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Van Dyken PC, Yang K, Faria AV, Sawa A, MacKinley M, Khan AR, Palaniyappan L. Stable White Matter Structure in the First Three Years After Psychosis Onset. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2025; 5:100472. [PMID: 40231305 PMCID: PMC11994302 DOI: 10.1016/j.bpsgos.2025.100472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2024] [Revised: 02/08/2025] [Accepted: 02/11/2025] [Indexed: 04/16/2025] Open
Abstract
Background White matter alterations observed using diffusion weighted imaging have become a hallmark of chronic schizophrenia, but it is unclear when these changes arise over the course of the disease. Nearly all studies reported to date have been cross-sectional, so despite their large sample sizes, they cannot determine whether changes accumulate as a degenerative process or patients with preexisting white matter damage are predisposed to more chronic forms of schizophrenia. Methods We examined 160 scans comprising 2 years of annual follow-up data from 42 control participants and 28 patients with schizophrenia recruited in the first 2 years since their diagnosis, totaling 2 to 3 scans per participant. We also examined 6-month follow-up data obtained from an ultra-high field (7T) scanner (68 scans; n = 19 patients with first-episode schizophrenia, n = 15 control participants) as a validation dataset. A longitudinal model was used to compare the trajectory of diffusion tensor parameters in patients and control participants. Results Positive and negative symptom scores were correlated with diffusion parameters using region of interest-based approaches. No longitudinal differences between patients and control participants were observed for any diffusion tensor imaging parameter in either dataset. However, we did observe consistent associations between white matter alterations and negative symptoms in both datasets. Conclusions White matter does not appear to be susceptible to schizophrenia-linked degeneration in the early stages of disease, but preexisting pathology may be linked to disease severity.
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Affiliation(s)
- Peter C. Van Dyken
- Neuroscience Graduate Program, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada
| | - Kun Yang
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Andreia V. Faria
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Akira Sawa
- Departments of Psychiatry, Neuroscience, Biomedical Engineering, Pharmacology, and Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Mental Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Michael MacKinley
- Lawson Health Research Institute, London Health Sciences Centre, London, Ontario, Canada
| | - Ali R. Khan
- Robarts Research Institute, Western University, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada
| | - Lena Palaniyappan
- Robarts Research Institute, Western University, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada
- Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada
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Goldwaser EL, Yuen A, Marshall W, Adhikari BM, Chiappelli J, van der Vaart A, Kvarta M, Ma Y, Du X, Gao S, Bruce H, Donnelly P, Mitchell B, Hong C, Wang DJJ, Kochunov P, Hong LE. Peripheral Microvascular and Cerebral White Matter Dysfunction in Schizophrenia: Implications of a Body-Brain Endothelial Pathophysiology. Schizophr Bull 2025:sbaf020. [PMID: 40036787 DOI: 10.1093/schbul/sbaf020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/06/2025]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia spectrum disorder (SSD) is a chronic neuropsychiatric illness accompanied by significant brain structural and functional abnormalities and higher rate of cardio- and cerebrovascular comorbidities. We hypothesized that genetic and environmental risk factors that led to SSD act throughout the body and demonstrated the association between lower integrity of peripheral vascular endothelium and white matter (WM) microstructure. STUDY DESIGN Microvascular endothelial function was evaluated using brachial artery post-occlusive reactive hyperemia (PORH), in which endothelial responses are measured under reduced blood flow and after blood flow is restored. White matter microstructure was assessed by multi-shell diffusion tensor imaging in n = 48 healthy controls (HCs) and n = 46 SSD. STUDY RESULTS Patients showed significantly lower PORH (F1,90 = 5.31, P = .02) effect and lower whole-brain fractional anisotropy (FA) values by diffusion imaging (F1,84 = 7.46, P = .008) with a group × post-occlusion time interaction effect (F3,90 = 4.58, P = .02). The PORH and whole-brain FA were significantly correlated in the full sample (r = 0.28, P = .01) and in SSD (r = 0.4, P = .008) separately, but not HC (r = 0.18, P = .28). CONCLUSIONS This study demonstrated, for the first time, significantly lower integrity of vascular endothelium in participants with SSD and showed that it is associated with WM microstructural abnormalities. Together, these findings support the need for a more holistic, body-brain approach to study the pathophysiology of SSD.
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Affiliation(s)
- Eric L Goldwaser
- Weill Cornell Medicine, Department of Psychiatry, New York, NY 10065, United States
| | - Alexa Yuen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21228, United States
| | - Wyatt Marshall
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21228, United States
| | - Bhim M Adhikari
- Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center, Houston, TX 77054, United States
| | - Joshua Chiappelli
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21228, United States
| | - Andrew van der Vaart
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21228, United States
| | - Mark Kvarta
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21228, United States
| | - Yizhou Ma
- Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center, Houston, TX 77054, United States
| | - Xiaoming Du
- Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center, Houston, TX 77054, United States
| | - Si Gao
- Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center, Houston, TX 77054, United States
| | - Heather Bruce
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21228, United States
| | - Patrick Donnelly
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21228, United States
| | - Braxton Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, United States
| | - Charles Hong
- Department of Medicine, Michigan State University College of Human Medicine, East Lansing, MI 48824, United States
- Henry Ford Health + Michigan State Health Sciences, Detroit, MI 48824, United States
| | - Danny J J Wang
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States
| | - Peter Kochunov
- Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center, Houston, TX 77054, United States
| | - L Elliot Hong
- Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center, Houston, TX 77054, United States
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3
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Kochunov P, Hong LE, Summerfelt A, Gao S, Brown PL, Terzi M, Acheson A, Woldorff MG, Fieremans E, Abdollahzadeh A, Sathyasaikumar KV, Clark SM, Schwarcz R, Shepard PD, Elmer GI. White matter and latency of visual evoked potentials during maturation: A miniature pig model of adolescent development. J Neurosci Methods 2024; 411:110252. [PMID: 39159872 PMCID: PMC11983141 DOI: 10.1016/j.jneumeth.2024.110252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 07/17/2024] [Accepted: 08/16/2024] [Indexed: 08/21/2024]
Abstract
BACKGROUND Continuous myelination of cerebral white matter (WM) during adolescence overlaps with the formation of higher cognitive skills and the onset of many neuropsychiatric disorders. We developed a miniature-pig model of adolescent brain development for neuroimaging and neurophysiological assessment during this critical period. Minipigs have gyroencephalic brains with a large cerebral WM compartment and a well-defined adolescence period. METHODS Eight Sinclair™ minipigs (Sus scrofa domestica) were evaluated four times during weeks 14-28 (40, 28 and 28 days apart) of adolescence using monocular visual stimulation (1 Hz)-evoked potentials and diffusion MRI (dMRI) of WM. The latency for the pre-positive 30 ms (PP30), positive 30 ms (P30) and negative 50 ms (N50) components of the flash visual evoked potentials (fVEPs) and their interhemispheric latency (IL) were recorded in the frontal, central and occipital areas during ten 60-second stimulations for each eye. The dMRI imaging protocol consisted of fifteen b-shells (b = 0-3500 s/mm2) with 32 directions/shell, providing measurements that included fractional anisotropy (FA), radial kurtosis, kurtosis anisotropy (KA), axonal water fraction (AWF), and the permeability-diffusivity index (PDI). RESULTS Significant reductions (p < 0.05) in the latency and IL of fVEP measurements paralleled significant rises in FA, KA, AWF and PDI over the same period. The longitudinal latency changes in fVEPs were primarily associated with whole-brain changes in diffusion parameters, while fVEP IL changes were related to maturation of the corpus callosum. CONCLUSIONS Good agreement between reduction in the latency of fVEPs and maturation of cerebral WM was interpreted as evidence for ongoing myelination and confirmation of the minipig as a viable research platform. Adolescent development in minipigs can be studied using human neuroimaging and neurophysiological protocols and followed up with more invasive assays to investigate key neurodevelopmental hypotheses in psychiatry.
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Affiliation(s)
- Peter Kochunov
- Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA; Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - L Elliot Hong
- Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ann Summerfelt
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Si Gao
- Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA; Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - P Leon Brown
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Matthew Terzi
- Department of Pathology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ashley Acheson
- Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Marty G Woldorff
- Center for Cognitive Neuroscience, Duke University, Durham, NC. USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Ali Abdollahzadeh
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Korrapati V Sathyasaikumar
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Sarah M Clark
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA; Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Robert Schwarcz
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Paul D Shepard
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Greg I Elmer
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
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4
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Farrher E, Grinberg F, Khechiashvili T, Neuner I, Konrad K, Shah NJ. Spatiotemporal Patterns of White Matter Maturation after Pre-Adolescence: A Diffusion Kurtosis Imaging Study. Brain Sci 2024; 14:495. [PMID: 38790472 PMCID: PMC11119177 DOI: 10.3390/brainsci14050495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
Diffusion tensor imaging (DTI) enables the assessment of changes in brain tissue microstructure during maturation and ageing. In general, patterns of cerebral maturation and decline render non-monotonic lifespan trajectories of DTI metrics with age, and, importantly, the rate of microstructural changes is heterochronous for various white matter fibres. Recent studies have demonstrated that diffusion kurtosis imaging (DKI) metrics are more sensitive to microstructural changes during ageing compared to those of DTI. In a previous work, we demonstrated that the Cohen's d of mean diffusional kurtosis (dMK) represents a useful biomarker for quantifying maturation heterochronicity. However, some inferences on the maturation grades of different fibre types, such as association, projection, and commissural, were of a preliminary nature due to the insufficient number of fibres considered. Hence, the purpose of this follow-up work was to further explore the heterochronicity of microstructural maturation between pre-adolescence and middle adulthood based on DTI and DKI metrics. Using the effect size of the between-group parametric changes and Cohen's d, we observed that all commissural fibres achieved the highest level of maturity, followed by the majority of projection fibres, while the majority of association fibres were the least matured. We also demonstrated that dMK strongly correlates with the maxima or minima of the lifespan curves of DTI metrics. Furthermore, our results provide substantial evidence for the existence of spatial gradients in the timing of white matter maturation. In conclusion, our data suggest that DKI provides useful biomarkers for the investigation of maturation spatial heterogeneity and heterochronicity.
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Affiliation(s)
- Ezequiel Farrher
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
| | - Farida Grinberg
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Neurology, RWTH Aachen University, 52074 Aachen, Germany
| | - Tamara Khechiashvili
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
| | - Irene Neuner
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
| | - Kerstin Konrad
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry and Psychotherapy, RWTH Aachen University, 52074 Aachen, Germany
- Institute of Neuroscience and Medicine 3, INM-3, Forschungszentrum Jülich, 52425 Jülich, Germany
- Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Neurology, RWTH Aachen University, 52074 Aachen, Germany
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
- Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, 52425 Jülich, Germany
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5
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Rivera AD, Normanton JR, Butt AM, Azim K. The Genomic Intersection of Oligodendrocyte Dynamics in Schizophrenia and Aging Unravels Novel Pathological Mechanisms and Therapeutic Potentials. Int J Mol Sci 2024; 25:4452. [PMID: 38674040 PMCID: PMC11050044 DOI: 10.3390/ijms25084452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/28/2024] [Accepted: 03/30/2024] [Indexed: 04/28/2024] Open
Abstract
Schizophrenia is a significant worldwide health concern, affecting over 20 million individuals and contributing to a potential reduction in life expectancy by up to 14.5 years. Despite its profound impact, the precise pathological mechanisms underlying schizophrenia continue to remain enigmatic, with previous research yielding diverse and occasionally conflicting findings. Nonetheless, one consistently observed phenomenon in brain imaging studies of schizophrenia patients is the disruption of white matter, the bundles of myelinated axons that provide connectivity and rapid signalling between brain regions. Myelin is produced by specialised glial cells known as oligodendrocytes, which have been shown to be disrupted in post-mortem analyses of schizophrenia patients. Oligodendrocytes are generated throughout life by a major population of oligodendrocyte progenitor cells (OPC), which are essential for white matter health and plasticity. Notably, a decline in a specific subpopulation of OPC has been identified as a principal factor in oligodendrocyte disruption and white matter loss in the aging brain, suggesting this may also be a factor in schizophrenia. In this review, we analysed genomic databases to pinpoint intersections between aging and schizophrenia and identify shared mechanisms of white matter disruption and cognitive dysfunction.
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Affiliation(s)
- Andrea D. Rivera
- Department of Neuroscience, Institute of Human Anatomy, University of Padova, Via A. Gabelli 65, 35127 Padua, Italy;
| | - John R. Normanton
- GliaGenesis Limited, Orchard Lea, Horns Lane, Oxfordshire, Witney OX29 8NH, UK; (J.R.N.); (K.A.)
| | - Arthur M. Butt
- GliaGenesis Limited, Orchard Lea, Horns Lane, Oxfordshire, Witney OX29 8NH, UK; (J.R.N.); (K.A.)
- School of Pharmacy and Biomedical Science, University of Portsmouth, Hampshire PO1 2UP, UK
| | - Kasum Azim
- GliaGenesis Limited, Orchard Lea, Horns Lane, Oxfordshire, Witney OX29 8NH, UK; (J.R.N.); (K.A.)
- Independent Data Lab UG, Frauenmantelanger 31, 80937 Munich, Germany
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Porgalı Zayman E, Erbay MF. Neuroanatomical comparison of treatment-resistant and treatment-responsive schizophrenia patients using the cloud-based brain magnetic resonance image segmentation and parcellation system: An MRIcloud study. Psychiatry Res Neuroimaging 2024; 339:111789. [PMID: 38354479 DOI: 10.1016/j.pscychresns.2024.111789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 12/22/2023] [Accepted: 01/08/2024] [Indexed: 02/16/2024]
Abstract
Recent developments in neuroimaging have improved our understanding of the biological mechanisms underlying schizophrenia. However, neuroimaging findings in treatment-resistant schizophrenia (TRS) remain unclear. In the present study, we aimed to explore potential neuroanatomical regions that may be associated with treatment resistance in schizophrenia patients by comparing neuroanatomical regions of TRS and non-TRS patients using the MRICloud method. A total of 33 schizophrenia patients (meeting DSM 5 diagnostic criteria for schizophrenia) were included in the study. Patients were dichotomized into TRS (n = 18) and non-TRS (n = 15) groups, and all patients underwent MRI. Neuroanatomical regions of TRS and non-TRS patients were compared using the MRICloud method. Disease severity was measured using the Positive and Negative Syndrome Scale (PANSS). Interestingly, a statistically significant greater left Corpus Collosum (CC) thickness was found in TRS patients compared to non-TRS patients. It is clear that further studies comparing TRS patients with non-TRS patients are needed, and these studies should focus on the circuits in the corpus callosum that are thought to play a role in treatment resistance. Further longitudinal studies are also needed to complement the cross-sectional studies, using a multimodal imaging approach in the patients with clearly defined TRS criteria.
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Gangadin SS, Mandl RCW, de Witte LD, van Haren NEM, Schutte MJL, Begemann MJH, Kahn RS, Sommer IEC. Lower fractional anisotropy without evidence for neuro-inflammation in patients with early-phase schizophrenia spectrum disorders. Schizophr Res 2024; 264:557-566. [PMID: 36577563 DOI: 10.1016/j.schres.2022.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 12/05/2022] [Accepted: 12/07/2022] [Indexed: 12/28/2022]
Abstract
Various lines of research suggest immune dysregulation as a potential therapeutic target for negative and cognitive symptoms in schizophrenia spectrum disorders (SSD). Immune dysregulation would lead to higher extracellular free-water (EFW) in cerebral white matter (WM), which may partially underlie the frequently reported lower fractional anisotropy (FA) in SSD. We aim to investigate differences in EFW concentrations - a presumed proxy for neuro-inflammation - between early-phase SSD patients (n = 55) and healthy controls (HC; n = 37), and to explore immunological and cognitive correlates. To increase specificity for EFW, we study several complementary magnetic resonance imaging contrasts that are sensitive to EFW. FA, mean diffusivity (MD), magnetization transfer ratio (MTR), myelin water fraction (MWF) and quantitative T1 and T2 were calculated from diffusion-weighted imaging (DWI), magnetization transfer imaging (MTI) and multicomponent driven equilibrium single-pulse observation of T1/T2 (mcDESPOT). For each measure, WM skeletons were constructed with tract-based spatial statistics. Multivariate SSD-HC comparisons with WM skeletons and their average values (i.e. global WM) were not statistically significant. In voxel-wise analyses, FA was significantly lower in SSD in the genu of the corpus callosum and in the left superior longitudinal fasciculus (p < 0.04). Global WM measures did not correlate with immunological markers (i.e. IL1-RA, IL-6, IL-8, IL-10 and CRP) or cognition in HC and SSD after corrections for multiple comparisons. We confirmed lower FA in early-phase SSD patients. However, nonFA measures did not provide additional evidence for immune dysregulation or for higher EFW as the primary mechanism underlying the reported lower FA values in SSD.
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Affiliation(s)
- Shiral S Gangadin
- Section Cognitive Neuroscience, Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
| | - René C W Mandl
- Section Cognitive Neuroscience, Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Lot D de Witte
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
| | - Neeltje E M van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center-Sophia Children's Hospital, Rotterdam, the Netherlands.
| | - Maya J L Schutte
- Section Cognitive Neuroscience, Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
| | - Marieke J H Begemann
- Section Cognitive Neuroscience, Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
| | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
| | - Iris E C Sommer
- Section Cognitive Neuroscience, Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
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Kochunov P, Ma Y, Hatch KS, Gao S, Acheson A, Jahanshad N, Thompson PM, Adhikari BM, Bruce H, Van der Vaart A, Chiappelli J, Du X, Sotiras A, Kvarta MD, Ma T, Chen S, Hong LE. Ancestral, Pregnancy, and Negative Early-Life Risks Shape Children's Brain (Dis)similarity to Schizophrenia. Biol Psychiatry 2023; 94:332-340. [PMID: 36948435 PMCID: PMC10511664 DOI: 10.1016/j.biopsych.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 03/07/2023] [Accepted: 03/07/2023] [Indexed: 03/24/2023]
Abstract
BACKGROUND Familial, obstetric, and early-life environmental risks for schizophrenia spectrum disorder (SSD) alter normal cerebral development, leading to the formation of characteristic brain deficit patterns prior to onset of symptoms. We hypothesized that the insidious effects of these risks may increase brain similarity to adult SSD deficit patterns in prepubescent children. METHODS We used data collected by the Adolescent Brain Cognitive Development (ABCD) Study (N = 8940, age = 9.9 ± 0.1 years, 4307/4633 female/male), including 727 (age = 9.9 ± 0.1 years, 351/376 female/male) children with family history of SSD, to evaluate unfavorable cerebral effects of ancestral SSD history, pre/perinatal environment, and negative early-life environment. We used a regional vulnerability index to measure the alignment of a child's cerebral patterns with the adult SSD pattern derived from a large meta-analysis of case-control differences. RESULTS In children with a family history of SSD, the regional vulnerability index captured significantly more variance in ancestral history than traditional whole-brain and regional brain measurements. In children with and without family history of SSD, the regional vulnerability index also captured more variance associated with negative pre/perinatal environment and early-life experiences than traditional brain measurements. CONCLUSIONS In summary, in a cohort in which most children will not develop SSD, familial, pre/perinatal, and early developmental risks can alter brain patterns in the direction observed in adult patients with SSD. Individual similarity to adult SSD patterns may provide an early biomarker of the effects of genetic and developmental risks on the brain prior to psychotic or prodromal symptom onset.
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Affiliation(s)
- Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland.
| | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Kathryn S Hatch
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Si Gao
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Ashley Acheson
- Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of University of the Sunshine Coast, Marina del Rey, California
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of University of the Sunshine Coast, Marina del Rey, California
| | - Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Heather Bruce
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Andrew Van der Vaart
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Joshua Chiappelli
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Xiaoming Du
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Aris Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Mark D Kvarta
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, University of Maryland, College Park, Maryland
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
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Rootes-Murdy K, Edmond JT, Jiang W, Rahaman MA, Chen J, Perrone-Bizzozero NI, Calhoun VD, van Erp TGM, Ehrlich S, Agartz I, Jönsson EG, Andreassen OA, Westlye LT, Wang L, Pearlson GD, Glahn DC, Hong E, Buchanan RW, Kochunov P, Voineskos A, Malhotra A, Tamminga CA, Liu J, Turner JA. Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach. Front Hum Neurosci 2022; 16:1001692. [PMID: 36438633 PMCID: PMC9684186 DOI: 10.3389/fnhum.2022.1001692] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 10/17/2022] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND Structural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotypes associated with SZ and BP may allow for a more precise categorization than the current diagnostic criteria. In this study, we sought to identify GM alterations that were unique to each disorder and whether those alterations were also related to unique symptom profiles. MATERIALS AND METHODS We analyzed the GM patterns and clinical symptom presentations using independent component analysis (ICA), hierarchical clustering, and n-way biclustering in a large (N ∼ 3,000), merged dataset of neuroimaging data from healthy volunteers (HV), and individuals with either SZ or BP. RESULTS Component A showed a SZ and BP < HV GM pattern in the bilateral insula and cingulate gyrus. Component B showed a SZ and BP < HV GM pattern in the cerebellum and vermis. There were no significant differences between diagnostic groups in these components. Component C showed a SZ < HV and BP GM pattern bilaterally in the temporal poles. Hierarchical clustering of the PANSS scores and the ICA components did not yield new subgroups. N-way biclustering identified three unique subgroups of individuals within the sample that mapped onto different combinations of ICA components and symptom profiles categorized by the PANSS but no distinct diagnostic group differences. CONCLUSION These multivariate results show that diagnostic boundaries are not clearly related to structural differences or distinct symptom profiles. Our findings add support that (1) BP tend to have less severe symptom profiles when compared to SZ on the PANSS without a clear distinction, and (2) all the gray matter alterations follow the pattern of SZ < BP < HV without a clear distinction between SZ and BP.
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Affiliation(s)
- Kelly Rootes-Murdy
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jesse T. Edmond
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Wenhao Jiang
- Department of Psychosomatics and Psychiatry, Medical School, Zhongda Hospital, Institute of Psychosomatics, Southeast University, Nanjing, China
| | - Md A. Rahaman
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | | | - Vince D. Calhoun
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Theo G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| | - Stefan Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Ingrid Agartz
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institute and Stockholm Health Care Services, Stockholm, Sweden
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Erik G. Jönsson
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institute and Stockholm Health Care Services, Stockholm, Sweden
| | - Ole A. Andreassen
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Lei Wang
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, United States
| | - Godfrey D. Pearlson
- Department of Psychiatry, Yale University, New Haven, CT, United States
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, United States
| | - David C. Glahn
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, United States
- Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Elliot Hong
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Robert W. Buchanan
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Aristotle Voineskos
- Department of Psychiatry, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
| | - Anil Malhotra
- Division of Psychiatry Research, Zucker Hillside Hospital, Queens, NY, United States
| | - Carol A. Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical School, Dallas, TX, United States
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jessica A. Turner
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, United States
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10
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Roddy DW, Roman E, Nasa A, Gazzaz A, Zainy A, Burke T, Staines L, Kelleher I, O'Neill A, Clarke M, O'Hanlon E, Cannon M. Microstructural changes along the cingulum in young adolescents with psychotic experiences: An along-tract analysis. Eur J Neurosci 2022; 56:5116-5131. [PMID: 36004608 PMCID: PMC9825926 DOI: 10.1111/ejn.15806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 07/30/2022] [Accepted: 08/18/2022] [Indexed: 01/11/2023]
Abstract
Psychotic experiences (PEs) such as hallucinations and delusions are common among young people without psychiatric diagnoses and are associated with connectivity and white matter abnormalities, particularly in the limbic system. Using diffusion magnetic resonance imaging (MRI) in adolescents with reported PEs and matched controls, we examined the cingulum white matter tract along its length rather than as the usually reported single indivisible structure. Complex regional differences in diffusion metrics were found along the bundle at key loci following Bonferroni significance adjustment (p < .00013) with moderate to large effect sizes (.11-.76) throughout all significant subsegments. In this prospective community-based cohort of school-age children, these findings suggest that white matter alterations in the limbic system may be more common in the general non-clinical adolescent population than previously thought. Such white matter alternations may only be uncovered using a similar more granular along-tract analysis of white matter tracts.
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Affiliation(s)
- Darren William Roddy
- Department of PsychiatryRoyal College of Surgeons in IrelandDublinIreland,Trinity College Institute of Neuroscience, Lloyd BuildingTrinity College DublinDublinIreland
| | - Elena Roman
- Department of PsychiatryRoyal College of Surgeons in IrelandDublinIreland
| | - Anurag Nasa
- Trinity College Institute of Neuroscience, Lloyd BuildingTrinity College DublinDublinIreland
| | - Areej Gazzaz
- Department of PsychiatryRoyal College of Surgeons in IrelandDublinIreland
| | - Ahmed Zainy
- Department of PsychiatryRoyal College of Surgeons in IrelandDublinIreland
| | - Tom Burke
- Department of PsychiatryRoyal College of Surgeons in IrelandDublinIreland
| | - Lorna Staines
- Department of PsychiatryRoyal College of Surgeons in IrelandDublinIreland
| | - Ian Kelleher
- Department of PsychiatryRoyal College of Surgeons in IrelandDublinIreland
| | - Aisling O'Neill
- Department of PsychiatryRoyal College of Surgeons in IrelandDublinIreland
| | - Mary Clarke
- Department of PsychiatryRoyal College of Surgeons in IrelandDublinIreland
| | - Erik O'Hanlon
- Department of PsychiatryRoyal College of Surgeons in IrelandDublinIreland,Trinity College Institute of Neuroscience, Lloyd BuildingTrinity College DublinDublinIreland
| | - Mary Cannon
- Department of PsychiatryRoyal College of Surgeons in IrelandDublinIreland
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11
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Rivera AD, Azim K, Macchi V, Porzionato A, Butt AM, De Caro R. Epidermal Growth Factor Pathway in the Age-Related Decline of Oligodendrocyte Regeneration. Front Cell Neurosci 2022; 16:838007. [PMID: 35370556 PMCID: PMC8968959 DOI: 10.3389/fncel.2022.838007] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 02/23/2022] [Indexed: 01/01/2023] Open
Abstract
Oligodendrocytes (OLs) are specialized glial cells that myelinate CNS axons. OLs are generated throughout life from oligodendrocyte progenitor cells (OPCs) via a series of tightly controlled differentiation steps. Life-long myelination is essential for learning and to replace myelin lost in age-related pathologies such as Alzheimer's disease (AD) as well as white matter pathologies such as multiple sclerosis (MS). Notably, there is considerable myelin loss in the aging brain, which is accelerated in AD and underpins the failure of remyelination in secondary progressive MS. An important factor in age-related myelin loss is a marked decrease in the regenerative capacity of OPCs. In this review, we will contextualize recent advances in the key role of Epidermal Growth Factor (EGF) signaling in regulating multiple biological pathways in oligodendroglia that are dysregulated in aging.
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Affiliation(s)
- Andrea D. Rivera
- Department of Neuroscience, Institute of Human Anatomy, University of Padua, Padua, Italy
| | - Kasum Azim
- Department of Neurology, Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany
| | - Veronica Macchi
- Department of Neuroscience, Institute of Human Anatomy, University of Padua, Padua, Italy
| | - Andrea Porzionato
- Department of Neuroscience, Institute of Human Anatomy, University of Padua, Padua, Italy
| | - Arthur M. Butt
- School of Pharmacy and Biomedical Science, University of Portsmouth, Portsmouth, United Kingdom
| | - Raffaele De Caro
- Department of Neuroscience, Institute of Human Anatomy, University of Padua, Padua, Italy
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12
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Huang J, Ke P, Chen X, Li S, Zhou J, Xiong D, Huang Y, Li H, Ning Y, Duan X, Li X, Zhang W, Wu F, Wu K. Multimodal Magnetic Resonance Imaging Reveals Aberrant Brain Age Trajectory During Youth in Schizophrenia Patients. Front Aging Neurosci 2022; 14:823502. [PMID: 35309897 PMCID: PMC8929292 DOI: 10.3389/fnagi.2022.823502] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 01/18/2022] [Indexed: 11/13/2022] Open
Abstract
Accelerated brain aging had been widely reported in patients with schizophrenia (SZ). However, brain aging trajectories in SZ patients have not been well-documented using three-modal magnetic resonance imaging (MRI) data. In this study, 138 schizophrenia patients and 205 normal controls aged 20–60 were included and multimodal MRI data were acquired for each individual, including structural MRI, resting state-functional MRI and diffusion tensor imaging. The brain age of each participant was estimated by features extracted from multimodal MRI data using linear multiple regression. The correlation between the brain age gap and chronological age in SZ patients was best fitted by a positive quadratic curve with a peak chronological age of 47.33 years. We used the peak to divide the subjects into a youth group and a middle age group. In the normal controls, brain age matched chronological age well for both the youth and middle age groups, but this was not the case for schizophrenia patients. More importantly, schizophrenia patients exhibited increased brain age in the youth group but not in the middle age group. In this study, we aimed to investigate brain aging trajectories in SZ patients using multimodal MRI data and revealed an aberrant brain age trajectory in young schizophrenia patients, providing new insights into the pathophysiological mechanisms of schizophrenia.
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Affiliation(s)
- Jiayuan Huang
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
| | - Pengfei Ke
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Xiaoyi Chen
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
| | - Shijia Li
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Jing Zhou
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Dongsheng Xiong
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Yuanyuan Huang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Hehua Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Yuping Ning
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Xujun Duan
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Wensheng Zhang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Fengchun Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
- *Correspondence: Fengchun Wu,
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
- Guangdong Province Key Laboratory of Biomedical Engineering, South China University of Technology, Guangzhou, China
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
- Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- Kai Wu,
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13
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Luttenbacher I, Phillips A, Kazemi R, Hadipour AL, Sanghvi I, Martinez J, Adamson MM. Transdiagnostic role of glutamate and white matter damage in neuropsychiatric disorders: A Systematic Review. J Psychiatr Res 2022; 147:324-348. [PMID: 35151030 DOI: 10.1016/j.jpsychires.2021.12.042] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/08/2021] [Accepted: 12/19/2021] [Indexed: 12/09/2022]
Abstract
Neuropsychiatric disorders including generalized anxiety disorder (GAD), obsessive-compulsive disorder (OCD), major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ) have been considered distinct categories of diseases despite their overlapping characteristics and symptomatology. We aimed to provide an in-depth review elucidating the role of glutamate/Glx and white matter (WM) abnormalities in these disorders from a transdiagnostic perspective. The PubMed online database was searched for studies published between 2010 and 2021. After careful screening, 401 studies were included. The findings point to decreased levels of glutamate in the Anterior Cingulate Cortex in both SZ and BD, whereas Glx is elevated in the Hippocampus in SZ and MDD. With regard to WM abnormalities, the Corpus Callosum and superior Longitudinal Fascicle were the most consistently identified brain regions showing decreased fractional anisotropy (FA) across all the reviewed disorders, except GAD. Additionally, the Uncinate Fasciculus displayed decreased FA in all disorders, except OCD. Decreased FA was also found in the inferior Longitudinal Fasciculus, inferior Fronto-Occipital Fasciculus, Thalamic Radiation, and Corona Radiata in SZ, BD, and MDD. Decreased FA in the Fornix and Corticospinal Tract were found in BD and SZ patients. The Cingulum and Anterior Limb of Internal Capsule exhibited decreased FA in MDD and SZ patients. The results suggest a gradual increase in severity from GAD to SZ defined by the number of brain regions with WM abnormality which may be partially caused by abnormal glutamate levels. WM damage could thus be considered a potential marker of some of the main neuropsychiatric disorders.
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Affiliation(s)
- Ines Luttenbacher
- Department of Social & Behavioral Sciences, University of Amsterdam, Amsterdam, Netherlands; Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Angela Phillips
- Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Reza Kazemi
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
| | - Abed L Hadipour
- Department of Cognitive Sciences, University of Messina, Messina, Italy
| | - Isha Sanghvi
- Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Department of Neuroscience, University of Southern California, Los Angeles, CA, USA
| | - Julian Martinez
- Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Palo Alto University, Palo Alto, CA, USA
| | - Maheen M Adamson
- Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
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14
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Ryan MC, Hong LE, Hatch KS, Gao S, Chen S, Haerian K, Wang J, Goldwaser EL, Du X, Adhikari BM, Bruce H, Hare S, Kvarta MD, Jahanshad N, Nichols TE, Thompson PM, Kochunov P. The additive impact of cardio-metabolic disorders and psychiatric illnesses on accelerated brain aging. Hum Brain Mapp 2022; 43:1997-2010. [PMID: 35112422 PMCID: PMC8933252 DOI: 10.1002/hbm.25769] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Revised: 11/28/2021] [Accepted: 12/28/2021] [Indexed: 12/24/2022] Open
Abstract
Severe mental illnesses (SMI) including major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia spectrum disorder (SSD) elevate accelerated brain aging risks. Cardio‐metabolic disorders (CMD) are common comorbidities in SMI and negatively impact brain health. We validated a linear quantile regression index (QRI) approach against the machine learning “BrainAge” index in an independent SSD cohort (N = 206). We tested the direct and additive effects of SMI and CMD effects on accelerated brain aging in the N = 1,618 (604 M/1,014 F, average age = 63.53 ± 7.38) subjects with SMI and N = 11,849 (5,719 M/6,130 F; 64.42 ± 7.38) controls from the UK Biobank. Subjects were subdivided based on diagnostic status: SMI+/CMD+ (N = 665), SMI+/CMD− (N = 964), SMI−/CMD+ (N = 3,765), SMI−/CMD− (N = 8,083). SMI (F = 40.47, p = 2.06 × 10−10) and CMD (F = 24.69, p = 6.82 × 10−7) significantly, independently impacted whole‐brain QRI in SMI+. SSD had the largest effect (Cohen’s d = 1.42) then BD (d = 0.55), and MDD (d = 0.15). Hypertension had a significant effect on SMI+ (d = 0.19) and SMI− (d = 0.14). SMI effects were direct, independent of MD, and remained significant after correcting for effects of antipsychotic medications. Whole‐brain QRI was significantly (p < 10−16) associated with the volume of white matter hyperintensities (WMH). However, WMH did not show significant association with SMI and was driven by CMD, chiefly hypertension (p < 10−16). We used a simple and robust index, QRI, the demonstrate additive effect of SMI and CMD on accelerated brain aging. We showed a greater effect of psychiatric illnesses on QRI compared to cardio‐metabolic illness. Our findings suggest that subjects with SMI should be among the targets for interventions to protect against age‐related cognitive decline.
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Affiliation(s)
- Meghann C Ryan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Kathryn S Hatch
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Si Gao
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA.,Division of Biostatistics and Bioinformatics, Department of Public Health and Epidemiology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Krystl Haerian
- Department of Clinical Research and Leadership, School of Medicine and Health Sciences, George Washington University, Washington, District of Columbia, USA
| | - Jingtao Wang
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA.,Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Eric L Goldwaser
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Xiaoming Du
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Heather Bruce
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Stephanie Hare
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Mark D Kvarta
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | - Thomas E Nichols
- Nuffield Department of Population Health of the University of Oxford, Oxford, UK
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
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15
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Ballester PL, Romano MT, de Azevedo Cardoso T, Hassel S, Strother SC, Kennedy SH, Frey BN. Brain age in mood and psychotic disorders: a systematic review and meta-analysis. Acta Psychiatr Scand 2022; 145:42-55. [PMID: 34510423 DOI: 10.1111/acps.13371] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 09/05/2021] [Accepted: 09/07/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To evaluate whether accelerated brain aging occurs in individuals with mood or psychotic disorders. METHODS A systematic review following PRISMA guidelines was conducted. A meta-analysis was then performed to assess neuroimaging-derived brain age gap in three independent groups: (1) schizophrenia and first-episode psychosis, (2) major depressive disorder, and (3) bipolar disorder. RESULTS A total of 18 papers were included. The random-effects model meta-analysis showed a significantly increased neuroimaging-derived brain age gap relative to age-matched controls for the three major psychiatric disorders, with schizophrenia (3.08; 95%CI [2.32; 3.85]; p < 0.01) presenting the largest effect, followed by bipolar disorder (1.93; [0.53; 3.34]; p < 0.01) and major depressive disorder (1.12; [0.41; 1.83]; p < 0.01). The brain age gap was larger in older compared to younger individuals. CONCLUSION Individuals with mood and psychotic disorders may undergo a process of accelerated brain aging reflected in patterns captured by neuroimaging data. The brain age gap tends to be more pronounced in older individuals, indicating a possible cumulative biological effect of illness burden.
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Affiliation(s)
- Pedro L Ballester
- Neuroscience Graduate Program, McMaster University, Hamilton, Ontario, Canada
| | - Maria T Romano
- Integrated Science Undergraduate Program, McMaster University, Hamilton, Ontario, Canada
| | - Taiane de Azevedo Cardoso
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Stefanie Hassel
- Mathison Centre for Mental Health Research and Education, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.,Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Sidney H Kennedy
- Centre for Depression and Suicide Studies, and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada.,Mood Disorders Treatment and Research Centre, and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, Ontario, Canada
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16
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Kochunov P, Hong LE, Dennis EL, Morey RA, Tate DF, Wilde EA, Logue M, Kelly S, Donohoe G, Favre P, Houenou J, Ching CRK, Holleran L, Andreassen OA, van Velzen LS, Schmaal L, Villalón-Reina JE, Bearden CE, Piras F, Spalletta G, van den Heuvel OA, Veltman DJ, Stein DJ, Ryan MC, Tan Y, van Erp TGM, Turner JA, Haddad L, Nir TM, Glahn DC, Thompson PM, Jahanshad N. ENIGMA-DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross-diagnostic psychiatric research. Hum Brain Mapp 2022; 43:194-206. [PMID: 32301246 PMCID: PMC8675425 DOI: 10.1002/hbm.24998] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 03/06/2020] [Accepted: 03/17/2020] [Indexed: 12/25/2022] Open
Abstract
The ENIGMA-DTI (diffusion tensor imaging) workgroup supports analyses that examine the effects of psychiatric, neurological, and developmental disorders on the white matter pathways of the human brain, as well as the effects of normal variation and its genetic associations. The seven ENIGMA disorder-oriented working groups used the ENIGMA-DTI workflow to derive patterns of deficits using coherent and coordinated analyses that model the disease effects across cohorts worldwide. This yielded the largest studies detailing patterns of white matter deficits in schizophrenia spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), traumatic brain injury (TBI), and 22q11 deletion syndrome. These deficit patterns are informative of the underlying neurobiology and reproducible in independent cohorts. We reviewed these findings, demonstrated their reproducibility in independent cohorts, and compared the deficit patterns across illnesses. We discussed translating ENIGMA-defined deficit patterns on the level of individual subjects using a metric called the regional vulnerability index (RVI), a correlation of an individual's brain metrics with the expected pattern for a disorder. We discussed the similarity in white matter deficit patterns among SSD, BD, MDD, and OCD and provided a rationale for using this index in cross-diagnostic neuropsychiatric research. We also discussed the difference in deficit patterns between idiopathic schizophrenia and 22q11 deletion syndrome, which is used as a developmental and genetic model of schizophrenia. Together, these findings highlight the importance of collaborative large-scale research to provide robust and reproducible effects that offer insights into individual vulnerability and cross-diagnosis features.
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Affiliation(s)
- Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Emily L Dennis
- Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital, Boston, Massachusetts, USA
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California, USA
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, Utah, USA
- George E. Wahlen VA, Salt Lake City, Utah, USA
| | - Rajendra A Morey
- Brain Imaging and Analysis Center, Duke University, Durham, North Carolina, USA
| | - David F Tate
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, Utah, USA
- George E. Wahlen VA, Salt Lake City, Utah, USA
| | - Elisabeth A Wilde
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, Utah, USA
- George E. Wahlen VA, Salt Lake City, Utah, USA
| | - Mark Logue
- VA Boston Healthcare System, National Center for PTSD, Boston, Massachusetts, USA
- Boston University School of Medicine, Department of Psychiatry, Boston, Massachusetts, USA
- Boston University School of Medicine, Biomedical Genetics, Boston, Massachusetts, USA
- Boston University School of Public Health, Department of Biostatistics, Boston, Massachusetts, USA
| | - Sinead Kelly
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Gary Donohoe
- Centre for Neuroimaging and Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - Pauline Favre
- Neurospin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
- INSERM Unit U955, team "Translational Neuro-Psychiatry", Créteil, France
| | - Josselin Houenou
- Neurospin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
- INSERM Unit U955, team "Translational Neuro-Psychiatry", Créteil, France
- Psychiatry Department, Assistance Publique-Hôpitaux de Paris (AP-HP), CHU Mondor, Créteil, France
- Faculté de Médecine, Université Paris Est Créteil, Créteil, France
| | - Christopher R K Ching
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California, USA
| | - Laurena Holleran
- Centre for Neuroimaging and Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Laura S van Velzen
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
- Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, Australia
| | - Lianne Schmaal
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
- Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, Australia
| | - Julio E Villalón-Reina
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California, USA
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, California, USA
- Department of Psychology, University of California at Los Angeles, Los Angeles, California, USA
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
- Division of Neuropsychiatry, Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas, USA
| | - Odile A van den Heuvel
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Dick J Veltman
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Dan J Stein
- Department of Psychiatry & Neuroscience Institute, University of Cape Town, SA MRC Unit on Risk & Resilience in Mental Disorders, Cape Town, South Africa
| | - Meghann C Ryan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Yunlong Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry, University of California Irvine, Irvine, California, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, California, USA
| | - Jessica A Turner
- Department of Psychology and Neuroscience Institute, Georgia State University, Atlanta, Georgia, USA
| | - Liz Haddad
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California, USA
| | - Talia M Nir
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California, USA
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Olin Neuropsychiatric Research Center, Hartford Hospital, Hartford, Connecticut, USA
| | - Paul M Thompson
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California, USA
| | - Neda Jahanshad
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California, USA
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17
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White matter brain aging in relationship to schizophrenia and its cognitive deficit. Schizophr Res 2021; 230:9-16. [PMID: 33667860 PMCID: PMC8222174 DOI: 10.1016/j.schres.2021.02.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 01/26/2021] [Accepted: 02/18/2021] [Indexed: 11/21/2022]
Abstract
We hypothesized that cerebral white matter deficits in schizophrenia (SZ) are driven in part by accelerated white matter aging and are associated with cognitive deficits. We used a machine learning model to predict individual age from diffusion tensor imaging features and calculated the delta age (Δage) as the difference between predicted and chronological age. Through this approach, we translated multivariate white matter imaging features into an age-scaled metric and used it to test the temporal trends of accelerated aging-related white matter deficit in SZ and its association with the cognition. A feature selection procedure was first employed to choose fractional anisotropy values in 34 of 43 white fiber tracts. Using these features, a machine learning model was trained based on a training set consisted of 107 healthy controls (HC). The brain age of 166 SZs and 107 HCs in the testing set were calculated using this model. Then, we examined the SZ-HC group effect on Δage and whether this effect was moderated by chronological age using the regression spline model. The results showed that Δage was significantly elevated in the age > 30 group in patients (p < 0.001) but not in age ≤ 30 group (p = 0.364). Δage in patients was significantly and negatively associated with both working memory (β = -0.176, p = 0.007) and processing speed (β = -0.519, p = 0.035) while adjusting sex and chronological age. Overall, these findings indicate that the Δage is elevated in SZs and become significantly from the third decade of life; the increase of Δage in SZs is associated with the declined neurocognitive performance.
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18
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D'Amore F, Grinberg F, Mauler J, Galldiks N, Blazhenets G, Farrher E, Filss C, Stoffels G, Mottaghy FM, Lohmann P, Shah NJ, Langen KJ. Combined 18F-FET PET and diffusion kurtosis MRI in posttreatment glioblastoma: differentiation of true progression from treatment-related changes. Neurooncol Adv 2021; 3:vdab044. [PMID: 34013207 PMCID: PMC8117449 DOI: 10.1093/noajnl/vdab044] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background Radiological differentiation of tumor progression (TPR) from treatment-related changes (TRC) in pretreated glioblastoma is crucial. This study aimed to explore the diagnostic value of diffusion kurtosis MRI combined with information derived from O-(2-[18F]-fluoroethyl)-l-tyrosine (18F-FET) PET for the differentiation of TPR from TRC in patients with pretreated glioblastoma. Methods Thirty-two patients with histomolecularly defined and pretreated glioblastoma suspected of having TPR were included in this retrospective study. Twenty-one patients were included in the TPR group, and 11 patients in the TRC group, as assessed by neuropathology or clinicoradiological follow-up. Three-dimensional (3D) regions of interest were generated based on increased 18F-FET uptake using a tumor-to-brain ratio of 1.6. Furthermore, diffusion MRI kurtosis maps were obtained from the same regions of interest using co-registered 18F-FET PET images, and advanced histogram analysis of diffusion kurtosis map parameters was applied to generated 3D regions of interest. Diagnostic accuracy was analyzed by receiver operating characteristic curve analysis and combinations of PET and MRI parameters using multivariate logistic regression. Results Parameters derived from diffusion MRI kurtosis maps show high diagnostic accuracy, up to 88%, for differentiating between TPR and TRC. Logistic regression revealed that the highest diagnostic accuracy of 94% (area under the curve, 0.97; sensitivity, 94%; specificity, 91%) was achieved by combining the maximum tumor-to-brain ratio of 18F-FET uptake and diffusion MRI kurtosis metrics. Conclusions The combined use of 18F-FET PET and MRI diffusion kurtosis maps appears to be a promising approach to improve the differentiation of TPR from TRC in pretreated glioblastoma and warrants further investigation.
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Affiliation(s)
- Francesco D'Amore
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany.,Department of Neuroradiology, Circolo Hospital and Macchi Foundation, Varese, Italy
| | - Farida Grinberg
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany
| | - Jörg Mauler
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany
| | - Norbert Galldiks
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany.,Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Center for Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Duesseldorf, Germany
| | - Ganna Blazhenets
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany.,Department of Nuclear Medicine, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ezequiel Farrher
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany
| | - Christian Filss
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany.,Department of Nuclear Medicine, RWTH Aachen University, Aachen, Germany
| | - Gabriele Stoffels
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany
| | - Felix M Mottaghy
- Center for Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Duesseldorf, Germany.,Department of Nuclear Medicine, RWTH Aachen University, Aachen, Germany.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands.,Department of Stereotaxy and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany.,Department of Stereotaxy and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nadim Jon Shah
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany.,Department of Neurology, RWTH Aachen University, Aachen, Germany.,JARA-BRAIN-Translational Medicine, Aachen, Germany
| | - Karl-Josef Langen
- Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany.,Center for Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Duesseldorf, Germany.,Department of Nuclear Medicine, RWTH Aachen University, Aachen, Germany
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19
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Kraguljac NV, Monroe WS, Anthony T, Jindal RD, Hill H, Lahti AC. Neurite Orientation Dispersion and Density Imaging (NODDI) and Duration of Untreated Psychosis in Antipsychotic Medication-Naïve First Episode Psychosis Patients. NEUROIMAGE. REPORTS 2021; 1:100005. [PMID: 36969709 PMCID: PMC10038586 DOI: 10.1016/j.ynirp.2021.100005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Background Diffusion tensor imaging suggests that white matter alterations are already evident in first episode psychosis patients (FEP) and may become more prominent as the duration of untreated psychosis (DUP) increases. But because the tensor model lacks specificity, it remains unclear how to interpret findings on a biological level. Here, we used a biophysical diffusion model, Neurite Orientation Dispersion and Density Imaging (NODDI), to map microarchitecture in FEP, and to investigate associations between DUP and microarchitectural integrity. Methods We scanned 78 antipsychotic medication-naïve FEP and 64 healthy controls using a multi-shell diffusion weighted sequence and used the NODDI toolbox to compute neurite density (ND), orientation dispersion index (ODI) and extracellular free water (FW) maps. AFNI's 3dttest++ was used to compare diffusion maps between groups and to perform regression analyses with DUP. Results We found that ND was decreased in commissural and association fibers but increased in projection fibers in FEP. ODI was largely increased regardless of fiber type, and FW showed a mix of increase in decrease across fiber tracts. We also demonstrated associations between DUP and microarchitecture for all NODDI indices. Conclusions We demonstrated that complex microarchitecture abnormalities are already evident in antipsychotic-naïve FEP. ND alterations are differentially expressed depending on fiber type, while decreased fiber complexity appears to be a uniform marker of white matter deficit in the illness. Importantly, we identified an empirical link between longer DUP and greater white matter pathology across NODDI indices, underscoring the critical importance of early intervention in this devastating illness.
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Affiliation(s)
- Nina Vanessa Kraguljac
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham
| | - William Stonewall Monroe
- Department of Electrical and Computer Engineering/ IT Research Computing, University of Alabama at Birmingham
| | - Thomas Anthony
- Department of Electrical and Computer Engineering/ IT Research Computing, University of Alabama at Birmingham
| | | | - Harrison Hill
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham
| | - Adrienne Carol Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham
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20
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Kochunov P, Zavaliangos-Petropulu A, Jahanshad N, Thompson PM, Ryan MC, Chiappelli J, Chen S, Du X, Hatch K, Adhikari B, Sampath H, Hare S, Kvarta M, Goldwaser E, Yang F, Olvera RL, Fox PT, Curran JE, Blangero J, Glahn DC, Tan Y, Hong LE. A White Matter Connection of Schizophrenia and Alzheimer's Disease. Schizophr Bull 2021; 47:197-206. [PMID: 32681179 PMCID: PMC7825012 DOI: 10.1093/schbul/sbaa078] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Schizophrenia (SZ) is a severe psychiatric illness associated with an elevated risk for developing Alzheimer's disease (AD). Both SZ and AD have white matter abnormalities and cognitive deficits as core disease features. We hypothesized that aging in SZ patients may be associated with the development of cerebral white matter deficit patterns similar to those observed in AD. We identified and replicated aging-related increases in the similarity between white matter deficit patterns in patients with SZ and AD. The white matter "regional vulnerability index" (RVI) for AD was significantly higher in SZ patients compared with healthy controls in both the independent discovery (Cohen's d = 0.44, P = 1·10-5, N = 173 patients/230 control) and replication (Cohen's d = 0.78, P = 9·10-7, N = 122 patients/64 controls) samples. The degree of overlap with the AD deficit pattern was significantly correlated with age in patients (r = .21 and .29, P < .01 in discovery and replication cohorts, respectively) but not in controls. Elevated RVI-AD was significantly associated with cognitive measures in both SZ and AD. Disease and cognitive specificities were also tested in patients with mild cognitive impairment and showed intermediate overlap. SZ and AD have diverse etiologies and clinical courses; our findings suggest that white matter deficits may represent a key intersecting point for these 2 otherwise distinct diseases. Identifying mechanisms underlying this white matter deficit pattern may yield preventative and treatment targets for cognitive deficits in both SZ and AD patients.
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Affiliation(s)
- Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - Artemis Zavaliangos-Petropulu
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California of USC, Marina del Rey, CA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California of USC, Marina del Rey, CA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California of USC, Marina del Rey, CA
| | - Meghann C Ryan
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - Joshua Chiappelli
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - Shuo Chen
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - Xiaoming Du
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - Kathryn Hatch
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - Bhim Adhikari
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - Hemalatha Sampath
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - Stephanie Hare
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - Mark Kvarta
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - Eric Goldwaser
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - Fude Yang
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, P. R. China
| | - Rene L Olvera
- Department of Psychiatry, University of Texas Health Science Center at San Antonio, San Antonio, TX
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX
| | - David C Glahn
- Department of Psychiatry, Boston Children’s Hospital, Harvard Medical School, Boston, MA
| | - Yunlong Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, P. R. China
| | - L Elliot Hong
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
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21
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Lynn JD, Anand C, Arshad M, Homayouni R, Rosenberg DR, Ofen N, Raz N, Stanley JA. Microstructure of Human Corpus Callosum across the Lifespan: Regional Variations in Axon Caliber, Density, and Myelin Content. Cereb Cortex 2021; 31:1032-1045. [PMID: 32995843 PMCID: PMC7906774 DOI: 10.1093/cercor/bhaa272] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 08/21/2020] [Accepted: 08/21/2020] [Indexed: 12/13/2022] Open
Abstract
The myeloarchitecture of the corpus callosum (CC) is characterized as a mosaic of distinct differences in fiber density of small- and large-diameter axons along the anterior-posterior axis; however, regional and age differences across the lifespan are not fully understood. Using multiecho T2 magnetic resonance imaging combined with multi-T2 fitting, the myelin water fraction (MWF) and geometric-mean of the intra-/extracellular water T2 (geomT2IEW) in 395 individuals (7-85 years; 41% males) were examined. The approach was validated where regional patterns along the CC closely resembled the histology; MWF matched mean axon diameter and geomT2IEW mirrored the density of large-caliber axons. Across the lifespan, MWF exhibited a quadratic association with age in all 10 CC regions with evidence of a positive linear MWF-age relationship among younger participants and minimal age differences in the remainder of the lifespan. Regarding geomT2IEW, a significant linear age × region interaction reflected positive linear age dependence mostly prominent in the regions with the highest density of small-caliber fibers-genu and splenium. In all, these two indicators characterize distinct attributes that are consistent with histology, which is a first. In addition, these results conform to rapid developmental progression of CC myelination leveling in middle age as well as age-related degradation of axon sheaths in older adults.
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Affiliation(s)
- Jonathan D Lynn
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit MI 48201, USA
- Institute of Gerontology, Wayne State University, Detroit MI 48202, USA
| | - Chaitali Anand
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit MI 48201, USA
- Institute of Gerontology, Wayne State University, Detroit MI 48202, USA
| | - Muzamil Arshad
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit MI 48201, USA
| | - Roya Homayouni
- Institute of Gerontology, Wayne State University, Detroit MI 48202, USA
- Department of Psychology, Wayne State University, Detroit MI 48201, USA
| | - David R Rosenberg
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit MI 48201, USA
| | - Noa Ofen
- Institute of Gerontology, Wayne State University, Detroit MI 48202, USA
- Department of Psychology, Wayne State University, Detroit MI 48201, USA
- Lifespan Cognitive Neuroscience, Merrill Palmer Skillman Institute, Wayne State University, Detroit MI 14195, USA
| | - Naftali Raz
- Institute of Gerontology, Wayne State University, Detroit MI 48202, USA
- Department of Psychology, Wayne State University, Detroit MI 48201, USA
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin 14195, Germany
| | - Jeffrey A Stanley
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit MI 48201, USA
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22
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Qiu X, Lu S, Zhou M, Yan W, Du J, Zhang A, Xie S, Zhang R. The Relationship Between Abnormal Resting-State Functional Connectivity of the Left Superior Frontal Gyrus and Cognitive Impairments in Youth-Onset Drug-Naïve Schizophrenia. Front Psychiatry 2021; 12:679642. [PMID: 34721094 PMCID: PMC8548582 DOI: 10.3389/fpsyt.2021.679642] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 09/02/2021] [Indexed: 11/25/2022] Open
Abstract
Objective: Age of onset is one of the heterogeneous factors in schizophrenia, and an earlier onset of the disease indicated a worse prognosis. The left superior frontal gyrus (SFG) is involved in numerous cognitive and motor control tasks. Hence, we explored the relationship between abnormal changes in SFG resting-state functional connectivity (rsFC) and cognitive function in the peak age of incidence to understand better the pathophysiological mechanism in youth-onset drug-naïve schizophrenia to search for reliable biomarkers. Methods: About 66 youth-onset drug-naïve schizophrenia patients and 59 healthy controls (HCs) were included in this study. Abnormal connectivity changes in the left SFG and whole brain were measured using the region of interest (ROI) rsFC analysis method. The cognitive function was assessed using the MATRICS Consensus Cognitive Battery (MCCB), and the severity of the clinical symptoms was evaluated by positive and negative syndrome scale (PANSS). Furthermore, we analyzed the relationships among abnormal FC values, cognition scores, and clinical symptoms. Results: We found decreased FC between left SFG and bilateral precuneus (PCUN), right hippocampus, right parahippocampal gyrus, left thalamus, left caudate, insula, and right superior parietal lobule (SPL), whereas increased FC was seen between the left SFG and right middle frontal gyrus (MFG) in the youth-onset drug-naïve schizophrenia group, compared with HCs. Meanwhile, the T-scores were lower in each cognitive domain than HCs. Moreover, in the youth-onset drug-naive schizophrenia group, the insula was negatively correlated with processing speed. No significant correlations were found between the FC-value and PANSS score. Conclusions: Our findings suggest widespread FC network abnormalities in the left SFG and widespread cognitive impairments in the early stages of schizophrenia. The dysfunctional connectivity of the left SFG may be a potential pathophysiological mechanism in youth-onset drug-naïve schizophrenia.
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Affiliation(s)
- Xiaolei Qiu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shuiping Lu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Min Zhou
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wei Yan
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jinglun Du
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Aoshuang Zhang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shiping Xie
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Rongrong Zhang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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Hegde RR, Kelly S, Lutz O, Guimond S, Karayumak SC, Mike L, Mesholam-Gately RI, Pasternak O, Kubicki M, Eack SM, Keshavan MS. Association of white matter microstructure and extracellular free-water with cognitive performance in the early course of schizophrenia. Psychiatry Res Neuroimaging 2020; 305:111159. [PMID: 32919288 DOI: 10.1016/j.pscychresns.2020.111159] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 07/26/2020] [Accepted: 08/12/2020] [Indexed: 10/23/2022]
Abstract
Schizophrenia (SZ) is proposed as a disorder of dysconnectivity underlying cognitive impairments and clinical manifestations. Although previous studies have shown extracellular changes in white matter of first-episode SZ, little is known about the transition period towards chronicity and its association with cognition. Free-water (FW) imaging was applied to 79 early course SZ participants and 29 controls to detect white matter axonal and extracellular differences during this phase of illness. Diffusion-weighted images were collected from two sites, harmonized, and processed using a pipeline separately modeling water diffusion in tissue (FAt) and extracellular space (FW). Tract-Based Spatial Statistics was performed using the ENIGMA-DTI protocols. SZ showed FAt reductions in the posterior thalamic radiation (PTR) and FW elevations in the cingulum compared to controls, suggesting FAt and FW changes in the early course of SZ. In SZ, greater FAt of the fornix & stria terminalis (FXST) was positively associated with Theory of Mind performance; average whole-brain FAt, FAt of the FXST and the PTR were positively associated with greater working memory performance; average whole-brain FAt was positively associated with visual learning. Further studies are necessary to better understand the neurobiological mechanisms of SZ for developing intervention strategies to preserve brain structure and function.
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Affiliation(s)
- Rachal R Hegde
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Sinead Kelly
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Olivia Lutz
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Synthia Guimond
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA; The Royal's Institute of Mental Health Research, Department of Psychiatry, University of Ottawa, ON, Canada
| | - Suheyla Cetin Karayumak
- Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Luke Mike
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Raquelle I Mesholam-Gately
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Marek Kubicki
- Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Shaun M Eack
- Department of Psychiatry and School of Social Work, University of Pittsburgh, Pittsburgh, PA, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
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Assessment of brain cholesterol metabolism biomarker 24S-hydroxycholesterol in schizophrenia. NPJ SCHIZOPHRENIA 2020; 6:34. [PMID: 33219208 PMCID: PMC7680117 DOI: 10.1038/s41537-020-00121-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 09/03/2020] [Indexed: 11/08/2022]
Abstract
Plasma 24S-hydroxycholesterol mostly originates in brain tissue and likely reflects the turnover of cholesterol in the central nervous system. As cholesterol is disproportionally enriched in many key brain structures, 24S-hydroxycholesterol is a promising biomarker for psychiatric and neurologic disorders that impact brain structure. We hypothesized that, as schizophrenia patients have widely reported gray and white matter deficits, they would have abnormal levels of plasma 24S-hydroxycholesterol, and that plasma levels of 24S-hydroxycholesterol would be associated with brain structural and functional biomarkers for schizophrenia. Plasma levels of 24S-hydroxycholesterol were measured in 226 individuals with schizophrenia and 204 healthy controls. The results showed that levels of 24S-hydroxycholesterol were not significantly different between patients and controls. Age was significantly and negatively correlated with 24S-hydroxycholesterol in both groups, and in both groups, females had significantly higher levels of 24S-hydroxycholesterol compared to males. Levels of 24S-hydroxycholesterol were not related to average fractional anisotropy of white matter or cortical thickness, or to cognitive deficits in schizophrenia. Based on these results from a large sample and using multiple brain biomarkers, we conclude there is little to no value of plasma 24S-hydroxycholesterol as a brain metabolite biomarker for schizophrenia.
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Kwon D, Pfefferbaum A, Sullivan EV, Pohl KM. Regional growth trajectories of cortical myelination in adolescents and young adults: longitudinal validation and functional correlates. Brain Imaging Behav 2020; 14:242-266. [PMID: 30406353 DOI: 10.1007/s11682-018-9980-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Adolescence is a time of continued cognitive and emotional evolution occurring with continuing brain development involving synaptic pruning and cortical myelination. The hypothesis of this study is that heavy myelination occurs in cortical regions with relatively direct, predetermined circuitry supporting unimodal sensory or motor functions and shows a steep developmental slope during adolescence (12-21 years) until young adulthood (22-35 years) when further myelination decelerates. By contrast, light myelination occurs in regions with highly plastic circuitry supporting complex functions and follows a delayed developmental trajectory. In support of this hypothesis, cortical myelin content was estimated and harmonized across publicly available datasets provided by the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) and the Human Connectome Project (HCP). The cross-sectional analysis of 226 no-to-low alcohol drinking NCANDA adolescents revealed relatively steeper age-dependent trajectories of myelin growth in unimodal primary motor cortex and flatter age-dependent trajectories in multimodal mid/posterior cingulate cortices. This pattern of continued myelination showed smaller gains when the same analyses were performed on 686 young adults of the HCP cohort free of neuropsychiatric diagnoses. Critically, a predicted correlation between a motor task and myelin content in motor or cingulate cortices was found in the NCANDA adolescents, supporting the functional relevance of this imaging neurometric. Furthermore, the regional trajectory slopes were confirmed by performing longitudinally consistent analysis of cortical myelin. In conclusion, coordination of myelin content and circuit complexity continues to develop throughout adolescence, contributes to performance maturation, and may represent active cortical development climaxing in young adulthood.
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Affiliation(s)
- Dongjin Kwon
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Center for Health Sciences, SRI International, 333 Ravenswood Avenue, Menlo Park, CA, 94025, USA
| | - Adolf Pfefferbaum
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Center for Health Sciences, SRI International, 333 Ravenswood Avenue, Menlo Park, CA, 94025, USA
| | - Edith V Sullivan
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Kilian M Pohl
- Center for Health Sciences, SRI International, 333 Ravenswood Avenue, Menlo Park, CA, 94025, USA.
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DeLisi LE. What a Clinician Should Know About the Neurobiology of Schizophrenia: A Historical Perspective to Current Understanding. FOCUS (AMERICAN PSYCHIATRIC PUBLISHING) 2020; 18:368-374. [PMID: 33343248 PMCID: PMC7725146 DOI: 10.1176/appi.focus.20200022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The brain is no doubt the "organ" of psychiatry; yet, over the years, few evidence-based classifications of psychiatric disorders have been based on brain mechanisms. The National Institute of Mental Health notably proposed one such system, known as Research Domain Criteria, although it has not yet influenced any changes in the DSM. Of all the major psychiatric disorders, the brain has been studied most extensively in schizophrenia, with its speculative pathology first documented by Emil Kraepelin as early as the beginning of the 20th century. Subsequently, the revolution in technology over the past 50 years has changed how investigators are able to view the brain before death without performing biopsies. Schizophrenia is thus found to have both structural and functional widespread brain anomalies that likely lead to its clinical deterioration. At the onset of illness, acquiring an MRI scan could be part of the routine evaluation to determine how progressive the disease has so far been. However, this practice is not yet recognized by the American Psychiatric Association in any of its guidelines on the treatment of schizophrenia.
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Affiliation(s)
- Lynn E DeLisi
- Department of Psychiatry, Harvard Medical School, Boston, and Cambridge Health Alliance, Cambridge Hospital, Cambridge, Massachusetts
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Chen J, Zhang G, Chiou W, Laidlaw DH, Auchus AP. Measuring the Effects of Scalar and Spherical Colormaps on Ensembles of DMRI Tubes. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2020; 26:2818-2833. [PMID: 30763242 DOI: 10.1109/tvcg.2019.2898438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
We report empirical study results on the color encoding of ensemble scalar and orientation to visualize diffusion magnetic resonance imaging (DMRI) tubes. The experiment tested six scalar colormaps for average fractional anisotropy (FA) tasks (grayscale, blackbody, diverging, isoluminant-rainbow, extended-blackbody, and coolwarm) and four three-dimensional (3D) spherical colormaps for tract tracing tasks (uniform gray, absolute, eigenmaps, and Boy's surface embedding). We found that extended-blackbody, coolwarm, and blackbody remain the best three approaches for identifying ensemble average in 3D. Isoluminant-rainbow colormap led to the same ensemble mean accuracy as other colormaps. However, more than 50 percent of the answers consistently had higher estimates of the ensemble average, independent of the mean values. The number of hues, not luminance, influences ensemble estimates of mean values. For ensemble orientation-tracing tasks, we found that both Boy's surface embedding (greatest spatial resolution and contrast) and absolute colormaps (lowest spatial resolution and contrast) led to more accurate answers than the eigenmaps scheme (medium resolution and contrast), acting as the uncanny-valley phenomenon of visualization design in terms of accuracy. Absolute colormap broadly used in brain science is a good default spherical colormap. We could conclude from our study that human visual processing of a chunk of colors differs from that of single colors.
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28
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Tønnesen S, Kaufmann T, de Lange AMG, Richard G, Doan NT, Alnæs D, van der Meer D, Rokicki J, Moberget T, Maximov II, Agartz I, Aminoff SR, Beck D, Barch DM, Beresniewicz J, Cervenka S, Fatouros-Bergman H, Craven AR, Flyckt L, Gurholt TP, Haukvik UK, Hugdahl K, Johnsen E, Jönsson EG, Kolskår KK, Kroken RA, Lagerberg TV, Løberg EM, Nordvik JE, Sanders AM, Ulrichsen K, Andreassen OA, Westlye LT. Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder: A Multisample Diffusion Tensor Imaging Study. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:1095-1103. [PMID: 32859549 DOI: 10.1016/j.bpsc.2020.06.014] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 06/15/2020] [Accepted: 06/26/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts. METHODS We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18-94 years of age) and applied the models to the test sets including 648 patients with SZ (18-66 years of age), 185 patients with BD (18-64 years of age), and 990 HC subjects (17-68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. RESULTS Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen's d = -0.29) and patients with BD (Cohen's d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy-based models showed larger group differences than the models based on other DTI-derived metrics. CONCLUSIONS Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.
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Affiliation(s)
- Siren Tønnesen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ann-Marie G de Lange
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom
| | - Geneviève Richard
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Nhat Trung Doan
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Bjørknes University College, Oslo, Norway
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Jaroslav Rokicki
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Torgeir Moberget
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ivan I Maximov
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm Region, Stockholm, Sweden; Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Sofie R Aminoff
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Early Intervention in Psychosis Advisory Unit for South East Norway, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dani Beck
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Deanna M Barch
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri; Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri; Department of Radiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Justyna Beresniewicz
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway; NORMENT, Haukeland University Hospital, Bergen, Norway
| | - Simon Cervenka
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm Region, Stockholm, Sweden; Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Helena Fatouros-Bergman
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm Region, Stockholm, Sweden; Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Alexander R Craven
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway; NORMENT, Haukeland University Hospital, Bergen, Norway; Department of Clinical Engineering, Haukeland University Hospital, Bergen, Norway
| | - Lena Flyckt
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm Region, Stockholm, Sweden; Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Tiril P Gurholt
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Unn K Haukvik
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Adult Psychiatry Unit, Department of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Kenneth Hugdahl
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway; NORMENT, Haukeland University Hospital, Bergen, Norway; Department of Radiology, Haukeland University Hospital, Bergen, Norway; Department of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Erik Johnsen
- Department of Clinical Medicine (K1), University of Bergen, Bergen, Norway
| | - Erik G Jönsson
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm Region, Stockholm, Sweden; Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | | | - Knut K Kolskår
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Sunnaas Rehabilitation Hospital HF, Nesodden, Norway
| | - Rune Andreas Kroken
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway; NORMENT, Haukeland University Hospital, Bergen, Norway
| | - Trine V Lagerberg
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Else-Marie Løberg
- Department of Clinical Psychology, University of Bergen, Bergen, Norway; NORMENT, Haukeland University Hospital, Bergen, Norway; Department of Addiction Medicine, Haukeland University Hospital, Bergen, Norway
| | | | - Anne-Marthe Sanders
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Sunnaas Rehabilitation Hospital HF, Nesodden, Norway
| | - Kristine Ulrichsen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Sunnaas Rehabilitation Hospital HF, Nesodden, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
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The effects of age and sex on cognitive impairment in schizophrenia: Findings from the Consortium on the Genetics of Schizophrenia (COGS) study. PLoS One 2020; 15:e0232855. [PMID: 32401791 PMCID: PMC7219730 DOI: 10.1371/journal.pone.0232855] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 04/22/2020] [Indexed: 01/05/2023] Open
Abstract
Recently emerging evidence indicates accelerated age-related changes in the structure and function of the brain in schizophrenia, raising a question about its potential consequences on cognitive function. Using a large sample of schizophrenia patients and controls and a battery of tasks across multiple cognitive domains, we examined whether patients show accelerated age-related decline in cognition and whether an age-related effect differ between females and males. We utilized data of 1,415 schizophrenia patients and 1,062 healthy community collected by the second phase of the Consortium on the Genetics of Schizophrenia (COGS-2). A battery of cognitive tasks included the Letter-Number Span Task, two forms of the Continuous Performance Test, the California Verbal Learning Test, Second Edition, the Penn Emotion Identification Test and the Penn Facial Memory Test. The effect of age and gender on cognitive performance was examined with a general linear model. We observed age-related changes on most cognitive measures, which was similar between males and females. Compared to controls, patients showed greater deterioration in performance on attention/vigilance and greater slowness of processing social information with increasing age. However, controls showed greater age-related changes in working memory and verbal memory compared to patients. Age-related changes (η2p of 0.001 to .008) were much smaller than between-group differences (η2p of 0.005 to .037). This study found that patients showed continued decline of cognition on some domains but stable impairment or even less decline on other domains with increasing age. These findings indicate that age-related changes in cognition in schizophrenia are subtle and not uniform across multiple cognitive domains.
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Yamada S, Takahashi S, Ohoshi Y, Ishida T, Tsuji T, Shinosaki K, Terada M, Ukai S. Widespread white matter microstructural abnormalities and cognitive impairment in schizophrenia, bipolar disorder, and major depressive disorder: Tract-based spatial statistics study. Psychiatry Res Neuroimaging 2020; 298:111045. [PMID: 32087457 DOI: 10.1016/j.pscychresns.2020.111045] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 02/01/2020] [Accepted: 02/06/2020] [Indexed: 12/12/2022]
Affiliation(s)
- Shinichi Yamada
- Department of Neuropsychiatry, Wakayama Medical University, 811-1, Kimiidera, Wakayama 641-0012 Japan.
| | - Shun Takahashi
- Department of Neuropsychiatry, Wakayama Medical University, 811-1, Kimiidera, Wakayama 641-0012 Japan
| | - Yuji Ohoshi
- Department of Neuropsychiatry, Wakayama Medical University, 811-1, Kimiidera, Wakayama 641-0012 Japan
| | - Takuya Ishida
- Department of Neuropsychiatry, Wakayama Medical University, 811-1, Kimiidera, Wakayama 641-0012 Japan
| | - Tomikimi Tsuji
- Department of Neuropsychiatry, Wakayama Medical University, 811-1, Kimiidera, Wakayama 641-0012 Japan
| | | | | | - Satoshi Ukai
- Department of Neuropsychiatry, Wakayama Medical University, 811-1, Kimiidera, Wakayama 641-0012 Japan
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31
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Karlsgodt KH. White Matter Microstructure across the Psychosis Spectrum. Trends Neurosci 2020; 43:406-416. [PMID: 32349908 DOI: 10.1016/j.tins.2020.03.014] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 03/12/2020] [Accepted: 03/30/2020] [Indexed: 12/11/2022]
Abstract
Diffusion-weighted imaging (DWI) is a neuroimaging technique that has allowed us an unprecedented look at the role that white matter microstructure may play in mental illnesses, such as psychosis. Psychosis-related illnesses, including schizophrenia, are increasingly viewed as existing along a spectrum; spectrums may be defined based on factors such as stage of illness, symptom severity, or genetic liability. This review first focuses on an overview of some of the recent findings from DWI studies. Then, it examines the ways in which DWI analyses have been extended across the broader psychosis spectrum, or spectrums, and what we have learned from such approaches.
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Affiliation(s)
- Katherine H Karlsgodt
- Departments of Psychology and Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, 1285 Franz Hall, Box 951563, Los Angeles, CA 90095, USA.
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32
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Wisner KM, Chiappelli J, Savransky A, Fisseha F, Rowland LM, Kochunov P, Hong LE. Cingulum and abnormal psychological stress response in schizophrenia. Brain Imaging Behav 2020; 14:548-561. [PMID: 31123971 PMCID: PMC6874732 DOI: 10.1007/s11682-019-00120-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Stress is implicated in many aspects of schizophrenia, including heightened distress intolerance. We examined how affect and microstructure of major brain tracts involved in regulating affect may contribute to distress intolerance in schizophrenia. Patients with schizophrenia spectrum disorders (n = 78) and community controls (n = 95) completed diffusion weighted imaging and performed psychological stress tasks. Subjective affect was collected pre and post stressors. Individuals who did not persist during one or both stress tasks were considered distress intolerant (DI), and otherwise distress tolerant (DT). Fractional anisotropy (FA) of the dorsal cingulum showed a significant diagnosis x DT/DI phenotype interaction (p = 0.003). Post-hoc tests showed dorsal cingulum FA was significantly lower in DI patients compared with DI controls (p < 0.001), but not different between DT groups (p = 0.27). Regarding affect responses to stress, irritability showed the largest stress-related change (p < 0.001), but irritability changes were significantly reduced in DI patients compared to DI controls (p = 0.006). The relationship between irritability change and performance errors also differed among patients (ρ = -0.29, p = 0.011) and controls (ρ = 0.21, p = 0.042). Further modeling highlighted the explanatory power of dorsal cingulum for predicting DI even after performance and irritability were taken into account. Distress intolerance during psychological stress exposure is related to microstructural properties of the dorsal cingulum, a key structure for cognitive control and emotion regulation. In schizophrenia, the affective response to psychological stressors is abnormal, and distress intolerant patients had significantly reduced dorsal cingulum FA compared to distress intolerant controls. The findings provide new insight regarding distress intolerance in schizophrenia.
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Affiliation(s)
- Krista M Wisner
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, P.O. Box 21247, Baltimore, MD, 21228, USA.
| | - Joshua Chiappelli
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, P.O. Box 21247, Baltimore, MD, 21228, USA
| | - Anya Savransky
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, P.O. Box 21247, Baltimore, MD, 21228, USA
| | - Feven Fisseha
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, P.O. Box 21247, Baltimore, MD, 21228, USA
| | - Laura M Rowland
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, P.O. Box 21247, Baltimore, MD, 21228, USA
| | - Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, P.O. Box 21247, Baltimore, MD, 21228, USA
| | - L Elliot Hong
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, P.O. Box 21247, Baltimore, MD, 21228, USA
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Thompson PM, Jahanshad N, Ching CRK, Salminen LE, Thomopoulos SI, Bright J, Baune BT, Bertolín S, Bralten J, Bruin WB, Bülow R, Chen J, Chye Y, Dannlowski U, de Kovel CGF, Donohoe G, Eyler LT, Faraone SV, Favre P, Filippi CA, Frodl T, Garijo D, Gil Y, Grabe HJ, Grasby KL, Hajek T, Han LKM, Hatton SN, Hilbert K, Ho TC, Holleran L, Homuth G, Hosten N, Houenou J, Ivanov I, Jia T, Kelly S, Klein M, Kwon JS, Laansma MA, Leerssen J, Lueken U, Nunes A, Neill JO, Opel N, Piras F, Piras F, Postema MC, Pozzi E, Shatokhina N, Soriano-Mas C, Spalletta G, Sun D, Teumer A, Tilot AK, Tozzi L, van der Merwe C, Van Someren EJW, van Wingen GA, Völzke H, Walton E, Wang L, Winkler AM, Wittfeld K, Wright MJ, Yun JY, Zhang G, Zhang-James Y, Adhikari BM, Agartz I, Aghajani M, Aleman A, Althoff RR, Altmann A, Andreassen OA, Baron DA, Bartnik-Olson BL, Marie Bas-Hoogendam J, Baskin-Sommers AR, Bearden CE, Berner LA, Boedhoe PSW, Brouwer RM, Buitelaar JK, Caeyenberghs K, Cecil CAM, Cohen RA, Cole JH, Conrod PJ, De Brito SA, de Zwarte SMC, Dennis EL, Desrivieres S, Dima D, Ehrlich S, Esopenko C, Fairchild G, Fisher SE, Fouche JP, Francks C, et alThompson PM, Jahanshad N, Ching CRK, Salminen LE, Thomopoulos SI, Bright J, Baune BT, Bertolín S, Bralten J, Bruin WB, Bülow R, Chen J, Chye Y, Dannlowski U, de Kovel CGF, Donohoe G, Eyler LT, Faraone SV, Favre P, Filippi CA, Frodl T, Garijo D, Gil Y, Grabe HJ, Grasby KL, Hajek T, Han LKM, Hatton SN, Hilbert K, Ho TC, Holleran L, Homuth G, Hosten N, Houenou J, Ivanov I, Jia T, Kelly S, Klein M, Kwon JS, Laansma MA, Leerssen J, Lueken U, Nunes A, Neill JO, Opel N, Piras F, Piras F, Postema MC, Pozzi E, Shatokhina N, Soriano-Mas C, Spalletta G, Sun D, Teumer A, Tilot AK, Tozzi L, van der Merwe C, Van Someren EJW, van Wingen GA, Völzke H, Walton E, Wang L, Winkler AM, Wittfeld K, Wright MJ, Yun JY, Zhang G, Zhang-James Y, Adhikari BM, Agartz I, Aghajani M, Aleman A, Althoff RR, Altmann A, Andreassen OA, Baron DA, Bartnik-Olson BL, Marie Bas-Hoogendam J, Baskin-Sommers AR, Bearden CE, Berner LA, Boedhoe PSW, Brouwer RM, Buitelaar JK, Caeyenberghs K, Cecil CAM, Cohen RA, Cole JH, Conrod PJ, De Brito SA, de Zwarte SMC, Dennis EL, Desrivieres S, Dima D, Ehrlich S, Esopenko C, Fairchild G, Fisher SE, Fouche JP, Francks C, Frangou S, Franke B, Garavan HP, Glahn DC, Groenewold NA, Gurholt TP, Gutman BA, Hahn T, Harding IH, Hernaus D, Hibar DP, Hillary FG, Hoogman M, Hulshoff Pol HE, Jalbrzikowski M, Karkashadze GA, Klapwijk ET, Knickmeyer RC, Kochunov P, Koerte IK, Kong XZ, Liew SL, Lin AP, Logue MW, Luders E, Macciardi F, Mackey S, Mayer AR, McDonald CR, McMahon AB, Medland SE, Modinos G, Morey RA, Mueller SC, Mukherjee P, Namazova-Baranova L, Nir TM, Olsen A, Paschou P, Pine DS, Pizzagalli F, Rentería ME, Rohrer JD, Sämann PG, Schmaal L, Schumann G, Shiroishi MS, Sisodiya SM, Smit DJA, Sønderby IE, Stein DJ, Stein JL, Tahmasian M, Tate DF, Turner JA, van den Heuvel OA, van der Wee NJA, van der Werf YD, van Erp TGM, van Haren NEM, van Rooij D, van Velzen LS, Veer IM, Veltman DJ, Villalon-Reina JE, Walter H, Whelan CD, Wilde EA, Zarei M, Zelman V. ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries. Transl Psychiatry 2020; 10:100. [PMID: 32198361 PMCID: PMC7083923 DOI: 10.1038/s41398-020-0705-1] [Show More Authors] [Citation(s) in RCA: 355] [Impact Index Per Article: 71.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 12/11/2019] [Accepted: 12/20/2019] [Indexed: 02/07/2023] Open
Abstract
This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.
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Affiliation(s)
- Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Lauren E Salminen
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Joanna Bright
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Bernhard T Baune
- Department of Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Sara Bertolín
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, Barcelona, Spain
| | - Janita Bralten
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Willem B Bruin
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Robin Bülow
- Institute for Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Jian Chen
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
| | - Yann Chye
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, VIC, Australia
| | - Udo Dannlowski
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Carolien G F de Kovel
- Biometris Wageningen University and Research, Wageningen, The Netherlands
- Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Gary Donohoe
- The Center for Neuroimaging and Cognitive Genomics, School of Psychology, National University of Ireland, Galway, Ireland
| | - Lisa T Eyler
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Desert-Pacific Mental Illness Research, Education, and Clinical Center, VA San Diego Healthcare System, San Diego, CA, USA
| | - Stephen V Faraone
- Departments of Psychiatry and of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Pauline Favre
- INSERM Unit 955 Team 15 'Translational Psychiatry', Créteil, France
- NeuroSpin, UNIACT Lab, Psychiatry Team, CEA Saclay, Gif-Sur-Yvette, France
| | - Courtney A Filippi
- National Institute of Mental Health, National of Health, Bethesda, MD, USA
| | - Thomas Frodl
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Magdeburg, Germany
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Daniel Garijo
- Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA
| | - Yolanda Gil
- Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA
- Department of Computer Science, University of Southern California, Los Angeles, CA, USA
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany
| | - Katrina L Grasby
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Tomas Hajek
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
- National Institute of Mental Health, Klecany, Czech Republic
| | - Laura K M Han
- Department of Psychiatry, Amsterdam University Medical Centers, VU University Medical Center, GGZ inGeest, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Sean N Hatton
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA
- Brain and Mind Centre, University of Sydney, Sydney, Australia
| | - Kevin Hilbert
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Tiffany C Ho
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA
- Department of Psychiatry & Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Laurena Holleran
- The Center for Neuroimaging and Cognitive Genomics, School of Psychology, National University of Ireland, Galway, Ireland
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Norbert Hosten
- Institute for Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Josselin Houenou
- INSERM Unit 955 Team 15 'Translational Psychiatry', Créteil, France
- NeuroSpin, UNIACT Lab, Psychiatry Team, CEA Saclay, Gif-Sur-Yvette, France
- APHP, Mondor University Hospitals, School of Medicine, DMU Impact, Psychiatry Department, Créteil, France
| | - Iliyan Ivanov
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Centre for Population Neuroscience and Precision Medicine (PONS), MRC SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Sinead Kelly
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA
| | - Marieke Klein
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Max A Laansma
- Department of Anatomy & Neurosciences, Amsterdam UMC, Location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Jeanne Leerssen
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Ulrike Lueken
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Abraham Nunes
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
- Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
| | - Joseph O' Neill
- Child & Adolescent Psychiatry, University of California, Los Angeles, Los Angeles, CA, USA
| | - Nils Opel
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Federica Piras
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Merel C Postema
- Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Elena Pozzi
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
- Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, VIC, Australia
| | - Natalia Shatokhina
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Carles Soriano-Mas
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, Barcelona, Spain
- CIBERSAM-G17, Madrid, Spain
- Department of Psychobiology and Methodology in Health Sciences, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Daqiang Sun
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Mental Health, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Amanda K Tilot
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Leonardo Tozzi
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Celia van der Merwe
- Stanley Center for Psychiatric Research, The Broad Institute, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Eus J W Van Someren
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
- Psychiatry and Integrative Neurophysiology, VU University, Amsterdam UMC, Amsterdam, The Netherlands
| | - Guido A van Wingen
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research, Partner Site Greifswald, Greifswald, Germany
| | - Esther Walton
- Department of Psychology, University of Bath, Bath, UK
| | - Lei Wang
- Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Anderson M Winkler
- National Institute of Mental Health, National of Health, Bethesda, MD, USA
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - Je-Yeon Yun
- Seoul National University Hospital, Seoul, Republic of Korea
- Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Guohao Zhang
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, MD, USA
| | - Yanli Zhang-James
- Departments of Psychiatry and of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Bhim M Adhikari
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health & Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Moji Aghajani
- Department of Psychiatry, Amsterdam UMC, Location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Research & Innovation, GGZ InGeest, Amsterdam, The Netherlands
| | - André Aleman
- University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Robert R Althoff
- Psychiatry, Pediatrics, and Psychological Sciences, University of Vermont, Burlington, VT, USA
| | - Andre Altmann
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health & Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - David A Baron
- Provost and Senior Vice President, Western University of Health Sciences, Pomona, CA, USA
| | | | - Janna Marie Bas-Hoogendam
- Institute of Psychology, Leiden University, Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | | | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Laura A Berner
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Premika S W Boedhoe
- Department of Psychiatry, Amsterdam UMC, Location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Rachel M Brouwer
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jan K Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Burwood, VIC, Australia
| | - Charlotte A M Cecil
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Centre, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Ronald A Cohen
- Center for Cognitive Aging and Memory, University of Florida, Gainesville, FL, USA
- Clinical and Health Psychology, Gainesville, FL, USA
| | - James H Cole
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, London, UK
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Patricia J Conrod
- Universite de Montreal, Centre de Recherche CHU Ste-Justine, Montreal, QC, Canada
| | - Stephane A De Brito
- School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham, UK
| | - Sonja M C de Zwarte
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Emily L Dennis
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
- Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sylvane Desrivieres
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Danai Dima
- Department of Psychology, School of Arts and Social Sciences, City, University of London, London, UK
- Department of Neuroimaging, Institute of Psychology, Psychiatry and Neurosciences, King's College London, London, UK
| | - Stefan Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Carrie Esopenko
- Department of Rehabilitation and Movement Sciences, School of Health Professions, Rutgers Biomedical Health Sciences, Newark, NJ, USA
| | | | - Simon E Fisher
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Jean-Paul Fouche
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
- SU/UCT MRC Unit on Risk & Resilience in Mental Disorders, University of Stellenbosch, Stellenbosch, South Africa
| | - Clyde Francks
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- University of British Columbia, Vancouver, Canada
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Hugh P Garavan
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford, CT, USA
| | - Nynke A Groenewold
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Tiril P Gurholt
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health & Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Boris A Gutman
- Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
- Institute for Information Transmission Problems, Kharkevich Institute, Moscow, Russian Federation
| | - Tim Hahn
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Ian H Harding
- Turner Institute for Brain and Mental Health & School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - Dennis Hernaus
- Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | | | - Frank G Hillary
- Department of Psychology, Penn State University, University Park, PA, USA
- Social Life and Engineering Sciences Imaging Center, University Park, PA, USA
| | - Martine Hoogman
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Hilleke E Hulshoff Pol
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | | | - George A Karkashadze
- Research and Scientific Institute of Pediatrics and Child Health, CCH RAS, Ministry of Science and Higher Education, Moscow, Russian Federation
| | - Eduard T Klapwijk
- Institute of Psychology, Leiden University, Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Rebecca C Knickmeyer
- Department of Pediatrics, Michigan State University, East Lansing, MI, USA
- Institute for Quantitative Health Science and Engineering, East Lansing, MI, USA
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Peter Kochunov
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Inga K Koerte
- Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- CBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Xiang-Zhen Kong
- Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Sook-Lei Liew
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Chan Division of Occupational Science and Occupational Therapy, Los Angeles, CA, USA
| | - Alexander P Lin
- Center for Clinical Spectroscopy, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Mark W Logue
- National Center for PTSD at Boston VA Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
- Biomedical Genetics, Boston University School of Medicine, Boston, MA, USA
| | - Eileen Luders
- School of Psychology, University of Auckland, Auckland, New Zealand
- Laboratory of Neuro Imaging, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Fabio Macciardi
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, USA
| | - Scott Mackey
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | | | - Carrie R McDonald
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA
- Psychiatry, San Diego, CA, USA
| | - Agnes B McMahon
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
- The Kavli Foundation, Los Angeles, CA, USA
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Gemma Modinos
- Department of Neuroimaging, Institute of Psychology, Psychiatry and Neurosciences, King's College London, London, UK
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Rajendra A Morey
- Department of Psychiatry, Duke University School of Medicine, Durham, NC, USA
- Mental Illness Research Education and Clinical Center, Durham VA Medical Center, Durham, NC, USA
| | - Sven C Mueller
- Experimental Clinical & Health Psychology, Ghent University, Ghent, Belgium
- Department of Personality, Psychological Assessment and Treatment, University of Deusto, Bilbao, Spain
| | | | - Leyla Namazova-Baranova
- Research and Scientific Institute of Pediatrics and Child Health, CCH RAS, Ministry of Science and Higher Education, Moscow, Russian Federation
- Department of Pediatrics, Russian National Research Medical University MoH RF, Moscow, Russian Federation
| | - Talia M Nir
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Alexander Olsen
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Physical Medicine and Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | | | - Daniel S Pine
- National Institute of Mental Health Intramural Research Program, Bethesda, MD, USA
| | - Fabrizio Pizzagalli
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Miguel E Rentería
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jonathan D Rohrer
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | | | - Lianne Schmaal
- Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, VIC, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine (PONS), MRC SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Psychiatry and Psychotherapy, Charite, Humboldt University, Berlin, Germany
| | - Mark S Shiroishi
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
- Department of Radiology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Sanjay M Sisodiya
- Department of Clinical and Experimental Epilepsy, University College London, London, UK
- Chalfont Centre for Epilepsy, Chalfont St Peter, UK
| | - Dirk J A Smit
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Ida E Sønderby
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health & Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Dan J Stein
- Department of Psychiatry & Neuroscience Institute, SA MRC Unit on Risk & Resilience in Mental Disorders, Cape Town, South Africa
| | - Jason L Stein
- Department of Genetics & UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Masoud Tahmasian
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, I. R., Iran
| | - David F Tate
- Department of Neurology, TBI and Concussion Center, Salt Lake City, UT, USA
- Missouri Institute of Mental Health, Berkeley, MO, USA
| | - Jessica A Turner
- Psychology Department & Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Odile A van den Heuvel
- Department of Anatomy & Neurosciences, Amsterdam UMC, Location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Psychiatry, Amsterdam UMC, Location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Nic J A van der Wee
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Ysbrand D van der Werf
- Department of Anatomy & Neurosciences, Amsterdam UMC, Location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, USA
| | - Neeltje E M van Haren
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Daan van Rooij
- Donders Centre for Cognitive Neuroimaging, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Laura S van Velzen
- Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, VIC, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Ilya M Veer
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam UMC, Location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Julio E Villalon-Reina
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Henrik Walter
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Christopher D Whelan
- Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin, Ireland
- Research and Early Development, Biogen Inc, Cambridge, MA, USA
| | - Elisabeth A Wilde
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
- VA Salt Lake City Healthcare System, Salt Lake City, UT, USA
- Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
| | - Mojtaba Zarei
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, I. R., Iran
| | - Vladimir Zelman
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Skolkovo Institute of Science and Technology, Moscow, Russian Federation
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Kochunov P, Huang J, Chen S, Li Y, Tan S, Fan F, Feng W, Wang Y, Rowland LM, Savransky A, Du X, Chiappelli J, Chen S, Jahanshad N, Thompson PM, Ryan MC, Adhikari B, Sampath H, Cui Y, Wang Z, Yang F, Tan Y, Hong LE. White Matter in Schizophrenia Treatment Resistance. Am J Psychiatry 2019; 176:829-838. [PMID: 31352812 PMCID: PMC6773514 DOI: 10.1176/appi.ajp.2019.18101212] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
OBJECTIVE Failure of antipsychotic medications to resolve symptoms in patients with schizophrenia creates a clinical challenge that is known as treatment resistance. The causes of treatment resistance are unknown, but it is associated with earlier age at onset and more severe cognitive deficits. The authors tested the hypothesis that white matter deficits that are involved in both neurodevelopment and severity of cognitive deficits in schizophrenia are associated with a higher risk of treatment resistance. METHODS The study sample (N=122; mean age, 38.2 years) included schizophrenia patients at treatment initiation (N=45), patients whose symptoms were treatment responsive (N=40), and patients whose symptoms were treatment resistant (N=37), as well as healthy control subjects (N=78; mean age, 39.2 years). White matter regional vulnerability index (RVI) was tested as a predictor of treatment resistance and cognitive deficits. Higher RVI is indicative of better agreement between diffusion tensor imaging fractional anisotropy across the brain in an individual and the pattern identified by the largest-to-date meta-analysis of white matter deficits in schizophrenia. RESULTS Patients with treatment-resistant symptoms showed the highest white matter RVI (mean=0.38 [SD=0.2]), which was significantly higher than the RVI among patients with treatment-responsive symptoms (mean=0.30 [SD=0.02]). At the onset of treatment, schizophrenia patients showed significantly higher RVI than healthy control subjects (mean=0.18 [SD=0.03] and mean=0.13 [SD=0.02], respectively). RVIs were significantly correlated with performance on processing speed and negative symptoms. CONCLUSIONS Schizophrenia affects white matter microstructure in specific regional patterns. Susceptibility to white matter regional deficits is associated with an increased likelihood of treatment resistance. Developments to overcome schizophrenia treatment resistance should consider white matter as an important target.
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Affiliation(s)
- Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA,Corresponding Authors: Dr. Kochunov (), Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA, Phone: (410) 402-6110, Fax: (410) 402-6778; Dr. Tan (), Beijing Huilongguan Hospital, Peking University, Huilongguan Clinical Medical School, Beijing, P. R. China, Phone: (800) 010-83024532, Fax: (800) 010-83020156
| | - Junchao Huang
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, P. R. China
| | - Song Chen
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, P. R. China
| | - Yanli Li
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, P. R. China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, P. R. China
| | - Fengmei Fan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, P. R. China
| | - Wei Feng
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, P. R. China
| | - Yunhui Wang
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, P. R. China
| | - Laura M. Rowland
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Anya Savransky
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Xiaoming Du
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Joshua Chiappelli
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Meghann C. Ryan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bhim Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Hemalatha Sampath
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yimin Cui
- Department of Pharmacy, Peking University First Hospital, Beijing, P.R. China
| | - Zhiren Wang
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, P. R. China
| | - Fude Yang
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, P. R. China
| | - Yunlong Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, P. R. China,Corresponding Authors: Dr. Kochunov (), Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA, Phone: (410) 402-6110, Fax: (410) 402-6778; Dr. Tan (), Beijing Huilongguan Hospital, Peking University, Huilongguan Clinical Medical School, Beijing, P. R. China, Phone: (800) 010-83024532, Fax: (800) 010-83020156
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
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Sheffield JM, Rogers BP, Blackford JU, Heckers S, Woodward ND. Accelerated Aging of Functional Brain Networks Supporting Cognitive Function in Psychotic Disorders. Biol Psychiatry 2019; 86:240-248. [PMID: 30739807 PMCID: PMC6609513 DOI: 10.1016/j.biopsych.2018.12.016] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 12/03/2018] [Accepted: 12/04/2018] [Indexed: 12/18/2022]
Abstract
BACKGROUND Across networks, connectivity within the frontoparietal network (FPN) and cingulo-opercular network (CON) exhibits reductions earliest during healthy aging, contributing to cognitive impairment. Individuals with psychotic disorders demonstrate evidence of accelerated aging across multiple biological systems. By leveraging a large sample of patients with psychosis from early to chronic illness stages, this study sought to determine whether the CON and FPN exhibit evidence of accelerated aging in psychotic disorders, confirm associations between network efficiency and cognition, and determine whether reduced network efficiency is observed in early-stage psychosis. METHODS Resting-state functional magnetic resonance imaging and cognitive data were obtained on 240 patients with psychotic disorder and 178 healthy control participants (HCs). Global efficiency, a measure of functional integration, was calculated for the CON, FPN, subcortical network, and visual network. Associations with age and cognition were assessed and compared between groups. RESULTS Consistent with accelerated aging, significant group by age interactions reflected significantly stronger relationships between efficiency and age in patients with psychosis than in HCs for both the CON (psychosis: r = -.37; HC: r = -.16) and FPN (psychosis: r = -.31; HC: r = -.05). Accelerated aging was not observed in either the subcortical or visual network, suggesting specificity for cognitive networks that decline earliest in healthy aging. Replicating prior findings, efficiency of both the CON and FPN correlated with cognitive function across all participants (rs > .11, ps < .031). Furthermore, patients with chronic psychosis (p = .004), but not patients with early psychosis (p = .553), exhibited significantly lower FPN efficiency compared with HCs. CONCLUSIONS Functional integration of higher-order cognitive networks is intact in early psychosis but exhibits evidence of accelerated aging, suggesting the potential for intervention targeting cognition within the early psychosis period.
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Affiliation(s)
- Julia M Sheffield
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University School of Medicine, Nashville, Tennessee.
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Sciences, Nashville, Tennessee
| | - Jennifer U Blackford
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University School of Medicine, Nashville, Tennessee; Tennessee Valley Health Service, Department of Veterans Affairs Medical Center, Nashville, Tennessee
| | - Stephan Heckers
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Neil D Woodward
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University School of Medicine, Nashville, Tennessee
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Adhikari BM, Hong LE, Sampath H, Chiappelli J, Jahanshad N, Thompson PM, Rowland LM, Calhoun VD, Du X, Chen S, Kochunov P. Functional network connectivity impairments and core cognitive deficits in schizophrenia. Hum Brain Mapp 2019; 40:4593-4605. [PMID: 31313441 DOI: 10.1002/hbm.24723] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 07/03/2019] [Accepted: 07/08/2019] [Indexed: 12/19/2022] Open
Abstract
Cognitive deficits contribute to functional disability in patients with schizophrenia and may be related to altered functional networks that serve cognition. We evaluated the integrity of major functional networks and assessed their role in supporting two cognitive functions affected in schizophrenia: processing speed (PS) and working memory (WM). Resting-state functional magnetic resonance imaging (rsfMRI) data, N = 261 patients and 327 controls, were aggregated from three independent cohorts and evaluated using Enhancing NeuroImaging Genetics through Meta Analysis rsfMRI analysis pipeline. Meta- and mega-analyses were used to evaluate patient-control differences in functional connectivity (FC) measures. Canonical correlation analysis was used to study the association between cognitive deficits and FC measures. Patients showed consistent patterns of cognitive and resting-state FC (rsFC) deficits across three cohorts. Patient-control differences in rsFC calculated using seed-based and dual-regression approaches were consistent (Cohen's d: 0.31 ± 0.09 and 0.29 ± 0.08, p < 10-4 ). RsFC measures explained 12-17% of the individual variations in PS and WM in the full sample and in patients and controls separately, with the strongest correlations found in salience, auditory, somatosensory, and default-mode networks. The pattern of association between rsFC (within-network) and PS (r = .45, p = .07) and WM (r = .36, p = .16), and rsFC (between-network) and PS (r = .52, p = 8.4 × 10-3 ) and WM (r = .47, p = .02), derived from multiple networks was related to effect size of patient-control differences in the functional networks. No association was detected between rsFC and current medication dose or psychosis ratings. Patients demonstrated significant reduction in several FC networks that may partially underlie some of the core neurocognitive deficits in schizophrenia. The strength of connectivity-cognition relationships in different networks was strongly associated with network's vulnerability to schizophrenia.
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Affiliation(s)
- Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Hemalatha Sampath
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Joshua Chiappelli
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine of USC, Marina del Rey, California
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine of USC, Marina del Rey, California
| | - Laura M Rowland
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Vince D Calhoun
- Department of Electrical and Computer Engineering, The Mind Research Network, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico
| | - Xiaoming Du
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
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Li M, Becker B, Zheng J, Zhang Y, Chen H, Liao W, Duan X, Liu H, Zhao J, Chen H. Dysregulated Maturation of the Functional Connectome in Antipsychotic-Naïve, First-Episode Patients With Adolescent-Onset Schizophrenia. Schizophr Bull 2019; 45:689-697. [PMID: 29850901 PMCID: PMC6483582 DOI: 10.1093/schbul/sby063] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
BACKGROUND Schizophrenia has been conceptualized as a brain network disorder rooted in dysregulated neurodevelopmental processes. Recent neuroimaging studies revealed disrupted brain connectomic organization in adult schizophrenia patients. However, altered developmental trajectories of the functional connectome during the adolescent maturational stage have not been examined. METHODS The present study combined functional MRI with a graph theoretical approach to examine functional network topology and its age-related development in 39 medication naïve, first-episode patients with adolescent-onset schizophrenia and 31 matched controls (age range: 12-18 years). RESULTS Patients demonstrated impaired large-scale integration as reflected by reduced global efficiency as well as decreased regional nodal efficiency in highly integrative network hubs, most consistently the hippocampal formation and the precuneus. Furthermore, the left hippocampus showed opposite age-efficiency associations in healthy controls and patients, indicating dysregulated maturational trajectories in adolescent schizophrenia and a particular vulnerability of this region during early pathological attack. CONCLUSIONS The findings allow an integrative perspective on network and neurodevelopmental perspectives on schizophrenia, suggesting that dysregulated maturation of the functional connectome during adolescence might reflect an early marker for the disorder.
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Affiliation(s)
- Meiling Li
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Junjie Zheng
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yan Zhang
- Department of Psychiatry, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Heng Chen
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA
| | - Jingping Zhao
- Institute of Mental Health, the Second Xiangya Hospital, Central South University, Changsha, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China,To whom correspondence should be addressed; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 610054, China; tel: 86 13808003171, fax: 86 2883208238, e-mail:
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38
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Micro- and Macrostructural White Matter Integrity in Never-Treated and Currently Unmedicated Patients With Schizophrenia and Effects of Short-Term Antipsychotic Treatment. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:462-471. [PMID: 30852126 DOI: 10.1016/j.bpsc.2019.01.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 12/17/2018] [Accepted: 01/08/2019] [Indexed: 01/26/2023]
Abstract
BACKGROUND Schizophrenia is associated with progressive white matter changes, but it is unclear whether antipsychotic medications contribute to these. Our objective was to characterize effects of short-term treatment with risperidone on white matter diffusion indices. METHODS We recruited 42 patients with schizophrenia (30 never treated and 12 currently untreated) and 42 matched healthy control subjects in this prospective case-control neuroimaging study. Patients received a 6-week trial of risperidone. Using diffusion tensor imaging, we assessed microstructural (fractional anisotropy, mean diffusivity, and radial diffusivity) and macrostructural (radial fiber trophy) white matter integrity deficits in unmedicated patients compared with control subjects and change in white matter integrity in patients before and after antipsychotic treatment (mean risperidone dose at end point was 3.73 ± 1.72 mg). RESULTS At baseline, fractional anisotropy was decreased in the left medial temporal white matter (cluster extent: 123 voxels; Montreal Neurological Institute peak coordinates: x = -51, y = -44, z = -7; α < .05), and mean diffusivity was increased in the fusiform/lingual gyrus white matter extending to the hippocampal part of the cingulum (cluster extent: 185 voxels; peak coordinates: x = -27, y = -49, z = 2; α < .04) in patients compared with control subjects. Radial diffusivity and macrostructure were not abnormal. None of the diffusion indices showed a significant change after 6 weeks of treatment with both voxelwise and whole-brain white matter analyses. CONCLUSIONS We demonstrate microstructural white matter integrity abnormalities in the absence of macrostructural impairment in unmedicated patients with primarily early-stage schizophrenia. In our data, we found no significant white matter changes after short-term treatment with risperidone.
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Tønnesen S, Kaufmann T, Doan NT, Alnæs D, Córdova-Palomera A, Meer DVD, Rokicki J, Moberget T, Gurholt TP, Haukvik UK, Ueland T, Lagerberg TV, Agartz I, Andreassen OA, Westlye LT. White matter aberrations and age-related trajectories in patients with schizophrenia and bipolar disorder revealed by diffusion tensor imaging. Sci Rep 2018; 8:14129. [PMID: 30237410 PMCID: PMC6147807 DOI: 10.1038/s41598-018-32355-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 09/06/2018] [Indexed: 12/18/2022] Open
Abstract
Supported by histological and genetic evidence implicating myelin, neuroinflammation and oligodendrocyte dysfunction in schizophrenia spectrum disorders (SZ), diffusion tensor imaging (DTI) studies have consistently shown white matter (WM) abnormalities when compared to healthy controls (HC). The diagnostic specificity remains unclear, with bipolar disorders (BD) frequently conceptualized as a less severe clinical manifestation along a psychotic spectrum. Further, the age-related dynamics and possible sex differences of WM abnormalities in SZ and BD are currently understudied. Using tract-based spatial statistics (TBSS) we compared DTI-based microstructural indices between SZ (n = 128), BD (n = 61), and HC (n = 293). We tested for age-by-group and sex-by-group interactions, computed effect sizes within different age-bins and within genders. TBSS revealed global reductions in fractional anisotropy (FA) and increases in radial (RD) diffusivity in SZ compared to HC, with strongest effects in the body and splenium of the corpus callosum, and lower FA in SZ compared to BD in right inferior longitudinal fasciculus and right inferior fronto-occipital fasciculus, and no significant differences between BD and HC. The results were not strongly dependent on age or sex. Despite lack of significant group-by-age interactions, a sliding-window approach supported widespread WM involvement in SZ with most profound differences in FA from the late 20 s.
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Affiliation(s)
- Siren Tønnesen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Tobias Kaufmann
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Nhat Trung Doan
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dag Alnæs
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Aldo Córdova-Palomera
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dennis van der Meer
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jaroslav Rokicki
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Torgeir Moberget
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Tiril P Gurholt
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Unn K Haukvik
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Torill Ueland
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Trine Vik Lagerberg
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
- Department of Psychology, University of Oslo, Oslo, Norway.
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Bogart LJ, O’Donnell P. Multiple long-range inputs evoke NMDA currents in prefrontal cortex fast-spiking interneurons. Neuropsychopharmacology 2018; 43:2101-2108. [PMID: 29483660 PMCID: PMC6098111 DOI: 10.1038/s41386-018-0029-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Revised: 01/24/2018] [Accepted: 01/31/2018] [Indexed: 11/09/2022]
Abstract
Several aspects of schizophrenia can be mimicked acutely in healthy human volunteers via administration of NMDA glutamate receptor (NMDAR) antagonists. As these agents decrease firing rates in prefrontal cortical (PFC) GABAergic fast-spiking interneurons (FSI) in animal studies, a leading hypothesis on schizophrenia pathophysiology is that NMDAR in FSI are impaired. However, whole-cell recordings of FSI in slices of adult mouse PFC revealed limited amounts of NMDAR-mediated current. Since those studies used local electrical stimulation to activate a heterogeneous set of synaptic inputs to the recorded cell, it is unclear whether specific afferent inputs may preferentially drive NMDAR responses in FSI. Here, we expressed opsins in discrete brain regions projecting to the PFC in adult male mice, enabling light-activation of defined, homogenous sets of long-range inputs to FSI and pyramidal neurons recorded in slices containing medial PFC (mPFC). Stimulation of axons originating from either the contralateral mPFC, ventral hippocampus, or mediodorsal thalamus evoked NMDAR-mediated currents in the vast majority of FSI and in all pyramidal neurons recorded. The observation that multiple long-range inputs to mPFC FSI elicit NMDAR currents suggests that the NMDAR-hypofunction model of schizophrenia may still imply a loss of interneuron inputs, but the sources of reduced excitation may originate from sites upstream of the PFC.
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Affiliation(s)
- Luke Joseph Bogart
- 0000 0000 8800 7493grid.410513.2Internal Medicine Research Unit, Pfizer Inc., Cambridge, MA 02139 USA
| | - Patricio O’Donnell
- 0000 0000 8800 7493grid.410513.2Internal Medicine Research Unit, Pfizer Inc., Cambridge, MA 02139 USA
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41
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Ganjgahi H, Winkler AM, Glahn DC, Blangero J, Donohue B, Kochunov P, Nichols TE. Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes. Nat Commun 2018; 9:3254. [PMID: 30108209 PMCID: PMC6092439 DOI: 10.1038/s41467-018-05444-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2017] [Accepted: 07/09/2018] [Indexed: 01/05/2023] Open
Abstract
Genome wide association (GWA) analysis of brain imaging phenotypes can advance our understanding of the genetic basis of normal and disorder-related variation in the brain. GWA approaches typically use linear mixed effect models to account for non-independence amongst subjects due to factors, such as family relatedness and population structure. The use of these models with high-dimensional imaging phenotypes presents enormous challenges in terms of computational intensity and the need to account multiple testing in both the imaging and genetic domain. Here we present a method that makes mixed models practical with high-dimensional traits by a combination of a transformation applied to the data and model, and the use of a non-iterative variance component estimator. With such speed enhancements permutation tests are feasible, which allows inference on powerful spatial tests like the cluster size statistic.
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Affiliation(s)
- Habib Ganjgahi
- Department of Statistics, University of Oxford, Oxford, UK
- Medical Research Council Harwell Institute, Harwell, UK
| | - Anderson M Winkler
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Big Data Analytics Group, Hospital Israelita Albert Einstein, São Paulo, SP, Brazil
| | - David C Glahn
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - John Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Brian Donohue
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Thomas E Nichols
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- Department of Statistics, University of Warwick, Coventry, UK.
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42
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Kelly S, Jahanshad N, Zalesky A, Kochunov P, Agartz I, Alloza C, Andreassen OA, Arango C, Banaj N, Bouix S, Bousman CA, Brouwer RM, Bruggemann J, Bustillo J, Cahn W, Calhoun V, Cannon D, Carr V, Catts S, Chen J, Chen JX, Chen X, Chiapponi C, Cho KK, Ciullo V, Corvin AS, Crespo-Facorro B, Cropley V, De Rossi P, Diaz-Caneja CM, Dickie EW, Ehrlich S, Fan FM, Faskowitz J, Fatouros-Bergman H, Flyckt L, Ford JM, Fouche JP, Fukunaga M, Gill M, Glahn DC, Gollub R, Goudzwaard ED, Guo H, Gur RE, Gur RC, Gurholt TP, Hashimoto R, Hatton SN, Henskens FA, Hibar DP, Hickie IB, Hong LE, Horacek J, Howells FM, Hulshoff Pol HE, Hyde CL, Isaev D, Jablensky A, Jansen PR, Janssen J, Jönsson EG, Jung LA, Kahn RS, Kikinis Z, Liu K, Klauser P, Knöchel C, Kubicki M, Lagopoulos J, Langen C, Lawrie S, Lenroot RK, Lim KO, Lopez-Jaramillo C, Lyall A, Magnotta V, Mandl RCW, Mathalon DH, McCarley RW, McCarthy-Jones S, McDonald C, McEwen S, McIntosh A, Melicher T, Mesholam-Gately RI, Michie PT, Mowry B, Mueller BA, Newell DT, O'Donnell P, Oertel-Knöchel V, Oestreich L, Paciga SA, Pantelis C, Pasternak O, Pearlson G, Pellicano GR, Pereira A, Pineda Zapata J, et alKelly S, Jahanshad N, Zalesky A, Kochunov P, Agartz I, Alloza C, Andreassen OA, Arango C, Banaj N, Bouix S, Bousman CA, Brouwer RM, Bruggemann J, Bustillo J, Cahn W, Calhoun V, Cannon D, Carr V, Catts S, Chen J, Chen JX, Chen X, Chiapponi C, Cho KK, Ciullo V, Corvin AS, Crespo-Facorro B, Cropley V, De Rossi P, Diaz-Caneja CM, Dickie EW, Ehrlich S, Fan FM, Faskowitz J, Fatouros-Bergman H, Flyckt L, Ford JM, Fouche JP, Fukunaga M, Gill M, Glahn DC, Gollub R, Goudzwaard ED, Guo H, Gur RE, Gur RC, Gurholt TP, Hashimoto R, Hatton SN, Henskens FA, Hibar DP, Hickie IB, Hong LE, Horacek J, Howells FM, Hulshoff Pol HE, Hyde CL, Isaev D, Jablensky A, Jansen PR, Janssen J, Jönsson EG, Jung LA, Kahn RS, Kikinis Z, Liu K, Klauser P, Knöchel C, Kubicki M, Lagopoulos J, Langen C, Lawrie S, Lenroot RK, Lim KO, Lopez-Jaramillo C, Lyall A, Magnotta V, Mandl RCW, Mathalon DH, McCarley RW, McCarthy-Jones S, McDonald C, McEwen S, McIntosh A, Melicher T, Mesholam-Gately RI, Michie PT, Mowry B, Mueller BA, Newell DT, O'Donnell P, Oertel-Knöchel V, Oestreich L, Paciga SA, Pantelis C, Pasternak O, Pearlson G, Pellicano GR, Pereira A, Pineda Zapata J, Piras F, Potkin SG, Preda A, Rasser PE, Roalf DR, Roiz R, Roos A, Rotenberg D, Satterthwaite TD, Savadjiev P, Schall U, Scott RJ, Seal ML, Seidman LJ, Shannon Weickert C, Whelan CD, Shenton ME, Kwon JS, Spalletta G, Spaniel F, Sprooten E, Stäblein M, Stein DJ, Sundram S, Tan Y, Tan S, Tang S, Temmingh HS, Westlye LT, Tønnesen S, Tordesillas-Gutierrez D, Doan NT, Vaidya J, van Haren NEM, Vargas CD, Vecchio D, Velakoulis D, Voineskos A, Voyvodic JQ, Wang Z, Wan P, Wei D, Weickert TW, Whalley H, White T, Whitford TJ, Wojcik JD, Xiang H, Xie Z, Yamamori H, Yang F, Yao N, Zhang G, Zhao J, van Erp TGM, Turner J, Thompson PM, Donohoe G. Widespread white matter microstructural differences in schizophrenia across 4322 individuals: results from the ENIGMA Schizophrenia DTI Working Group. Mol Psychiatry 2018; 23:1261-1269. [PMID: 29038599 PMCID: PMC5984078 DOI: 10.1038/mp.2017.170] [Show More Authors] [Citation(s) in RCA: 486] [Impact Index Per Article: 69.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Revised: 05/02/2017] [Accepted: 06/07/2017] [Indexed: 12/15/2022]
Abstract
The regional distribution of white matter (WM) abnormalities in schizophrenia remains poorly understood, and reported disease effects on the brain vary widely between studies. In an effort to identify commonalities across studies, we perform what we believe is the first ever large-scale coordinated study of WM microstructural differences in schizophrenia. Our analysis consisted of 2359 healthy controls and 1963 schizophrenia patients from 29 independent international studies; we harmonized the processing and statistical analyses of diffusion tensor imaging (DTI) data across sites and meta-analyzed effects across studies. Significant reductions in fractional anisotropy (FA) in schizophrenia patients were widespread, and detected in 20 of 25 regions of interest within a WM skeleton representing all major WM fasciculi. Effect sizes varied by region, peaking at (d=0.42) for the entire WM skeleton, driven more by peripheral areas as opposed to the core WM where regions of interest were defined. The anterior corona radiata (d=0.40) and corpus callosum (d=0.39), specifically its body (d=0.39) and genu (d=0.37), showed greatest effects. Significant decreases, to lesser degrees, were observed in almost all regions analyzed. Larger effect sizes were observed for FA than diffusivity measures; significantly higher mean and radial diffusivity was observed for schizophrenia patients compared with controls. No significant effects of age at onset of schizophrenia or medication dosage were detected. As the largest coordinated analysis of WM differences in a psychiatric disorder to date, the present study provides a robust profile of widespread WM abnormalities in schizophrenia patients worldwide. Interactive three-dimensional visualization of the results is available at www.enigma-viewer.org.
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Affiliation(s)
- S Kelly
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA,Harvard Medical School, Boston, MA, USA,Imaging Genetics Center, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA. E-mail:
| | - N Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - A Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - P Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - I Agartz
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - C Alloza
- University of Edinburgh, Edinburgh, UK
| | | | - C Arango
- Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, Madrid, Spain
| | - N Banaj
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - S Bouix
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - C A Bousman
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, VIC, Australia,Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia,Department of General Practice, The University of Melbourne, Parkville, VIC, Australia,Swinburne University of Technology, Melbourne, VIC, Australia
| | - R M Brouwer
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - J Bruggemann
- Neuroscience Research Australia and School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - J Bustillo
- University of New Mexico, Albuquerque, NM, USA
| | - W Cahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - V Calhoun
- The Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA,The Mind Research Network, Albuquerque, NM, USA
| | - D Cannon
- Centre for Neuroimaging and Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - V Carr
- Neuroscience Research Australia and School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - S Catts
- Discipline of Psychiatry, School of Medicine, University of Queensland, Herston, QLD, Australia
| | - J Chen
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
| | - J-x Chen
- Beijing Huilongguan Hospital, Beijing, China
| | - X Chen
- Worldwide Research and Development, Pfizer, Cambridge, MA, USA
| | | | - Kl K Cho
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - V Ciullo
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - A S Corvin
- Department of Psychiatry and Neuropsychiatric Genetics Research Group, Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland
| | - B Crespo-Facorro
- University Hospital Marqués de Valdecilla, IDIVAL, Department of Medicine and Psychiatry, School of Medicine, University of Cantabria, Santander, Spain,CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Santander, Spain
| | - V Cropley
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - P De Rossi
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy,Department NESMOS, Faculty of Medicine and Psychology, University ‘Sapienza’ of Rome, Rome, Italy,Department of Neurology and Psychiatry, Sapienza University of Rome, Rome, Italy
| | - C M Diaz-Caneja
- Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, Madrid, Spain
| | - E W Dickie
- Center for Addiction and Mental Health, Toronto, ON, Canada
| | - S Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences, Technische Universität Dresden, Faculty of Medicine, University Hospital C.G. Carus, Dresden, Germany
| | - F-m Fan
- Beijing Huilongguan Hospital, Beijing, China
| | - J Faskowitz
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - H Fatouros-Bergman
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
| | - L Flyckt
- University of New South Wales, School of Psychiatry, Sydney, NSW, Australia,The University of Queensland, Queensland Brain Institute and Centre for Advanced Imaging, Brisbane, QLD, Australia
| | - J M Ford
- University of California, VAMC, San Francisco, CA, USA
| | - J-P Fouche
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - M Fukunaga
- Division of Cerebral Integration, National Institute for Physiological Sciences, Aichi, Japan
| | - M Gill
- Department of Psychiatry and Neuropsychiatric Genetics Research Group, Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland
| | - D C Glahn
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital and Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - R Gollub
- Harvard Medical School, Boston, MA, USA,Departments of Psychiatry and Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - E D Goudzwaard
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - H Guo
- Zhumadian Psychiatry Hospital, Henan Province, China
| | - R E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - R C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - T P Gurholt
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - R Hashimoto
- Molecular Research Center for Children's Mental Development, United Graduate School of Child Development, Osaka University, Osaka, Japan,Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - S N Hatton
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - F A Henskens
- School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, NSW, Australia,Health Behaviour Research Group, University of Newcastle, Callaghan, NSW, Australia,Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - D P Hibar
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - I B Hickie
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - L E Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - J Horacek
- National Institute of Mental Health, Klecany, Czech Republic,Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - F M Howells
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - H E Hulshoff Pol
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - C L Hyde
- Worldwide Research and Development, Pfizer, Cambridge, MA, USA
| | - D Isaev
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - A Jablensky
- University of Western Australia, Perth, WA, Australia
| | - P R Jansen
- Erasmus University Medical Center, Rotterdam, The Netherlands
| | - J Janssen
- Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, Madrid, Spain,Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - E G Jönsson
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
| | - L A Jung
- Laboratory for Neuroimaging, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University, Frankfurt/Main, Germany
| | - R S Kahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Z Kikinis
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - K Liu
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - P Klauser
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, VIC, Australia,Brain and Mental Health Laboratory, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, VIC, Australia,Department of Psychiatry, Lausanne University Hospital (CHUV), University of Lausanne, Lausanne, Switzerland
| | - C Knöchel
- Laboratory for Neuroimaging, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University, Frankfurt/Main, Germany
| | - M Kubicki
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - J Lagopoulos
- Sunshine Coast Mind and Neuroscience Institute, University of the Sunshine Coast QLD, Australia, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - C Langen
- Erasmus University Medical Center, Rotterdam, The Netherlands
| | - S Lawrie
- University of Edinburgh, Edinburgh, UK
| | - R K Lenroot
- Neuroscience Research Australia and School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - K O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - C Lopez-Jaramillo
- Research Group in Psychiatry (GIPSI), Department of Psychiatry, Faculty of Medicine, Universidad de Antioquia, Mood Disorder Program, Hospital Universitario San Vicente Fundación, Medellín, Colombia
| | - A Lyall
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - R C W Mandl
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - D H Mathalon
- University of California, VAMC, San Francisco, CA, USA
| | | | - S McCarthy-Jones
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - C McDonald
- Centre for Neuroimaging and Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - S McEwen
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - T Melicher
- Third Faculty of Medicine, Charles University, Prague, Czech Republic,The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - R I Mesholam-Gately
- Harvard Medical School and Massachusetts Mental Health Center Public Psychiatry Division of the Beth Israel Deaconess, Medical Center, Boston, MA, USA
| | - P T Michie
- Hunter Medical Research Institute, Newcastle, NSW, Australia,The University of Newcastle, Newcastle, NSW, Australia,Schizophrenia Research Institute, Sydney, NSW, Australia
| | - B Mowry
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia and Queensland Centre for Mental Health Research, Brisbane and Queensland Centre for Mental Health Research, Brisbane, QLD, Australia
| | - B A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - D T Newell
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - P O'Donnell
- Worldwide Research and Development, Pfizer, Cambridge, MA, USA
| | - V Oertel-Knöchel
- Laboratory for Neuroimaging, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University, Frankfurt/Main, Germany
| | - L Oestreich
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia and Queensland Centre for Mental Health Research, Brisbane and Queensland Centre for Mental Health Research, Brisbane, QLD, Australia
| | - S A Paciga
- Worldwide Research and Development, Pfizer, Cambridge, MA, USA
| | - C Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, VIC, Australia,Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia,Schizophrenia Research Institute, Sydney, NSW, Australia,Centre for Neural Engineering (CfNE), Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC, Australia
| | - O Pasternak
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - G Pearlson
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital and Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - G R Pellicano
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - A Pereira
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia
| | | | - F Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy,School of Biomedical Sciences, Faculty of Health, the University of Newcastle, Callaghan, NSW, Australia
| | - S G Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - A Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - P E Rasser
- Hunter Medical Research Institute, Newcastle, NSW, Australia,Priority Centre for Brain and Mental Health Research, The University of Newcastle, Newcastle, NSW, Australia
| | - D R Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - R Roiz
- University Hospital Marqués de Valdecilla, IDIVAL, Department of Medicine and Psychiatry, School of Medicine, University of Cantabria, Santander, Spain,CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Santander, Spain
| | - A Roos
- SU/UCT MRC Unit on Anxiety and Stress Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - D Rotenberg
- Center for Addiction and Mental Health, Toronto, ON, Canada
| | - T D Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - P Savadjiev
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - U Schall
- Hunter Medical Research Institute, Newcastle, NSW, Australia,Priority Centre for Brain and Mental Health Research, The University of Newcastle, Newcastle, NSW, Australia
| | - R J Scott
- Hunter Medical Research Institute, Newcastle, NSW, Australia,School of Biomedical Sciences, Faculty of Health, the University of Newcastle, Callaghan, NSW, Australia
| | - M L Seal
- Murdoch Childrens Research Institute, The Royal Children’s Hospital, Parkville, VIC, Australia
| | - L J Seidman
- Harvard Medical School, Boston, MA, USA,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA,Harvard Medical School and Massachusetts Mental Health Center Public Psychiatry Division of the Beth Israel Deaconess, Medical Center, Boston, MA, USA
| | - C Shannon Weickert
- Schizophrenia Research Institute, Sydney, NSW, Australia,Neuroscience Research Australia, Sydney, NSW, Australia,School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - C D Whelan
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - M E Shenton
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA,VA Boston Healthcare System, Boston, MA, USA
| | - J S Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - G Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy,Division of Neuropsychiatry, Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - F Spaniel
- National Institute of Mental Health, Klecany, Czech Republic,Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - E Sprooten
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital and Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - M Stäblein
- Laboratory for Neuroimaging, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University, Frankfurt/Main, Germany
| | - D J Stein
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa,Department of Psychiatry and MRC Unit on Anxiety and Stress Disorders, University of Cape Town, Cape Town, South Africa
| | - S Sundram
- Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia,Department of Psychiatry, School of Clinical Sciences, Monash University and Monash Health, Clayton, VIC, Australia
| | - Y Tan
- Beijing Huilongguan Hospital, Beijing, China
| | - S Tan
- Beijing Huilongguan Hospital, Beijing, China
| | - S Tang
- Chongqing Three Gorges Central Hospital, Chongqing, China
| | - H S Temmingh
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - L T Westlye
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Psychology, University of Oslo, Oslo, Norway
| | - S Tønnesen
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - D Tordesillas-Gutierrez
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Santander, Spain,Neuroimaging Unit, Technological Facilities, Valdecilla Biomedical Research Institute IDIVAL, Santander, Spain
| | - N T Doan
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - J Vaidya
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - N E M van Haren
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - C D Vargas
- Research Group in Psychiatry (GIPSI), Department of Psychiatry, Faculty of Medicine, Universidad de Antioquia, Medellín, Colombia
| | - D Vecchio
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - D Velakoulis
- Neuropsychiatry Unit, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - A Voineskos
- Kimel Family Translational Imaging-Genetics Research Laboratory, Campbell Family Mental Health Research Institute, CAMH Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - J Q Voyvodic
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Z Wang
- Beijing Huilongguan Hospital, Beijing, China
| | - P Wan
- Zhumadian Psychiatry Hospital, Henan Province, China
| | - D Wei
- Luoyang Fifth People's Hospital, Henan Province, China
| | - T W Weickert
- Schizophrenia Research Institute, Sydney, NSW, Australia,Neuroscience Research Australia, Sydney, NSW, Australia,School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - H Whalley
- University of Edinburgh, Edinburgh, UK
| | - T White
- Erasmus University Medical Center, Rotterdam, The Netherlands
| | - T J Whitford
- University of New South Wales, School of Psychiatry, Sydney, NSW, Australia
| | - J D Wojcik
- Harvard Medical School and Massachusetts Mental Health Center Public Psychiatry Division of the Beth Israel Deaconess, Medical Center, Boston, MA, USA
| | - H Xiang
- Chongqing Three Gorges Central Hospital, Chongqing, China
| | - Z Xie
- Worldwide Research and Development, Pfizer, Cambridge, MA, USA
| | - H Yamamori
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - F Yang
- Beijing Huilongguan Hospital, Beijing, China
| | - N Yao
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - G Zhang
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD, USA
| | - J Zhao
- Centre for Neuroimaging and Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland,School of Psychology, Shaanxi Normal University and Key Laboratory for Behavior and Cognitive Neuroscience of Shaanxi Province, Xi’an, Shaanxi, China
| | - T G M van Erp
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - J Turner
- Psychology Department & Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - P M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - G Donohoe
- Centre for Neuroimaging and Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
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Corpus callosum volumes in the 5 years following the first-episode of schizophrenia: Effects of antipsychotics, chronicity and maturation. NEUROIMAGE-CLINICAL 2018; 18:932-942. [PMID: 29876278 PMCID: PMC5988462 DOI: 10.1016/j.nicl.2018.03.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2017] [Revised: 02/19/2018] [Accepted: 03/14/2018] [Indexed: 01/27/2023]
Abstract
Background White matter (WM) structural changes, particularly affecting the corpus callosum (CC), seem to be critically implicated in psychosis. Whether such abnormalities are progressive or static is still a matter of debate in schizophrenia research. Aberrant maturation processes might also influence the longitudinal trajectory of age-related CC changes in schizophrenia patients. We investigated whether patients with first-episode schizophrenia-related psychoses (FESZ) would present longitudinal CC and whole WM volume changes over the 5 years after disease onset. Method Thirty-two FESZ patients and 34 controls recruited using a population-based design completed a 5-year assessment protocol, including structural MRI scanning at baseline and follow-up. The linear effects of disease duration, clinical outcome and antipsychotic (AP) use over time on WM and CC volumes were studied using both voxelwise and volume-based morphometry analyses. We also examined maturation/aging abnormalities through cross-sectional analyses of age-related trajectories of total WM and CC volume changes. Results No interaction between diagnosis and time was observed, and clinical outcome did not influence CC volumes in patients. On the other hand, FESZ patients continuously exposed to AP medication showed volume increase over time in posterior CC. Curve-estimation analyses revealed a different aging pattern in FESZ patients versus controls: while patients displayed a linear decline of total WM and anterior CC volumes with age, a non-linear trajectory of total WM and relative preservation of CC volumes were observed in controls. Conclusions Continuous AP exposure can influence CC morphology during the first years after schizophrenia onset. Schizophrenia is associated with an abnormal pattern of total WM and anterior CC aging during non-elderly adulthood, and this adds complexity to the discussion on the static or progressive nature of structural abnormalities in psychosis.
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Key Words
- AP, antipsychotics
- CC, corpus callosum
- Corpus callosum
- FEP, first episode of psychosis
- FESZ, First-episode of schizophrenia-related psychoses
- GM, gray matter
- MEM, mixed-effects model
- Magnetic resonance imaging
- Psychosis
- ROI, region-of-interest
- Schizophrenia
- VBM, voxel-based morphometry
- VolBM, volume-based morphometry
- WM, white matter
- White matter
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44
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Ryan MC, Sherman P, Rowland LM, Wijtenburg SA, Acheson A, Fieremans E, Veraart J, Novikov DS, Hong LE, Sladky J, Peralta PD, Kochunov P, McGuire SA. Miniature pig model of human adolescent brain white matter development. J Neurosci Methods 2018; 296:99-108. [PMID: 29277719 PMCID: PMC5817010 DOI: 10.1016/j.jneumeth.2017.12.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Revised: 12/21/2017] [Accepted: 12/21/2017] [Indexed: 12/14/2022]
Abstract
BACKGROUND Neuroscience research in brain development and disorders can benefit from an in vivo animal model that portrays normal white matter (WM) development trajectories and has a sufficiently large cerebrum for imaging with human MRI scanners and protocols. NEW METHOD Twelve three-month-old Sinclair™ miniature pigs (Sus scrofa domestica) were longitudinally evaluated during adolescent development using advanced diffusion weighted imaging (DWI) focused on cerebral WM. Animals had three MRI scans every 23.95 ± 3.73 days using a 3-T scanner. The DWI imaging protocol closely modeled advanced human structural protocols and consisted of fifteen b-shells (b = 0-3500 s/mm2) with 32-directions/shell. DWI data were analyzed using diffusion kurtosis and bi-exponential modeling that provided measurements that included fractional anisotropy (FA), radial kurtosis, kurtosis anisotropy (KA), axial kurtosis, tortuosity, and permeability-diffusivity index (PDI). RESULTS Significant longitudinal effects of brain development were observed for whole-brain average FA, KA, and PDI (all p < 0.001). There were expected regional differences in trends, with corpus callosum fibers showing the highest rate of change. COMPARISON WITH EXISTING METHOD(S) Pigs have a large, gyrencephalic brain that can be studied using clinical MRI scanners/protocols. Pigs are less complex than non-human primates thus satisfying the "replacement" principle of animal research. CONCLUSIONS Longitudinal effects were observed for whole-brain and regional diffusion measurements. The changes in diffusion measurements were interepreted as evidence for ongoing myelination and maturation of cerebral WM. Corpus callosum and superficial cortical WM showed the expected higher rates of change, mirroring results in humans.
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Affiliation(s)
- Meghann C Ryan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, 55 Wade Avenue, Catonsville, MD 21228, United States
| | - Paul Sherman
- U.S. Air Force School of Aerospace Medicine, Aeromedical Research Department, 2510 5th Street, Building 840, Wright-Patterson AFB, OH 45433-7913, United States
| | - Laura M Rowland
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, 55 Wade Avenue, Catonsville, MD 21228, United States
| | - S Andrea Wijtenburg
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, 55 Wade Avenue, Catonsville, MD 21228, United States
| | - Ashley Acheson
- Department of Psychiatry, University of Arkansas for Medical Sciences, 4301 W Markham St, Little Rock, AR 72205, United States
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, 660 1st Avenue, New York, NY 10016, United States
| | - Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, 660 1st Avenue, New York, NY 10016, United States
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, 660 1st Avenue, New York, NY 10016, United States
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, 55 Wade Avenue, Catonsville, MD 21228, United States
| | - John Sladky
- U.S. Air Force School of Aerospace Medicine, Aeromedical Research Department, 2510 5th Street, Building 840, Wright-Patterson AFB, OH 45433-7913, United States; Department of Neurology, 59th Medical Wing, 2200 Bergquist Drive, Suite 1, Joint Base San Antonio-Lackland AFB, TX 78236, United States
| | - P Dana Peralta
- Department of Neurology, 59th Medical Wing, 2200 Bergquist Drive, Suite 1, Joint Base San Antonio-Lackland AFB, TX 78236, United States
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, 55 Wade Avenue, Catonsville, MD 21228, United States.
| | - Stephen A McGuire
- U.S. Air Force School of Aerospace Medicine, Aeromedical Research Department, 2510 5th Street, Building 840, Wright-Patterson AFB, OH 45433-7913, United States; Department of Neurology, 59th Medical Wing, 2200 Bergquist Drive, Suite 1, Joint Base San Antonio-Lackland AFB, TX 78236, United States
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45
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Kochunov P, Dickie EW, Viviano JD, Turner J, Kingsley PB, Jahanshad N, Thompson PM, Ryan MC, Fieremans E, Novikov D, Veraart J, Hong EL, Malhotra AK, Buchanan RW, Chavez S, Voineskos AN. Integration of routine QA data into mega-analysis may improve quality and sensitivity of multisite diffusion tensor imaging studies. Hum Brain Mapp 2018; 39:1015-1023. [PMID: 29181875 PMCID: PMC5764798 DOI: 10.1002/hbm.23900] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 11/07/2017] [Accepted: 11/16/2017] [Indexed: 12/14/2022] Open
Abstract
A novel mega-analytical approach that reduced methodological variance was evaluated using a multisite diffusion tensor imaging (DTI) fractional anisotropy (FA) data by comparing white matter integrity in people with schizophrenia to controls. Methodological variance was reduced through regression of variance captured from quality assurance (QA) and by using Marchenko-Pastur Principal Component Analysis (MP-PCA) denoising. N = 192 (119 patients/73 controls) data sets were collected at three sites equipped with 3T MRI systems: GE MR750, GE HDx, and Siemens Trio. DTI protocol included five b = 0 and 60 diffusion-sensitized gradient directions (b = 1,000 s/mm2 ). In-house DTI QA protocol data was acquired weekly using a uniform phantom; factor analysis was used to distil into two orthogonal QA factors related to: SNR and FA. They were used as site-specific covariates to perform mega-analytic data aggregation. The effect size of patient-control differences was compared to these reported by the enhancing neuro imaging genetics meta-analysis (ENIGMA) consortium before and after regressing QA variance. Impact of MP-PCA filtering was evaluated likewise. QA-factors explained ∼3-4% variance in the whole-brain average FA values per site. Regression of QA factors improved the effect size of schizophrenia on whole brain average FA values-from Cohen's d = .53 to .57-and improved the agreement between the regional pattern of FA differences observed in this study versus ENIGMA from r = .54 to .70. Application of MP-PCA-denoising further improved the agreement to r = .81. Regression of methodological variances captured by routine QA and advanced denoising that led to a better agreement with a large mega-analytic study.
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Affiliation(s)
- Peter Kochunov
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimore
| | | | | | - Jessica Turner
- Department of PsychologyGeorgia State UniversityAtlantaGeorgia
| | - Peter B. Kingsley
- Department of RadiologyNorth Shore University HospitalManhassetNew York
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del ReyCAUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del ReyCAUSA
| | - Meghann C. Ryan
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimore
| | - Els Fieremans
- Department of Radiology, New York University School of MedicineNew YorkNew York
| | - Dmitry Novikov
- Department of Radiology, New York University School of MedicineNew YorkNew York
| | - Jelle Veraart
- Department of Radiology, New York University School of MedicineNew YorkNew York
| | - Elliot L. Hong
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimore
| | - Anil K. Malhotra
- Department of RadiologyNorth Shore University HospitalManhassetNew York
| | - Robert W. Buchanan
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimore
| | - Sofia Chavez
- Centre for Addiction and Mental HealthTorontoCanada
- Department of PsychiatryUniversity of TorontoCanada
| | - Aristotle N. Voineskos
- Centre for Addiction and Mental HealthTorontoCanada
- Department of PsychiatryUniversity of TorontoCanada
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Czepielewski LS, Massuda R, Panizzutti B, Grun LK, Barbé-Tuana FM, Teixeira AL, Barch DM, Gama CS. Telomere Length and CCL11 Levels are Associated With Gray Matter Volume and Episodic Memory Performance in Schizophrenia: Evidence of Pathological Accelerated Aging. Schizophr Bull 2018; 44:158-167. [PMID: 28338779 PMCID: PMC5767949 DOI: 10.1093/schbul/sbx015] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Schizophrenia (SZ) is associated with increased somatic morbidity and mortality, in addition to cognitive impairments similar to those seen in normal aging, which may suggest that pathological accelerated aging occurs in SZ. Therefore, we aim to evaluate the relationships of age, telomere length (TL), and CCL11 (aging and inflammatory biomarkers, respectively), gray matter (GM) volume and episodic memory performance in individuals with SZ compared to healthy controls (HC). One hundred twelve participants (48 SZ and 64 HC) underwent clinical and memory assessments, structural MRI, and had their peripheral blood drawn for biomarkers analysis. Comparisons of group means and correlations were performed. Participants with SZ had decreased TL and GM volume, increased CCL11, and worse memory performance compared to HC. In SZ, shorter TL was related to increased CCL11, and both biomarkers were related to reduced GM volume, all of which were related to worse memory performance. Older age was only associated with reduced GM, but longer duration of illness was related with all the aforementioned variables. Younger age of disease onset was associated with increased CCL11 levels and worse memory performance. In HC, there were no significant correlations except between memory and GM. Our results are consistent with the hypothesis of accelerated aging in SZ. These results may indicate that it is not age itself, but the impact of the disease associated with a pathological accelerated aging that leads to impaired outcomes in SZ.
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Affiliation(s)
- Leticia Sanguinetti Czepielewski
- Molecular Psychiatry Laboratory, Hospital de Clinicas de Porto Alegre, Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Raffael Massuda
- Departamento de Psiquiatria, Universidade Federal do Paraná, Curitiba, Brazil
| | - Bruna Panizzutti
- Molecular Psychiatry Laboratory, Hospital de Clinicas de Porto Alegre, Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Lucas Kich Grun
- Programa de Pós-Graduação em Ciências Biológicas: Bioquímica, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Florencia María Barbé-Tuana
- Programa de Pós-Graduação em Ciências Biológicas: Bioquímica, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Antonio Lucio Teixeira
- Laboratório Interdisciplinar de Investigação Médica, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Deanna M Barch
- Department of Psychological & Brain Sciences, Washington University in St Louis, St Louis, MO,Department of Psychiatry and Radiology, Washington University in St Louis, St Louis, MO
| | - Clarissa S Gama
- Molecular Psychiatry Laboratory, Hospital de Clinicas de Porto Alegre, Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil,To whom correspondence should be addressed; Hospital de Clínicas de Porto Alegre/CPE, Molecular Psychiatry Laboratory, Rua Ramiro Barcelos, 2350, Prédio Anexo, 90035-903 Porto Alegre, Brazil; tel: +55-51-33598845, fax: +55-51-33598846, e-mail:
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47
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van Mierlo HC, Wichers CGK, He Y, Sneeboer MAM, Radstake TRDJ, Kahn RS, Broen JCA, de Witte LD. Telomere quantification in frontal and temporal brain tissue of patients with schizophrenia. J Psychiatr Res 2017; 95:231-234. [PMID: 28910708 DOI: 10.1016/j.jpsychires.2017.09.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Revised: 09/01/2017] [Accepted: 09/05/2017] [Indexed: 02/02/2023]
Abstract
Recent imaging studies have suggested that accelerated aging occurs in schizophrenia. However, the exact cause of these findings is still unclear. In this study we measured telomere length, a marker for cell senescence, in gray and white matter brain tissue from the medial frontal gyrus (MFG) and superior temporal gyrus (STG) of 9 patients with schizophrenia and 11 controls. No alterations in telomere length were found in MFG gray and white matter and in STG gray matter. A significant reduction in telomere length was observed in STG white matter of patients with schizophrenia as compared to controls (fold change of -0.42, U = 5, P = 0.008). Our results support previous findings that telomere length in gray matter is not affected, whereas they suggest that increased cell senescence may affect white matter temporal brain tissue.
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Affiliation(s)
- Hans C van Mierlo
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Catharina G K Wichers
- Laboratory of Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yujie He
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Marjolein A M Sneeboer
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Timothy R D J Radstake
- Laboratory of Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht, The Netherlands; Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - René S Kahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jasper C A Broen
- Laboratory of Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht, The Netherlands; Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lot D de Witte
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
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48
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Kochunov P, Coyle TR, Rowland LM, Jahanshad N, Thompson PM, Kelly S, Du X, Sampath H, Bruce H, Chiappelli J, Ryan M, Fisseha F, Savransky A, Adhikari B, Chen S, Paciga SA, Whelan CD, Xie Z, Hyde CL, Chen X, Schubert CR, O’Donnell P, Hong LE. Association of White Matter With Core Cognitive Deficits in Patients With Schizophrenia. JAMA Psychiatry 2017; 74:958-966. [PMID: 28768312 PMCID: PMC5710230 DOI: 10.1001/jamapsychiatry.2017.2228] [Citation(s) in RCA: 113] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
IMPORTANCE Efforts to remediate the multiple cognitive function impairments in schizophrenia should consider white matter as one of the underlying neural mechanisms. OBJECTIVE To determine whether altered structural brain connectivity is responsible for 2 of the core cognitive deficits in schizophrenia- reduced information processing speed and impaired working memory. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study design took place in outpatient clinics from August 1, 2004, to August 31, 2015. Participants included 166 patients with schizophrenia and 213 healthy control individuals. These participants were from 3 independent cohorts, each of which had its own healthy control group. No participant had current or past neurological conditions or major medical conditions. Patients were diagnosed with either schizophrenia or schizoaffective disorder as defined by the DSM-IV. Controls had no Axis I psychiatric disorder. MAIN OUTCOMES AND MEASURES Mediation analyses and structural equation modeling were used to analyze the associations among processing speed, working memory, and white matter microstructures. Whole-brain and regional diffusion tensor imaging fractional anisotropy were used to measure white matter microstructures. RESULTS Of the study participants, the 166 patients with schizophrenia had a mean (SD) age of 38.2 (13.3) years and the 213 healthy controls had a mean (SD) age of 39.2 (14.0) years. There were significantly more male patients than controls in each of the 3 cohorts (117 [70%] vs 91 [43%]), but there were no significant differences in sex composition among the 3 cohorts. Patients had significantly reduced processing speed (Cohen d = 1.24; P = 6.91 × 10-30) and working memory deficits (Cohen d = 0.83; P = 1.10 × 10-14) as well as a significant whole-brain fractional anisotropy deficit (Cohen d = 0.63; P = 2.20 × 10-9). In schizophrenia, working memory deficit was mostly accounted for by processing speed deficit, but this deficit remained when accounting for working memory (Cohen d = 0.89; P = 2.21 × 10-17). Mediation analyses showed a significant association pathway from fractional anisotropy to processing speed to working memory (P = 5.01 × 10-7). The strength of this brain-to-cognition pathway in different white matter tracts was strongly associated with the severity of schizophrenia-associated fractional anisotropy deficits in the corresponding white matter tracts as determined by a meta-analysis (r = 0.85-0.94; all P < .001). The same pattern was observed in patients and controls either jointly or independently. CONCLUSIONS AND RELEVANCE Study findings suggest that (1) processing speed contributes to the association between white matter microstructure and working memory in schizophrenia and (2) white matter impairment in schizophrenia is regional tract-specific, particularly in tracts normally supporting processing speed performance.
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Affiliation(s)
- Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
| | - Thomas R. Coyle
- Department of Psychology, The University of Texas at San Antonio
| | - Laura M. Rowland
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
| | - Neda Jahanshad
- Imaging Genetics Center, Keck School of Medicine of the University of Southern California, Marina del Rey
| | - Paul M. Thompson
- Imaging Genetics Center, Keck School of Medicine of the University of Southern California, Marina del Rey
| | - Sinead Kelly
- Imaging Genetics Center, Keck School of Medicine of the University of Southern California, Marina del Rey
| | - Xiaoming Du
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
| | - Hemalatha Sampath
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
| | - Heather Bruce
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
| | - Joshua Chiappelli
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
| | - Meghann Ryan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
| | - Feven Fisseha
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
| | - Anya Savransky
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
| | - Bhim Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
| | - Sara A. Paciga
- Worldwide Research and Development, Pfizer Inc, Cambridge, Massachusetts
| | | | - Zhiyong Xie
- Worldwide Research and Development, Pfizer Inc, Cambridge, Massachusetts
| | - Craig L. Hyde
- Worldwide Research and Development, Pfizer Inc, Cambridge, Massachusetts
| | - Xing Chen
- Worldwide Research and Development, Pfizer Inc, Cambridge, Massachusetts
| | | | - Patricio O’Donnell
- Worldwide Research and Development, Pfizer Inc, Cambridge, Massachusetts
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
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Nenadić I, Dietzek M, Langbein K, Sauer H, Gaser C. BrainAGE score indicates accelerated brain aging in schizophrenia, but not bipolar disorder. Psychiatry Res Neuroimaging 2017; 266:86-89. [PMID: 28628780 DOI: 10.1016/j.pscychresns.2017.05.006] [Citation(s) in RCA: 107] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2017] [Accepted: 05/19/2017] [Indexed: 12/21/2022]
Abstract
BrainAGE (brain age gap estimation) is a novel morphometric parameter providing a univariate score derived from multivariate voxel-wise analyses. It uses a machine learning approach and can be used to analyse deviation from physiological developmental or aging-related trajectories. Using structural MRI data and BrainAGE quantification of acceleration or deceleration of in individual aging, we analysed data from 45 schizophrenia patients, 22 bipolar I disorder patients (mostly with previous psychotic symptoms / episodes), and 70 healthy controls. We found significantly higher BrainAGE scores in schizophrenia, but not bipolar disorder patients. Our findings indicate significantly accelerated brain structural aging in schizophrenia. This suggests, that despite the conceptualisation of schizophrenia as a neurodevelopmental disorder, there might be an additional progressive pathogenic component.
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Affiliation(s)
- Igor Nenadić
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany; Department of Psychiatry and Psychotherapy, Philipps University Marburg & Marburg University Hospital UKGM, Marburg, Germany.
| | - Maren Dietzek
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Kerstin Langbein
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Heinrich Sauer
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Christian Gaser
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany; Department of Neurology, Jena University Hospital, Jena, Germany
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Zhang G, Kochunov P, Hong E, Kelly S, Whelan C, Jahanshad N, Thompson P, Chen J. ENIGMA-Viewer: interactive visualization strategies for conveying effect sizes in meta-analysis. BMC Bioinformatics 2017; 18:253. [PMID: 28617224 PMCID: PMC5471941 DOI: 10.1186/s12859-017-1634-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Global scale brain research collaborations such as the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium are beginning to collect data in large quantity and to conduct meta-analyses using uniformed protocols. It becomes strategically important that the results can be communicated among brain scientists effectively. Traditional graphs and charts failed to convey the complex shapes of brain structures which are essential to the understanding of the result statistics from the analyses. These problems could be addressed using interactive visualization strategies that can link those statistics with brain structures in order to provide a better interface to understand brain research results. RESULTS We present ENIGMA-Viewer, an interactive web-based visualization tool for brain scientists to compare statistics such as effect sizes from meta-analysis results on standardized ROIs (regions-of-interest) across multiple studies. The tool incorporates visualization design principles such as focus+context and visual data fusion to enable users to better understand the statistics on brain structures. To demonstrate the usability of the tool, three examples using recent research data are discussed via case studies. CONCLUSIONS ENIGMA-Viewer supports presentations and communications of brain research results through effective visualization designs. By linking visualizations of both statistics and structures, users can gain more insights into the presented data that are otherwise difficult to obtain. ENIGMA-Viewer is an open-source tool, the source code and sample data are publicly accessible through the NITRC website ( http://www.nitrc.org/projects/enigmaviewer_20 ). The tool can also be directly accessed online ( http://enigma-viewer.org ).
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Affiliation(s)
- Guohao Zhang
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, 21250 MD USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, University of Maryland, Baltimore, 55 Wade Ave, Baltimore, 21228 MD USA
| | - Elliot Hong
- Maryland Psychiatric Research Center, University of Maryland, Baltimore, 55 Wade Ave, Baltimore, 21228 MD USA
| | - Sinead Kelly
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, 02215 MA USA
- Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St, Boston, 02115 MA USA
| | - Christopher Whelan
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, 1975 Zonal Ave, Los Angeles, 90033 LA USA
- Department of Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin, 123 St Stephen’s Green, Dublin 2, Ireland
| | - Neda Jahanshad
- Keck School of Medicine, University of Southern California, 1975 Zonal Ave, Los Angeles, 90033 LA USA
| | - Paul Thompson
- Keck School of Medicine, University of Southern California, 1975 Zonal Ave, Los Angeles, 90033 LA USA
| | - Jian Chen
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, 21250 MD USA
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