101
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Duerden EG, Chakravarty MM, Lerch JP, Taylor MJ. Sex-Based Differences in Cortical and Subcortical Development in 436 Individuals Aged 4-54 Years. Cereb Cortex 2019; 30:2854-2866. [PMID: 31814003 DOI: 10.1093/cercor/bhz279] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2019] [Revised: 10/14/2019] [Accepted: 10/19/2019] [Indexed: 11/13/2022] Open
Abstract
Sex-based differences in brain development have long been established in ex vivo studies. Recent in vivo studies using magnetic resonance imaging (MRI) have offered considerable insight into sex-based variations in brain maturation. However, reports of sex-based differences in cortical volumes and thickness are inconsistent. We examined brain maturation in a cross-sectional, single-site cohort of 436 individuals (201 [46%] males) aged 4-54 years (median = 16 years). Cortical thickness, cortical surface area, subcortical surface area, volumes of the cerebral cortex, white matter (WM), cortical and subcortical gray matter (GM), including the thalamic subnuclei, basal ganglia, and hippocampi were calculated using automatic segmentation pipelines. Subcortical structures demonstrated distinct curvilinear trajectories from the cortex, in both volumetric maturation and surface-area expansion in relation to age. Surface-area analysis indicated that dorsal regions of the thalamus, globus pallidus and striatum, regions demonstrating structural connectivity with frontoparietal cortices, exhibited extensive expansion with age, and were inversely related to changes seen in cortical maturation, which contracted with age. Furthermore, surface-area expansion was more robust in males in comparison to females. Age- and sex-related maturational changes may reflect alterations in dendritic and synaptic architecture known to occur during development from early childhood through to mid-adulthood.
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Affiliation(s)
- Emma G Duerden
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada.,Faculty of Education, Western University, London, Ontario, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada.,Departments of Psychiatry and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Jason P Lerch
- Wellcome Centre for Integrative Neuroimaging, University of Oxford.,Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Margot J Taylor
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Psychology, University of Toronto, Toronto, Ontario, Canada.,Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
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102
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Talpalaru A, Bhagwat N, Devenyi GA, Lepage M, Chakravarty MM. Identifying schizophrenia subgroups using clustering and supervised learning. Schizophr Res 2019; 214:51-59. [PMID: 31455518 DOI: 10.1016/j.schres.2019.05.044] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 05/28/2019] [Accepted: 05/30/2019] [Indexed: 01/18/2023]
Abstract
Schizophrenia has a 1% incidence rate world-wide and those diagnosed present with positive (e.g. hallucinations, delusions), negative (e.g. apathy, asociality), and cognitive symptoms. However, both symptom burden and associated brain alterations are highly heterogeneous and intimately linked to prognosis. In this study, we present a method to predict individual symptom profiles by first deriving clinical subgroups and then using machine learning methods to perform subject-level classification based on magnetic resonance imaging (MRI) derived neuroanatomical measures. Symptomatic and MRI data of 167 subjects were used. Subgroups were defined using hierarchical clustering of clinical data resulting in 3 stable clusters: 1) high symptom burden, 2) predominantly positive symptom burden, and 3) mild symptom burden. Cortical thickness estimates were obtained in 78 regions of interest and were input, along with demographic data, into three machine learning models (logistic regression, support vector machine, and random forest) to predict subgroups. Random forest performance metrics for predicting the group membership of the high and mild symptom burden groups exceeded those of the baseline comparison of the entire schizophrenia population versus normal controls (AUC: 0.81 and 0.78 vs. 0.75). Additionally, an analysis of the most important features in the random forest classification demonstrated consistencies with previous findings of regional impairments and symptoms of schizophrenia.
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Affiliation(s)
- Alexandra Talpalaru
- Biological & Biomedical Engineering, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada; Douglas Mental Health University Institute, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada.
| | - Nikhil Bhagwat
- Douglas Mental Health University Institute, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, 27 King's College Cir, Toronto, ON M5S 3H7, Canada
| | - Gabriel A Devenyi
- Douglas Mental Health University Institute, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada; Department of Psychiatry, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada
| | - Martin Lepage
- Douglas Mental Health University Institute, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada; Department of Psychiatry, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada
| | - M Mallar Chakravarty
- Biological & Biomedical Engineering, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada; Douglas Mental Health University Institute, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada; Department of Psychiatry, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada.
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103
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Schmitt JE, Giedd JN, Raznahan A, Neale MC. The Genetic Contributions to Maturational Coupling in the Human Cerebrum: A Longitudinal Pediatric Twin Imaging Study. Cereb Cortex 2019; 28:3184-3191. [PMID: 28968785 DOI: 10.1093/cercor/bhx190] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Indexed: 11/13/2022] Open
Abstract
Although prior studies have demonstrated that genetic factors play the dominant role in the patterning of the pediatric brain, it remains unclear how these patterns change over time. Using 1748 longitudinal anatomic MRI scans from 792 healthy twins and siblings, we quantified how genetically mediated inter-regional associations change over time via multivariate longitudinal structural equation modeling. These analyses found that genetic correlations for both lobar volumes and cortical thickness are dynamic, with relatively static effects on surface area. While genetic correlations for lobar volumes decrease over childhood and adolescence, in general they increase for cortical thickness in the second decade of life. Quantification of how genetic factors influence maturational coupling improves our understanding of typical neurodevelopment and informs future molecular genetic analyses.
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Affiliation(s)
- J Eric Schmitt
- Department of Radiology and Psychiatry, Brain Behavior Laboratory, Hospital of the University of Pennsylvania, Philadelphia PA, USA
| | - Jay N Giedd
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Armin Raznahan
- Developmental Neurogenomics Unit, National Institutes of Mental Health, Bethesda, MD, USA
| | - Michael C Neale
- Department of Psychiatry and Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980126, Richmond, VA, USA
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104
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Yang H, Li K, Liang X, Gu B, Wang L, Gong G, Feng F, You H, Hou B, Gong F, Zhu H, Pan H. Alterations in Cortical Thickness in Young Male Patients With Childhood-Onset Adult Growth Hormone Deficiency: A Morphometric MRI Study. Front Neurosci 2019; 13:1134. [PMID: 31695595 PMCID: PMC6817473 DOI: 10.3389/fnins.2019.01134] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 10/08/2019] [Indexed: 11/13/2022] Open
Abstract
Background The growth hormone (GH)/insulin-like growth factor-1 (IGF-1) axis plays an important role in brain structure and maintenance of brain function. There is a close correlation between serum GH and IGF1 levels and age-related cognitive function. The effects of childhood-onset growth hormone deficiency (GHD)on brain morphology are underestimated so far. Methods In this cross-sectional study, T1-weighted magnetic resonance imaging was assessed in 17 adult males with childhood-onset GHD and 17 age and gender-matched healthy controls. The cortical thickness was analyzed and compared between the two groups of subjects. Effects of disease status and hormone levels on cortical thickness were also evaluated. Results Although there was no difference in whole brain volume or gray matter volume between the two groups, there was decreased cortical thickness in the parahippocampal gyrus, posterior cingulate gyrus and occipital visual syncortex in the adult growth hormone deficiency (AGHD) group, and increased cortical thickness in a partial area of the frontal lobe, parietal lobe and occipital visual syncortex in AGHD group. Cortical thickness of the posterior cingulum gyrus was prominently associated with FT3 serum levels only in control group after adjusting of IGF-1 levels. Conclusion These results suggest that young adult male patients with childhood-onset GHD have alterations in cortical thickness in different brain lobes/regions.
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Affiliation(s)
- Hongbo Yang
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, The Translational Medicine Center of PUMCH, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Kang Li
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, The Translational Medicine Center of PUMCH, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xinyu Liang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Bin Gu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Linjie Wang
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, The Translational Medicine Center of PUMCH, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Hui You
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Bo Hou
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Fengying Gong
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, The Translational Medicine Center of PUMCH, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Huijuan Zhu
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, The Translational Medicine Center of PUMCH, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Hui Pan
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, The Translational Medicine Center of PUMCH, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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105
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The Cortical Neuroanatomy Related to Specific Neuropsychological Deficits in Alzheimer's Continuum. Dement Neurocogn Disord 2019; 18:77-95. [PMID: 31681443 PMCID: PMC6819670 DOI: 10.12779/dnd.2019.18.3.77] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 08/21/2019] [Accepted: 08/24/2019] [Indexed: 01/09/2023] Open
Abstract
Background and Purpose In Alzheimer's continuum (a comprehensive of preclinical Alzheimer's disease [AD], mild cognitive impairment [MCI] due to AD, and AD dementia), cognitive dysfunctions are often related to cortical atrophy in specific brain regions. The purpose of this study was to investigate the association between anatomical pattern of cortical atrophy and specific neuropsychological deficits. Methods A total of 249 participants with Alzheimer's continuum (125 AD dementia, 103 MCI due to AD, and 21 preclinical AD) who were confirmed to be positive for amyloid deposits were collected from the memory disorder clinic in the department of neurology at Samsung Medical Center in Korea between September 2013 and March 2018. To analyze neuropsychological test-specific neural correlates representing the relationship between cortical atrophy measured by cortical thickness and performance in specific neuropsychological tests, a linear regression analysis was performed. Two neural correlates acquired by 2 different standardized scores in neuropsychological tests were also compared. Results Cortical atrophy in several specific brain regions was associated with most neuropsychological deficits, including digit span backward, naming, drawing-copying, verbal and visual recall, semantic fluency, phonemic fluency, and response inhibition. There were a few differences between 2 neural correlates obtained by different z-scores. Conclusions The poor performance of most neuropsychological tests is closely related to cortical thinning in specific brain areas in Alzheimer's continuum. Therefore, the brain atrophy pattern in patients with Alzheimer's continuum can be predict by an accurate analysis of neuropsychological tests in clinical practice.
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106
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Multi-channeled MR brain image segmentation: A new automated approach combining BAT and clustering technique for better identification of heterogeneous tumors. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.05.007] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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107
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Application of an amyloid and tau classification system in subcortical vascular cognitive impairment patients. Eur J Nucl Med Mol Imaging 2019; 47:292-303. [DOI: 10.1007/s00259-019-04498-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 08/21/2019] [Indexed: 10/26/2022]
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108
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Jeon S, Kang JM, Seo S, Jeong HJ, Funck T, Lee SY, Park KH, Lee YB, Yeon BK, Ido T, Okamura N, Evans AC, Na DL, Noh Y. Topographical Heterogeneity of Alzheimer's Disease Based on MR Imaging, Tau PET, and Amyloid PET. Front Aging Neurosci 2019; 11:211. [PMID: 31481888 PMCID: PMC6710378 DOI: 10.3389/fnagi.2019.00211] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 07/26/2019] [Indexed: 11/13/2022] Open
Abstract
Alzheimer’s disease (AD) patients are known to have heterogeneous clinical presentation and pathologic patterns. We hypothesize that AD dementia can be categorized into subtypes based on multimodal imaging biomarkers such as magnetic resonance imaging (MRI), tau positron emission tomography (PET), and amyloid PET. We collected 3T MRI, 18F-THK5351 PET, and 18F-flutemetamol (FLUTE) PET data from 83 patients with AD dementia [Clinical Dementia Rating (CDR) ≤1] and 60 normal controls (NC), and applied surface-based analyses to measure cortical thickness, THK5351 standardized uptake value ratio (SUVR) and FLUTE SUVR for each participant. For the patient group, we performed an agglomerative hierarchical clustering analysis using the three multimodal imaging features on the vertices (n = 3 × 79,950). The identified AD subtypes were compared to NC using general linear models adjusting for age, sex, and years of education. We mapped the effect size within significant cortical regions reaching a corrected p-vertex <0.05 (random field theory). Our surface-based multimodal framework has revealed three distinct subtypes among AD patients: medial temporal-dominant subtype (MT, n = 44), parietal-dominant subtype (P, n = 19), and diffuse atrophy subtype (D, n = 20). The topography of cortical atrophy and THK5351 retention differentiates between the three subtypes. In the case of FLUTE, three subtypes did not show distinct topographical differences, although cortical composite retention was significantly higher in the P type than in the MT type. These three subtypes also differed in demographic and clinical features. In conclusion, AD patients may be clustered into three subtypes with distinct topographical features of cortical atrophy and tau deposition, although amyloid deposition may not differ across the subtypes in terms of topography.
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Affiliation(s)
- Seun Jeon
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Jae Myeong Kang
- Department of Psychiatry, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea
| | - Seongho Seo
- Department of Neuroscience, Gachon University College of Medicine, Incheon, South Korea
| | - Hye Jin Jeong
- Neuroscience Research Institute, Gachon University, Incheon, South Korea
| | - Thomas Funck
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sang-Yoon Lee
- Department of Neuroscience, Gachon University College of Medicine, Incheon, South Korea
| | - Kee Hyung Park
- Department of Neurology, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea
| | - Yeong-Bae Lee
- Department of Neurology, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea
| | - Byeong Kil Yeon
- Department of Psychiatry, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea
| | - Tatsuo Ido
- Neuroscience Research Institute, Gachon University, Incheon, South Korea
| | - Nobuyuki Okamura
- Division of Pharmacology, Faculty of Medicine, Tohoku Medical and Pharmaceutical University, Sendai, Japan
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Young Noh
- Department of Neurology, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea.,Department of Health Science and Technology, GAIHST, Gachon University, Incheon, South Korea
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109
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Kwak K, Yun HJ, Park G, Lee JM. Multi-Modality Sparse Representation for Alzheimer's Disease Classification. J Alzheimers Dis 2019; 65:807-817. [PMID: 29562503 DOI: 10.3233/jad-170338] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) and mild cognitive impairment (MCI) are age-related neurodegenerative diseases characterized by progressive loss of memory and irreversible cognitive functions. The hippocampus, a brain area critical for learning and memory processes, is especially susceptible to damage at early stages of AD. OBJECTIVE We aimed to develop prediction model using a multi-modality sparse representation approach. METHODS We proposed a sparse representation approach to the hippocampus using structural T1-weighted magnetic resonance imaging (MRI) and 18-fluorodeoxyglucose-positron emission tomography (FDG-PET) to distinguish AD/MCI from healthy control subjects (HCs). We considered structural and function information for the hippocampus and applied a sparse patch-based approach to effectively reduce the dimensions of neuroimaging biomarkers. RESULTS In experiments using Alzheimer's Disease Neuroimaging Initiative data, our proposed method demonstrated more reliable than previous classification studies. The effects of different parameters on segmentation accuracy were also evaluated. The mean classification accuracy obtained with our proposed method was 0.94 for AD/HCs, 0.82 for MCI/HCs, and 0.86 for AD/MCI. CONCLUSION We extracted multi-modal features from automatically defined hippocampal regions of training subjects and found this method to be discriminative and robust for AD and MCI classification. The extraction of features in T1 and FDG-PET images is expected to improve classification performance due to the relationship between brain structure and function.
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Affiliation(s)
- Kichang Kwak
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Hyuk Jin Yun
- Fetal Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Gilsoon Park
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
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110
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Le M, Tang LYW, Hernández-Torres E, Jarrett M, Brosch T, Metz L, Li DKB, Traboulsee A, Tam RC, Rauscher A, Wiggermann V. FLAIR 2 improves LesionTOADS automatic segmentation of multiple sclerosis lesions in non-homogenized, multi-center, 2D clinical magnetic resonance images. NEUROIMAGE-CLINICAL 2019; 23:101918. [PMID: 31491827 PMCID: PMC6646743 DOI: 10.1016/j.nicl.2019.101918] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 06/18/2019] [Accepted: 06/30/2019] [Indexed: 11/05/2022]
Abstract
Background Accurate segmentation of MS lesions on MRI is difficult and, if performed manually, time consuming. Automatic segmentations rely strongly on the image contrast and signal-to-noise ratio. Literature examining segmentation tool performances in real-world multi-site data acquisition settings is scarce. Objective FLAIR2, a combination of T2-weighted and fluid attenuated inversion recovery (FLAIR) images, improves tissue contrast while suppressing CSF. We compared the use of FLAIR and FLAIR2 in LesionTOADS, OASIS and the lesion segmentation toolbox (LST) when applied to non-homogenized, multi-center 2D-imaging data. Methods Lesions were segmented on 47 MS patient data sets obtained from 34 sites using LesionTOADS, OASIS and LST, and compared to a semi-automatically generated reference. The performance of FLAIR and FLAIR2 was assessed using the relative lesion volume difference (LVD), Dice coefficient (DSC), sensitivity (SEN) and symmetric surface distance (SSD). Performance improvements related to lesion volumes (LVs) were evaluated for all tools. For comparison, LesionTOADS was also used to segment lesions from 3 T single-center MR data of 40 clinically isolated syndrome (CIS) patients. Results Compared to FLAIR, the use of FLAIR2 in LesionTOADS led to improvements of 31.6% (LVD), 14.0% (DSC), 25.1% (SEN), and 47.0% (SSD) in the multi-center study. DSC and SSD significantly improved for larger LVs, while LVD and SEN were enhanced independent of LV. OASIS showed little difference between FLAIR and FLAIR2, likely due to its inherent use of T2w and FLAIR. LST replicated the benefits of FLAIR2 only in part, indicating that further optimization, particularly at low LVs is needed. In the CIS study, LesionTOADS did not benefit from the use of FLAIR2 as the segmentation performance for both FLAIR and FLAIR2 was heterogeneous. Conclusions In this real-world, multi-center experiment, FLAIR2 outperformed FLAIR in its ability to segment MS lesions with LesionTOADS. The computation of FLAIR2 enhanced lesion detection, at minimally increased computational time or cost, even retrospectively. Further work is needed to determine how LesionTOADS and other tools, such as LST, can optimally benefit from the improved FLAIR2 contrast. FLAIR2 improves automatic MS lesion segmentation with LesionTOADS compared to FLAIR. Segmentation similarity improves for higher lesion volumes, particularly for FLAIR2. FLAIR2 provides greater sensitivity independent of lesion volume than FLAIR alone. Other segmentation tools need further optimization to fully benefit from FLAIR2. FLAIR2 provides immediate benefits at 1.5 T and visually improves segmentation at 3 T.
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Affiliation(s)
- M Le
- MS/MRI Research Group (Division of Neurology), University of British Columbia, Vancouver, BC, Canada
| | - L Y W Tang
- MS/MRI Research Group (Division of Neurology), University of British Columbia, Vancouver, BC, Canada; Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - E Hernández-Torres
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada; UBC MRI Research Centre, University of British Columbia, Vancouver, BC, Canada; Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - M Jarrett
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada; Population Data BC, Vancouver, BC, Canada
| | - T Brosch
- MS/MRI Research Group (Division of Neurology), University of British Columbia, Vancouver, BC, Canada; Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada; Philips Medical Innovative Technologies, Hamburg, Germany
| | - L Metz
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - D K B Li
- MS/MRI Research Group (Division of Neurology), University of British Columbia, Vancouver, BC, Canada; Department of Radiology, University of British Columbia, Vancouver, BC, Canada; UBC MRI Research Centre, University of British Columbia, Vancouver, BC, Canada
| | - A Traboulsee
- Department of Neurology (Division of Medicine), University of British Columbia, Vancouver, BC, Canada
| | - R C Tam
- MS/MRI Research Group (Division of Neurology), University of British Columbia, Vancouver, BC, Canada; Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - A Rauscher
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada; BC Children's Hospital Research Institute, Canada; Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - V Wiggermann
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada; UBC MRI Research Centre, University of British Columbia, Vancouver, BC, Canada; Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.
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111
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Statistical framework for validation without ground truth of choroidal thickness changes detection. PLoS One 2019; 14:e0218776. [PMID: 31251762 PMCID: PMC6599222 DOI: 10.1371/journal.pone.0218776] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Accepted: 06/11/2019] [Indexed: 11/19/2022] Open
Abstract
Monitoring subtle choroidal thickness changes in the human eye delivers insight into the pathogenesis of various ocular diseases such as myopia and helps planning their treatment. However, a thorough evaluation of detection-performance is challenging as a ground truth for comparison is not available. Alternatively, an artificial ground truth can be generated by averaging the manual expert segmentations. This makes the ground truth very sensitive to ambiguities due to different interpretations by the experts. In order to circumvent this limitation, we present a novel validation approach that operates independently from a ground truth and is uniquely based on the common agreement between algorithm and experts. Utilizing an appropriate index, we compare the joint agreement of several raters with the algorithm and validate it against manual expert segmentation. To illustrate this, we conduct an observational study and evaluate the results obtained using our previously published registration-based method. In addition, we present an adapted state-of-the-art evaluation method, where a paired t-test is carried out after leaving out the results of one expert at the time. Automated and manual detection were performed on a dataset of 90 OCT 3D-volume stack pairs of healthy subjects between 8 and 18 years of age from Asian urban regions with a high prevalence of myopia.
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112
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Rostral-Caudal Hippocampal Functional Convergence Is Reduced Across the Alzheimer's Disease Spectrum. Mol Neurobiol 2019; 56:8336-8344. [PMID: 31230260 DOI: 10.1007/s12035-019-01671-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 06/03/2019] [Indexed: 10/26/2022]
Abstract
Beginning in the early stages of Alzheimer's disease (AD), the hippocampus reduces its functional connections to other cortical regions due to synaptic depletion. However, little is known regarding connectivity abnormalities within the hippocampus. Here, we describe rostral-caudal hippocampal convergence (rcHC), a metric of the overlap between the rostral and caudal hippocampal functional networks, across the clinical spectrum of AD. We predicted a decline in rostral-caudal hippocampal convergence in the early stages of the disease. Using fMRI, we generated resting-state hippocampal functional networks across 56 controls, 48 early MCI (EMCI), 35 late MCI (LMCI), and 31 AD patients from the Alzheimer's Disease Neuroimaging Initiative cohort. For each diagnostic group, we performed a conjunction analysis and compared the rostral and caudal hippocampal network changes using a mixed effects linear model to estimate the convergence and differences between these networks, respectively. The conjunction analysis showed a reduction of rostral-caudal hippocampal convergence strength from early MCI to AD, independent of hippocampal atrophy. Our results demonstrate a parallel between the functional convergence within the hippocampus and disease stage, which is independent of brain atrophy. These findings support the concept that network convergence might contribute as a biomarker for connectivity dysfunction in early stages of AD.
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113
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Shiohama T, McDavid J, Levman J, Takahashi E. The left lateral occipital cortex exhibits decreased thickness in children with sensorineural hearing loss. Int J Dev Neurosci 2019; 76:34-40. [PMID: 31173823 DOI: 10.1016/j.ijdevneu.2019.05.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 05/10/2019] [Accepted: 05/30/2019] [Indexed: 10/26/2022] Open
Abstract
Patients with sensorineural hearing loss (SNHL) tend to show language delay, executive functioning deficits, and visual cognitive impairment, even after intervention with hearing amplification and cochlear implants, which suggest altered brain structures and functions in SNHL patients. In this study, we investigated structural brain MRI in 30 children with SNHL (18 mild to moderate [M-M] SNHL and 12 moderately severe to profound [M-P] SNHL) by comparing gender- and age-matched normal controls (NC). Region-based analyses did not show statistically significant differences in volumes of the cerebrum, basal ganglia, cerebellum, and the ventricles between SNHL and NC. On surface-based analyses, the global and lobar cortical surface area, thickness, and volumes were not statistically significantly different between SNHL and NC participants. Regional surface areas, cortical thicknesses, and cortical volumes were statistically significantly smaller in M-P SNHL compared to NC in the left middle occipital cortex, and left inferior occipital cortex after a correction for multiple comparisons using random field theory (p < 0.02). These regions were identified as areas known to be related to high level visual cognition including the human middle temporal area, lateral occipital area, occipital face area, and V8. The observed regional decreased thickness in M-P SNHL may be associated with dysfunctions of visual cognition in SNHL detectable in a clinical setting.
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Affiliation(s)
- Tadashi Shiohama
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA.,Department of Pediatrics, Chiba University Hospital, Inohana 1-8-1, Chiba-shi, Chiba, 2608670, Japan
| | - Jeremy McDavid
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Jacob Levman
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA.,Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, 2323 Notre Dame Ave, Antigonish, Nova Scotia, B2G 2W5, Canada
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
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114
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Pascoal TA, Mathotaarachchi S, Kang MS, Mohaddes S, Shin M, Park AY, Parent MJ, Benedet AL, Chamoun M, Therriault J, Hwang H, Cuello AC, Misic B, Soucy JP, Aston JAD, Gauthier S, Rosa-Neto P. Aβ-induced vulnerability propagates via the brain's default mode network. Nat Commun 2019; 10:2353. [PMID: 31164641 PMCID: PMC6547716 DOI: 10.1038/s41467-019-10217-w] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 04/16/2019] [Indexed: 12/17/2022] Open
Abstract
The link between brain amyloid-β (Aβ), metabolism, and dementia symptoms remains a pressing question in Alzheimer's disease. Here, using positron emission tomography ([18F]florbetapir tracer for Aβ and [18F]FDG tracer for glucose metabolism) with a novel analytical framework, we found that Aβ aggregation within the brain's default mode network leads to regional hypometabolism in distant but functionally connected brain regions. Moreover, we found that an interaction between this hypometabolism with overlapping Aβ aggregation is associated with subsequent cognitive decline. These results were also observed in transgenic Aβ rats that do not form neurofibrillary tangles, which support these findings as an independent mechanism of cognitive deterioration. These results suggest a model in which distant Aβ induces regional metabolic vulnerability, whereas the interaction between local Aβ with a vulnerable environment drives the clinical progression of dementia.
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Affiliation(s)
- Tharick A Pascoal
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, H4H 1R3, Montreal, Canada
- Montreal Neurological Institute, H3A 2B4, Montreal, Canada
| | - Sulantha Mathotaarachchi
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, H4H 1R3, Montreal, Canada
| | - Min Su Kang
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, H4H 1R3, Montreal, Canada
- Montreal Neurological Institute, H3A 2B4, Montreal, Canada
| | - Sara Mohaddes
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, H4H 1R3, Montreal, Canada
| | - Monica Shin
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, H4H 1R3, Montreal, Canada
| | - Ah Yeon Park
- Statistical Laboratory, University of Cambridge, CB3 0WB, Cambridge, UK
| | - Maxime J Parent
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, H4H 1R3, Montreal, Canada
- Montreal Neurological Institute, H3A 2B4, Montreal, Canada
| | - Andrea L Benedet
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, H4H 1R3, Montreal, Canada
| | - Mira Chamoun
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, H4H 1R3, Montreal, Canada
| | - Joseph Therriault
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, H4H 1R3, Montreal, Canada
| | - Heungsun Hwang
- Department of Psychology, McGill University, Montreal, Canada
| | - A Claudio Cuello
- Department of Pharmacology and Therapeutics, McGill University, H3A 2T5, Montreal, Canada
- Alzheimer's Disease Research Unit, The McGill University Research Centre for Studies in Aging, H4H 1R3, Montreal, Canada
| | | | | | - John A D Aston
- Statistical Laboratory, University of Cambridge, CB3 0WB, Cambridge, UK
| | - Serge Gauthier
- Alzheimer's Disease Research Unit, The McGill University Research Centre for Studies in Aging, H4H 1R3, Montreal, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, H4H 1R3, Montreal, Canada.
- Montreal Neurological Institute, H3A 2B4, Montreal, Canada.
- Department of Pharmacology and Therapeutics, McGill University, H3A 2T5, Montreal, Canada.
- Alzheimer's Disease Research Unit, The McGill University Research Centre for Studies in Aging, H4H 1R3, Montreal, Canada.
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115
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Shiohama T, McDavid J, Levman J, Takahashi E. Quantitative brain morphological analysis in CHARGE syndrome. Neuroimage Clin 2019; 23:101866. [PMID: 31154243 PMCID: PMC6543177 DOI: 10.1016/j.nicl.2019.101866] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 05/10/2019] [Accepted: 05/19/2019] [Indexed: 11/01/2022]
Abstract
CHARGE syndrome (CS) is a rare congenital syndrome characterized by coloboma, heart anomaly, choanal atresia, retardation of growth and development, and genital and ear anomalies. While several neuroimaging studies have revealed abnormalities such as hypoplasia of the semicircular canal, olfactory nerve, cerebellum, and brainstem, no quantitative analysis of brain morphology in CS has been reported. We quantitatively investigated brain morphology in CS participants using structural magnetic resonance imaging (MRI) (N = 10, mean age 14.7 years old) and high-angular resolution diffusion MRI (HARDI) tractography (N = 8, mean age 19.4 years old) comparing with gender- and age-matched controls. Voxel-based analyses revealed decreased volume of the bilateral globus pallidus (left and right; p = 0.021 and 0.029), bilateral putamen (p = 0.016 and 0.011), left subthalamic nucleus (p = 0.012), bilateral cerebellum (p = 1.5 × 10-6 and 1.2 × 10-6), and brainstem (p = 0.031), and the enlargement of the lateral ventricles (p = 0.011 and 0.0031) bilaterally in CS. Surface-based analysis revealed asymmetrically increased cortical thickness in the right hemisphere (p = 0.013). The group-wise differences observed in global cortical volume, gyrification index, and left cortical thickness were not statistically significant. HARDI tractography revealed reduced volume, elongation, and higher ADC values in multiple fiber tracts in patients in CS compared to the controls, but FA values were not statistically significantly different between the two groups. Facial features are known to be asymmetric in CS, which has been recognized as an important symptom in CS. Our results revealed that the cortex in CS has an asymmetric appearance similar to the facial features. In addition, the signal pattern of high ADC with statistically unchanged FA values of tractography pathways indicated the presence of other pathogenesis than vasogenic edema or myelination dysfunction in developmental delay in CS.
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Affiliation(s)
- Tadashi Shiohama
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA; Department of Pediatrics, Chiba University Hospital, Inohana 1-8-1, Chiba-shi, Chiba 2608670, Japan.
| | - Jeremy McDavid
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Jacob Levman
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA; Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, 2323 Notre Dame Ave, Antigonish, Nova Scotia B2G 2W5, Canada
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA
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116
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Dickie EW, Anticevic A, Smith DE, Coalson TS, Manogaran M, Calarco N, Viviano JD, Glasser MF, Van Essen DC, Voineskos AN. Ciftify: A framework for surface-based analysis of legacy MR acquisitions. Neuroimage 2019; 197:818-826. [PMID: 31091476 DOI: 10.1016/j.neuroimage.2019.04.078] [Citation(s) in RCA: 99] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 04/26/2019] [Accepted: 04/29/2019] [Indexed: 10/26/2022] Open
Abstract
The preprocessing pipelines of the Human Connectome Project (HCP) were made publicly available for the neuroimaging community to apply the HCP analytic approach to data from non-HCP sources. The HCP analytic approach is surface-based for the cerebral cortex, uses the CIFTI "grayordinate" file format, provides greater statistical sensitivity than traditional volume-based analysis approaches, and allows for a more neuroanatomically-faithful representation of data. However, the HCP pipelines require the acquisition of specific images (namely T2w and field map) that historically have often not been acquired. Massive amounts of this 'legacy' data could benefit from the adoption of HCP-style methods. However, there is currently no published framework, to our knowledge, for adapting HCP preprocessing to "legacy" data. Here we present the ciftify project, a parsimonious analytic framework for adapting key modules from the HCP pipeline into existing structural workflows using FreeSurfer's recon_all structural and existing functional preprocessing workflows. Within this framework, any functional dataset with an accompanying (i.e. T1w) anatomical data can be analyzed in CIFTI format. To simplify usage for new data, the workflow has been bundled with fMRIPrep following the BIDS-app framework. Finally, we present the package and comment on future neuroinformatics advances that may accelerate the movement to a CIFTI-based grayordinate framework.
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Affiliation(s)
- Erin W Dickie
- Kimel Family Translational Imaging-Genetics Laboratory, Campbell Family Mental Health Research Institute, Centre of Addiction and Mental Health, Toronto Canada.
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Division of Neurocognition, Neurocomputation, & Neurogenetics (N3), Yale University School of Medicine, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA
| | - Dawn E Smith
- Kimel Family Translational Imaging-Genetics Laboratory, Campbell Family Mental Health Research Institute, Centre of Addiction and Mental Health, Toronto Canada
| | - Timothy S Coalson
- Departments of Radiology and Neuroscience, Washington University School of Medicine, St Louis, USA
| | - Mathuvanthi Manogaran
- Kimel Family Translational Imaging-Genetics Laboratory, Campbell Family Mental Health Research Institute, Centre of Addiction and Mental Health, Toronto Canada
| | - Navona Calarco
- Kimel Family Translational Imaging-Genetics Laboratory, Campbell Family Mental Health Research Institute, Centre of Addiction and Mental Health, Toronto Canada
| | - Joseph D Viviano
- Kimel Family Translational Imaging-Genetics Laboratory, Campbell Family Mental Health Research Institute, Centre of Addiction and Mental Health, Toronto Canada
| | - Matthew F Glasser
- Departments of Radiology and Neuroscience, Washington University School of Medicine, St Louis, USA; St. Luke's Hospital, Chesterfield, MO, USA
| | - David C Van Essen
- Departments of Radiology and Neuroscience, Washington University School of Medicine, St Louis, USA
| | - Aristotle N Voineskos
- Kimel Family Translational Imaging-Genetics Laboratory, Campbell Family Mental Health Research Institute, Centre of Addiction and Mental Health, Toronto Canada; Department of Psychiatry, University of Toronto, Toronto, Canada
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117
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Fontes K, Rohlicek CV, Saint-Martin C, Gilbert G, Easson K, Majnemer A, Marelli A, Chakravarty MM, Brossard-Racine M. Hippocampal alterations and functional correlates in adolescents and young adults with congenital heart disease. Hum Brain Mapp 2019; 40:3548-3560. [PMID: 31070841 DOI: 10.1002/hbm.24615] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 03/30/2019] [Accepted: 04/24/2019] [Indexed: 01/18/2023] Open
Abstract
There is a high prevalence of neurodevelopmental impairments in individuals living with congenital heart disease (CHD) and the neural correlates of these impairments are not yet fully understood. Recent studies have shown that hippocampal volume and shape differences may provide unique biomarkers for neurodevelopmental disorders. The hippocampus is vulnerable to early life injury, especially in populations at risk for hypoxemia or hemodynamic instability such as in neonates with CHD. We compared hippocampal gray and white matter volume and morphometry between youth born with CHD (n = 50) aged 16-24 years and healthy peers (n = 48). We also explored whether hippocampal gray and white matter volume and morphometry are associated with executive function and self-regulation deficits. To do so, participants underwent 3T brain magnetic resonance imaging and completed the self-reported Behavior Rating Inventory of Executive Function-Adult version. We found that youth with CHD had smaller hippocampal volumes (all statistics corrected for false discovery rate; q < 0.05) as compared to controls. We also observed significant smaller surface area bilaterally and inward displacement on the left hippocampus predominantly on the ventral side (q < 0.10) in the CHD group that were not present in the controls. Left CA1 and CA2/3 were negatively associated with working memory (p < .05). Here, we report, for the first-time, hippocampal morphometric alterations in youth born with CHD when compared to healthy peers, as well as, structure-function relationships between hippocampal volumes and executive function. These differences may reflect long lasting alterations in brain development specific to individual with CHD.
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Affiliation(s)
- Kimberly Fontes
- Advances in Brain and Child Health Development Research Laboratory, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | - Charles V Rohlicek
- Department of Pediatrics, Division of Cardiology, Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada
| | - Christine Saint-Martin
- Department of Medical Imaging, Division of Pediatric Radiology, Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada
| | | | - Kaitlyn Easson
- Advances in Brain and Child Health Development Research Laboratory, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | - Annette Majnemer
- School of Physical and Occupational Therapy, McGill University, Montreal, Quebec, Canada
| | - Ariane Marelli
- McGill Adult Unit for Congenital Heart Disease Excellence, McGill University, Montreal, Quebec, Canada
| | - M Mallar Chakravarty
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre - Douglas Mental Health University Institute, Verdun, Quebec, Canada.,Department of Psychiatry, McGill University, Montreal, Quebec, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Marie Brossard-Racine
- Advances in Brain and Child Health Development Research Laboratory, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada.,School of Physical and Occupational Therapy, McGill University, Montreal, Quebec, Canada.,Department of Pediatrics, Division of Neonatology, Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada
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118
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Albaugh MD, Hudziak JJ, Orr C, Spechler PA, Chaarani B, Mackey S, Lepage C, Fonov V, Rioux P, Evans AC, Banaschewski T, Bokde ALW, Bromberg U, Büchel C, Quinlan EB, Desrivières S, Flor H, Grigis A, Gowland P, Heinz A, Ittermann B, Martinot JL, Martinot MLP, Nees F, Orfanos DP, Paus T, Poustka L, Millenet S, Fröhner JH, Smolka MN, Walter H, Whelan R, Schumann G, Potter AS, Garavan H. Amygdalar reactivity is associated with prefrontal cortical thickness in a large population-based sample of adolescents. PLoS One 2019; 14:e0216152. [PMID: 31048888 PMCID: PMC6497259 DOI: 10.1371/journal.pone.0216152] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 04/15/2019] [Indexed: 11/18/2022] Open
Abstract
In structural neuroimaging studies, reduced cerebral cortical thickness in orbital and ventromedial prefrontal regions is frequently interpreted as reflecting an impaired ability to downregulate neuronal activity in the amygdalae. Unfortunately, little research has been conducted in order to test this conjecture. We examine the extent to which amygdalar reactivity is associated with cortical thickness in a population-based sample of adolescents. Data were obtained from the IMAGEN study, which includes 2,223 adolescents. While undergoing functional neuroimaging, participants passively viewed video clips of a face that started from a neutral expression and progressively turned angry, or, instead, turned to a second neutral expression. Left and right amygdala ROIs were used to extract mean BOLD signal change for the angry minus neutral face contrast for all subjects. T1-weighted images were processed through the CIVET pipeline (version 2.1.0). In variable-centered analyses, local cortical thickness was regressed against amygdalar reactivity using first and second-order linear models. In a follow-up person-centered analysis, we defined a "high reactive" group of participants based on mean amygdalar BOLD signal change for the angry minus neutral face contrast. Between-group differences in cortical thickness were examined ("high reactive" versus all other participants). A significant association was revealed between the continuous measure of amygdalar reactivity and bilateral ventromedial prefrontal cortical thickness in a second-order linear model (p < 0.05, corrected). The "high reactive" group, in comparison to all other participants, possessed reduced cortical thickness in bilateral orbital and ventromedial prefrontal cortices, bilateral anterior temporal cortices, left caudal middle temporal gyrus, and the left inferior and middle frontal gyri (p < 0.05, corrected). Results are consistent with non-human primate studies, and provide empirical support for an association between reduced prefrontal cortical thickness and amygdalar reactivity. Future research will likely benefit from investigating the degree to which psychopathology qualifies relations between prefrontal cortical structure and amygdalar reactivity.
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Affiliation(s)
- Matthew D. Albaugh
- Vermont Center for Children, Youth, and Families, Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, United States of America
| | - James. J. Hudziak
- Vermont Center for Children, Youth, and Families, Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, United States of America
| | - Catherine Orr
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, United States of America
| | - Philip A. Spechler
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, United States of America
| | - Bader Chaarani
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, United States of America
| | - Scott Mackey
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, United States of America
| | - Claude Lepage
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Vladimir Fonov
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Pierre Rioux
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Alan C. Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Arun L. W. Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Uli Bromberg
- University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | | | - Erin Burke Quinlan
- Medical Research Council—Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Sylvane Desrivières
- Medical Research Council—Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Charité –Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany [or depending on journal requirements can be: Physikalisch-Technische Bundesanstalt (PTB), Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes—Sorbonne Paris Cité; and Maison de Solenn, Paris, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”; University Paris Sud; University Paris Descartes; Sorbonne Universités; and AP-HP, Department of Child and AdolescentPsychiatryPitié-Salpêtrière Hospital, Paris, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | | | - Tomáš Paus
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital and Departments of Psychology and Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Göttingen, Germany
| | - Sabina Millenet
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Juliane H. Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N. Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Charité –Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Gunter Schumann
- Medical Research Council—Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Alexandra S. Potter
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, United States of America
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, United States of America
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119
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Youn JH, Ryu SH, Lee JY, Park S, Cho SJ, Kwon H, Yang JJ, Lee JM, Lee J, Kim S, Livingston G, Yoon DH. Brain structural changes after multi-strategic metamemory training in older adults with subjective memory complaints: A randomized controlled trial. Brain Behav 2019; 9:e01278. [PMID: 30916450 PMCID: PMC6520300 DOI: 10.1002/brb3.1278] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 03/01/2019] [Accepted: 03/06/2019] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Metamemory is the process of monitoring and controlling one's memory. Improving metamemory may reduce the memory problem in old age. We hypothesized that metamemory training (MMT) would improve cognition in older adults with subjective memory complaints and change the brain region related to metacognition. METHOD We recruited and randomized older adults to the multi-strategic memory training of 10 weekly 90-min sessions, based on the metamemory concept or usual care. Cognitive tests including the Elderly Verbal Learning Test, Simple Rey Figure Test, Digit Span, Spatial Span, Categorical Fluency, and the Boston Naming Test were done in 201 participants, together with magnetic resonance imaging (MRI) in 49 participants before and after training. RESULTS A total of 112 in the training group and 89 in the control group participated. The training group had a significant increase in long-term delayed free recall, categorical fluency, and the Boston Naming test. In MRI, the mean diffusivity of the bundles of axon tracts passing from the frontal lobe to the posterior end of the lateral sulcus decreased in the training group. CONCLUSION These results indicate that the MMT program has a positive impact on enhancing older people' cognitive performance. Improved white matter integrity in the anterior and posterior cerebrum and increased cortical thickness of prefrontal regions, which related to metacognition, possibly suggest that the effects of the MMT would be induced via the enhancement of cognitive control.
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Affiliation(s)
- Jung-Hae Youn
- Graduate School of Clinical Counseling Psychology, CHA University, Pocheon, Republic of Korea
| | - Seung-Ho Ryu
- Department of Psychiatry, School of Medicine, Konkuk University, Konkuk University Medical Center, Seoul, Republic of Korea
| | - Jun-Young Lee
- Department of Psychiatry, Seoul National University & SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
| | - Soowon Park
- Department of Education, Sejong University, Seoul, Republic of Korea
| | - Seong-Jin Cho
- Gachon University Gil Medical Center, Incheon, Republic of Korea
| | - Hunki Kwon
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
| | - Jin-Ju Yang
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
| | - Jiyeon Lee
- Department of Psychiatry, Seoul National University & SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
| | - Seolmin Kim
- Department of Psychiatry, Konkuk University Medical Center, Seoul, Republic of Korea
| | - Gill Livingston
- Division of Psychiatry, University College London, London, UK
| | - Dong Hyun Yoon
- Department of Psychiatry, Seoul National University & SMG-SNU Boramae Medical Center, Seoul, Republic of Korea.,Institute of Sports Science, Seoul National University, Seoul, Republic of Korea
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120
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Meyer PF, Tremblay-Mercier J, Leoutsakos J, Madjar C, Lafaille-Magnan ME, Savard M, Rosa-Neto P, Poirier J, Etienne P, Breitner J. INTREPAD: A randomized trial of naproxen to slow progress of presymptomatic Alzheimer disease. Neurology 2019; 92:e2070-e2080. [PMID: 30952794 PMCID: PMC6512884 DOI: 10.1212/wnl.0000000000007232] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 01/07/2019] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To evaluate the safety and efficacy of low-dose naproxen for prevention of progression in presymptomatic Alzheimer disease (AD) among cognitively intact persons at risk. METHODS Investigation of Naproxen Treatment Effects in Pre-symptomatic Alzheimer's Disease (INTREPAD), a 2-year double-masked pharmaco-prevention trial, enrolled 195 AD family history-positive elderly (mean age 63 years) participants screened carefully to exclude cognitive disorder (NCT-02702817). These were randomized 1:1 to naproxen sodium 220 mg twice daily or placebo. Multimodal imaging, neurosensory, cognitive, and (in ∼50%) CSF biomarker evaluations were performed at baseline, 3, 12, and 24 months. A modified intent-to-treat analysis considered 160 participants who remained on-treatment through their first follow-up examination. The primary outcome was rate of change in a multimodal composite presymptomatic Alzheimer Progression Score (APS). RESULTS Naproxen-treated individuals showed a clear excess of adverse events. Among treatment groups combined, the APS increased by 0.102 points/year (SE 0.014; p < 10-12), but rate of change showed little difference by treatment assignment (0.019 points/year). The treatment-related rate ratio of 1.16 (95% confidence interval 0.64-1.96) suggested that naproxen does not reduce the rate of APS progression by more than 36%. Secondary analyses revealed no notable treatment effects on individual CSF, cognitive, or neurosensory biomarker indicators of progressive presymptomatic AD. CONCLUSIONS In cognitively intact individuals at risk, sustained treatment with naproxen sodium 220 mg twice daily increases frequency of adverse health effects but does not reduce apparent progression of presymptomatic AD. CLASSIFICATION OF EVIDENCE This study provides Class I evidence that, for people who are cognitively intact, low-dose naproxen does not significantly reduce progression of a composite indicator of presymptomatic AD.
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Affiliation(s)
- Pierre-François Meyer
- From the McGill Centre for Integrative Neuroscience, Montreal Neurological Institute (C.M.), and McGill University Research Centre for Studies in Aging (M.S., P.R.-N.), McGill University (P.-F.M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.); StoP-AD Centre (P.-F.M., J.T.-M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.), Douglas Mental Health University Institute Research Centre, Montréal, Canada; and John Hopkins University (J.L.), Baltimore, MD
| | - Jennifer Tremblay-Mercier
- From the McGill Centre for Integrative Neuroscience, Montreal Neurological Institute (C.M.), and McGill University Research Centre for Studies in Aging (M.S., P.R.-N.), McGill University (P.-F.M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.); StoP-AD Centre (P.-F.M., J.T.-M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.), Douglas Mental Health University Institute Research Centre, Montréal, Canada; and John Hopkins University (J.L.), Baltimore, MD
| | - Jeannie Leoutsakos
- From the McGill Centre for Integrative Neuroscience, Montreal Neurological Institute (C.M.), and McGill University Research Centre for Studies in Aging (M.S., P.R.-N.), McGill University (P.-F.M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.); StoP-AD Centre (P.-F.M., J.T.-M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.), Douglas Mental Health University Institute Research Centre, Montréal, Canada; and John Hopkins University (J.L.), Baltimore, MD
| | - Cécile Madjar
- From the McGill Centre for Integrative Neuroscience, Montreal Neurological Institute (C.M.), and McGill University Research Centre for Studies in Aging (M.S., P.R.-N.), McGill University (P.-F.M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.); StoP-AD Centre (P.-F.M., J.T.-M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.), Douglas Mental Health University Institute Research Centre, Montréal, Canada; and John Hopkins University (J.L.), Baltimore, MD
| | - Marie-Elyse Lafaille-Magnan
- From the McGill Centre for Integrative Neuroscience, Montreal Neurological Institute (C.M.), and McGill University Research Centre for Studies in Aging (M.S., P.R.-N.), McGill University (P.-F.M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.); StoP-AD Centre (P.-F.M., J.T.-M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.), Douglas Mental Health University Institute Research Centre, Montréal, Canada; and John Hopkins University (J.L.), Baltimore, MD
| | - Melissa Savard
- From the McGill Centre for Integrative Neuroscience, Montreal Neurological Institute (C.M.), and McGill University Research Centre for Studies in Aging (M.S., P.R.-N.), McGill University (P.-F.M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.); StoP-AD Centre (P.-F.M., J.T.-M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.), Douglas Mental Health University Institute Research Centre, Montréal, Canada; and John Hopkins University (J.L.), Baltimore, MD
| | - Pedro Rosa-Neto
- From the McGill Centre for Integrative Neuroscience, Montreal Neurological Institute (C.M.), and McGill University Research Centre for Studies in Aging (M.S., P.R.-N.), McGill University (P.-F.M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.); StoP-AD Centre (P.-F.M., J.T.-M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.), Douglas Mental Health University Institute Research Centre, Montréal, Canada; and John Hopkins University (J.L.), Baltimore, MD
| | - Judes Poirier
- From the McGill Centre for Integrative Neuroscience, Montreal Neurological Institute (C.M.), and McGill University Research Centre for Studies in Aging (M.S., P.R.-N.), McGill University (P.-F.M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.); StoP-AD Centre (P.-F.M., J.T.-M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.), Douglas Mental Health University Institute Research Centre, Montréal, Canada; and John Hopkins University (J.L.), Baltimore, MD
| | - Pierre Etienne
- From the McGill Centre for Integrative Neuroscience, Montreal Neurological Institute (C.M.), and McGill University Research Centre for Studies in Aging (M.S., P.R.-N.), McGill University (P.-F.M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.); StoP-AD Centre (P.-F.M., J.T.-M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.), Douglas Mental Health University Institute Research Centre, Montréal, Canada; and John Hopkins University (J.L.), Baltimore, MD
| | - John Breitner
- From the McGill Centre for Integrative Neuroscience, Montreal Neurological Institute (C.M.), and McGill University Research Centre for Studies in Aging (M.S., P.R.-N.), McGill University (P.-F.M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.); StoP-AD Centre (P.-F.M., J.T.-M., M.-E.L.-M., P.R.-N., J.P., P.E., J.B.), Douglas Mental Health University Institute Research Centre, Montréal, Canada; and John Hopkins University (J.L.), Baltimore, MD.
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Schmitt JE, Neale MC, Clasen LS, Liu S, Seidlitz J, Pritikin JN, Chu A, Wallace GL, Lee NR, Giedd JN, Raznahan A. A Comprehensive Quantitative Genetic Analysis of Cerebral Surface Area in Youth. J Neurosci 2019; 39:3028-3040. [PMID: 30833512 PMCID: PMC6468099 DOI: 10.1523/jneurosci.2248-18.2019] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 01/21/2019] [Accepted: 01/29/2019] [Indexed: 11/21/2022] Open
Abstract
The genetics of cortical arealization in youth is not well understood. In this study, we use a genetically informative sample of 677 typically developing children and adolescents (mean age 12.72 years), high-resolution MRI, and quantitative genetic methodology to address several fundamental questions on the genetics of cerebral surface area. We estimate that >85% of the phenotypic variance in total brain surface area in youth is attributable to additive genetic factors. We also observed pronounced regional variability in the genetic influences on surface area, with the most heritable areas seen in primary visual and visual association cortex. A shared global genetic factor strongly influenced large areas of the frontal and temporal cortex, mirroring regions that are the most evolutionarily novel in humans relative to other primates. In contrast to studies on older populations, we observed statistically significant genetic correlations between measures of surface area and cortical thickness (rG = 0.63), suggestive of overlapping genetic influences between these endophenotypes early in life. Finally, we identified strong and highly asymmetric genetically mediated associations between Full-Scale Intelligence Quotient and left perisylvian surface area, particularly receptive language centers. Our findings suggest that spatially complex and temporally dynamic genetic factors are influencing cerebral surface area in our species.SIGNIFICANCE STATEMENT Over evolution, the human cortex has undergone massive expansion. In humans, patterns of neurodevelopmental expansion mirror evolutionary changes. However, there is a sparsity of information on how genetics impacts surface area maturation. Here, we present a systematic analysis of the genetics of cerebral surface area in youth. We confirm prior research that implicates genetics as the dominant force influencing individual differences in global surface area. We also find evidence that evolutionarily novel brain regions share common genetics, that overlapping genetic factors influence both area and thickness in youth, and the presence of strong genetically mediated associations between intelligence and surface area in language centers. These findings further elucidate the complex role that genetics plays in brain development and function.
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Affiliation(s)
- J Eric Schmitt
- Departments of Radiology and Psychiatry, Division of Neuroradiology, Brain Behavior Laboratory, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania 19104,
| | - Michael C Neale
- Departments of Psychiatry and Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia 23298
| | - Liv S Clasen
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland 20892
| | - Siyuan Liu
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland 20892
| | - Jakob Seidlitz
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland 20892
| | - Joshua N Pritikin
- Departments of Psychiatry and Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia 23298
| | - Alan Chu
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Gregory L Wallace
- Department of Speech, Language, and Hearing Sciences, George Washington University, Washington, DC 20052
| | - Nancy Raitano Lee
- Department of Psychology, Drexel University, Philadelphia, Pennsylvania 19104, and
| | - Jay N Giedd
- Department of Psychiatry, University of California at San Diego, La Jolla, California 92093
| | - Armin Raznahan
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland 20892
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Abstract
The most important goals of brain network analyses are to (a) detect pivotal regions and connections that contribute to disproportionate communication flow, (b) integrate global information, and (c) increase the brain network efficiency. Most centrality measures assume that information propagates in networks with the shortest connection paths, but this assumption is not true for most real networks given that information in the brain propagates through all possible paths. This study presents a methodological pipeline for identifying influential nodes and edges in human brain networks based on the self-regulating biological concept adopted from the Physarum model, thereby allowing the identification of optimal paths that are independent of the stated assumption. Network hubs and bridges were investigated in structural brain networks using the Physarum model. The optimal paths and fluid flow were used to formulate the Physarum centrality measure. Most network hubs and bridges are overlapped to some extent, but those based on Physarum centrality contain local and global information in the superior frontal, anterior cingulate, middle temporal gyrus, and precuneus regions. This approach also reduced individual variation. Our results suggest that the Physarum centrality presents a trade-off between the degree and betweenness centrality measures.
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123
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Kim SJ, Jung NY, Kim YJ, Park SB, Kim K, Kim Y, Jang H, Kim SE, Cho SH, Kim JP, Jung YH, Woo SY, Kim SW, Lockhart SN, Kim EJ, Kim HJ, Lee JM, Chin J, Na DL, Seo SW. Clinical Effects of Frontal Behavioral Impairment: Cortical Thickness and Cognitive Decline in Individuals with Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment. J Alzheimers Dis 2019; 69:213-225. [PMID: 30958372 DOI: 10.3233/jad-190007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Frontal behavioral impairment (FrBI) is commonly observed in various degenerative diseases and refers to various behavioral symptoms. OBJECTIVE We investigated the effects of the presence of FrBI on cortical thickness, and the longitudinal neuropsychological changes in people in the predementia stage. METHODS A total of 794 individuals completed neuropsychological tests and the Frontal Behavioral Inventory (FBI) Questionnaire, and underwent magnetic resonance (MR) scanning. Participants were analyzed and grouped into non-FrBI (FBI = 0) or FrBI (FBI≥1). Cortical thickness was measured on MR images using a surface-based method. RESULTS In total, 281 people with subjective cognitive decline (SCD) and 513 with amnestic mild cognitive impairment (aMCI) were assessed for FrBI. Relative to people without FrBI, those with FrBI presented reduced cortical thickness in the frontal, anterior temporal and lateral parietal regions (p < 0.05, FDR corrected). People with FrBI developed Alzheimer's disease, rather than behavioral variant frontotemporal dementia, as observed over seven years. Mixed effects models reported that people with FrBI have greater cognitive decline than those with non-FrBI in multiple domains, including language, memory, and executive functions (p < 0.05, FDR corrected). Furthermore, while negative FrBI symptoms (e.g., deficit behaviors) were associated with greater declines in multiple domains, positive FrBI symptoms (e.g., disinhibition symptoms) were related to declines in visuospatial function and verbal memory. Finally, the occurrence of both types of symptoms correlated with multi-domain cognitive decline. CONCLUSIONS FrBI predicted worse clinical outcomes, including reduced cortical thickness and cognitive decline, which are not necessarily specific to frontal dysfunction.
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Affiliation(s)
- Seung Joo Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.,Departments of Neurology, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Changwon, South Korea
| | - Na-Yeon Jung
- Department of Neurology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine and Medical Research Institute, Yangsan, South Korea
| | - Young Ju Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Seong Beom Park
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - KoWoon Kim
- Department of Neurology, Chonbuk National University Hospital, Chun-Ju, South Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University Hospital, Chuncheon, South Korea
| | - Hyemin Jang
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Si Eun Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.,Department of Neurology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, South Korea
| | - Soo Hyun Cho
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.,Department of Neurology, Chonnam National University Hospital, Gwangju, South Korea
| | - Jun Pyo Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Young Hee Jung
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Sook-Young Woo
- Biostatistics team, Samsung Biomedical Research Institute, Seoul, South Korea
| | - Seon Woo Kim
- Biostatistics team, Samsung Biomedical Research Institute, Seoul, South Korea
| | - Samuel N Lockhart
- Internal Medicine-Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, USA
| | - Eun-Joo Kim
- Department of Neurology, Pusan National University Hospital, Pusan National University School of Medicine and Medical Research Institute, Busan, South Korea
| | - Hee Jin Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Juhee Chin
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Duk L Na
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Sang Won Seo
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, South Korea.,Center for Clinical Epidemiology, Samsung Medical Center, Seoul, South Korea.,Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea
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124
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Narayanan A, Rajasekaran MP, Zhang Y, Govindaraj V, Thiyagarajan A. Multi-channeled MR brain image segmentation: A novel double optimization approach combined with clustering technique for tumor identification and tissue segmentation. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2018.12.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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125
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Nguyen TV, Jones SL, Gower T, Lew J, Albaugh MD, Botteron KN, Hudziak JJ, Fonov VS, Collins DL, Campbell BC, Booij L, Herba CM, Monnier P, Ducharme S, Waber D, McCracken JT. Age-specific associations between oestradiol, cortico-amygdalar structural covariance, and verbal and spatial skills. J Neuroendocrinol 2019; 31:e12698. [PMID: 30776161 PMCID: PMC6482064 DOI: 10.1111/jne.12698] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 01/19/2019] [Accepted: 02/13/2019] [Indexed: 01/02/2023]
Abstract
Oestradiol is known to play an important role in the developing human brain, although little is known about the entire network of potential regions that might be affected and how these effects may vary from childhood to early adulthood, which in turn can explain sexually differentiated behaviours. In the present study, we examined the relationships between oestradiol, cortico-amygdalar structural covariance, and cognitive or behavioural measures typically showing sex differences (verbal/spatial skills, anxious-depressed symptomatology) in 152 children and adolescents (aged 6-22 years). Cortico-amygdalar structural covariance shifted from positive to negative across the age range. Oestradiol was found to diminish the impact of age on cortico-amygdalar covariance for the pre-supplementary motor area/frontal eye field and retrosplenial cortex (across the age range), as well as for the posterior cingulate cortex (in older children). Moreover, the influence of oestradiol on age-related cortico-amygdalar networks was associated with higher word identification and spatial working memory (across the age range), as well as higher reading comprehension (in older children), although it did not impact anxious-depressed symptoms. There were no significant sex effects on any of the above relationships. These findings confirm the importance of developmental timing on oestradiol-related effects and hint at the non-sexually dimorphic role of oestradiol-related cortico-amygdalar structural networks in aspects of cognition distinct from emotional processes.
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Affiliation(s)
- Tuong-Vi Nguyen
- Department of Psychiatry, McGill University, Montreal, QC, Canada, H3A1A1
- Department of Obstetrics-Gynecology, McGill University Health Center, Montreal, QC, Canada, H4A 3J1
- Research Institute of the McGill University Health Center, Montreal, QC, Canada, H4A 3J1
| | - Sherri Lee Jones
- Department of Psychology, McGill University, Montreal, QC, Canada, H4A 3J1
- Douglas Mental Health University Institute, Verdun, QC, Canada, H4H 1R3
| | - Tricia Gower
- Department of Psychology, McGill University, Montreal, QC, Canada, H4A 3J1
| | - Jimin Lew
- Department of Psychology, McGill University, Montreal, QC, Canada, H4A 3J1
| | - Matthew D Albaugh
- Department of Psychology, University of Vermont, College of Medicine, Burlington, VT, USA, 05405
| | - Kelly N Botteron
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA, 63110
- Brain Development Cooperative Group
| | - James J Hudziak
- Department of Psychology, University of Vermont, College of Medicine, Burlington, VT, USA, 05405
- Brain Development Cooperative Group
| | - Vladimir S Fonov
- McConnell Brain imaging Centre, Montreal Neurological Institute, Montreal, QC Canada H3A 2B4
| | - D. Louis Collins
- McConnell Brain imaging Centre, Montreal Neurological Institute, Montreal, QC Canada H3A 2B4
| | - Benjamin C Campbell
- Department of Anthropology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA, 53211
| | - Linda Booij
- Department of Psychiatry, McGill University, Montreal, QC, Canada, H3A1A1
- Department of Psychology, Concordia University, Montreal, QC, Canada, H4B 1R6
- CHU Sainte Justine Hospital Research Centre, University of Montreal, Montreal, QC, Canada, H3T1C5
| | - Catherine M. Herba
- CHU Sainte Justine Hospital Research Centre, University of Montreal, Montreal, QC, Canada, H3T1C5
- Department of Psychology, Université du Québec à Montréal, Montreal, QC,
Canada
| | - Patricia Monnier
- Department of Obstetrics-Gynecology, McGill University Health Center, Montreal, QC, Canada, H4A 3J1
- Research Institute of the McGill University Health Center, Montreal, QC, Canada, H4A 3J1
| | - Simon Ducharme
- Department of Psychiatry, McGill University, Montreal, QC, Canada, H3A1A1
- McConnell Brain imaging Centre, Montreal Neurological Institute, Montreal, QC Canada H3A 2B4
- Department of Neurology & Neurosurgery, McGill University, Montreal, QC, Canada, H3A 1A1
| | - Deborah Waber
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA, 02115
| | - James T McCracken
- Brain Development Cooperative Group
- Department of Child and Adolescent Psychiatry, University of California in Los Angeles, Los Angeles, CA,
USA, 90024
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Frey BM, Petersen M, Mayer C, Schulz M, Cheng B, Thomalla G. Characterization of White Matter Hyperintensities in Large-Scale MRI-Studies. Front Neurol 2019; 10:238. [PMID: 30972001 PMCID: PMC6443932 DOI: 10.3389/fneur.2019.00238] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 02/22/2019] [Indexed: 01/18/2023] Open
Abstract
Background: White matter hyperintensities of presumed vascular origin (WMH) are a common finding in elderly people and a growing social malady in the aging western societies. As a manifestation of cerebral small vessel disease, WMH are considered to be a vascular contributor to various sequelae such as cognitive decline, dementia, depression, stroke as well as gait and balance problems. While pathophysiology and therapeutical options remain unclear, large-scale studies have improved the understanding of WMH, particularly by quantitative assessment of WMH. In this review, we aimed to provide an overview of the characteristics, research subjects and segmentation techniques of these studies. Methods: We performed a systematic review according to the PRISMA statement. One thousand one hundred and ninety-six potentially relevant articles were identified via PubMed search. Six further articles classified as relevant were added manually. After applying a catalog of exclusion criteria, remaining articles were read full-text and the following information was extracted into a standardized form: year of publication, sample size, mean age of subjects in the study, the cohort included, and segmentation details like the definition of WMH, the segmentation method, reference to methods papers as well as validation measurements. Results: Our search resulted in the inclusion and full-text review of 137 articles. One hundred and thirty-four of them belonged to 37 prospective cohort studies. Median sample size was 1,030 with no increase over the covered years. Eighty studies investigated in the association of WMH and risk factors. Most of them focussed on arterial hypertension, diabetes mellitus type II and Apo E genotype and inflammatory markers. Sixty-three studies analyzed the association of WMH and secondary conditions like cognitive decline, mood disorder and brain atrophy. Studies applied various methods based on manual (3), semi-automated (57), and automated segmentation techniques (75). Only 18% of the articles referred to an explicit definition of WMH. Discussion: The review yielded a large number of studies engaged in WMH research. A remarkable variety of segmentation techniques was applied, and only a minority referred to a clear definition of WMH. Most addressed topics were risk factors and secondary clinical conditions. In conclusion, WMH research is a vivid field with a need for further standardization regarding definitions and used methods.
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Affiliation(s)
- Benedikt M Frey
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Marvin Petersen
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Carola Mayer
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Maximilian Schulz
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Bastian Cheng
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Hwang J, Jeong JH, Yoon SJ, Park KW, Kim EJ, Yoon B, Jang JW, Kim HJ, Hong JY, Lee JM, Park H, Kang JH, Choi YH, Park G, Hong J, Byun MS, Yi D, Kim YK, Lee DY, Choi SH. Clinical and Biomarker Characteristics According to Clinical Spectrum of Alzheimer's Disease (AD) in the Validation Cohort of Korean Brain Aging Study for the Early Diagnosis and Prediction of AD. J Clin Med 2019; 8:jcm8030341. [PMID: 30862124 PMCID: PMC6463169 DOI: 10.3390/jcm8030341] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2019] [Revised: 03/04/2019] [Accepted: 03/07/2019] [Indexed: 01/09/2023] Open
Abstract
We aimed to present the study design of an independent validation cohort from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (AD) (KBASE-V) and to investigate the baseline characteristics of the participants according to the AD clinical spectrum. We recruited 71 cognitively normal (CN) participants, 96 with subjective cognitive decline (SCD), 72 with mild cognitive impairment (MCI), and 56 with AD dementia (ADD). The participants are followed for three years. The Consortium to Establish a Registry for AD scores was significantly different between all of the groups. The logical memory delayed recall scores were significantly different between all groups, except between the MCI and ADD groups. The Mini-Mental State Examination score, hippocampal volume, and cerebrospinal fluid (CSF) amyloid-β42 level were significant difference among the SCD, MCI, and ADD groups. The frequencies of participants with amyloid pathology according to PET or CSF studies were 8.9%, 25.6%, 48.3%, and 90.0% in the CN, SCD, MCI, and ADD groups, respectively. According to ATN classification, A+/T+/N+ or A+/T+/N- was observed in 0%, 15.5%, 31.0%, and 78.3% in the CN, SCD, MCI, and ADD groups, respectively. The KBASE-V showed a clear difference according to the AD clinical spectrum in neuropsychological tests and AD biomarkers.
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Affiliation(s)
- Jihye Hwang
- Department of Neurology, Keimyung University Dongsan Medical Center, Daegu 41931, Korea.
| | - Jee Hyang Jeong
- Department of Neurology, Ewha Womans University School of Medicine, Seoul 07985, Korea.
| | - Soo Jin Yoon
- Department of Neurology, Eulji University School of Medicine, Daejeon 35233, Korea.
| | - Kyung Won Park
- Department of Neurology, Dong-A Medical Center, Dong-A University College of Medicine, Busan 49201, Korea.
| | - Eun-Joo Kim
- Department of Neurology, Pusan National University Hospital, Pusan National University School of Medicine and Medical Research Institute, Busan 49241, Korea.
| | - Bora Yoon
- Department of Neurology, Konyang University College of Medicine, Daejeon 35365, Korea.
| | - Jae-Won Jang
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon 24289, Korea.
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.
| | - Jin Yong Hong
- Department of Neurology, Yonsei University Wonju College of Medicine, Wonju 26426, Korea.
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea.
| | - Hyuntae Park
- Department of Health Care and Science, Dong-A University, Busan 49315, Korea.
| | - Ju-Hee Kang
- Department of Pharmacology, Inha University School of Medicine, Incheon 22212, Korea.
| | - Yong-Ho Choi
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea.
| | - Gilsoon Park
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea.
| | - Jinwoo Hong
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea.
| | - Min Soo Byun
- Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul 03080, Korea.
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul 03080, Korea.
| | - Yu Kyeong Kim
- Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul 07061, Korea.
| | - Dong Young Lee
- Department of Neuropsychiatry, Seoul National University Hospital & Department of Seoul National University College of Medicine, Seoul 03080, Korea.
| | - Seong Hye Choi
- Department of Neurology, Inha University School of Medicine, Incheon 22332, Korea.
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Ahmadi A, Davoudi S, Daliri MR. Computer Aided Diagnosis System for multiple sclerosis disease based on phase to amplitude coupling in covert visual attention. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 169:9-18. [PMID: 30638593 DOI: 10.1016/j.cmpb.2018.11.006] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2018] [Revised: 11/03/2018] [Accepted: 11/23/2018] [Indexed: 05/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Computer Aided Diagnosis (CAD) techniques have widely been used in research to detect the neurological abnormalities and improve the consistency of diagnosis and treatment in medicine. In this study, a new CAD system based on EEG signals was developed. The motivation for the development of the CAD system was to diagnose multiple sclerosis (MS) disease during covert visual attention tasks. It is worth noting that research of this kind on the efficacy of attention tasks is limited in scope for MS patients; therefore, it is vital to develop a feature of EEG to characterize the patient's state with high sensitivity and specificity. METHODS We evaluated the use of phase-amplitude coupling (PAC) of EEG signals to diagnose MS. It is assumed that the role of PAC for information encoding during visual attention in MS is greatly unknown; therefore, we made an attempt to investigate it via CAD systems. The EEG signals were recorded from healthy and MS patients while performing new visual attention tasks. Machine learning algorithms were also used to identify the EEG signals as to whether the disease existed or not. The challenge regarding the dimensionality of the extracted features was addressed through selecting the relevant and efficient features using T-test and Bhattacharyya distance criteria, and the validity of the system was assessed through leave-one-subject-out cross-validation method. RESULTS Our findings indicated that online sequential extreme learning machine (OS-ELM) classifier with T-test feature selection method yielded peak accuracy, sensitivity and specificity in both color and direction tasks. These values were 91%, 83% and 96% for color task, and 90%, 82% and 96% for the direction task. CONCLUSIONS Based on the results, it can be concluded that this procedure can be used for the automatic diagnosis of early MS, and can also facilitate the treatment assessment in patients.
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Affiliation(s)
- Amirmasoud Ahmadi
- Neuroscience & Neuroengineering Research Lab., Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, 16846-13114, Tehran, Iran
| | - Saeideh Davoudi
- Neuroscience & Neuroengineering Research Lab., Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, 16846-13114, Tehran, Iran
| | - Mohammad Reza Daliri
- Neuroscience & Neuroengineering Research Lab., Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, 16846-13114, Tehran, Iran.
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129
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Clarifying associations between cortical thickness, subcortical structures, and a comprehensive assessment of clinical insight in enduring schizophrenia. Schizophr Res 2019; 204:245-252. [PMID: 30150023 DOI: 10.1016/j.schres.2018.08.024] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 07/31/2018] [Accepted: 08/13/2018] [Indexed: 12/24/2022]
Abstract
BACKGROUND The relationship between poor insight and less favorable outcomes in schizophrenia has promoted research efforts to understand its neurobiological basis. Thus far, research on neural correlates of insight has been constrained by small samples, incomplete insight assessments, and a focus on frontal lobes. The purpose of this study was to examine associations of cortical thickness and subcortical volumes, with a comprehensive assessment of clinical insight, in a large sample of enduring schizophrenia patients. METHODS Two dimensions of clinical insight previously identified by a factor analysis of 4 insight assessments were used: Awareness of Illness and Need for Treatment (AINT) and Awareness of Symptoms and Consequences (ASC). T1-weighted structural images were acquired on a 3 T MRI scanner for 110 schizophrenia patients and 69 healthy controls. MR images were processed using CIVET (version 2.0) and MAGeT and quality controlled pre and post-processing. Whole-brain and region-of-interest, vertex-wise linear models were applied between cortical thickness, and levels of AINT and ASC. Partial correlations were conducted between volumes of the amygdala, thalamus, striatum, and hippocampus and insight levels. RESULTS No significant associations between both insight factors and cortical thickness were observed. Moreover, no significant associations emerged between subcortical volumes and both insight factors. CONCLUSIONS These results do not replicate previous findings obtained with smaller samples using single-item measures of insight into illness, suggesting a limited role of neurobiological factors and a greater role of psychological processes in explaining levels of clinical insight.
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130
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Kim KW, Kwon H, Kim YE, Yoon CW, Kim YJ, Kim YB, Lee JM, Yoon WT, Kim HJ, Lee JS, Jang YK, Kim Y, Jang H, Ki CS, Youn YC, Shin BS, Bang OY, Kim GM, Chung CS, Kim SJ, Na DL, Duering M, Cho H, Seo SW. Multimodal imaging analyses in patients with genetic and sporadic forms of small vessel disease. Sci Rep 2019; 9:787. [PMID: 30692550 PMCID: PMC6349863 DOI: 10.1038/s41598-018-36580-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 11/24/2018] [Indexed: 11/09/2022] Open
Abstract
Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is thought to be a pure genetic form of subcortical vascular cognitive impairment (SVCI). The aim of this study was to compare white matter integrity and cortical thickness between typical CADASIL, a genetic form, and two sporadic forms of SVCI (with NOTCH3 and without NOTCH3 variants). We enrolled typical CADASIL patients (N = 11) and SVCI patients [with NOTCH3 variants (N = 15), without NOTCH3 variants (N = 101)]. To adjust the age difference, which reflects the known difference in clinical and radiologic courses between typical CADASIL patients and SVCI patients, we constructed a W-score of measurement for diffusion tensor image and cortical thickness. Typical CADASIL patients showed more frequent white matter hyperintensities in the bilateral posterior temporal region compared to SVCI patients (p < 0.001, uncorrected). We found that SVCI patients, regardless of the presence of NOTCH3 variants, showed significantly greater microstructural alterations (W-score, p < 0.05, FWE-corrected) and cortical thinning (W-score, p < 0.05, FDR-corrected) than typical CADASIL patients. In this study, typical CADASIL and SVCI showed distinct anatomic vulnerabilities in the cortical and subcortical structures. However, there was no difference between SVCI with NOTCH3 variants and SVCI without NOTCH3 variants.
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Affiliation(s)
- Ko Woon Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Department of Neurology, Chonbuk National University Medical School & Hospital, Jeonju, Korea
| | - Hunki Kwon
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea.,Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Young-Eun Kim
- Genome Research Center, Green Cross Genome, Yong-in, Korea
| | - Cindy W Yoon
- Department of Neurology, Inha University School of Medicine, Incheon, Korea
| | - Yeo Jin Kim
- Department of Neurology, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Korea
| | - Yong Bum Kim
- Department of Neurology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jong Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Won Tae Yoon
- Department of Neurology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jin San Lee
- Department of Neurology, Kyung Hee University Hospital, Seoul, Korea
| | - Young Kyoung Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Chang-Seok Ki
- Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Young Chul Youn
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, Korea
| | - Byoung-Soo Shin
- Department of Neurology, Chonbuk National University Medical School & Hospital, Jeonju, Korea
| | - Oh Young Bang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Gyeong-Moon Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Chin-Sang Chung
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Seung Joo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU, Munich, Germany
| | - Hanna Cho
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, and Departments of, Clinical Research Design and Evaluation, Seoul, Korea.
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. .,Neuroscience Center, Samsung Medical Center, Seoul, Korea. .,Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea.
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131
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Shiohama T, Levman J, Takahashi E. Surface- and voxel-based brain morphologic study in Rett and Rett-like syndrome with MECP2 mutation. Int J Dev Neurosci 2019; 73:83-88. [PMID: 30690146 DOI: 10.1016/j.ijdevneu.2019.01.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 12/20/2018] [Accepted: 01/23/2019] [Indexed: 12/21/2022] Open
Abstract
Rett syndrome (RTT) is a rare congenital disorder which in most cases (95%) is caused by methyl-CpG binding protein 2 (MECP2) mutations. RTT is characterized by regression in global development, epilepsy, autistic features, acquired microcephaly, habitual hand clapping, loss of purposeful hand skills, and autonomic dysfunctions. Although the literature has demonstrated decreased volumes of the cerebrum, cerebellum, and the caudate nucleus in RTT patients, surface-based brain morphology including cortical thickness and cortical gyrification analyses are lacking in RTT. We present quantitative surface- and voxel-based morphological measurements in young children with RTT and Rett-like syndrome (RTT-l) with MECP2 mutations. The 8 structural T1-weighted MR images were obtained from 7 female patients with MECP2 mutations (3 classic RTT, 2 variant RTT, and 2 RTT-l) (mean age 5.2 [standard deviation 3.3] years old). Our analyses demonstrated decreased total volumes of the cerebellum in RTT/RTT-l compared to gender- and age-matched controls (t (22)=-2.93, p = .008, Cohen's d = 1.27). In contrast, global cerebral cortical surface areas, global/regional cortical thicknesses, the degree of global gyrification, and global/regional gray and white matter volumes were not statistically significantly different between the two groups. Our findings, as well as literature findings, suggest that early brain abnormalities associated with RTT/RTT-l (with MECP2 mutations) can be detected as regionally decreased cerebellar volumes. Decreased cerebellar volume may be helpful for understanding the etiology of RTT/RTT-l.
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Affiliation(s)
- Tadashi Shiohama
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA; Department of Pediatrics, Chiba University Hospital, Inohana 1-8-1, Chiba-shi, Chiba, 2608670, Japan.
| | - Jacob Levman
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA; Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, 2323 Notre Dame Ave, Antigonish, Nova Scotia, B2G 2W5, Canada
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
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132
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Konishi K, Joober R, Poirier J, MacDonald K, Chakravarty M, Patel R, Breitner J, Bohbot VD. Healthy versus Entorhinal Cortical Atrophy Identification in Asymptomatic APOE4 Carriers at Risk for Alzheimer's Disease. J Alzheimers Dis 2019; 61:1493-1507. [PMID: 29278888 PMCID: PMC5798531 DOI: 10.3233/jad-170540] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Early detection of Alzheimer’s disease (AD) has been challenging as current biomarkers are invasive and costly. Strong predictors of future AD diagnosis include lower volume of the hippocampus and entorhinal cortex, as well as the ɛ4 allele of the Apolipoprotein E gene (APOE) gene. Therefore, studying functions that are critically mediated by the hippocampus and entorhinal cortex, such as spatial memory, in APOE ɛ4 allele carriers, may be key to the identification of individuals at risk of AD, prior to the manifestation of cognitive impairments. Using a virtual navigation task developed in-house, specifically designed to assess spatial versus non-spatial strategies, the current study is the first to differentiate functional and structural differences within APOE ɛ4 allele carriers. APOE ɛ4 allele carriers that predominantly use non-spatial strategies have decreased fMRI activity in the hippocampus and increased atrophy in the hippocampus, entorhinal cortex, and fimbria compared to APOE ɛ4 allele carriers who use spatial strategies. In contrast, APOE ɛ4 allele carriers who use spatial strategies have grey matter levels comparable to non-APOE ɛ4 allele carriers. Furthermore, in a leave-one-out analysis, grey matter in the entorhinal cortex could predict navigational strategy with 92% accuracy.
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Affiliation(s)
- Kyoko Konishi
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Ridha Joober
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Judes Poirier
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Kathleen MacDonald
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Mallar Chakravarty
- Department of Biomedical Engineering, Brain Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Raihaan Patel
- Department of Biomedical Engineering, Brain Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - John Breitner
- Department of Psychiatry, Centre for Studies on Prevention of Alzheimer's Disease (StoP-AD), Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Véronique D Bohbot
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
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133
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A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy. ALGORITHMS 2019. [DOI: 10.3390/a12010014] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Diabetic retinopathy (DR) is a complication of diabetes and is known as visual impairment, and is diagnosed in various ethnicities of the working-age population worldwide. Fundus angiography is a widely applicable modality used by ophthalmologists and computerized applications to detect DR-based clinical features such as microaneurysms (MAs), hemorrhages (HEMs), and exudates (EXs) for early screening of DR. Fundus images are usually acquired using funduscopic cameras in varied light conditions and angles. Therefore, these images are prone to non-uniform illumination, poor contrast, transmission error, low brightness, and noise problems. This paper presents a novel and real-time mechanism of fundus image enhancement used for early grading of diabetic retinopathy, macular degeneration, retinal neoplasms, and choroid disruptions. The proposed system is based on two folds: (i) An RGB fundus image is initially taken and converted into a color appearance module (called lightness and denoted as J) of the CIECAM02 color space model to obtain image information in grayscale with bright light. Afterwards, in step (ii), the achieved J component is processed using a nonlinear contrast enhancement approach to improve the textural and color features of the fundus image without any further extraction steps. To test and evaluate the strength of the proposed technique, several performance and quality parameters—namely peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), entropy (content information), histograms (intensity variation), and a structure similarity index measure (SSIM)—were applied to 1240 fundus images comprised of two publicly available datasets, DRIVE and MESSIDOR. It was determined from the experiments that the proposed enhancement procedure outperformed histogram-based approaches in terms of contrast, sharpness of fundus features, and brightness. This further revealed that it can be a suitable preprocessing tool for segmentation and classification of DR-related features algorithms.
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134
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Xie T, Zhang X, Tang X, Zhang H, Yu M, Gong G, Wang X, Evans A, Zhang Z, He Y. Mapping Convergent and Divergent Cortical Thinning Patterns in Patients With Deficit and Nondeficit Schizophrenia. Schizophr Bull 2019; 45:211-221. [PMID: 29272543 PMCID: PMC6293229 DOI: 10.1093/schbul/sbx178] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Deficit schizophrenia (DS) is a homogeneous subtype of schizophrenia characterized by primary and enduring negative symptoms. However, the underlying neuroanatomical substrate of DS remains poorly understood. Here, we collected high-resolution structural magnetic resonance images of 115 participants, including 33 DS patients, 41 nondeficit schizophrenia (NDS) patients, and 41 healthy controls (HCs), and calculated the cortical thickness and surface area for statistical comparisons among the 3 groups. Relative to the control group, both the DS and NDS groups exhibited convergent cortical thinning in the bilateral inferior frontal gyri and the left superior temporal gyrus. The cortical thinning in the right inferior frontal cortex in the patient group was significantly positively correlated with declines of cognitive flexibility and visuospatial memory. Importantly, compared to the NDS group, the DS group exhibited a more widespread cortical thinning pattern, with the most significant differences in the left temporo-parietal junction area. For the surface area measurement, no significant group differences were observed. Collectively, these results highlight the convergent and divergent cortical thinning patterns between patients with DS and NDS, which provide critical insights into the neuroanatomical substrate of DS and improve our understanding of the biological mechanism that contributes to the negative symptoms and cognitive impairments in DS.
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Affiliation(s)
- Teng Xie
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xiangrong Zhang
- Department of Geriatric Psychiatry, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China,Department of Neuropsychiatry, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Xiaowei Tang
- Department of Psychiatry, Wutaishan Hospital of Yangzhou, Yangzhou, China
| | - Hongying Zhang
- Department of Radiology, Subei People’s Hospital of Jiangsu Province, Yangzhou University, Yangzhou, China
| | - Miao Yu
- Department of Neuropsychiatry, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Gaolang Gong
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xiang Wang
- Medical Psychological Institute of the Second Xiangya Hospital, Central South University, Changsha, China
| | - Alan Evans
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Canada
| | - Zhijun Zhang
- Department of Neuropsychiatry, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China,To whom correspondence should be addressed; National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Key Laboratory of Brain Imaging and Connectomics, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China. E-mail:
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135
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Jang JW, Kim Y, Choi YH, Lee JM, Yoon B, Park KW, Kim SE, Kim HJ, Yoon SJ, Jeong JH, Kim EJ, Jung NY, Hwang J, Kang JH, Hong JY, Choi SH. Association of Nutritional Status with Cognitive Stage in the Elderly Korean Population: The Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease. J Clin Neurol 2019; 15:292-300. [PMID: 31286699 PMCID: PMC6620466 DOI: 10.3988/jcn.2019.15.3.292] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Revised: 12/15/2018] [Accepted: 12/18/2018] [Indexed: 01/08/2023] Open
Abstract
Background and Purpose Epidemiological studies have suggested the presence of strong correlations among diet, lifestyle, and dementia onset. However, these studies have unfortunately had major limitations due to their inability to fully control the various potential confounders affecting the nutritional status. The purpose of the current study was to determine the nutritional status of participants in the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease (KBASE) and to identify clinical risk factors for being at risk of malnutrition or being malnourished. Methods Baseline data from 212 participants [119 cognitively unimpaired (CU), 56 with mild cognitive impairment (MCI), and 37 with dementia] included in the KBASE database were analyzed. All participants underwent a comprehensive cognitive test and MRI at baseline. The presence of malnutrition at baseline was measured by the Mini Nutritional Assessment score. We examined the cross-sectional relationships of clinical findings with nutritional status using multiple logistic regression applied to variables for which p<0.2 in the univariate analysis. Differences in cortical thickness according to the nutritional status were also investigated. Results After adjustment for demographic, nutritional, and neuropsychological factors, participants with dementia had a significantly higher odds ratio (OR) for being at risk of malnutrition or being malnourished than CU participants [OR=5.98, 95% CI=1.20–32.97] whereas participants with MCI did not (OR=0.62, 95% CI=0.20–1.83). Cortical thinning in the at-risk/malnutrition group was observed in the left temporal area. Conclusions Dementia was found to be an independent predictor for the risk of malnutrition compared with CU participants. Our findings further suggest that cortical thinning in left temporal regions is related to the nutritional status.
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Affiliation(s)
- Jae Won Jang
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, Korea
| | - Yong Ho Choi
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Jong Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Bora Yoon
- Department of Neurology, Konyang University College of Medicine, Daejeon, Korea
| | - Kyung Won Park
- Department of Neurology, Dong-A Medical Center, Dong-A University College of Medicine, Busan, Korea
| | - Si Eun Kim
- Department of Neurology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Soo Jin Yoon
- Department of Neurology, Eulji University College of Medicine, Daejeon, Korea
| | - Jee Hyang Jeong
- Department of Neurology, Ewha Womans University School of Medicine, Seoul, Korea
| | - Eun Joo Kim
- Department of Neurology, Pusan National University School of Medicine, Busan, Korea
| | - Na Yeon Jung
- Department of Neurology, Pusan National University School of Medicine, Busan, Korea
| | - Jihye Hwang
- Department of Neurology, Keimyung University Dongsan Medical Center, Daegu, Korea
| | - Ju Hee Kang
- Department of Pharmacology and Medicinal Toxicology Research Center, Inha University School of Medicine, Incheon, Korea
| | - Jin Yong Hong
- Department of Neurology, Yonsei University Wonju College of Medicine, Wonju, Korea.
| | - Seong Hye Choi
- Department of Neurology, Inha University School of Medicine, Incheon, Korea.
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136
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Longitudinally Mapping Childhood Socioeconomic Status Associations with Cortical and Subcortical Morphology. J Neurosci 2018; 39:1365-1373. [PMID: 30587541 DOI: 10.1523/jneurosci.1808-18.2018] [Citation(s) in RCA: 114] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 10/31/2018] [Accepted: 11/16/2018] [Indexed: 11/21/2022] Open
Abstract
Childhood socioeconomic status (SES) impacts cognitive development and mental health, but its association with human structural brain development is not yet well characterized. Here, we analyzed 1243 longitudinally acquired structural MRI scans from 623 youth (299 female/324 male) to investigate the relation between SES and cortical and subcortical morphology between ages 5 and 25 years. We found positive associations between SES and total volumes of the brain, cortical sheet, and four separate subcortical structures. These associations were stable between ages 5 and 25. Surface-based shape analysis revealed that higher SES is associated with areal expansion of lateral prefrontal, anterior cingulate, lateral temporal, and superior parietal cortices and ventrolateral thalamic, and medial amygdalo-hippocampal subregions. Meta-analyses of functional imaging data indicate that cortical correlates of SES are centered on brain systems subserving sensorimotor functions, language, memory, and emotional processing. We further show that anatomical variation within a subset of these cortical regions partially mediates the positive association between SES and IQ. Finally, we identify neuroanatomical correlates of SES that exist above and beyond accompanying variation in IQ. Although SES is clearly a complex construct that likely relates to development through diverse, nondeterministic processes, our findings elucidate potential neuroanatomical mediators of the association between SES and cognitive outcomes.SIGNIFICANCE STATEMENT Childhood socioeconomic status (SES) has been associated with developmental disparities in mental health, cognitive ability, and academic achievement, but efforts to understand underlying SES-brain relationships are ongoing. Here, we leverage a unique developmental neuroimaging dataset to longitudinally map the associations between SES and regional brain anatomy at high spatiotemporal resolution. We find widespread associations between SES and global cortical and subcortical volumes and surface area and localize these correlations to a distributed set of cortical, thalamic, and amygdalo-hippocampal subregions. Anatomical variation within a subset of these regions partially mediates the positive relationship between SES and IQ. Our findings help to localize cortical and subcortical systems that represent candidate biological substrates for the known relationships between SES and cognition.
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Developmental changes of cortical white-gray contrast as predictors of autism diagnosis and severity. Transl Psychiatry 2018; 8:249. [PMID: 30446637 PMCID: PMC6240045 DOI: 10.1038/s41398-018-0296-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 09/18/2018] [Accepted: 10/05/2018] [Indexed: 12/29/2022] Open
Abstract
Recent studies suggest that both cortical gray and white-matter microstructural characteristics are distinct for subjects with autism. There is a lack of evidence regarding how these characteristics change in a developmental context. We analysed a longitudinal/cross-sectional dataset of 402 magnetic resonance imaging (MRI) scans (171 subjects with autism and 231 with typical development) from the Autism Brain Imaging Data Exchange, cohorts I-II (ABIDE-I-II). In the longitudinal sample, we computed the rate of change in the white-gray contrast, a measure which has been related to age and cognitive performance, at the boundary of the cerebral cortex. Then, we devised an analogous metric for the cross-sectional sample of the ABIDE dataset to measure age-related differences in cortical contrast. Further, we developed a probabilistic model to predict the diagnostic group in the longitudinal sample of the cortical contrast change data, using results obtained from the cross-sectional sample. In both subsets, we observed a similar overall pattern of greater decrease within the autistic population in intensity contrast for most cortical regions (81%), with occasional increases, mostly in primary sensory regions. This pattern correlated well with raw and calibrated behavioural scores. The prediction results show 76% accuracy for the whole-cortex diagnostic prediction and 86% accuracy in prediction using the motor system alone. Our results support a contrast change analysis strategy that appears sensitive in predicting diagnostic outcome and symptom severity in autism spectrum disorder, and is readily extensible to other MRI-based studies of neurodevelopmental cohorts.
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138
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Hafizi S, Guma E, Koppel A, Da Silva T, Kiang M, Houle S, Wilson AA, Rusjan PM, Chakravarty MM, Mizrahi R. TSPO expression and brain structure in the psychosis spectrum. Brain Behav Immun 2018; 74:79-85. [PMID: 29906515 PMCID: PMC6289857 DOI: 10.1016/j.bbi.2018.06.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 06/06/2018] [Accepted: 06/09/2018] [Indexed: 01/18/2023] Open
Abstract
Psychosis is associated with abnormal structural changes in the brain including decreased regional brain volumes and abnormal brain morphology. However, the underlying causes of these structural abnormalities are less understood. The immune system, including microglial activation, has been implicated in the pathophysiology of psychosis. Although previous studies have suggested a connection between peripheral proinflammatory cytokines and structural brain abnormalities in schizophrenia, no in-vivo studies have investigated whether microglial activation is also linked to brain structure alterations previously observed in schizophrenia and its putative prodrome. In this study, we investigated the link between mitochondrial 18 kDa translocator protein (TSPO) and structural brain characteristics (i.e. regional brain volume, cortical thickness, and hippocampal shape) in key brain regions such as dorsolateral prefrontal cortex and hippocampus of a large group of participants (N = 90) including individuals at clinical high risk (CHR) for psychosis, first-episode psychosis (mostly antipsychotic-naïve) patients, and healthy volunteers. The participants underwent structural brain MRI scan and [18F]FEPPA positron emission tomography (PET) targeting TSPO. A significant [18F]FEPPA binding-by-group interaction was observed in morphological measures across the left hippocampus. In first-episode psychosis, we observed associations between [18F]FEPPA VT (total volume of distribution) and outward and inward morphological alterations, respectively, in the dorsal and ventro-medial portions of the left hippocampus. These associations were not significant in CHR or healthy volunteers. There was no association between [18F]FEPPA VT and other structural brain characteristics. Our findings suggest a link between TSPO expression and alterations in hippocampal morphology in first-episode psychosis.
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Affiliation(s)
- Sina Hafizi
- Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Elisa Guma
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Alex Koppel
- Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Tania Da Silva
- Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Michael Kiang
- Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada,Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Sylvain Houle
- Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada,Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Alan A. Wilson
- Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada,Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Pablo M. Rusjan
- Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada,Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - M. Mallar Chakravarty
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada,Departments of Psychiatry and Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Romina Mizrahi
- Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.
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139
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van Erp TGM, Walton E, Hibar DP, Schmaal L, Jiang W, Glahn DC, Pearlson GD, Yao N, Fukunaga M, Hashimoto R, Okada N, Yamamori H, Bustillo JR, Clark VP, Agartz I, Mueller BA, Cahn W, de Zwarte SMC, Hulshoff Pol HE, Kahn RS, Ophoff RA, van Haren NEM, Andreassen OA, Dale AM, Doan NT, Gurholt TP, Hartberg CB, Haukvik UK, Jørgensen KN, Lagerberg TV, Melle I, Westlye LT, Gruber O, Kraemer B, Richter A, Zilles D, Calhoun VD, Crespo-Facorro B, Roiz-Santiañez R, Tordesillas-Gutiérrez D, Loughland C, Carr VJ, Catts S, Cropley VL, Fullerton JM, Green MJ, Henskens F, Jablensky A, Lenroot RK, Mowry BJ, Michie PT, Pantelis C, Quidé Y, Schall U, Scott RJ, Cairns MJ, Seal M, Tooney PA, Rasser PE, Cooper G, Weickert CS, Weickert TW, Morris DW, Hong E, Kochunov P, Beard LM, Gur RE, Gur RC, Satterthwaite TD, Wolf DH, Belger A, Brown GG, Ford JM, Macciardi F, Mathalon DH, O’Leary DS, Potkin SG, Preda A, Voyvodic J, Lim KO, McEwen S, Yang F, Tan Y, Tan S, Wang Z, Fan F, Chen J, Xiang H, Tang S, Guo H, Wan P, Wei D, Bockholt HJ, Ehrlich S, Wolthusen RPF, King MD, Shoemaker JM, Sponheim SR, De Haan L, Koenders L, et alvan Erp TGM, Walton E, Hibar DP, Schmaal L, Jiang W, Glahn DC, Pearlson GD, Yao N, Fukunaga M, Hashimoto R, Okada N, Yamamori H, Bustillo JR, Clark VP, Agartz I, Mueller BA, Cahn W, de Zwarte SMC, Hulshoff Pol HE, Kahn RS, Ophoff RA, van Haren NEM, Andreassen OA, Dale AM, Doan NT, Gurholt TP, Hartberg CB, Haukvik UK, Jørgensen KN, Lagerberg TV, Melle I, Westlye LT, Gruber O, Kraemer B, Richter A, Zilles D, Calhoun VD, Crespo-Facorro B, Roiz-Santiañez R, Tordesillas-Gutiérrez D, Loughland C, Carr VJ, Catts S, Cropley VL, Fullerton JM, Green MJ, Henskens F, Jablensky A, Lenroot RK, Mowry BJ, Michie PT, Pantelis C, Quidé Y, Schall U, Scott RJ, Cairns MJ, Seal M, Tooney PA, Rasser PE, Cooper G, Weickert CS, Weickert TW, Morris DW, Hong E, Kochunov P, Beard LM, Gur RE, Gur RC, Satterthwaite TD, Wolf DH, Belger A, Brown GG, Ford JM, Macciardi F, Mathalon DH, O’Leary DS, Potkin SG, Preda A, Voyvodic J, Lim KO, McEwen S, Yang F, Tan Y, Tan S, Wang Z, Fan F, Chen J, Xiang H, Tang S, Guo H, Wan P, Wei D, Bockholt HJ, Ehrlich S, Wolthusen RPF, King MD, Shoemaker JM, Sponheim SR, De Haan L, Koenders L, Machielsen MW, van Amelsvoort T, Veltman DJ, Assogna F, Banaj N, de Rossi P, Iorio M, Piras F, Spalletta G, McKenna PJ, Pomarol-Clotet E, Salvador R, Corvin A, Donohoe G, Kelly S, Whelan CD, Dickie EW, Rotenberg D, Voineskos A, Ciufolini S, Radua J, Dazzan P, Murray R, Marques TR, Simmons A, Borgwardt S, Egloff L, Harrisberger F, Riecher-Rössler A, Smieskova R, Alpert KI, Wang L, Jönsson EG, Koops S, Sommer IEC, Bertolino A, Bonvino A, Di Giorgio A, Neilson E, Mayer AR, Stephen JM, Kwon JS, Yun JY, Cannon DM, McDonald C, Lebedeva I, Tomyshev AS, Akhadov T, Kaleda V, Fatouros-Bergman H, Flyckt L, Busatto GF, Rosa PGP, Serpa MH, Zanetti MV, Hoschl C, Skoch A, Spaniel F, Tomecek D, Hagenaars SP, McIntosh AM, Whalley HC, Lawrie SM, Knöchel C, Oertel-Knöchel V, Stäblein M, Howells FM, Stein DJ, Temmingh H, Uhlmann A, Lopez-Jaramillo C, Dima D, McMahon A, Faskowitz JI, Gutman BA, Jahanshad N, Thompson PM, Turner JA. Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium. Biol Psychiatry 2018; 84:644-654. [PMID: 29960671 PMCID: PMC6177304 DOI: 10.1016/j.biopsych.2018.04.023] [Show More Authors] [Citation(s) in RCA: 592] [Impact Index Per Article: 84.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 04/19/2018] [Accepted: 04/20/2018] [Indexed: 12/31/2022]
Abstract
BACKGROUND The profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group. METHODS The study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11-78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10-87 years; 53% male) assessed with standardized methods at 39 centers worldwide. RESULTS Compared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d = -0.530/-0.516) and smaller surface area (left/right hemisphere: Cohen's d = -0.251/-0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset. CONCLUSIONS The findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia.
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Affiliation(s)
- Theo GM. van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, USA
| | - Esther Walton
- Imaging Genetics and Neuroinformatics Lab, Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Derrek P. Hibar
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA,Janssen Research & Development, San Diego, CA, USA
| | - 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,Department of Psychiatry and Amsterdam Neuroscience, VU University Medical Center, Amsterdam, The Netherlands
| | - Wenhao Jiang
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - David C. Glahn
- Department of Psychiatry, Yale University, New Haven, CT, USA,Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - Godfrey D. Pearlson
- Department of Psychiatry, Yale University, New Haven, CT, USA,Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - Nailin Yao
- Department of Psychiatry, Yale University, New Haven, CT, USA,Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - Masaki Fukunaga
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
| | - Ryota Hashimoto
- Molecular Research Center for Children’s Mental Development, United Graduate School of Child Development, Osaka University, Suita, Osaka, Japan,Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Naohiro Okada
- Department of Neuropsychiatry, Graduate school of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Hidenaga Yamamori
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | | | - Vincent P. Clark
- University of New Mexico, Albuquerque, NM, USA,Mind Research Network, Albuquerque, NM, USA
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway,Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden
| | - Bryon A. Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - Wiepke Cahn
- Department of Psychiatry and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sonja MC. de Zwarte
- Department of Psychiatry and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hilleke E. Hulshoff Pol
- Department of Psychiatry and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - René S. Kahn
- Department of Psychiatry and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Roel A. Ophoff
- Department of Psychiatry and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands,UCLA Center for Neurobehavioral Genetics, Los Angeles, CA, USA
| | - Neeltje EM. van Haren
- Department of Psychiatry and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), K.G. Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Anders M. Dale
- Departments of Neurosciences, Radiology, Psychiatry, and Cognitive Science, UCSD, La Jolla, CA, USA,Center for Translational Imaging and Precision Medicine, San Diego, CA, USA
| | - Nhat Trung Doan
- Norwegian Centre for Mental Disorders Research (NORMENT), K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tiril P. Gurholt
- Norwegian Centre for Mental Disorders Research (NORMENT), K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Cecilie B. Hartberg
- Norwegian Centre for Mental Disorders Research (NORMENT), K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Unn K. Haukvik
- Norwegian Centre for Mental Disorders Research (NORMENT), K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), K.G. Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Kjetil N. Jørgensen
- Norwegian Centre for Mental Disorders Research (NORMENT), K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Trine V. Lagerberg
- Norwegian Centre for Mental Disorders Research (NORMENT), K.G. Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingrid Melle
- Norwegian Centre for Mental Disorders Research (NORMENT), K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), K.G. Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), K.G. Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway,Department of Psychology, University of Oslo, Oslo, Norway
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University Hospital, Heidelberg, Germany,Center for Translational Research in Systems Neuroscience and Psychiatry, Department of Psychiatry, Georg August University, Göttingen, Germany
| | - Bernd Kraemer
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University Hospital, Heidelberg, Germany,Center for Translational Research in Systems Neuroscience and Psychiatry, Department of Psychiatry, Georg August University, Göttingen, Germany
| | - Anja Richter
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University Hospital, Heidelberg, Germany,Center for Translational Research in Systems Neuroscience and Psychiatry, Department of Psychiatry, Georg August University, Göttingen, Germany
| | - David Zilles
- Center for Translational Research in Systems Neuroscience and Psychiatry, Department of Psychiatry, Georg August University, Göttingen, Germany,Department of Psychiatry, University Medical Center Göttingen, Gottingen, Germany
| | - Vince D. Calhoun
- University of New Mexico, Albuquerque, NM, USA,Mind Research Network, Albuquerque, NM, USA
| | - Benedicto Crespo-Facorro
- Department of Psychiatry, University Hospital Marqués de Valdecilla, School of Medicine, University of Cantabria-IDIVAL, Santander, Spain,CIBERSAM, Centro Investigación Biomédica en Red de Salud Mental, Santander, Spain
| | - Roberto Roiz-Santiañez
- Department of Psychiatry, University Hospital Marqués de Valdecilla, School of Medicine, University of Cantabria-IDIVAL, Santander, Spain,CIBERSAM, Centro Investigación Biomédica en Red de Salud Mental, Santander, Spain
| | - Diana Tordesillas-Gutiérrez
- Department of Psychiatry, University Hospital Marqués de Valdecilla, School of Medicine, University of Cantabria-IDIVAL, Santander, Spain,CIBERSAM, Centro Investigación Biomédica en Red de Salud Mental, Santander, Spain,Neuroimaging Unit.Technological Facilities, Valdecilla Biomedical Research Institute IDIVAL, Santander, Cantabria, Spain, Dresden, Dresden, Germany
| | - Carmel Loughland
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Vaughan J. Carr
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia,Monash University, Melbourne, Australia
| | | | - Vanessa L. Cropley
- Melbourne Neuropsychiatry Centre, University of Melbourne & Melbourne Health, Melbourne, VIC, Australia
| | - Janice M. Fullerton
- Neuroscience Research Australia, Sydney, NSW, Australia,School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Melissa J. Green
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia,Neuroscience Research Australia, Sydney, NSW, Australia
| | - Frans Henskens
- PRC for Health Behaviour, and FEBE, University of Newcastle Australia, Newcastle, NSW, Australia
| | | | - Rhoshel K. Lenroot
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia,Neuroscience Research Australia, Sydney, NSW, Australia
| | - Bryan J. Mowry
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia,Queensland Centre for Mental Health Research, The University of Queensland, Brisbane, QLD, Australia
| | - Patricia T. Michie
- School of Psychology, University of Newcastle, Newcastle, NSW, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, University of Melbourne & Melbourne Health, Melbourne, VIC, Australia,Florey Institute of Neuroscience and Mental Health, University of Melbourne, VIC, Australia
| | - Yann Quidé
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia,Neuroscience Research Australia, Sydney, NSW, Australia
| | - Ulrich Schall
- The University of Newcastle, Priority Research Centres for Brain & Mental Health and Grow Up Well, Newcastle, NSW, Australia,Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Rodney J. Scott
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, NSW, Australia,Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Murray J. Cairns
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, NSW, Australia,Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Marc Seal
- Murdoch Children’s Research Institute, Melbourne, VIC, Australia
| | - Paul A. Tooney
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, NSW, Australia,The University of Newcastle, Priority Research Centres for Brain & Mental Health and Grow Up Well, Newcastle, NSW, Australia,The University of Newcastle, Priority Research Centre for Brain & Mental Health, Newcastle, NSW, Australia
| | - Paul E. Rasser
- The University of Newcastle, Priority Research Centre for Brain & Mental Health, Newcastle, NSW, Australia
| | - Gavin Cooper
- The University of Newcastle, Priority Research Centre for Brain & Mental Health, Newcastle, NSW, Australia
| | - Cynthia Shannon Weickert
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia,Neuroscience Research Australia, Sydney, NSW, Australia
| | - Thomas W. Weickert
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia,Neuroscience Research Australia, Sydney, NSW, Australia
| | - Derek W. Morris
- Centre for Neuroimaging & Cognitive Genomics, School of Psychology and Department of Biochemistry, National University of Ireland Galway, Galway, Ireland,Neuropsychiatric Genetics Research Group, Department of Psychiatry and Trinity College Institute of Neuroscience, Trinity College, Dublin, Ireland
| | - Elliot Hong
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Lauren M. Beard
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Daniel H. Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA,Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Gregory G. Brown
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Judith M. Ford
- University of California, San Francisco, San Francisco, CA, USA,San Francisco VA Medical Center, San Francisco, CA, USA
| | - Fabio Macciardi
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, USA
| | - Daniel H. Mathalon
- University of California, San Francisco, San Francisco, CA, USA,San Francisco VA Medical Center, San Francisco, CA, USA
| | | | - Steven G. Potkin
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, USA
| | - James Voyvodic
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Kelvin O. Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - Sarah McEwen
- Department of Psychiatry & Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Fude Yang
- Psychiatry Research Center, Beijing Huilongguan hospital, Beijing, China
| | - Yunlong Tan
- Psychiatry Research Center, Beijing Huilongguan hospital, Beijing, China
| | - Shuping Tan
- Psychiatry Research Center, Beijing Huilongguan hospital, Beijing, China
| | - Zhiren Wang
- Psychiatry Research Center, Beijing Huilongguan hospital, Beijing, China
| | - Fengmei Fan
- Psychiatry Research Center, Beijing Huilongguan hospital, Beijing, China
| | - Jingxu Chen
- Psychiatry Research Center, Beijing Huilongguan hospital, Beijing, China
| | - Hong Xiang
- Chongqing Three Gorges Central Hospital, Chongqing, China
| | - Shiyou Tang
- Chongqing Three Gorges Central Hospital, Chongqing, China
| | - Hua Guo
- Zhumadian Psychiatry Hospital, Henan province, Zhumadian, China
| | - Ping Wan
- Zhumadian Psychiatry Hospital, Henan province, Zhumadian, China
| | - Dong Wei
- Luoyang Fifth People’s Hospital, Henan province, Luoyang, China
| | - Henry J. Bockholt
- Mind Research Network, Albuquerque, NM, USA,Department of Psychiatry, University of Iowa, Iowa City, IA, USA,Advanced Biomedical Informatics Group, LLC, Iowa City, IA, USA
| | - Stefan Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Germany, Dresden, Germany,Massachusetts General Hospital/Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Psychiatric Neuroimaging Research Program
| | - Rick PF. Wolthusen
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Germany, Dresden, Germany,Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA,Emotion and Social Neuroscience Laboratory, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | | | | | - Scott R. Sponheim
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA,Minneapolis VA HCS, Minneapolis, MN, USA
| | - Lieuwe De Haan
- Department of psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Laura Koenders
- Department of psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Marise W. Machielsen
- Department of psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Therese van Amelsvoort
- Department of Psychiatry & Psychology, Maastricht University, Maastricht, The Netherlands
| | - Dick J. Veltman
- Department of Psychiatry, Vrije Universiteit Medical Center, Amsterdam, The Netherlands
| | - Francesca Assogna
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy,Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche “Enrico Fermi”, Rome, Italy
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Pietro de Rossi
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy,NESMOS Department, Faculty of Medicine and Psychology, University “Sapienza” of Rome, Rome, Italy,Department of Neurology and Psychiatry, Sapienza University of Rome, Rome, Italy
| | - Mariangela Iorio
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy,Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche “Enrico Fermi”, Rome, Italy
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy,Beth K. and Stuart C. Yudofsky Division of Neuropsychiatry, Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Tx USA
| | - Peter J. McKenna
- FIDMAG Germanes Hospitalaries Research Foundation, Barcelona, Spain,CIBERSAM, Centro Investigación Biomédica en Red de Salud Mental, Barcelona, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalaries Research Foundation, Barcelona, Spain,CIBERSAM, Centro Investigación Biomédica en Red de Salud Mental, Barcelona, Spain
| | - Raymond Salvador
- FIDMAG Germanes Hospitalaries Research Foundation, Barcelona, Spain,CIBERSAM, Centro Investigación Biomédica en Red de Salud Mental, Barcelona, Spain
| | - Aiden Corvin
- Neuropsychiatric Genetics Research Group, Department of Psychiatry and Trinity College Institute of Neuroscience, Trinity College, Dublin, Ireland
| | - Gary Donohoe
- Centre for Neuroimaging & Cognitive Genomics, School of Psychology and Department of Biochemistry, National University of Ireland Galway, Galway, Ireland,Neuropsychiatric Genetics Research Group, Department of Psychiatry and Trinity College Institute of Neuroscience, Trinity College, Dublin, Ireland
| | - Sinead Kelly
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA,Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Christopher D. Whelan
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | | | | | | | - Simone Ciufolini
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Joaquim Radua
- Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden,FIDMAG Germanes Hospitalaries Research Foundation, Barcelona, Spain,CIBERSAM, Centro Investigación Biomédica en Red de Salud Mental, Barcelona, Spain,Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust
| | - Robin Murray
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Tiago Reis Marques
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Andrew Simmons
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | | | - Laura Egloff
- University of Basel Psychiatric Hospital, Basel, Switzerland
| | | | | | | | - Kathryn I. Alpert
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA,Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Erik G. Jönsson
- Norwegian Centre for Mental Disorders Research (NORMENT), K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden
| | - Sanne Koops
- Department of Psychiatry and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Iris EC. Sommer
- Department of Psychiatry and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy
| | - Aurora Bonvino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy
| | | | - Emma Neilson
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | | | | | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea,Department of Brain & Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea
| | - 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
| | - Dara M. Cannon
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, H91 TK33 Galway, Ireland
| | - Colm McDonald
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, H91 TK33 Galway, Ireland
| | | | | | - Tolibjohn Akhadov
- Children’s Clinical and Research Institute of Emergency Surgery and Trauma, Moscow, Russia
| | | | - Helena Fatouros-Bergman
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Lena Flyckt
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | | | - Geraldo F. Busatto
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil,Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, São Paulo, Brazil
| | - Pedro GP. Rosa
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil,Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, São Paulo, Brazil
| | - Mauricio H. Serpa
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil,Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, São Paulo, Brazil
| | - Marcus V. Zanetti
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil,Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, São Paulo, Brazil
| | - Cyril Hoschl
- National Institute of Mental Health, Klecany, Czech Republic
| | - Antonin Skoch
- National Institute of Mental Health, Klecany, Czech Republic,MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Filip Spaniel
- National Institute of Mental Health, Klecany, Czech Republic
| | - David Tomecek
- National Institute of Mental Health, Klecany, Czech Republic
| | - Saskia P. Hagenaars
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom,Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew M. McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Heather C. Whalley
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephen M. Lawrie
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Christian Knöchel
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, Frankfurt, Germany
| | - Viola Oertel-Knöchel
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, Frankfurt, Germany
| | - Michael Stäblein
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Frankfurt, Frankfurt, Germany
| | - Fleur M. Howells
- University of Cape Town Dept of Psychiatry, Groote Schuur Hospital (J2), Cape Town South Africa
| | - Dan J. Stein
- University of Cape Town Dept of Psychiatry, Groote Schuur Hospital (J2), Cape Town South Africa,MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - Henk Temmingh
- University of Cape Town Dept of Psychiatry, Groote Schuur Hospital (J2), Cape Town South Africa
| | - Anne Uhlmann
- University of Cape Town Dept of Psychiatry, Groote Schuur Hospital (J2), Cape Town South Africa,MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
| | - Carlos Lopez-Jaramillo
- Research Group in Psychiatry, Department of Psychiatry, Faculty of Medicine, Universidad de Antioquia, Medellin, Colombia
| | - Danai Dima
- Department of Psychology, City, University of London, London, United Kingdom,Department of Neuroimaging, IOPPN, King’s College London, London, United Kingdom
| | - Agnes McMahon
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Joshua I. Faskowitz
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Boris A. Gutman
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Jessica A. Turner
- Imaging Genetics and Neuroinformatics Lab, Department of Psychology, Georgia State University, Atlanta, GA, USA,Mind Research Network, Albuquerque, NM, USA
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140
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Xia Z, Zhang L, Hoeft F, Gu B, Gong G, Shu H. Neural Correlates of Oral Word Reading, Silent Reading Comprehension, and Cognitive Subcomponents. INTERNATIONAL JOURNAL OF BEHAVIORAL DEVELOPMENT 2018; 42:342-356. [PMID: 29904229 DOI: 10.1177/0165025417727872] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The ability to read is essential for cognitive development. To deepen our understanding of reading acquisition, we explored the neuroanatomical correlates (cortical thickness (CT)) of word reading fluency and sentence comprehension efficiency in Chinese with a group of typically developing children (N = 21; 12 females and 9 males; age range 10.7-12.3 years). Then, we investigated the relationship between the CT of reading-defined regions and the cognitive subcomponents of reading to determine whether our study lends support to the multi-component model. The results demonstrated that children's performance on oral word reading was positively correlated with CT in the left superior temporal gyrus (LSTG), inferior temporal gyrus (LITG), supramarginal gyrus (LSMG) and right superior temporal gyrus (RSTG). Moreover, CT in the LSTG, LSMG and LITG uniquely predicted children's phonetic representation, phonological awareness, and orthography-phonology mapping skills, respectively. By contrast, children's performance on sentence reading comprehension was positively correlated with CT in the left parahippocampus (LPHP) and right calcarine fissure (RV1). As for the subcomponents of reading, CT in the LPHP was exclusively correlated with morphological awareness, whereas CT in the RV1 was correlated with orthography-semantic mapping. Taken together, these findings indicate that the reading network of typically developing children consists of multiple subdivisions, thus providing neuroanatomical evidence in support of the multi-componential view of reading.
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Affiliation(s)
- Zhichao Xia
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, China.,Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco (UCSF), USA
| | - Linjun Zhang
- Faculty of Linguistic Sciences and KIT-BLCU MEG Laboratory for Brain Science, Beijing Language and Culture University, China
| | - Fumiko Hoeft
- Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco (UCSF), USA.,Precision Learning Center (PrecL), UC, USA.,Dyslexia Center, UCSF, USA.,Haskins Laboratories, 300 George Street #900, New Haven, USA.,Department of Neuropsychiatry, Keio University School of Medicine, Japan
| | - Bin Gu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, China
| | - Hua Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, China
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141
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Kang K, Kwak K, Yoon U, Lee JM. Lateral Ventricle Enlargement and Cortical Thinning in Idiopathic Normal-pressure Hydrocephalus Patients. Sci Rep 2018; 8:13306. [PMID: 30190599 PMCID: PMC6127145 DOI: 10.1038/s41598-018-31399-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 08/14/2018] [Indexed: 01/26/2023] Open
Abstract
We utilized three-dimensional, surface-based, morphometric analysis to investigate ventricle shape between 2 groups: (1) idiopathic normal-pressure hydrocephalus (INPH) patients who had a positive response to the cerebrospinal fluid tap test (CSFTT) and (2) healthy controls. The aims were (1) to evaluate the location of INPH-related structural abnormalities of the lateral ventricles and (2) to investigate relationships between lateral ventricular enlargement and cortical thinning in INPH patients. Thirty-three INPH patients and 23 healthy controls were included in this study. We used sparse canonical correlation analysis to show correlated regions of ventricular surface expansion and cortical thinning. Significant surface expansion in the INPH group was observed mainly in clusters bilaterally located in the superior portion of the lateral ventricles, adjacent to the high convexity of the frontal and parietal regions. INPH patients showed a significant bilateral expansion of both the temporal horns of the lateral ventricles and the medial aspects of the frontal horns of the lateral ventricles to surrounding brain regions, including the medial frontal lobe. Ventricular surface expansion was associated with cortical thinning in the bilateral orbitofrontal cortex, bilateral rostral anterior cingulate cortex, left parahippocampal cortex, left temporal pole, right insula, right inferior temporal cortex, and right fusiform gyrus. These results suggest that patients with INPH have unique patterns of ventricular surface expansion. Our findings encourage future studies to elucidate the underlying mechanism of lateral ventricular morphometric abnormalities in INPH patients.
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Affiliation(s)
- Kyunghun Kang
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea.,Department of Neurology, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Kichang Kwak
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Uicheul Yoon
- Department of Biomedical Engineering, Daegu Catholic University, Gyeongsan-si, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea.
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142
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Nadig A, Reardon PK, Seidlitz J, McDermott CL, Blumenthal JD, Clasen LS, Lalonde F, Lerch JP, Chakravarty MM, Raznahan A. Carriage of Supernumerary Sex Chromosomes Decreases the Volume and Alters the Shape of Limbic Structures. eNeuro 2018; 5:ENEURO.0265-18.2018. [PMID: 30713992 PMCID: PMC6354783 DOI: 10.1523/eneuro.0265-18.2018] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 08/30/2018] [Accepted: 09/24/2018] [Indexed: 01/10/2023] Open
Abstract
Sex chromosome aneuploidy (SCA) increases risk for several psychiatric disorders associated with the limbic system, including mood and autism spectrum disorders. Thus, SCA offers a genetics-first model for understanding the biological basis of psychopathology. Additionally, the sex-biased prevalence of many psychiatric disorders could potentially reflect sex chromosome dosage effects on brain development. To clarify how limbic anatomy varies across sex and sex chromosome complement, we characterized amygdala and hippocampus structure in a uniquely large sample of patients carrying supernumerary sex chromosomes (n = 132) and typically developing controls (n = 166). After adjustment for sex-differences in brain size, karyotypically normal males (XY) and females (XX) did not differ in volume or shape of either structure. In contrast, all SCAs were associated with lowered amygdala volume relative to gonadally-matched controls. This effect was robust to three different methods for total brain volume adjustment, including an allometric analysis that derived normative scaling rules for these structures in a separate, typically developing population (n = 79). Hippocampal volume was insensitive to SCA after adjustment for total brain volume. However, surface-based analysis revealed that SCA, regardless of specific karyotype, was consistently associated with a spatially specific pattern of shape change in both amygdala and hippocampus. In particular, SCA was accompanied by contraction around the basomedial nucleus of the amygdala and an area crossing the hippocampal tail. These results demonstrate the power of SCA as a model to understand how copy number variation can precipitate changes in brain systems relevant to psychiatric disease.
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Affiliation(s)
- Ajay Nadig
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland 20892
| | - Paul K. Reardon
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland 20892
| | - Jakob Seidlitz
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland 20892
| | - Cassidy L. McDermott
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland 20892
| | - Jonathan D. Blumenthal
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland 20892
| | - Liv S. Clasen
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland 20892
| | - Francois Lalonde
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland 20892
| | - Jason P. Lerch
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5T 1R8, Canada
- Neurosciences and Mental Health, the Hospital for Sick Children, Toronto, Ontario M5T 3H7, Canada
| | - M. Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, Quebec H3A OG4, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A OG4, Canada
| | - Armin Raznahan
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland 20892
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143
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Lee YG, Jeon S, Yoo HS, Chung SJ, Lee SK, Lee PH, Sohn YH, Yun M, Evans AC, Ye BS. Amyloid-β-related and unrelated cortical thinning in dementia with Lewy bodies. Neurobiol Aging 2018; 72:32-39. [PMID: 30205358 DOI: 10.1016/j.neurobiolaging.2018.08.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 07/19/2018] [Accepted: 08/04/2018] [Indexed: 12/11/2022]
Abstract
Coexisting Alzheimer's disease (AD) pathology is common in patients with dementia with Lewy bodies (DLB). To evaluate the cortical thinning in patients with DLB considering the effect of amyloid-β (Aβ), we compared the regional cortical thickness between control subjects and patients with DLB with abnormal dopamine transporter imaging. Seventeen (43.6%) of 39 patients with DLB and no control subjects had significant Aβ deposition on 18F-florbetaben positron emission tomography. Compared to control (n = 15), Aβ-negative DLB group (n = 21) had cortical thinning in the bilateral insula, entorhinal, basal frontal, and occipito-parietal cortices. Compared to Aβ-negative DLB, Aβ-positive DLB group (n = 15) had a lower cortical thickness in the AD-prone brain regions in addition to the bilateral occipital, basal frontal, and somatomotor cortices. After controlling for the amount of Aβ deposition, DLB group had cortical thinning in the same regions affected in the Aβ-negative DLB group. In summary, patients with DLB had an Aβ-independent cortical thinning, while Aβ was associated with additional cortical thinning in the AD-prone brain regions and the aggravation of DLB-specific cortical thinning.
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Affiliation(s)
- Young-Gun Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Seun Jeon
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Han Soo Yoo
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Seok Jong Chung
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Phil Hyu Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Young Ho Sohn
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Mijin Yun
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Byoung Seok Ye
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea.
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144
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Pascoal TA, Shin M, Kang MS, Chamoun M, Chartrand D, Mathotaarachchi S, Bennacef I, Therriault J, Ng KP, Hopewell R, Bouhachi R, Hsiao HH, Benedet AL, Soucy JP, Massarweh G, Gauthier S, Rosa-Neto P. In vivo quantification of neurofibrillary tangles with [ 18F]MK-6240. ALZHEIMERS RESEARCH & THERAPY 2018; 10:74. [PMID: 30064520 PMCID: PMC6069775 DOI: 10.1186/s13195-018-0402-y] [Citation(s) in RCA: 113] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Accepted: 07/06/2018] [Indexed: 01/02/2023]
Abstract
BACKGROUND Imaging agents capable of quantifying the brain's tau aggregates will allow a more precise staging of Alzheimer's disease (AD). The aim of the present study was to examine the in vitro properties as well as the in vivo kinetics, using gold standard methods, of the novel positron emission tomography (PET) tau imaging agent [18F]MK-6240. METHODS In vitro properties of [18F]MK-6240 were estimated with autoradiography in postmortem brain tissues of 14 subjects (seven AD patients and seven age-matched controls). In vivo quantification of [18F]MK-6240 binding was performed in 16 subjects (four AD patients, three mild cognitive impairment patients, six healthy elderly individuals, and three healthy young individuals) who underwent 180-min dynamic scans; six subjects had arterial sampling for metabolite correction. Simplified approaches for [18F]MK-6240 quantification were validated using full kinetic modeling with metabolite-corrected arterial input function. All participants also underwent amyloid-PET and structural magnetic resonance imaging. RESULTS In vitro [18F]MK-6240 uptake was higher in AD patients than in age-matched controls in brain regions expected to contain tangles such as the hippocampus, whereas no difference was found in the cerebellar gray matter. In vivo, [18F]MK-6240 displayed favorable kinetics with rapid brain delivery and washout. The cerebellar gray matter had low binding across individuals, showing potential for use as a reference region. A reversible two-tissue compartment model well described the time-activity curves across individuals and brain regions. Distribution volume ratios using the plasma input and standardized uptake value ratios (SUVRs) calculated after the binding approached equilibrium (90 min) were correlated and higher in mild cognitive impairment or AD dementia patients than in controls. Reliability analysis revealed robust SUVRs calculated from 90 to 110 min, while earlier time points provided inaccurate estimates. CONCLUSIONS This evaluation shows an [18F]MK-6240 distribution in concordance with postmortem studies and that simplified quantitative approaches such as the SUVR offer valid estimates of neurofibrillary tangle load 90 min post injection. [18F]MK-6240 is a promising tau tracer with the potential to be applied in the disease diagnosis and assessment of therapeutic interventions.
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Affiliation(s)
- Tharick A Pascoal
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, 6825 LaSalle Boulevard, Verdun, QC, H4H 1R3, Canada.,Montreal Neurological Institute, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| | - Monica Shin
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, 6825 LaSalle Boulevard, Verdun, QC, H4H 1R3, Canada
| | - Min Su Kang
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, 6825 LaSalle Boulevard, Verdun, QC, H4H 1R3, Canada.,Montreal Neurological Institute, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| | - Mira Chamoun
- Montreal Neurological Institute, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| | - Daniel Chartrand
- Montreal Neurological Institute, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| | - Sulantha Mathotaarachchi
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, 6825 LaSalle Boulevard, Verdun, QC, H4H 1R3, Canada.,Montreal Neurological Institute, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| | - Idriss Bennacef
- Translational Biomarkers, Merck & Co., Inc., 770 Sumneytown Pike, West Point, PA, 19486, USA
| | - Joseph Therriault
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, 6825 LaSalle Boulevard, Verdun, QC, H4H 1R3, Canada
| | - Kok Pin Ng
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, 6825 LaSalle Boulevard, Verdun, QC, H4H 1R3, Canada
| | - Robert Hopewell
- Montreal Neurological Institute, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| | - Reda Bouhachi
- Montreal Neurological Institute, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| | - Hung-Hsin Hsiao
- Montreal Neurological Institute, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| | - Andrea L Benedet
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, 6825 LaSalle Boulevard, Verdun, QC, H4H 1R3, Canada
| | - Jean-Paul Soucy
- Montreal Neurological Institute, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| | - Gassan Massarweh
- Montreal Neurological Institute, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| | - Serge Gauthier
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, 6825 LaSalle Boulevard, Verdun, QC, H4H 1R3, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, 6825 LaSalle Boulevard, Verdun, QC, H4H 1R3, Canada. .,Montreal Neurological Institute, 3801 University Street, Montreal, QC, H3A 2B4, Canada. .,Douglas Hospital, McGill University, 6875 La Salle Blvd-FBC room 3149, Montreal, QC, H4H 1R3, Canada.
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145
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Smart K, Cox SML, Nagano-Saito A, Rosa-Neto P, Leyton M, Benkelfat C. Test-retest variability of [ 11 C]ABP688 estimates of metabotropic glutamate receptor subtype 5 availability in humans. Synapse 2018; 72:e22041. [PMID: 29935121 DOI: 10.1002/syn.22041] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 06/01/2018] [Accepted: 06/06/2018] [Indexed: 12/12/2022]
Abstract
[11 C]ABP688 is a positron emission tomography (PET) radioligand that binds selectively to metabotropic glutamate type 5 receptors (mGluR5). The use of this tracer has identified receptor binding changes in clinical populations, and has been informative in drug occupancy studies. However, previous studies have found significant increases in [11 C]ABP688 binding in the later scan of same-day comparisons, and estimates of test-retest reliability under consistent scanning conditions are not available. The objective of this study was to assess the variability of [11 C]ABP688 binding in healthy people in scans performed at the same time of day. Two [11 C]ABP688 scans were acquired in eight healthy volunteers (6 women, 2 men) using a high-resolution research tomograph (HRRT). Scans were acquired 3 weeks apart with start times between 10:00am and 1:30pm. Mean mGluR5 binding potential (BPND ) values were calculated across cortical, striatal and limbic brain regions. Participants reported on subjective mood state after each scan and blood samples were drawn for cortisol analysis. No significant change in BPND between scans was observed. Variability in BPND values of 11-21% was observed across regions, with the greatest change in the hippocampus and amygdala. Reliability was low to moderate. BPND was not statistically related to scan start time, subjective anxiety, serum cortisol levels, or menstrual phase in women. Overall, [11 C]ABP688 BPND estimates show moderate variability in healthy people. Reliability is fair in cortical and striatal regions, and lower in limbic regions. Future research using this ligand should account for this in study design and analysis.
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Affiliation(s)
- Kelly Smart
- Department of Psychiatry, McGill University, 1033 Pine Ave W, Montreal, Quebec, H3A 1A1, Canada
| | - Sylvia M L Cox
- Department of Psychiatry, McGill University, 1033 Pine Ave W, Montreal, Quebec, H3A 1A1, Canada
| | - Atsuko Nagano-Saito
- Department of Psychiatry, McGill University, 1033 Pine Ave W, Montreal, Quebec, H3A 1A1, Canada
| | - Pedro Rosa-Neto
- Department of Neurology and Neurosurgery, McGill University, Montreal Neurological Institute, 3801 University Ave, Montreal, Quebec, H3A 2B4, Canada.,Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, 6825 Boulevard LaSalle, Verdun, Quebec, H4H 1R3, Canada
| | - Marco Leyton
- Department of Psychiatry, McGill University, 1033 Pine Ave W, Montreal, Quebec, H3A 1A1, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal Neurological Institute, 3801 University Ave, Montreal, Quebec, H3A 2B4, Canada
| | - Chawki Benkelfat
- Department of Psychiatry, McGill University, 1033 Pine Ave W, Montreal, Quebec, H3A 1A1, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal Neurological Institute, 3801 University Ave, Montreal, Quebec, H3A 2B4, Canada
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146
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Wagstyl K, Lepage C, Bludau S, Zilles K, Fletcher PC, Amunts K, Evans AC. Mapping Cortical Laminar Structure in the 3D BigBrain. Cereb Cortex 2018; 28:2551-2562. [PMID: 29901791 PMCID: PMC5998962 DOI: 10.1093/cercor/bhy074] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 03/08/2018] [Accepted: 03/13/2018] [Indexed: 11/30/2022] Open
Abstract
Histological sections offer high spatial resolution to examine laminar architecture of the human cerebral cortex; however, they are restricted by being 2D, hence only regions with sufficiently optimal cutting planes can be analyzed. Conversely, noninvasive neuroimaging approaches are whole brain but have relatively low resolution. Consequently, correct 3D cross-cortical patterns of laminar architecture have never been mapped in histological sections. We developed an automated technique to identify and analyze laminar structure within the high-resolution 3D histological BigBrain. We extracted white matter and pial surfaces, from which we derived histologically verified surfaces at the layer I/II boundary and within layer IV. Layer IV depth was strongly predicted by cortical curvature but varied between areas. This fully automated 3D laminar analysis is an important requirement for bridging high-resolution 2D cytoarchitecture and in vivo 3D neuroimaging. It lays the foundation for in-depth, whole-brain analyses of cortical layering.
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Affiliation(s)
- Konrad Wagstyl
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Neurology and Neurosurgery, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Claude Lepage
- Department of Neurology and Neurosurgery, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Sebastian Bludau
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Karl Zilles
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen, Germany
- JARA—Translational Brain Medicine, Aachen, Aachen, Germany
| | - Paul C Fletcher
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- C. and O. Vogt-Institute for Brain Research, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Alan C Evans
- Department of Neurology and Neurosurgery, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
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147
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Lee K, Khoo HM, Lina JM, Dubeau F, Gotman J, Grova C. Disruption, emergence and lateralization of brain network hubs in mesial temporal lobe epilepsy. NEUROIMAGE-CLINICAL 2018; 20:71-84. [PMID: 30094158 PMCID: PMC6070692 DOI: 10.1016/j.nicl.2018.06.029] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2017] [Revised: 05/26/2018] [Accepted: 06/27/2018] [Indexed: 01/05/2023]
Abstract
Hubs of brain networks are brain regions exhibiting denser connections than others, promoting long-range communication. Studies suggested the reorganization of hubs in epilepsy. The patterns of connector hub abnormalities specific to mesial temporal lobe epilepsy (mTLE) are unclear. We wish to quantify connector hub abnormalities in mTLE and identify epilepsy-related resting state networks involving abnormal connector hubs. A recently developed sparsity-based analysis of reliable k-hubness (SPARK) allowed us to address this question by using resting state functional MRI in 20 mTLE patients and 17 healthy controls. Handling the multicollinearity of functional networks, SPARK measures a new metric of hubness by counting the number (k) of networks involved in each voxel, and identifies which networks are actually associated to each connector hub. This measure provides new information about the network architecture involving connector hubs and a realistic range of k-hubness. We quantified the disruption and emergence of connector hubs in individual epileptic subjects and assessed the lateralization of networks involving connector hubs. In mTLE, we found pathological disruptions of normal connector hubs in the mTL and within the default mode network. Right mTLE had remarkably higher emergence of new connector hubs in the mTL than left mTLE. Different patterns of lateralization of the salience network involving the abnormal hippocampus were found in right versus left mTLE. The temporal, cerebellar, default mode, subcortical and motor networks also contributed to the lateralization of hippocampal networks. We finally observed an asymmetrical connector hub reorganization and overall regularization of epilepsy-related resting state networks in mTLE, characterized by the disruption of distant connections and the emergence of local connections. Individually reproducible brain network hubs in mesial Temporal Lobe Epilepsy (mTLE). We observed asymmetrical connector hub reorganization and network regularization in mTLE. We found connector hub disruptions within the mTL and default mode network. Emergence of new connector hubs in the mTL was prominent in right but not in left mTLE. Lateralization of hippocampal connectivity was associated with the salience network.
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Affiliation(s)
- Kangjoo Lee
- Multimodal Functional Imaging Lab, Department of Biomedical Engineering, McGill University, Duff Medical Building, 3775 Rue University, Montreal, QC H3A 2B4, Canada; Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Canada.
| | - Hui Ming Khoo
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Canada; Department of Neurosurgery, Osaka University, 2-2 Yamadaoka, Suita, Osaka Prefecture, 565-0871, Japan
| | - Jean-Marc Lina
- École de Technologie Supérieure, 1100 Rue Notre-Dame O, Montreal, QC H3C 1K3, Canada; Centre de Recherches Mathématiques, Université de Montréal, Pavillon André-Aisenstadt 2920 Chemin de la tour, Montreal, QC H3T 1J4, Canada
| | - François Dubeau
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Canada
| | - Jean Gotman
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Canada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Department of Biomedical Engineering, McGill University, Duff Medical Building, 3775 Rue University, Montreal, QC H3A 2B4, Canada; Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Canada; Centre de Recherches Mathématiques, Université de Montréal, Pavillon André-Aisenstadt 2920 Chemin de la tour, Montreal, QC H3T 1J4, Canada; Department of Physics and PERFORM Centre, Concordia University, 7200 Rue Sherbrooke St. W, Montreal, QC H4B 1R6, Canada
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148
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Knight J, Taylor GW, Khademi A. Voxel-Wise Logistic Regression and Leave-One-Source-Out Cross Validation for white matter hyperintensity segmentation. Magn Reson Imaging 2018; 54:119-136. [PMID: 29932970 DOI: 10.1016/j.mri.2018.06.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 06/11/2018] [Accepted: 06/13/2018] [Indexed: 12/21/2022]
Abstract
Many algorithms have been proposed for automated segmentation of white matter hyperintensities (WMH) in brain MRI. Yet, broad uptake of any particular algorithm has not been observed. In this work, we argue that this may be due to variable and suboptimal validation data and frameworks, precluding direct comparison of methods on heterogeneous data. As a solution, we present Leave-One-Source-Out Cross Validation (LOSO-CV), which leverages all available data for performance estimation, and show that this gives more realistic (lower) estimates of segmentation algorithm performance on data from different scanners. We also develop a FLAIR-only WMH segmentation algorithm: Voxel-Wise Logistic Regression (VLR), inspired by the open-source Lesion Prediction Algorithm (LPA). Our variant facilitates more accurate parameter estimation, and permits intuitive interpretation of model parameters. We illustrate the performance of the VLR algorithm using the LOSO-CV framework with a dataset comprising freely available data from several recent competitions (96 images from 7 scanners). The performance of the VLR algorithm (median Similarity Index of 0.69) is compared to its LPA predecessor (0.58), and the results of the VLR algorithm in the 2017 WMH Segmentation Competition are also presented.
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Affiliation(s)
- Jesse Knight
- University of Guelph, 50 Stone Rd E, Guelph, Canada.
| | - Graham W Taylor
- University of Guelph, 50 Stone Rd E, Guelph, Canada; Vector Institute, 101 College Street, Toronto, Suite HL30B, Canada
| | - April Khademi
- Ryerson University, 350 Victoria St, Toronto, Canada
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149
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Pascoal TA, Mathotaarachchi S, Shin M, Park AY, Mohades S, Benedet AL, Kang MS, Massarweh G, Soucy JP, Gauthier S, Rosa-Neto P. Amyloid and tau signatures of brain metabolic decline in preclinical Alzheimer's disease. Eur J Nucl Med Mol Imaging 2018; 45:1021-1030. [PMID: 29396637 PMCID: PMC5915512 DOI: 10.1007/s00259-018-3933-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 01/02/2018] [Indexed: 01/18/2023]
Abstract
PURPOSE We aimed to determine the amyloid (Aβ) and tau biomarker levels associated with imminent Alzheimer's disease (AD) - related metabolic decline in cognitively normal individuals. METHODS A threshold analysis was performed in 120 cognitively normal elderly individuals by modelling 2-year declines in brain glucose metabolism measured with [18F]fluorodeoxyglucose ([18F]FDG) as a function of [18F]florbetapir Aβ positron emission tomography (PET) and cerebrospinal fluid phosphorylated tau biomarker thresholds. Additionally, using a novel voxel-wise analytical framework, we determined the sample sizes needed to test an estimated 25% drugeffect with 80% of power on changes in FDG uptake over 2 years at every brain voxel. RESULTS The combination of [18F]florbetapir standardized uptake value ratios and phosphorylated-tau levels more than one standard deviation higher than their respective thresholds for biomarker abnormality was the best predictor of metabolic decline in individuals with preclinical AD. We also found that a clinical trial using these thresholds would require as few as 100 individuals to test a 25% drug effect on AD-related metabolic decline over 2 years. CONCLUSIONS These results highlight the new concept that combined Aβ and tau thresholds can predict imminent neurodegeneration as an alternative framework with a high statistical power for testing the effect of disease-modifying therapies on [18F]FDG uptake decline over a typical 2-year clinical trial period in individuals with preclinical AD.
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Affiliation(s)
- Tharick A Pascoal
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, 6875 La Salle Blvd - FBC room 3149, Montreal, QC, H4H 1R3, Canada
| | - Sulantha Mathotaarachchi
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, 6875 La Salle Blvd - FBC room 3149, Montreal, QC, H4H 1R3, Canada
| | - Monica Shin
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, 6875 La Salle Blvd - FBC room 3149, Montreal, QC, H4H 1R3, Canada
| | - Ah Yeon Park
- Statistical Laboratory, University of Cambridge, Cambridge, UK
| | - Sara Mohades
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, 6875 La Salle Blvd - FBC room 3149, Montreal, QC, H4H 1R3, Canada
| | - Andrea L Benedet
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, 6875 La Salle Blvd - FBC room 3149, Montreal, QC, H4H 1R3, Canada
| | - Min Su Kang
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, 6875 La Salle Blvd - FBC room 3149, Montreal, QC, H4H 1R3, Canada
| | | | - Jean-Paul Soucy
- Montreal Neurological Institute, Montreal, Canada
- PERFORM Centre, Concordia University, Montreal, Canada
| | - Serge Gauthier
- Alzheimer's Disease Research Unit, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, 6875 La Salle Blvd - FBC room 3149, Montreal, QC, H4H 1R3, Canada.
- Montreal Neurological Institute, Montreal, Canada.
- Alzheimer's Disease Research Unit, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montreal, Canada.
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada.
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150
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Lewis JD, Evans AC, Tohka J. T1 white/gray contrast as a predictor of chronological age, and an index of cognitive performance. Neuroimage 2018; 173:341-350. [DOI: 10.1016/j.neuroimage.2018.02.050] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 02/19/2018] [Accepted: 02/25/2018] [Indexed: 11/30/2022] Open
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