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Nothdurfter D, Jawinski P, Markett S. White Matter Tract Integrity Is Reduced in Depression and in Individuals With Genetic Liability to Depression. Biol Psychiatry 2024; 95:1063-1071. [PMID: 38103877 DOI: 10.1016/j.biopsych.2023.11.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 11/06/2023] [Accepted: 11/26/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND While major depression has been linked to changes in white matter architecture, it remains unclear whether risk factors for depression are directly associated with these alterations. We reexamined white matter fiber tracts in individuals with depressive symptoms and investigated the connection between genetic and environmental risk for depression and structural changes in the brain. METHODS We included 19,183 participants from the UK Biobank imaging cohort, with depression status and adverse life experience based on questionnaire data and genetic liability for depression quantified by polygenic scores. The integrity of 27 white matter tracts was assessed using mean fractional anisotropy derived from diffusion magnetic resonance imaging. RESULTS White matter integrity was reduced, particularly in thalamic and intracortical fiber tracts, in individuals with depressive symptoms, independent of current symptom status. In a group of healthy individuals without depression, increasing genetic risk and increasing environmental risk were associated with reduced integrity in relevant fiber tracts, particularly in thalamic radiations. This association was stronger than expected based on statistical dependencies between samples, as confirmed by subsequent in silico simulations and permutation tests. CONCLUSIONS White matter alterations in thalamic and association tracts are associated with depressive symptoms and genetic risk for depression in unaffected individuals, suggesting an intermediate phenotype at the brain level.
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Affiliation(s)
- David Nothdurfter
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Philippe Jawinski
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany.
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2
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Poirier SE, Suskin NG, Khaw AV, Thiessen JD, Shoemaker JK, Anazodo UC. Probing Evidence of Cerebral White Matter Microstructural Disruptions in Ischemic Heart Disease Before and Following Cardiac Rehabilitation: A Diffusion Tensor MR Imaging Study. J Magn Reson Imaging 2024; 59:2137-2149. [PMID: 37589418 DOI: 10.1002/jmri.28964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023] Open
Abstract
BACKGROUND Ischemic heart disease (IHD) is linked to brain white matter (WM) breakdown but how age or disease effects WM integrity, and whether it is reversible using cardiac rehabilitation (CR), remains unclear. PURPOSE To assess the effects of brain aging, cardiovascular disease, and CR on WM microstructure in brains of IHD patients following a cardiac event. STUDY TYPE Retrospective. POPULATION Thirty-five IHD patients (9 females; mean age = 59 ± 8 years), 21 age-matched healthy controls (10 females; mean age = 59 ± 8 years), and 25 younger controls (14 females; mean age = 26 ± 4 years). FIELD STRENGTH/SEQUENCE 3 T diffusion-weighted imaging with single-shot echo planar imaging acquired at 3 months and 9 months post-cardiac event. ASSESSMENT Tract-based spatial statistics (TBSS) and tractometry were used to compare fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) in cerebral WM between: 1) older and younger controls to distinguish age-related from disease-related WM changes; 2) IHD patients at baseline (pre-CR) and age-matched controls to investigate if cardiovascular disease exacerbates age-related WM changes; and 3) IHD patients pre-CR and post-CR to investigate the neuroplastic effect of CR on WM microstructure. STATISTICAL TESTS Two-sample unpaired t-test (age: older vs. younger controls; IHD: IHD pre-CR vs. age-matched controls). One-sample paired t-test (CR: IHD pre- vs. post-CR). Statistical threshold: P < 0.05 (FWE-corrected). RESULTS TBSS and tractometry revealed widespread WM changes in older controls compared to younger controls while WM clusters of decreased FA in the fornix and increased MD in body of corpus callosum were observed in IHD patients pre-CR compared to age-matched controls. Robust WM improvements (increased FA, increased AD) were observed in IHD patients post-CR. DATA CONCLUSION In IHD, both brain aging and cardiovascular disease may contribute to WM disruptions. IHD-related WM disruptions may be favorably modified by CR. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Stefan E Poirier
- Lawson Imaging, Lawson Health Research Institute, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Neville G Suskin
- Division of Cardiology, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Alexander V Khaw
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Jonathan D Thiessen
- Lawson Imaging, Lawson Health Research Institute, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Joel K Shoemaker
- School of Kinesiology, Western University, London, Ontario, Canada
| | - Udunna C Anazodo
- Lawson Imaging, Lawson Health Research Institute, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Research Centre for Studies in Aging, McGill University, Montréal, Québec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
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3
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Kerpel A, Davenport E, Proskovec AL, Xi Y, Berry JD, Yetkin Z, Maldjian J, Yu FF. Antidepressant-related microstructural changes in the external capsule. Brain Imaging Behav 2024:10.1007/s11682-024-00891-w. [PMID: 38814546 DOI: 10.1007/s11682-024-00891-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2024] [Indexed: 05/31/2024]
Abstract
Several magnetic resonance imaging (MRI) studies have reported that antidepressant medications are strongly linked to brain microstructural alterations. Notably, external capsule alterations have been reported to be a biological marker for therapeutic response. However, prior studies did not investigate whether a change in the neurite density or directional coherence of white matter (WM) fibers underlies the observed microstructural alterations. This MRI-based case-control study examined the relationship between patients' current use of antidepressant medications and advanced measurements of external capsule WM microstructure derived from multishell diffusion imaging using neurite orientation dispersion and density imaging (NODDI). The study compared a group of thirty-five participants who were taking antidepressant medications comprising selective serotonin reuptake inhibitors (SSRIs) (n = 25) and serotonin and norepinephrine reuptake inhibitors (SNRIs) with a control group of thirty-five individuals matched in terms of age, sex, race, and atherosclerotic cardiovascular risk factors. All participants were selected from the Dallas Heart Study phase 2, a multi-ethnic, population-based cohort study. A series of multiple linear regression analyses were conducted to predict microstructural characteristics of the bilateral external capsule using age, sex, and antidepressant medications as predictor variables. There was significantly reduced neurite density in the bilateral external capsules of patients taking SSRIs. Increased orientation dispersion in the external capsule was predominantly seen in patients taking SNRIs. Our findings suggest an association between specific external capsule microstructural changes and antidepressant medications, including reduced neurite density for SSRIs and increased orientation dispersion for SNRIs.
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Affiliation(s)
- Ariel Kerpel
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
| | - Elizabeth Davenport
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Amy L Proskovec
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Yin Xi
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Jarett D Berry
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Zerrin Yetkin
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Joseph Maldjian
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Fang F Yu
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
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4
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Parker KJ, Kabir IE, Doyley MM, Faiyaz A, Uddin MN, Flores G, Schifitto G. Brain elastography in aging relates to fluid/solid trendlines. Phys Med Biol 2024; 69:115037. [PMID: 38670141 DOI: 10.1088/1361-6560/ad4446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/26/2024] [Indexed: 04/28/2024]
Abstract
The relatively new tools of brain elastography have established a general trendline for healthy, aging adult humans, whereby the brain's viscoelastic properties 'soften' over many decades. Earlier studies of the aging brain have demonstrated a wide spectrum of changes in morphology and composition towards the later decades of lifespan. This leads to a major question of causal mechanisms: of the many changes documented in structure and composition of the aging brain, which ones drive the long term trendline for viscoelastic properties of grey matter and white matter? The issue is important for illuminating which factors brain elastography is sensitive to, defining its unique role for study of the brain and clinical diagnoses of neurological disease and injury. We address these issues by examining trendlines in aging from our elastography data, also utilizing data from an earlier landmark study of brain composition, and from a biophysics model that captures the multiscale biphasic (fluid/solid) structure of the brain. Taken together, these imply that long term changes in extracellular water in the glymphatic system of the brain along with a decline in the extracellular matrix have a profound effect on the measured viscoelastic properties. Specifically, the trendlines indicate that water tends to replace solid fraction as a function of age, then grey matter stiffness decreases inversely as water fraction squared, whereas white matter stiffness declines inversely as water fraction to the 2/3 power, a behavior consistent with the cylindrical shape of the axons. These unique behaviors point to elastography of the brain as an important macroscopic measure of underlying microscopic structural change, with direct implications for clinical studies of aging, disease, and injury.
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Affiliation(s)
- Kevin J Parker
- Department of Electrical and Computer Engineering, University of Rochester, 724 Computer Studies Building, Box 270231, Rochester, NY 14627, United States of America
- Department of Biomedical Engineering, University of Rochester, 204 Goergen Hall, Box 270168, Rochester, NY 14627, United States of America
- Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Box 648, Rochester, NY 14642, United States of America
| | - Irteza Enan Kabir
- Department of Electrical and Computer Engineering, University of Rochester, 724 Computer Studies Building, Box 270231, Rochester, NY 14627, United States of America
| | - Marvin M Doyley
- Department of Electrical and Computer Engineering, University of Rochester, 724 Computer Studies Building, Box 270231, Rochester, NY 14627, United States of America
- Department of Biomedical Engineering, University of Rochester, 204 Goergen Hall, Box 270168, Rochester, NY 14627, United States of America
- Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Box 648, Rochester, NY 14642, United States of America
| | - Abrar Faiyaz
- Department of Electrical and Computer Engineering, University of Rochester, 724 Computer Studies Building, Box 270231, Rochester, NY 14627, United States of America
| | - Md Nasir Uddin
- Department of Biomedical Engineering, University of Rochester, 204 Goergen Hall, Box 270168, Rochester, NY 14627, United States of America
- Department of Neurology, University of Rochester Medical Center, 601 Elmwood Ave, Box 673, Rochester, NY 14642, United States of America
| | - Gilmer Flores
- Department of Biomedical Engineering, University of Rochester, 204 Goergen Hall, Box 270168, Rochester, NY 14627, United States of America
| | - Giovanni Schifitto
- Department of Electrical and Computer Engineering, University of Rochester, 724 Computer Studies Building, Box 270231, Rochester, NY 14627, United States of America
- Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Box 648, Rochester, NY 14642, United States of America
- Department of Neurology, University of Rochester Medical Center, 601 Elmwood Ave, Box 673, Rochester, NY 14642, United States of America
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5
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Baranyi G, Buchanan CR, Conole ELS, Backhouse EV, Maniega SM, Valdés Hernández MDC, Bastin ME, Wardlaw J, Deary IJ, Cox SR, Pearce J. Life-course neighbourhood deprivation and brain structure in older adults: the Lothian Birth Cohort 1936. Mol Psychiatry 2024:10.1038/s41380-024-02591-9. [PMID: 38773266 DOI: 10.1038/s41380-024-02591-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 04/29/2024] [Accepted: 05/02/2024] [Indexed: 05/23/2024]
Abstract
Neighbourhood disadvantage may be associated with brain health but the importance of exposure at different stages of the life course is poorly understood. Utilising the Lothian Birth Cohort 1936, we explored the relationship between residential neighbourhood deprivation from birth to late adulthood, and global and local neuroimaging measures at age 73. A total of 689 participants had at least one valid brain measures (53% male); to maximise the sample size structural equation models with full information maximum likelihood were conducted. Residing in disadvantaged neighbourhoods in mid- to late adulthood was associated with smaller total brain (β = -0.06; SE = 0.02; sample size[N] = 658; number of pairwise complete observations[n]=390), grey matter (β = -0.11; SE = 0.03; N = 658; n = 390), and normal-appearing white matter volumes (β = -0.07; SE = 0.03; N = 658; n = 390), thinner cortex (β = -0.14; SE = 0.06; N = 636; n = 379), and lower general white matter fractional anisotropy (β = -0.19; SE = 0.06; N = 665; n = 388). We also found some evidence on the accumulating impact of neighbourhood deprivation from birth to late adulthood on age 73 total brain (β = -0.06; SE = 0.02; N = 658; n = 276) and grey matter volumes (β = -0.10; SE = 0.04; N = 658; n = 276). Local analysis identified affected focal cortical areas and specific white matter tracts. Among individuals belonging to lower social classes, the brain-neighbourhood associations were particularly strong, with the impact of neighbourhood deprivation on total brain and grey matter volumes, and general white matter fractional anisotropy accumulating across the life course. Our findings suggest that living in deprived neighbourhoods across the life course, but especially in mid- to late adulthood, is associated with adverse brain morphologies, with lower social class amplifying the vulnerability.
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Affiliation(s)
- Gergő Baranyi
- Centre for Research on Environment, Society and Health, School of GeoSciences, The University of Edinburgh, Edinburgh, UK.
| | - Colin R Buchanan
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Eleanor L S Conole
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Ellen V Backhouse
- Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, UK
| | - Susana Muñoz Maniega
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
- Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, The University of Edinburgh, Edinburgh, UK
| | - María Del C Valdés Hernández
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
- Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, The University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
- Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, The University of Edinburgh, Edinburgh, UK
| | - Joanna Wardlaw
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
- Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, The University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Jamie Pearce
- Centre for Research on Environment, Society and Health, School of GeoSciences, The University of Edinburgh, Edinburgh, UK
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6
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Chen J, Bayanagari VL, Chung S, Wang Y, Lui YW. Deep learning with diffusion MRI as in vivo microscope reveals sex-related differences in human white matter microstructure. Sci Rep 2024; 14:9835. [PMID: 38744901 PMCID: PMC11094063 DOI: 10.1038/s41598-024-60340-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 04/22/2024] [Indexed: 05/16/2024] Open
Abstract
Biological sex is a crucial variable in neuroscience studies where sex differences have been documented across cognitive functions and neuropsychiatric disorders. While gross statistical differences have been previously documented in macroscopic brain structure such as cortical thickness or region size, less is understood about sex-related cellular-level microstructural differences which could provide insight into brain health and disease. Studying these microstructural differences between men and women paves the way for understanding brain disorders and diseases that manifest differently in different sexes. Diffusion MRI is an important in vivo, non-invasive methodology that provides a window into brain tissue microstructure. Our study develops multiple end-to-end classification models that accurately estimates the sex of a subject using volumetric diffusion MRI data and uses these models to identify white matter regions that differ the most between men and women. 471 male and 560 female healthy subjects (age range, 22-37 years) from the Human Connectome Project are included. Fractional anisotropy, mean diffusivity and mean kurtosis are used to capture brain tissue microstructure characteristics. Diffusion parametric maps are registered to a standard template to reduce bias that can arise from macroscopic anatomical differences like brain size and contour. This study employ three major model architectures: 2D convolutional neural networks, 3D convolutional neural networks and Vision Transformer (with self-supervised pretraining). Our results show that all 3 models achieve high sex classification performance (test AUC 0.92-0.98) across all diffusion metrics indicating definitive differences in white matter tissue microstructure between males and females. We further use complementary model architectures to inform about the pattern of detected microstructural differences and the influence of short-range versus long-range interactions. Occlusion analysis together with Wilcoxon signed-rank test is used to determine which white matter regions contribute most to sex classification. The results indicate that sex-related differences manifest in both local features as well as global features / longer-distance interactions of tissue microstructure. Our highly consistent findings across models provides new insight supporting differences between male and female brain cellular-level tissue organization particularly in the central white matter.
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Affiliation(s)
- Junbo Chen
- Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, 370 Jay Street, 9th Floor, Brooklyn, NY, 11201, USA.
| | - Vara Lakshmi Bayanagari
- Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, 370 Jay Street, 9th Floor, Brooklyn, NY, 11201, USA
| | - Sohae Chung
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Yao Wang
- Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, 370 Jay Street, 9th Floor, Brooklyn, NY, 11201, USA
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY, USA
| | - Yvonne W Lui
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
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7
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Farrher E, Grinberg F, Khechiashvili T, Neuner I, Konrad K, Shah NJ. Spatiotemporal Patterns of White Matter Maturation after Pre-Adolescence: A Diffusion Kurtosis Imaging Study. Brain Sci 2024; 14:495. [PMID: 38790472 PMCID: PMC11119177 DOI: 10.3390/brainsci14050495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
Diffusion tensor imaging (DTI) enables the assessment of changes in brain tissue microstructure during maturation and ageing. In general, patterns of cerebral maturation and decline render non-monotonic lifespan trajectories of DTI metrics with age, and, importantly, the rate of microstructural changes is heterochronous for various white matter fibres. Recent studies have demonstrated that diffusion kurtosis imaging (DKI) metrics are more sensitive to microstructural changes during ageing compared to those of DTI. In a previous work, we demonstrated that the Cohen's d of mean diffusional kurtosis (dMK) represents a useful biomarker for quantifying maturation heterochronicity. However, some inferences on the maturation grades of different fibre types, such as association, projection, and commissural, were of a preliminary nature due to the insufficient number of fibres considered. Hence, the purpose of this follow-up work was to further explore the heterochronicity of microstructural maturation between pre-adolescence and middle adulthood based on DTI and DKI metrics. Using the effect size of the between-group parametric changes and Cohen's d, we observed that all commissural fibres achieved the highest level of maturity, followed by the majority of projection fibres, while the majority of association fibres were the least matured. We also demonstrated that dMK strongly correlates with the maxima or minima of the lifespan curves of DTI metrics. Furthermore, our results provide substantial evidence for the existence of spatial gradients in the timing of white matter maturation. In conclusion, our data suggest that DKI provides useful biomarkers for the investigation of maturation spatial heterogeneity and heterochronicity.
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Affiliation(s)
- Ezequiel Farrher
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
| | - Farida Grinberg
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Neurology, RWTH Aachen University, 52074 Aachen, Germany
| | - Tamara Khechiashvili
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
| | - Irene Neuner
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
| | - Kerstin Konrad
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry and Psychotherapy, RWTH Aachen University, 52074 Aachen, Germany
- Institute of Neuroscience and Medicine 3, INM-3, Forschungszentrum Jülich, 52425 Jülich, Germany
- Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Neurology, RWTH Aachen University, 52074 Aachen, Germany
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
- Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, 52425 Jülich, Germany
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8
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Baciu M, Roger E. Finding the Words: How Does the Aging Brain Process Language? A Focused Review of Brain Connectivity and Compensatory Pathways. Top Cogn Sci 2024. [PMID: 38734967 DOI: 10.1111/tops.12736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/30/2024] [Accepted: 05/02/2024] [Indexed: 05/13/2024]
Abstract
As people age, there is a natural decline in cognitive functioning and brain structure. However, the relationship between brain function and cognition in older adults is neither straightforward nor uniform. Instead, it is complex, influenced by multiple factors, and can vary considerably from one person to another. Reserve, compensation, and maintenance mechanisms may help explain why some older adults can maintain high levels of performance while others struggle. These mechanisms are often studied concerning memory and executive functions that are particularly sensitive to the effects of aging. However, language abilities can also be affected by age, with changes in production fluency. The impact of brain changes on language abilities needs to be further investigated to understand the dynamics and patterns of aging, especially successful aging. We previously modeled several compensatory profiles of language production and lexical access/retrieval in aging within the Lexical Access and Retrieval in Aging (LARA) model. In the present paper, we propose an extended version of the LARA model, called LARA-Connectivity (LARA-C), incorporating recent evidence on brain connectivity. Finally, we discuss factors that may influence the strategies implemented with aging. The LARA-C model can serve as a framework to understand individual performance and open avenues for possible personalized interventions.
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Affiliation(s)
- Monica Baciu
- LPNC, Psychology Department, Grenoble Alps University
- Neurology Department, Grenoble Alps University Hospital
| | - Elise Roger
- LPNC, Psychology Department, Grenoble Alps University
- Communication and Aging Laboratory, Research Center of the University Institute of Geriatrics of Montreal
- Faculty of Medicine, University of Montreal
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9
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Windener F, Grewing L, Thomas C, Dorion MF, Otteken M, Kular L, Jagodic M, Antel J, Albrecht S, Kuhlmann T. Physiological aging and inflammation-induced cellular senescence may contribute to oligodendroglial dysfunction in MS. Acta Neuropathol 2024; 147:82. [PMID: 38722375 PMCID: PMC11082024 DOI: 10.1007/s00401-024-02733-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/12/2024] [Accepted: 04/15/2024] [Indexed: 05/12/2024]
Abstract
Aging affects all cell types in the CNS and plays an important role in CNS diseases. However, the underlying molecular mechanisms driving these age-associated changes and their contribution to diseases are only poorly understood. The white matter in the aging brain as well as in diseases, such as Multiple sclerosis is characterized by subtle abnormalities in myelin sheaths and paranodes, suggesting that oligodendrocytes, the myelin-maintaining cells of the CNS, lose the capacity to preserve a proper myelin structure and potentially function in age and certain diseases. Here, we made use of directly converted oligodendrocytes (dchiOL) from young, adult and old human donors to study age-associated changes. dchiOL from all three age groups differentiated in an comparable manner into O4 + immature oligodendrocytes, but the proportion of MBP + mature dchiOL decreased with increasing donor age. This was associated with an increased ROS production and upregulation of cellular senescence markers such as CDKN1A, CDKN2A in old dchiOL. Comparison of the transcriptomic profiles of dchiOL from adult and old donors revealed 1324 differentially regulated genes with limited overlap with transcriptomic profiles of the donors' fibroblasts or published data sets from directly converted human neurons or primary rodent oligodendroglial lineage cells. Methylome analyses of dchiOL and human white matter tissue samples demonstrate that chronological and epigenetic age correlate in CNS white matter as well as in dchiOL and resulted in the identification of an age-specific epigenetic signature. Furthermore, we observed an accelerated epigenetic aging of the myelinated, normal appearing white matter of multiple sclerosis (MS) patients compared to healthy individuals. Impaired differentiation and upregulation of cellular senescence markers could be induced in young dchiOL in vitro using supernatants from pro-inflammatory microglia. In summary, our data suggest that physiological aging as well as inflammation-induced cellular senescence contribute to oligodendroglial pathology in inflammatory demyelinating diseases such as MS.
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Affiliation(s)
- Farina Windener
- Institute of Neuropathology, University Hospital Münster, 48149, Münster, Germany
| | - Laureen Grewing
- Institute of Neuropathology, University Hospital Münster, 48149, Münster, Germany
| | - Christian Thomas
- Institute of Neuropathology, University Hospital Münster, 48149, Münster, Germany
| | - Marie-France Dorion
- Neuroimmunology Unit, Montreal Neurological Institute and Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - Marie Otteken
- Institute of Neuropathology, University Hospital Münster, 48149, Münster, Germany
| | - Lara Kular
- Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Maja Jagodic
- Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Jack Antel
- Neuroimmunology Unit, Montreal Neurological Institute and Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - Stefanie Albrecht
- Institute of Neuropathology, University Hospital Münster, 48149, Münster, Germany
| | - Tanja Kuhlmann
- Institute of Neuropathology, University Hospital Münster, 48149, Münster, Germany.
- Neuroimmunology Unit, Montreal Neurological Institute and Department of Neurology and Neurosurgery, McGill University, Montreal, Canada.
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10
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Benavidez SM, Abaryan Z, Kim GS, Laltoo E, McCracken JT, Thompson PM, Lawrence KE. Sex Differences in the Brain's White Matter Microstructure during Development assessed using Advanced Diffusion MRI Models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.02.578712. [PMID: 38352346 PMCID: PMC10862784 DOI: 10.1101/2024.02.02.578712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Typical sex differences in white matter (WM) microstructure during development are incompletely understood. Here we evaluated sex differences in WM microstructure during typical brain development using a sample of neurotypical individuals across a wide developmental age (N=239, aged 5-22 years). We used the conventional diffusion-weighted MRI (dMRI) model, diffusion tensor imaging (DTI), and two advanced dMRI models, the tensor distribution function (TDF) and neurite orientation dispersion density imaging (NODDI) to assess WM microstructure. WM microstructure exhibited significant, regionally consistent sex differences across the brain during typical development. Additionally, the TDF model was most sensitive in detecting sex differences. These findings highlight the importance of considering sex in neurodevelopmental research and underscore the value of the advanced TDF model.
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Affiliation(s)
- Sebastian M Benavidez
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Zvart Abaryan
- Children's Hospital of Los Angeles, Los Angeles, CA, USA
| | - Gaon S Kim
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Emily Laltoo
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - James T McCracken
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Katherine E Lawrence
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
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11
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Wang Z, Li X, Wang J, Yang W, Dove A, Lu W, Qi X, Sindi S, Xu W. Association of past and current sleep duration with structural brain differences: A large population-based study from the UK Biobank. Sleep Med 2024; 119:179-186. [PMID: 38692219 DOI: 10.1016/j.sleep.2024.04.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 04/19/2024] [Accepted: 04/24/2024] [Indexed: 05/03/2024]
Abstract
OBJECTIVE This study aimed to examine the association between past/current sleep duration and macro-/micro-structural brain outcomes and explore whether hypertension or social activity plays a role in such association. METHODS Within the UK Biobank, 40 436 dementia-free participants (age 40-70 years) underwent a baseline assessment followed by a brain magnetic resonance imaging (MRI) scan 9 years later. Past (baseline) and current (MRI scans) sleep duration (hours/day) were recorded and classified as short (≤5), intermediate (6-8), and long (≥9). Brain structural volumes and diffusion markers were assessed by MRI scans. RESULTS Compared with past intermediate sleep, past short sleep was related to smaller cortex volumes (standardized β [95 % CI]: -0.04 [-0.07, -0.02]) and lower regional fractional anisotropy (FA) (-0.08 [-0.13, -0.03]), while past long sleep was related to smaller regional subcortical volumes (standardized β: -0.04 to -0.07 for thalamus, accumbens, and hippocampus). Compared to current intermediate sleep, current short sleep was associated with smaller cortex volumes (-0.03 [-0.05, -0.01]), greater white matter hyperintensities (WMH) volumes (0.04 [0.01, 0.08]), and lower regional FA (-0.07 [-0.11, -0.02]). However, current long sleep was related to smaller total brain (-0.03 [-0.05, -0.02]), grey matter (-0.05 [-0.07, -0.03]), cortex (-0.05 [-0.07, -0.03]), regional subcortical volumes [standardized β: -0.05 to -0.09 for putamen, thalamus, hippocampus, and accumbens]), greater WMH volumes (0.06 [0.03, 0.09]), as well as lower regional FA (-0.05 [-0.09, -0.02]). The association between current long sleep duration and poor brain health was stronger among people with hypertension or low frequency of social activity (all Pinteraction <0.05). CONCLUSIONS Both past and current short/long sleep are associated with smaller brain volume and poorer white matter health in the brain, especially in individuals with hypertension and low frequency of social activity. Our findings highlight the need to maintain 6-8 h' sleep duration for healthy brain aging.
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Affiliation(s)
- Zhiyu Wang
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin, China; Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China; Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China
| | - Xuerui Li
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin, China
| | - Jiao Wang
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China; Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China; Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Wenzhe Yang
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China; Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China; Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Abigail Dove
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Wenli Lu
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China; Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China; Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Xiuying Qi
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China; Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China; Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China.
| | - Shireen Sindi
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Ageing Epidemiology Research Unit (AGE), School of Public Health, Faculty of Medicine, Imperial College London, UK
| | - Weili Xu
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China; Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China; Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China; Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
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12
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Schrempft S, Trofimova O, Künzi M, Ramponi C, Lutti A, Kherif F, Latypova A, Vollenweider P, Marques-Vidal P, Preisig M, Kliegel M, Stringhini S, Draganski B. The Neurobiology of Life Course Socioeconomic Conditions and Associated Cognitive Performance in Middle to Late Adulthood. J Neurosci 2024; 44:e1231232024. [PMID: 38499361 PMCID: PMC11044112 DOI: 10.1523/jneurosci.1231-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 03/20/2024] Open
Abstract
Despite major advances, our understanding of the neurobiology of life course socioeconomic conditions is still scarce. This study aimed to provide insight into the pathways linking socioeconomic exposures-household income, last known occupational position, and life course socioeconomic trajectories-with brain microstructure and cognitive performance in middle to late adulthood. We assessed socioeconomic conditions alongside quantitative relaxometry and diffusion-weighted magnetic resonance imaging indicators of brain tissue microstructure and cognitive performance in a sample of community-dwelling men and women (N = 751, aged 50-91 years). We adjusted the applied regression analyses and structural equation models for the linear and nonlinear effects of age, sex, education, cardiovascular risk factors, and the presence of depression, anxiety, and substance use disorders. Individuals from lower-income households showed signs of advanced brain white matter (WM) aging with greater mean diffusivity (MD), lower neurite density, lower myelination, and lower iron content. The association between household income and MD was mediated by neurite density (B = 0.084, p = 0.003) and myelination (B = 0.019, p = 0.009); MD partially mediated the association between household income and cognitive performance (B = 0.017, p < 0.05). Household income moderated the relation between WM microstructure and cognitive performance, such that greater MD, lower myelination, or lower neurite density was only associated with poorer cognitive performance among individuals from lower-income households. Individuals from higher-income households showed preserved cognitive performance even with greater MD, lower myelination, or lower neurite density. These findings provide novel mechanistic insights into the associations between socioeconomic conditions, brain anatomy, and cognitive performance in middle to late adulthood.
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Affiliation(s)
- Stephanie Schrempft
- Unit of Population Epidemiology, Division of Primary Care, Geneva University Hospitals, Geneva CH-1205, Switzerland
| | - Olga Trofimova
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne CH-1015, Switzerland
- Swiss Institute of Bioinformatics, Lausanne CH-1015, Switzerland
| | - Morgane Künzi
- Swiss National Centre of Competences in Research, "LIVES - Overcoming Vulnerability: Life Course Perspectives," University of Lausanne and University of Geneva, Lausanne CH-1015 and Carouge CH-1227, Switzerland
- Department of Psychology, University of Geneva, Geneva CH-1205, Switzerland
- Center for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Carouge CH-1227, Switzerland
| | - Cristina Ramponi
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Adeliya Latypova
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Martin Preisig
- Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne CH-1008, Switzerland
| | - Matthias Kliegel
- Swiss National Centre of Competences in Research, "LIVES - Overcoming Vulnerability: Life Course Perspectives," University of Lausanne and University of Geneva, Lausanne CH-1015 and Carouge CH-1227, Switzerland
- Department of Psychology, University of Geneva, Geneva CH-1205, Switzerland
- Center for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Carouge CH-1227, Switzerland
| | - Silvia Stringhini
- Unit of Population Epidemiology, Division of Primary Care, Geneva University Hospitals, Geneva CH-1205, Switzerland
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva CH-1211, Switzerland
- University Centre for General Medicine and Public Health, University of Lausanne, Lausanne CH-1005, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
- Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, D-04303 Leipzig, Germany
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13
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Rivera AD, Normanton JR, Butt AM, Azim K. The Genomic Intersection of Oligodendrocyte Dynamics in Schizophrenia and Aging Unravels Novel Pathological Mechanisms and Therapeutic Potentials. Int J Mol Sci 2024; 25:4452. [PMID: 38674040 PMCID: PMC11050044 DOI: 10.3390/ijms25084452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/28/2024] [Accepted: 03/30/2024] [Indexed: 04/28/2024] Open
Abstract
Schizophrenia is a significant worldwide health concern, affecting over 20 million individuals and contributing to a potential reduction in life expectancy by up to 14.5 years. Despite its profound impact, the precise pathological mechanisms underlying schizophrenia continue to remain enigmatic, with previous research yielding diverse and occasionally conflicting findings. Nonetheless, one consistently observed phenomenon in brain imaging studies of schizophrenia patients is the disruption of white matter, the bundles of myelinated axons that provide connectivity and rapid signalling between brain regions. Myelin is produced by specialised glial cells known as oligodendrocytes, which have been shown to be disrupted in post-mortem analyses of schizophrenia patients. Oligodendrocytes are generated throughout life by a major population of oligodendrocyte progenitor cells (OPC), which are essential for white matter health and plasticity. Notably, a decline in a specific subpopulation of OPC has been identified as a principal factor in oligodendrocyte disruption and white matter loss in the aging brain, suggesting this may also be a factor in schizophrenia. In this review, we analysed genomic databases to pinpoint intersections between aging and schizophrenia and identify shared mechanisms of white matter disruption and cognitive dysfunction.
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Affiliation(s)
- Andrea D. Rivera
- Department of Neuroscience, Institute of Human Anatomy, University of Padova, Via A. Gabelli 65, 35127 Padua, Italy;
| | - John R. Normanton
- GliaGenesis Limited, Orchard Lea, Horns Lane, Oxfordshire, Witney OX29 8NH, UK; (J.R.N.); (K.A.)
| | - Arthur M. Butt
- GliaGenesis Limited, Orchard Lea, Horns Lane, Oxfordshire, Witney OX29 8NH, UK; (J.R.N.); (K.A.)
- School of Pharmacy and Biomedical Science, University of Portsmouth, Hampshire PO1 2UP, UK
| | - Kasum Azim
- GliaGenesis Limited, Orchard Lea, Horns Lane, Oxfordshire, Witney OX29 8NH, UK; (J.R.N.); (K.A.)
- Independent Data Lab UG, Frauenmantelanger 31, 80937 Munich, Germany
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14
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McCrady BS, Claus E, Witkiewitz K, Shiver A, Swartz M, Chávez R. Neurocognitive and neurobehavioral mechanisms of change following psychological treatment for alcohol use disorder. Contemp Clin Trials 2024; 142:107538. [PMID: 38615751 DOI: 10.1016/j.cct.2024.107538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 04/08/2024] [Accepted: 04/11/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND Although modestly effective treatments exist for alcohol use disorder (AUD), many individuals return to heavy drinking after treatment, suggesting the need for better understanding of factors that contribute to maintaining abstinence or drinking reductions. Whereas past studies identified what treatments work for AUD, recent studies focus more on why particular treatments work, and the mechanisms by which treatment leads to change. This focus on mechanisms of behavior change (MOBC) may inform the process by which treatment leads to better outcomes, and also may lead to new treatments or modifications of existing treatments that target empirically supported mechanisms known to lead to change. There is a paucity of studies examining MOBC from a neurocognitive perspective. METHOD To address this gap in knowledge, the study described here is examining emotional reactivity, alcohol cue reactivity, and cognitive control as potential MOBC at three levels of analysis - self-report, behavior, and neural. RESULTS One hundred ten treatment-seeking individuals with an AUD are being randomized to receive 8 sessions of either Cognitive Behavioral Treatment (CBT) or Mindfulness Based Treatment (MBT) after up to 4 sessions of a platform treatment focused on enhancing motivation to change. To establish the temporal relationship between changes in drinking and changes in MOBC, patients are assessed at baseline, during and immediately after treatment, and 9- and 15-months post-baseline. Relationships between changes in drinking and changes in the proposed MOBC will be examined using advanced mixed modeling techniques. CONCLUSIONS Results should advance AUD treatment by targeting treatments to neurocognitive MOBC.
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Affiliation(s)
- Barbara S McCrady
- Center on Alcohol, Substance use, And Addictions; Department of Psychology, University of New Mexico, United States.
| | - Eric Claus
- Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106, United States; The Pennsylvania State University, United States
| | - Katie Witkiewitz
- Center on Alcohol, Substance use, And Addictions; Department of Psychology, University of New Mexico, United States
| | - Alicia Shiver
- Center on Alcohol, Substance use, And Addictions; Department of Psychology, University of New Mexico, United States
| | - Megan Swartz
- Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106, United States
| | - Roberta Chávez
- Center on Alcohol, Substance use, And Addictions; Department of Psychology, University of New Mexico, United States
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15
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Huang R, Wang A, Zhang Y, Li G, Lin Y, Ba X, Bao X, Li Y, Zhang G. Alterations of the cerebral microstructure in patients with noise-induced hearing loss: A diffusion tensor imaging study. Brain Behav 2024; 14:e3479. [PMID: 38648388 PMCID: PMC11034863 DOI: 10.1002/brb3.3479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 02/16/2024] [Accepted: 02/27/2024] [Indexed: 04/25/2024] Open
Abstract
OBJECTIVE To explore the changes in the cerebral microstructure of patients with noise-induced hearing loss (NIHL) using diffusion tensor imaging (DTI). METHOD Overall, 122 patients with NIHL (mild [MP, n = 79], relatively severe patients [including moderate and severe; RSP, n = 32], and undetermined [lost to follow-up, n = 11]) and 84 healthy controls (HCs) were enrolled. All clinical data, including age, education level, hearing threshold, occupation type, noise exposure time, and some scale scores (including the Mini-Mental State Examination [MMSE], tinnitus handicap inventory [THI], and Hamilton Anxiety Scale [HAMA]), were collected and analyzed. All participants underwent T1WI3DFSPGR and DTI, and tract-based spatial statistics and region of interest (ROI) analysis were used for assessment. RESULTS The final sample included 71 MP, 28 RSP, and 75 HCs. The HAMA scores of the three groups were significantly different (p < .05). The noise exposure times, hearing thresholds, and HAMA scores of the MP and RSP were significantly different (p < .05). The noise exposure time was positively correlated with the hearing threshold and negatively correlated with the HAMA scores (p < .05), whereas the THI scores were positively correlated with the hearing threshold (p < .05). DTI analysis showed that all DTI parameters (fractional anisotropy [FA], axial diffusivity [AD], mean diffusivity [MD], and radial diffusivity [RD]) were significantly different in the left inferior longitudinal fasciculus (ILF) and left inferior fronto-occipital fasciculus (IFOF) for the three groups (p < .05). In addition, the FA values were significantly lower in the bilateral corticospinal tract (CST), right fronto-pontine tract (FPT), right forceps major, left superior longitudinal fasciculus (temporal part) (SLF), and left cingulum (hippocampus) (C-H) of the MP and RSP than in those of the HCs (p < .05); the AD values showed diverse changes in the bilateral CST, left IFOF, right anterior thalamic radiation, right external capsule (EC), right SLF, and right superior cerebellar peduncle (SCP) of the MP and RSP relative to those of the HC (p < .05). However, there were no significant differences among the bilateral auditory cortex ROIs of the three groups (p > .05). There was a significant negative correlation between the FA and HAMA scores for the left IFOF/ILF, right FPT, left SLF, and left C-H for the three groups (p < .05). There was a significant positive correlation between the AD and HAMA scores for the left IFOF/ILF and right EC of the three groups (p < .05). There were significantly positive correlations between the RD/MD and HAMA scores in the left IFOF/ILF of the three groups (p < .05). There was a significant negative correlation between the AD in the right SCP and noise exposure time of the MP and RSP groups (p < .05). The AD, MD, and RD in the left ROI were significantly positively correlated with hearing threshold in the MP and RSP groups (p < .05), whereas FA in the right ROI was significantly positively correlated with the HAMA scores for the three groups (p < .05). CONCLUSION The changes in the white matter (WM) microstructure may be related to hearing loss caused by noise exposure, and the WM structural abnormalities in patients with NIHL were mainly located in the syndesmotic fibers of the temporooccipital region, which affected the auditory and language pathways. This confirmed that the auditory pathways have abnormal structural connectivity in patients with NIHL.
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Affiliation(s)
- Ranran Huang
- Department of RadiologyYantaishan HospitalYantaiP. R. China
| | - Aijie Wang
- Department of RadiologyYantaishan HospitalYantaiP. R. China
| | - Yafei Zhang
- Department of RadiologyYantaishan HospitalYantaiP. R. China
| | - Guochao Li
- Department of RadiologyYantaishan HospitalYantaiP. R. China
| | - Yi Lin
- Department of RadiologyYantaishan HospitalYantaiP. R. China
| | - Xinru Ba
- Department of RadiologyYantaishan HospitalYantaiP. R. China
| | - Xianghua Bao
- Department of OccupationalYantaishan HospitalYantaiP. R. China
| | - Yunxin Li
- Department of RadiologyYantaishan HospitalYantaiP. R. China
| | - Guowei Zhang
- Department of RadiologyYantaishan HospitalYantaiP. R. China
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16
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Papazoglou S, Ashtarayeh M, Oeschger JM, Callaghan MF, Does MD, Mohammadi S. Insights and improvements in correspondence between axonal volume fraction measured with diffusion-weighted MRI and electron microscopy. NMR IN BIOMEDICINE 2024; 37:e5070. [PMID: 38098204 DOI: 10.1002/nbm.5070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 09/25/2023] [Accepted: 10/19/2023] [Indexed: 02/17/2024]
Abstract
Biophysical diffusion-weighted imaging (DWI) models are increasingly used in neuroscience to estimate the axonal water fraction (f AW ), which in turn is key for noninvasive estimation of the axonal volume fraction (f A ). These models require thorough validation by comparison with a reference method, for example, electron microscopy (EM). While EM studies often neglect the unmyelinated axons and solely report the fraction of myelinated axons, in DWI both myelinated and unmyelinated axons contribute to the DWI signal. However, DWI models often include simplifications, for example, the neglect of differences in the compartmental relaxation times or fixed diffusivities, which in turn might affect the estimation off AW . We investigate whether linear calibration parameters (scaling and offset) can improve the comparability between EM- and DWI-based metrics off A . To this end, we (a) used six DWI models based on the so-called standard model of white matter (WM), including two models with fixed compartmental diffusivities (e.g., neurite orientation dispersion and density imaging, NODDI) and four models that fitted the compartmental diffusivities (e.g., white matter tract integrity, WMTI), and (b) used a multimodal data set including ex vivo diffusion DWI and EM data in mice with a broad dynamic range of fibre volume metrics. We demonstrated that the offset is associated with the volume fraction of unmyelinated axons and the scaling factor is associated with different compartmentalT 2 and can substantially enhance the comparability between EM- and DWI-based metrics off A . We found that DWI models that fitted compartmental diffusivities provided the most accurate estimates of the EM-basedf A . Finally, we introduced a more efficient hybrid calibration approach, where only the offset is estimated but the scaling is fixed to a theoretically predicted value. Using this approach, a similar one-to-one correspondence to EM was achieved for WMTI. The method presented can pave the way for use of validated DWI-based models in clinical research and neuroscience.
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Affiliation(s)
- Sebastian Papazoglou
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Max Planck Research Group MR Physics, Max Planck Institute for Human Development, Berlin, Germany
| | - Mohammad Ashtarayeh
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jan Malte Oeschger
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Mark D Does
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Electrical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Siawoosh Mohammadi
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Max Planck Research Group MR Physics, Max Planck Institute for Human Development, Berlin, Germany
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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17
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Tato-Fernández C, Ekblad LL, Pietilä E, Saunavaara V, Helin S, Parkkola R, Zetterberg H, Blennow K, Rinne JO, Snellman A. Cognitively healthy APOE4/4 carriers show white matter impairment associated with serum NfL and amyloid-PET. Neurobiol Dis 2024; 192:106439. [PMID: 38365046 DOI: 10.1016/j.nbd.2024.106439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/09/2024] [Accepted: 02/13/2024] [Indexed: 02/18/2024] Open
Abstract
Except for aging, carrying the APOE ε4 allele (APOE4) is the most important risk factor for sporadic Alzheimer's disease. APOE4 carriers may have reduced capacity to recycle lipids, resulting in white matter microstructural abnormalities. In this study, we evaluated whether white matter impairment measured by diffusion tensor imaging (DTI) differs between healthy individuals with a different number of APOE4 alleles, and whether white matter impairment associates with brain beta-amyloid (Aβ) load and serum levels of neurofilament light chain (NfL). We studied 96 participants (APOE3/3, N = 37; APOE3/4, N = 39; APOE4/4, N = 20; mean age 70.7 (SD 5.22) years, 63% females) with a brain MRI including a DTI sequence (N = 96), Aβ-PET (N = 89) and a venous blood sample for the serum NfL concentration measurement (N = 88). Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AxD) in six a priori-selected white matter regions-of-interest (ROIs) were compared between the groups using ANCOVA, with sex and age as covariates. A voxel-weighted average of FA, MD, RD and AxD was calculated for each subject, and correlations with Aβ-PET and NfL levels were evaluated. APOE4/4 carriers exhibited a higher MD and a higher RD in the body of corpus callosum than APOE3/4 (p = 0.0053 and p = 0.0049, respectively) and APOE3/3 (p = 0.026 and p = 0.042). APOE4/4 carriers had a higher AxD than APOE3/4 (p = 0.012) and APOE3/3 (p = 0.040) in the right cingulum adjacent to cingulate cortex. In the total sample, composite MD, RD and AxD positively correlated with the cortical Aβ load (r = 0.26 to 0.33, p < 0.013 for all) and with serum NfL concentrations (r = 0.31 to 0.36, p < 0.0028 for all). In conclusion, increased local diffusivity was detected in cognitively unimpaired APOE4/4 homozygotes compared to APOE3/4 and APOE3/3 carriers, and increased diffusivity correlated with biomarkers of Alzheimer's disease and neurodegeneration. White matter impairment seems to be an early phenomenon in the Alzheimer's disease pathologic process in APOE4/4 homozygotes.
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Affiliation(s)
- Claudia Tato-Fernández
- Turku PET Centre, Turku University Hospital, Turku, Finland; Turku PET Centre, University of Turku, Turku, Finland.
| | - Laura L Ekblad
- Turku PET Centre, Turku University Hospital, Turku, Finland; Turku PET Centre, University of Turku, Turku, Finland; Department of Geriatric Medicine, Turku University Hospital, Turku, Finland
| | - Elina Pietilä
- Turku PET Centre, Turku University Hospital, Turku, Finland; Turku PET Centre, University of Turku, Turku, Finland
| | - Virva Saunavaara
- Turku PET Centre, Turku University Hospital, Turku, Finland; Turku PET Centre, University of Turku, Turku, Finland; Department of Medical Physics, Division of Medical Imaging, Turku University Hospital, Finland
| | - Semi Helin
- Turku PET Centre, University of Turku, Turku, Finland
| | - Riitta Parkkola
- Department of Radiology, Turku University Hospital, Turku, Finland; Department of Radiology, University of Turku, Turku, Finland
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK; UK Dementia Research Institute at UCL, London, UK; Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Paris Brain Institute, ICM, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France; Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, Department of Neurology, Institute on Aging and Brain Disorders, University of Science and Technology of China and First Affiliated Hospital of USTC, Hefei, PR China
| | - Juha O Rinne
- Turku PET Centre, Turku University Hospital, Turku, Finland; Turku PET Centre, University of Turku, Turku, Finland; InFLAMES Research Flagship, University of Turku, Turku, Finland
| | - Anniina Snellman
- Turku PET Centre, Turku University Hospital, Turku, Finland; Turku PET Centre, University of Turku, Turku, Finland
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Jochems ACC, Muñoz Maniega S, Clancy U, Arteaga C, Jaime Garcia D, Chappell FM, Hewins W, Locherty R, Backhouse EV, Barclay G, Jardine C, McIntyre D, Gerrish I, Kampaite A, Sakka E, Valdés Hernández M, Wiseman S, Bastin ME, Stringer MS, Thrippleton MJ, Doubal FN, Wardlaw JM. Magnetic Resonance Imaging Tissue Signatures Associated With White Matter Changes Due to Sporadic Cerebral Small Vessel Disease Indicate That White Matter Hyperintensities Can Regress. J Am Heart Assoc 2024; 13:e032259. [PMID: 38293936 PMCID: PMC11056146 DOI: 10.1161/jaha.123.032259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 12/21/2023] [Indexed: 02/01/2024]
Abstract
BACKGROUND White matter hyperintensities (WMHs) might regress and progress contemporaneously, but we know little about underlying mechanisms. We examined WMH change and underlying quantitative magnetic resonance imaging tissue measures over 1 year in patients with minor ischemic stroke with sporadic cerebral small vessel disease. METHODS AND RESULTS We defined areas of stable normal-appearing white matter, stable WMHs, progressing and regressing WMHs based on baseline and 1-year brain magnetic resonance imaging. In these areas we assessed tissue characteristics with quantitative T1, fractional anisotropy (FA), mean diffusivity (MD), and neurite orientation dispersion and density imaging (baseline only). We compared tissue signatures cross-sectionally between areas, and longitudinally within each area. WMH change masks were available for N=197. Participants' mean age was 65.61 years (SD, 11.10), 59% had a lacunar infarct, and 68% were men. FA and MD were available for N=195, quantitative T1 for N=182, and neurite orientation dispersion and density imaging for N=174. Cross-sectionally, all 4 tissue classes differed for FA, MD, T1, and Neurite Density Index. Longitudinally, in regressing WMHs, FA increased with little change in MD and T1 (difference estimate, 0.011 [95% CI, 0.006-0.017]; -0.002 [95% CI, -0.008 to 0.003] and -0.003 [95% CI, -0.009 to 0.004]); in progressing and stable WMHs, FA decreased (-0.022 [95% CI, -0.027 to -0.017] and -0.009 [95% CI, -0.011 to -0.006]), whereas MD and T1 increased (progressing WMHs, 0.057 [95% CI, 0.050-0.063], 0.058 [95% CI, 0.050 -0.066]; stable WMHs, 0.054 [95% CI, 0.045-0.063], 0.049 [95% CI, 0.039-0.058]); and in stable normal-appearing white matter, MD increased (0.004 [95% CI, 0.003-0.005]), whereas FA and T1 slightly decreased and increased (-0.002 [95% CI, -0.004 to -0.000] and 0.005 [95% CI, 0.001-0.009]). CONCLUSIONS Quantitative magnetic resonance imaging shows that WMHs that regress have less abnormal microstructure at baseline than stable WMHs and follow trajectories indicating tissue improvement compared with stable and progressing WMHs.
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Affiliation(s)
- Angela C. C. Jochems
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Susana Muñoz Maniega
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Una Clancy
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Carmen Arteaga
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Daniela Jaime Garcia
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Francesca M. Chappell
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Will Hewins
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Rachel Locherty
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Ellen V. Backhouse
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Gayle Barclay
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Charlotte Jardine
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Donna McIntyre
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Iona Gerrish
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Agniete Kampaite
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
| | - Eleni Sakka
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
| | - Maria Valdés Hernández
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Stewart Wiseman
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Mark E. Bastin
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
| | - Michael S. Stringer
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Michael J. Thrippleton
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Fergus N. Doubal
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Joanna M. Wardlaw
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
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Qiang Y, Deng Y, Yu J. Associations of blood cell indices and anemia with risk of incident dementia. Alzheimers Dement 2024; 20:1466-1467. [PMID: 38009709 PMCID: PMC10916996 DOI: 10.1002/alz.13548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 11/29/2023]
Affiliation(s)
- Yi‐Xuan Qiang
- Department of Neurology and National Center for Neurological DisordersHuashan HospitalState Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceShanghai Medical College, Fudan UniversityShanghaiChina
| | - Yue‐Ting Deng
- Department of Neurology and National Center for Neurological DisordersHuashan HospitalState Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceShanghai Medical College, Fudan UniversityShanghaiChina
| | - Jin‐Tai Yu
- Department of Neurology and National Center for Neurological DisordersHuashan HospitalState Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceShanghai Medical College, Fudan UniversityShanghaiChina
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Hafiz R, Irfanoglu MO, Nayak A, Pierpaoli C. "Pscore": A Novel Percentile-Based Metric to Accurately Assess Individual Deviations in Non-Gaussian Distributions of Quantitative MRI Metrics. J Magn Reson Imaging 2024. [PMID: 38291798 DOI: 10.1002/jmri.29248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/08/2024] [Accepted: 01/09/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND Quantitative magnetic resonance imaging (MRI) metrics could be used in personalized medicine to assess individuals against normative distributions. Conventional Zscore analysis is inadequate in the presence of non-Gaussian distributions. Therefore, if quantitative MRI metrics deviate from normality, an alternative is needed. PURPOSE To confirm non-Gaussianity of diffusion MRI (dMRI) metrics on a publicly available dataset, and to propose a novel percentile-based method, "Pscore" to address this issue. STUDY TYPE Retrospective cohort. POPULATION Nine hundred and sixty-one healthy young adults (age: 22-35 years, females: 53%) from the Human Connectome Project. FIELD STRENGTH/SEQUENCE 3-T, spin-echo diffusion echo-planar imaging, T1-weighted: MPRAGE. ASSESSMENT The dMRI data were preprocessed using the TORTOISE pipeline. Forty-eight regions of interest (ROIs) from the JHU atlas were redrawn on a study-specific diffusion tensor (DT) template and average values were computed from various DT and mean apparent propagator (MAP) metrics. For each ROI, percentile ranks across participants were computed to generate "Pscores"-which normalized the difference between the median and a participant's value with the corresponding difference between the median and the 5th/95th percentile values. STATISTICAL TESTS ROI-wise distributions were assessed using log transformations, Zscore, and the "Pscore" methods. The percentages of extreme values above-95th and below-5th percentile boundaries (PEV>95 (%), PEV<5 (%)) were also assessed in the overall white matter. Bootstrapping was performed to test the reliability of Pscores in small samples (N = 100) using 100 iterations. RESULTS The dMRI metric distributions were systematically non-Gaussian, including positively skewed (eg, mean and radial diffusivity) and negatively skewed (eg, fractional and propagator anisotropy) metrics. This resulted in unbalanced tails in Zscore distributions (PEV>95 ≠ 5%, PEV<5 ≠ 5%) whereas "Pscore" distributions were symmetric and balanced (PEV>95 = PEV<5 = 5%); even for small bootstrapped samples (averagePEV > 95 ¯ = PEV < 5 ¯ = 5 ± 0 % $$ \overline{{\mathrm{PEV}}_{>95}}=\overline{{\mathrm{PEV}}_{<5}}=5\pm 0\% $$ [SD]). DATA CONCLUSION The inherent skewness observed for dMRI metrics may preclude the use of conventional Zscore analysis. The proposed "Pscore" method may help estimating individual deviations more accurately in skewed normative data, even from small datasets. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Rakibul Hafiz
- Laboratory on Quantitative Medical Imaging, National Institute of Biomedical Imaging and Bioengineering, Bethesda, Maryland, USA
| | - M Okan Irfanoglu
- Laboratory on Quantitative Medical Imaging, National Institute of Biomedical Imaging and Bioengineering, Bethesda, Maryland, USA
| | - Amritha Nayak
- Laboratory on Quantitative Medical Imaging, National Institute of Biomedical Imaging and Bioengineering, Bethesda, Maryland, USA
- Military Traumatic Brain Injury Initiative (MTBI2-formerly known as the Center for Neuroscience and Regenerative Medicine [CNRM]), Bethesda, Maryland, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA
| | - Carlo Pierpaoli
- Laboratory on Quantitative Medical Imaging, National Institute of Biomedical Imaging and Bioengineering, Bethesda, Maryland, USA
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21
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Rivier CA, Renedo DB, de Havenon A, Sunmonu NA, Gill TM, Payabvash S, Sheth KN, Falcone GJ. Association of Poor Oral Health With Neuroimaging Markers of White Matter Injury in Middle-Aged Participants in the UK Biobank. Neurology 2024; 102:e208010. [PMID: 38165331 PMCID: PMC10870735 DOI: 10.1212/wnl.0000000000208010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 10/03/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Poor oral health is a modifiable risk factor that is associated with clinically observed cardiovascular disease. However, the relationship between oral and brain health is not well understood. We tested the hypothesis that poor oral health is associated with worse neuroimaging brain health profiles in middle-aged persons without stroke or dementia. METHODS We performed a 2-stage cross-sectional neuroimaging study using UK Biobank data. First, we tested for association between self-reported poor oral health and MRI neuroimaging markers of brain health. Second, we used Mendelian randomization (MR) analyses to test for association between genetically determined poor oral health and the same neuroimaging markers. Poor oral health was defined as the presence of dentures or loose teeth. As instruments for the MR analysis, we used 116 independent DNA sequence variants linked to increased composite risk of dentures or teeth that are decayed, missing, or filled. Neuroimaging markers of brain health included white matter hyperintensity (WMH) volume and aggregate measures of fractional anisotropy (FA) and mean diffusivity (MD), 2 metrics indicative of white matter tract disintegrity obtained through diffusion tensor imaging across 48 brain regions. RESULTS We included 40,175 persons (mean age 55 years, female sex 53%) enrolled from 2006 to 2010, who underwent a dedicated research brain MRI between 2014 and 2016. Among participants, 5,470 (14%) had poor oral health. Poor oral health was associated with a 9% increase in WMH volume (β = 0.09, SD = 0.014, p < 0.001), 10% change in aggregate FA score (β = 0.10, SD = 0.013, p < 0.001), and 5% change in aggregate MD score (β = 0.05, SD = 0.013, p < 0.001). Genetically determined poor oral health was associated with a 30% increase in WMH volume (β = 0.30, SD = 0.06, p < 0.001), 43% change in aggregate FA score (β = 0.43, SD = 0.06, p < 0.001), and 10% change in aggregate MD score (β = 0.10, SD = 0.03, p < 0.01). DISCUSSION Among middle age Britons without stroke or dementia, poor oral health was associated with worse neuroimaging brain health profiles. Genetic analyses confirmed these associations, supporting a potentially causal association. Because the neuroimaging markers evaluated in this study precede and are established risk factors of stroke and dementia, our results suggest that oral health, an easily modifiable process, may be a promising target for very early interventions focused on improving brain health.
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Affiliation(s)
- Cyprien A Rivier
- From the Departments of Neurology (C.A.R., D.B.R., A.d.H., N.A.S., K.N.S., G.J.F.), Internal Medicine (T.M.G.), and Radiology (S.P.), Yale University School of Medicine; and Yale Center for Brain and Mind Health (C.A.R., A.d.H., S.P., K.N.S., G.J.F.), New Haven, CT
| | - Daniela B Renedo
- From the Departments of Neurology (C.A.R., D.B.R., A.d.H., N.A.S., K.N.S., G.J.F.), Internal Medicine (T.M.G.), and Radiology (S.P.), Yale University School of Medicine; and Yale Center for Brain and Mind Health (C.A.R., A.d.H., S.P., K.N.S., G.J.F.), New Haven, CT
| | - Adam de Havenon
- From the Departments of Neurology (C.A.R., D.B.R., A.d.H., N.A.S., K.N.S., G.J.F.), Internal Medicine (T.M.G.), and Radiology (S.P.), Yale University School of Medicine; and Yale Center for Brain and Mind Health (C.A.R., A.d.H., S.P., K.N.S., G.J.F.), New Haven, CT
| | - N Abimbola Sunmonu
- From the Departments of Neurology (C.A.R., D.B.R., A.d.H., N.A.S., K.N.S., G.J.F.), Internal Medicine (T.M.G.), and Radiology (S.P.), Yale University School of Medicine; and Yale Center for Brain and Mind Health (C.A.R., A.d.H., S.P., K.N.S., G.J.F.), New Haven, CT
| | - Thomas M Gill
- From the Departments of Neurology (C.A.R., D.B.R., A.d.H., N.A.S., K.N.S., G.J.F.), Internal Medicine (T.M.G.), and Radiology (S.P.), Yale University School of Medicine; and Yale Center for Brain and Mind Health (C.A.R., A.d.H., S.P., K.N.S., G.J.F.), New Haven, CT
| | - Seyedmehdi Payabvash
- From the Departments of Neurology (C.A.R., D.B.R., A.d.H., N.A.S., K.N.S., G.J.F.), Internal Medicine (T.M.G.), and Radiology (S.P.), Yale University School of Medicine; and Yale Center for Brain and Mind Health (C.A.R., A.d.H., S.P., K.N.S., G.J.F.), New Haven, CT
| | - Kevin N Sheth
- From the Departments of Neurology (C.A.R., D.B.R., A.d.H., N.A.S., K.N.S., G.J.F.), Internal Medicine (T.M.G.), and Radiology (S.P.), Yale University School of Medicine; and Yale Center for Brain and Mind Health (C.A.R., A.d.H., S.P., K.N.S., G.J.F.), New Haven, CT
| | - Guido J Falcone
- From the Departments of Neurology (C.A.R., D.B.R., A.d.H., N.A.S., K.N.S., G.J.F.), Internal Medicine (T.M.G.), and Radiology (S.P.), Yale University School of Medicine; and Yale Center for Brain and Mind Health (C.A.R., A.d.H., S.P., K.N.S., G.J.F.), New Haven, CT
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Robinson TD, Chad JA, Sun YL, Chang PTH, Chen JJ. Developmental order, fibre caliber, and vascularization predict tract-wise declines: Testing retrogenesis and physiological predictions in white matter aging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.20.576373. [PMID: 38328223 PMCID: PMC10849490 DOI: 10.1101/2024.01.20.576373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
To understand the consistently observed spatial distribution of white-matter (WM) aging, developmentally driven theories of retrogenesis have gained traction, positing that the order WM development predicts declines. Regions that develop first are often expected to deteriorate the last, i.e. "last-in-first-out". Alternatively, regions which develop most rapidly may also decline most rapidly in aging, or the "gains-predict-loss" model. The validity of such theories remains uncertain, in part due to lack of clarity on the definition of developmental order. Our recent findings also suggest that WM degeneration may vary by physiological parameters such as perfusion. Furthermore, it is informative to link perfusion to fibre metabolic need, which varies with fibre size. Here we address the question of whether WM degeneration is determined by development trajectory or physiological state across both microstructural and perfusion measures using data drawn from the Human Connectome Project in Aging (HCP-A). Our results indicate that developmental order of tract myelination provides the strongest support for the retrogenesis hypothesis, with the last to complete myelination the first to decline. Moreover, higher mean axon diameter and lower macrovascular density are associated with lower degrees of WM degeneration across measures. Tract perfusion, in turn also tends to be higher and the arterial transit time longer for tracts that appear first. These findings suggest that WM degeneration in different tracts may be governed by their developmental trajectories and physiology, and ultimately influenced by each tract's metabolic demand.
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Burzynska AZ, Anderson C, Arciniegas DB, Calhoun V, Choi IY, Mendez Colmenares A, Kramer AF, Li K, Lee J, Lee P, Thomas ML. Correlates of axonal content in healthy adult span: Age, sex, myelin, and metabolic health. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2024; 6:100203. [PMID: 38292016 PMCID: PMC10827486 DOI: 10.1016/j.cccb.2024.100203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 02/01/2024]
Abstract
As the emerging treatments that target grey matter pathology in Alzheimer's Disease have limited effectiveness, there is a critical need to identify new neural targets for treatments. White matter's (WM) metabolic vulnerability makes it a promising candidate for new interventions. This study examined the age and sex differences in estimates of axonal content, as well the associations of with highly prevalent modifiable health risk factors such as metabolic syndrome and adiposity. We estimated intra-axonal volume fraction (ICVF) using the Neurite Orientation Dispersion and Density Imaging (NODDI) in a sample of 89 cognitively and neurologically healthy adults (20-79 years). We showed that ICVF correlated positively with age and estimates of myelin content. The ICVF was also lower in women than men, across all ages, which difference was accounted for by intracranial volume. Finally, we found no association of metabolic risk or adiposity scores with the current estimates of ICVF. In addition, the previously observed adiposity-myelin associations (Burzynska et al., 2023) were independent of ICVF. Although our findings confirm the vulnerability of axons to aging, they suggest that metabolic dysfunction may selectively affect myelin content, at least in cognitively and neurologically healthy adults with low metabolic risk, and when using the specific MRI techniques. Future studies need to revisit our findings using larger samples and different MRI approaches, and identify modifiable factors that accelerate axonal deterioration as well as mechanisms linking peripheral metabolism with the health of myelin.
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Affiliation(s)
- Agnieszka Z Burzynska
- The BRAiN lab, Department of Human Development and Family Studies/Molecular, Cellular and Integrative Neurosciences, Colorado State University, Fort Collins, CO, USA
| | - Charles Anderson
- Department of Computer Science, Colorado State University, Fort Collins, CO, USA
| | - David B. Arciniegas
- Marcus Institute for Brain Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - In-Young Choi
- Department of Neurology, Department of Radiology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, USA
| | - Andrea Mendez Colmenares
- The BRAiN lab, Department of Human Development and Family Studies/Molecular, Cellular and Integrative Neurosciences, Colorado State University, Fort Collins, CO, USA
| | - Arthur F Kramer
- Beckman Institute for Advanced Science and Technology at the University of Illinois, IL, USA
- Center for Cognitive & Brain Health, Northeastern University, Boston, MA, USA
| | - Kaigang Li
- Department of Health and Exercise Science, Colorado State University, Fort Collins, CO, USA
| | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Phil Lee
- Department of Radiology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, USA
| | - Michael L Thomas
- Department of Psychology, Colorado State University, Fort Collins, CO, USA
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Hafiz R, Irfanoglu MO, Nayak A, Pierpaoli C. 'Pscore' - A Novel Percentile-Based Metric to Accurately Assess Individual Deviations in Non-Gaussian Distributions of Quantitative MRI Metrics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.10.571016. [PMID: 38105995 PMCID: PMC10723480 DOI: 10.1101/2023.12.10.571016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
BACKGROUND Quantitative MRI metrics could be used in personalized medicine to assess individuals against normative distributions. Conventional Zscore analysis is inadequate in the presence of non-Gaussian distributions. Therefore, if quantitative MRI metrics deviate from normality, an alternative is needed. PURPOSE To confirm non-Gaussianity of diffusion MRI (dMRI) metrics on a publicly available dataset, and to propose a novel percentile-based method, 'Pscore' to address this issue. STUDY TYPE Retrospective cohort. POPULATION 961 healthy young-adults (age:22-35 years, Females:53%) from the Human Connectome Project. FIELD STRENGTH/SEQUENCE 3-T, spin-echo diffusion echo-planar imaging, T1-weighted: MPRAGE. ASSESSMENT The dMRI data were preprocessed using the TORTOISE pipeline. Forty-eight regions of interest (ROIs) from the JHU-atlas were redrawn on a study-specific diffusion tensor (DT) template and average values were computed from various DT and mean apparent propagator (MAP) metrics. For each ROI, percentile ranks across participants were computed to generate 'Pscores'- which normalized the difference between the median and a participant's value with the corresponding difference between the median and the 5th/95th percentile values. STATISTICAL TESTS ROI-wise distributions were assessed using Log transformations, Zscore, and the 'Pscore' methods. The percentages of extreme values above-95th and below-5th percentile boundaries (PEV < 5 ( % ) ,PEV < 5 ( % ) ) were also assessed in the overall white matter. Bootstrapping was performed to test the reliability of Pscores in small samples (n=100) using 100 iterations. RESULTS The dMRI metric distributions were systematically non-Gaussian, including positively skewed (e.g., mean and radial distributions P E V > 95 ≠ 5 % , P E V < 5 ≠ 5 % whereas 'Pscore' distributions were symmetric and balanced P E V > 95 = P E V < 5 = 5 % ; even for small bootstrapped samples (average P E V > 95 ¯ = P E V < 5 ¯ = 5 ± 0 % S D ). DATA CONCLUSION The inherent skewness observed for dMRI metrics may preclude the use of conventional Zscore analysis. The proposed 'Pscore' method may help estimating individual deviations more accurately in skewed normative data, even from small datasets.
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Affiliation(s)
- Rakibul Hafiz
- Laboratory on Quantitative Medical Imaging, National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD
| | - M. Okan Irfanoglu
- Laboratory on Quantitative Medical Imaging, National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD
| | - Amritha Nayak
- Laboratory on Quantitative Medical Imaging, National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD
- Military Traumatic Brain Injury Initiative (MTBI2 – formerly known as the Center for Neuroscience and Regenerative Medicine [CNRM]) Bethesda, MD
- The Henry Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD
| | - Carlo Pierpaoli
- Laboratory on Quantitative Medical Imaging, National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD
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Kraeutner SN, Rubino C, Ferris JK, Rinat S, Penko L, Chiu L, Greeley B, Jones CB, Larssen BC, Boyd LA. Frontoparietal function and underlying structure reflect capacity for motor skill acquisition during healthy aging. Neurobiol Aging 2024; 133:78-86. [PMID: 37918189 DOI: 10.1016/j.neurobiolaging.2023.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 09/22/2023] [Accepted: 10/17/2023] [Indexed: 11/04/2023]
Abstract
While capacity for motor skill acquisition changes with healthy aging, there has been little consideration of how age-related changes in brain function or baseline brain structure support motor skill acquisition. We examined: (1) age-dependent changes in functional reorganization related to frontoparietal regions during motor skill acquisition, and (2) whether capacity for motor skill acquisition relates to baseline white matter microstructure in frontoparietal tracts. Healthy older and younger adults engaged in 4 weeks of skilled motor practice. Resting-state functional connectivity (rsFC) assessed functional reorganization before and after practice. Diffusion tensor imaging indexed microstructure of a frontoparietal tract at baseline, generated by rsFC seeds. Motor skill acquisition was associated with decreases in rsFC in healthy older adults and increases in rsFC in healthy younger adults. Frontoparietal tract microstructure was lower in healthy older versus younger adults, yet it was negatively associated with rate of skill acquisition regardless of group. Findings indicate that age-dependent alterations in frontoparietal function and baseline structure of a frontoparietal tract reflect capacity for motor skill acquisition.
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Affiliation(s)
- Sarah N Kraeutner
- Department of Psychology, University of British Columbia, Kelowna, British Columbia, Canada; Djavad Mowafaghian, Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada.
| | - Cristina Rubino
- Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Jennifer K Ferris
- Gerontology Research Centre, Simon Fraser University, Vancouver, British Columbia, Canada
| | - Shie Rinat
- Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Lauren Penko
- Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Larissa Chiu
- Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Brian Greeley
- Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Christina B Jones
- Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Beverley C Larssen
- Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Lara A Boyd
- Djavad Mowafaghian, Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada; Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada
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Blöchl M, Schaare HL, Kumral D, Gaebler M, Nestler S, Villringer A. Vascular risk factors, white matter microstructure, and depressive symptoms: a longitudinal analysis in the UK Biobank. Psychol Med 2024; 54:125-135. [PMID: 37016768 DOI: 10.1017/s0033291723000697] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
BACKGROUND Cumulative burden from vascular risk factors (VRFs) has been associated with an increased risk of depressive symptoms in mid- and later life. It has been hypothesised that this association arises because VRFs disconnect fronto-subcortical white matter tracts involved in mood regulation, which puts older adults at higher risk of developing depressive symptoms. However, evidence for the hypothesis that disconnection of white matter tracts underlies the association between VRF burden and depressive symptoms from longitudinal studies is scarce. METHODS This preregistered study analysed longitudinal data from 6,964 middle-aged and older adults from the UK Biobank who participated in consecutive assessments of VRFs, brain imaging, and depressive symptoms. Using mediation modelling, we directly tested to what extend white matter microstructure mediates the longitudinal association between VRF burden and depressive symptoms. RESULTS VRF burden showed a small association with depressive symptoms at follow-up. However, there was no evidence that fractional anisotropy (FA) of white matter tracts mediated this association. Additional analyses also yielded no mediating effects using alternative operationalisations of VRF burden, mean diffusivity (MD) of single tracts, or overall average of tract-based white matter microstructure (global FA, global MD, white matter hyperintensity volume). CONCLUSIONS Our results lend no support to the hypothesis that disconnection of white matter tracts underlies the association between VRF burden and depressive symptoms, while highlighting the relevance of using longitudinal data to directly test pathways linking vascular and mental health.
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Affiliation(s)
- Maria Blöchl
- Department for Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- International Max Planck Research School: Neuroscience of Communication: Structure, Function, and Plasticity, Leipzig, Germany
- Department of Psychology, University of Münster, Münster, Germany
| | - H Lina Schaare
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour) Research Centre Jülich, Germany
| | - Deniz Kumral
- Institute of Psychology, Neuropsychology, University of Freiburg, Freiburg, Germany
- Clinical Psychology and Psychotherapy Unit, Institute of Psychology, University of Freiburg, Freiburg, Germany
| | - Michael Gaebler
- Department for Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Faculty of Philosophy, Humboldt-Universität zu Berlin, Berlin School of Mind and Brain, MindBrainBody Institute
- Max Planck Dahlem Campus of Cognition, Berlin, Germany
| | - Steffen Nestler
- Department of Psychology, University of Münster, Münster, Germany
| | - Arno Villringer
- Department for Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University Clinic Leipzig, Leipzig, Germany
- Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
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Groechel RC, Alosco ML, Dixon D, Tripodis Y, Mez J, Goldstein L, Budson AE, Qiu WQ, Killiany RJ. Associations between white matter integrity of the cingulum bundle, surrounding gray matter regions, and cognition across the dementia continuum. J Comp Neurol 2023; 531:2162-2171. [PMID: 38010204 PMCID: PMC10841586 DOI: 10.1002/cne.25564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 10/19/2023] [Accepted: 10/21/2023] [Indexed: 11/29/2023]
Abstract
INTRODUCTION Previous Alzheimer's disease and related dementias (AD/ADRD) research studies have illustrated the significance of studying alterations in white matter (WM). Fewer studies have examined how WM integrity, measured with diffusion tensor imaging (DTI), is associated with volume of gray matter (GM) regions and measures of cognitive function in aged participants spanning the dementia continuum. METHODS Magnetic resonance imaging and cognitive data were collected from 241 Boston University Alzheimer's Disease Research Center participants who spanned from cognitively normal controls to amnestic mild cognitive impairment to having dementia. Primary DTI tracts of interest were the cingulum ventral (CV) and cingulum dorsal (CD) pathways. GM regions of interest (ROIs) were in the medial temporal lobe (MTL), prefrontal cortex, and retrosplenial cortex. Analyses of covariance models were used to assess differences in WM integrity across groups (control, amnestic mild cognitive impairment, and dementia). Multiple linear regression models were used to assess associations between WM integrity and GM volume, and with measures of memory and executive function. RESULTS Differences in WM integrity were shown in both cingulum pathways in participants across the dementia continuum. Associations between WM integrity of both cingulum pathways and volume of selected GM ROIs were widespread. Functionally significant associations were found between WM of the CV pathway and memory, independent of MTL GM volume. DISCUSSION Differences in WM integrity of the cingulum bundle and surrounding GM ROI are likely related to the progression of AD/ADRD. Such differences should continue to be studied, particularly in association with memory performance.
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Affiliation(s)
- Renée C. Groechel
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine
- National Institute of Neurological Disorders & Stroke Intramural Research Program
| | - Michael L. Alosco
- Boston University Alzheimer’s Disease Research Center
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine
- Boston University Chronic Traumatic Encephalopathy Center
| | - Diane Dixon
- Boston University Alzheimer’s Disease Research Center
- Biostatistics and Epidemiology Data Analytics Center, Boston University School of Public Health
| | - Yorghos Tripodis
- Boston University Alzheimer’s Disease Research Center
- Department of Biostatistics, Boston University School of Public Health
| | - Jesse Mez
- Boston University Alzheimer’s Disease Research Center
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine
- Boston University Chronic Traumatic Encephalopathy Center
| | - Lee Goldstein
- Boston University Alzheimer’s Disease Research Center
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine
| | - Andrew E. Budson
- Boston University Alzheimer’s Disease Research Center
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine
- Neurology Service, VA Boston Healthcare System
| | - Wei Qiao Qiu
- Boston University Alzheimer’s Disease Research Center
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine
| | - Ronald J. Killiany
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine
- Boston University Alzheimer’s Disease Research Center
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine
- Department of Environmental Health, Boston University School of Public Health
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Wang Y, Wen J, Xin J, Zhang Y, Xie H, Tang Y. 3DCNN predicting brain age using diffusion tensor imaging. Med Biol Eng Comput 2023; 61:3335-3344. [PMID: 37672142 DOI: 10.1007/s11517-023-02915-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 08/16/2023] [Indexed: 09/07/2023]
Abstract
Neuroimaging-based brain age prediction using deep learning is gaining popularity. However, few studies have attempted to leverage diffusion tensor imaging (DTI) to predict brain age. In this study, we proposed a 3D convolutional neural network model (3DCNN) and trained it on fractional anisotropy (FA) data from six publicly available datasets (n = 2406, age = 17-60) to estimate brain age. Implementing a two-loop nested cross-validation scheme with a tenfold cross-validation procedure, we achieved a robust prediction performance of a mean absolute error (MAE) of 2.785 and a correlation coefficient of 0.932. We also employed Grad-Cam++ to visualize the salient features of the proposed model. We identified a few highly salient fiber tracts, including the genu of corpus callosum and the left cerebellar peduncle, among others that play a pivotal role in our model. In sum, our model reliably predicted brain age and provided novel insight into age-related changes in brains' axonal structure.
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Affiliation(s)
- Yuqi Wang
- School of Computer Science and Engineering, Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Jingxi Wen
- School of Computer Science and Engineering, Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Jiang Xin
- School of Computer Science and Engineering, Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Yunhao Zhang
- School of Computer Science and Engineering, Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Hua Xie
- Department of Psychology, University of Maryland, College Park, MD, 20742, USA.
- Center for Neuroscience Research, Children's National Hospital, Washington, D.C, 20010, USA.
| | - Yan Tang
- School of Computer Science and Engineering, Central South University, Changsha, 410008, Hunan, People's Republic of China.
- Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004, Guangxi, People's Republic of China.
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Xu X, Lin L, Wu S, Sun S. Exploring Successful Cognitive Aging: Insights Regarding Brain Structure, Function, and Demographics. Brain Sci 2023; 13:1651. [PMID: 38137099 PMCID: PMC10741933 DOI: 10.3390/brainsci13121651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 11/24/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023] Open
Abstract
In the realm of cognitive science, the phenomenon of "successful cognitive aging" stands as a hallmark of individuals who exhibit cognitive abilities surpassing those of their age-matched counterparts. However, it is paramount to underscore a significant gap in the current research, which is marked by a paucity of comprehensive inquiries that deploy substantial sample sizes to methodically investigate the cerebral biomarkers and contributory elements underpinning this cognitive success. It is within this context that our present study emerges, harnessing data derived from the UK Biobank. In this study, a highly selective cohort of 1060 individuals aged 65 and above was meticulously curated from a larger pool of 17,072 subjects. The selection process was guided by their striking cognitive resilience, ascertained via rigorous evaluation encompassing both generic and specific cognitive assessments, compared to their peers within the same age stratum. Notably, the cognitive abilities of the chosen participants closely aligned with the cognitive acumen commonly observed in middle-aged individuals. Our study leveraged a comprehensive array of neuroimaging-derived metrics, obtained from three Tesla MRI scans (T1-weighted images, dMRI, and resting-state fMRI). The metrics included image-derived phenotypes (IDPs) that addressed grey matter morphology, the strength of brain network connectivity, and the microstructural attributes of white matter. Statistical analyses were performed employing ANOVA, Mann-Whitney U tests, and chi-square tests to evaluate the distinctive aspects of IDPs pertinent to the domain of successful cognitive aging. Furthermore, these analyses aimed to elucidate lifestyle practices that potentially underpin the maintenance of cognitive acumen throughout the aging process. Our findings unveiled a robust and compelling association between heightened cognitive aptitude and the integrity of white matter structures within the brain. Furthermore, individuals who exhibited successful cognitive aging demonstrated markedly enhanced activity in the cerebral regions responsible for auditory perception, voluntary motor control, memory retention, and emotional regulation. These advantageous cognitive attributes were mirrored in the health-related lifestyle choices of the surveyed cohort, characterized by elevated educational attainment, a lower incidence of smoking, and a penchant for moderate alcohol consumption. Moreover, they displayed superior grip strength and enhanced walking speeds. Collectively, these findings furnish valuable insights into the multifaceted determinants of successful cognitive aging, encompassing both neurobiological constituents and lifestyle practices. Such comprehensive comprehension significantly contributes to the broader discourse on aging, thereby establishing a solid foundation for the formulation of targeted interventions aimed at fostering cognitive well-being among aging populations.
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Affiliation(s)
- Xinze Xu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (X.X.); (S.W.); (S.S.)
| | - Lan Lin
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (X.X.); (S.W.); (S.S.)
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Shuicai Wu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (X.X.); (S.W.); (S.S.)
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Shen Sun
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (X.X.); (S.W.); (S.S.)
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
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Guan Y, Cheng CH, Bellomo LI, Narain S, Bigornia SJ, Garelnabi MO, Scott T, Ordovás JM, Tucker KL, Bhadelia R, Koo BB. APOE4 allele-specific associations between diet, multimodal biomarkers, and cognition among Puerto Rican adults in Massachusetts. Front Aging Neurosci 2023; 15:1285333. [PMID: 38035273 PMCID: PMC10684694 DOI: 10.3389/fnagi.2023.1285333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 10/25/2023] [Indexed: 12/02/2023] Open
Abstract
Background Apolipoprotein E (APOE) is the strongest genetic risk factor for sporadic Alzheimer's Disease (AD), and the ε4 allele (APOE4) may interact with lifestyle factors that relate to brain structural changes, underlying the increased risk of AD. However, the exact role of APOE4 in mediating interactions between the peripheral circulatory system and the central nervous system, and how it may link to brain and cognitive aging requires further elucidation. In this analysis, we investigated the association between APOE4 carrier status and multimodal biomarkers (diet, blood markers, clinical diagnosis, brain structure, and cognition) in the context of gene-environment interactions. Methods Participants were older adults from a longitudinal observational study, the Boston Puerto Rican Health Study (BPRHS), who self-identified as of Puerto Rican descent. Demographics, APOE genotype, diet, blood, and clinical data were collected at baseline and at approximately 12th year, with the addition of multimodal brain magnetic resonance imaging (MRI) (T1-weighted and diffusion) and cognitive testing acquired at 12-year. Measures were compared between APOE4 carriers and non-carriers, and associations between multimodal variables were examined using correlation and multivariate network analyses within each group. Results A total of 156 BPRHS participants (mean age at imaging = 68 years, 77% female, mean follow-up 12.7 years) with complete multimodal data were included in the current analysis. APOE4 carriers (n = 43) showed reduced medial temporal lobe (MTL) white matter (WM) microstructural integrity and lower mini-mental state examination (MMSE) score than non-carriers (n = 113). This pattern was consistent with an independent sample from the Alzheimer's Disease Neuroimaging Initiative (ADNI) of n = 283 non-Hispanic White adults without dementia (mean age = 75, 40% female). Within BPRHS, carriers showed distinct connectivity patterns between multimodal biomarkers, characterized by stronger direct network connections between baseline diet/blood markers with 12-year blood/clinical measures, and between blood markers (especially lipids and cytokines) and WM. Cardiovascular burden (i.e., hypertension and diabetes status) was associated with WM integrity for both carriers and non-carriers. Conclusion APOE4 carrier status affects interactions between dietary factors, multimodal blood biomarkers, and MTL WM integrity across ~12 years of follow-up, which may reflect increased peripheral-central systems crosstalk following blood-brain barrier breakdown in carriers.
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Affiliation(s)
- Yi Guan
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Chia Hsin Cheng
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Luis I. Bellomo
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Sriman Narain
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Sherman J. Bigornia
- Department of Agriculture, Nutrition, and Food Systems, College of Life Sciences and Agriculture, University of New Hampshire, Durham, NH, United States
| | - Mahdi O. Garelnabi
- Department of Public Health, Zuckerberg College of Health Sciences, University of Massachusetts Lowell, Lowell, MA, United States
| | - Tammy Scott
- School of Medicine, Tufts University, Boston, MA, United States
| | - José M. Ordovás
- Nutrition and Genomics Laboratory, J.M.-US Department of Agriculture Human Nutrition Research Center on Aging at Tufts University, Boston, MA, United States
- IMDEA Alimentacion, Madrid, Spain
- CIBER Fisiopatologia de la Obesidad y la Nutricion (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Katherine L. Tucker
- Department of Biomedical and Nutritional Sciences, Zuckerberg College of Health Sciences, University of Massachusetts Lowell, Lowell, MA, United States
- Center for Population Health, University of Massachusetts Lowell, Lowell, MA, United States
| | - Rafeeque Bhadelia
- Neuroradiology Section, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Bang-Bon Koo
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
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Wu Y, Ridwan AR, Niaz MR, Bennett DA, Arfanakis K. High resolution 0.5mm isotropic T 1-weighted and diffusion tensor templates of the brain of non-demented older adults in a common space for the MIITRA atlas. Neuroimage 2023; 282:120387. [PMID: 37783362 PMCID: PMC10625170 DOI: 10.1016/j.neuroimage.2023.120387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 09/22/2023] [Indexed: 10/04/2023] Open
Abstract
High quality, high resolution T1-weighted (T1w) and diffusion tensor imaging (DTI) brain templates located in a common space can enhance the sensitivity and precision of template-based neuroimaging studies. However, such multimodal templates have not been constructed for the older adult brain. The purpose of this work which is part of the MIITRA atlas project was twofold: (A) to develop 0.5 mm isotropic resolution T1w and DTI templates that are representative of the brain of non-demented older adults and are located in the same space, using advanced multimodal template construction techniques and principles of super resolution on data from a large, diverse, community cohort of 400 non-demented older adults, and (B) to systematically compare the new templates to other standardized templates. It was demonstrated that the new MIITRA-0.5mm T1w and DTI templates are well-matched in space, exhibit good definition of brain structures, including fine structures, exhibit higher image sharpness than other standardized templates, and are free of artifacts. The MIITRA-0.5mm T1w and DTI templates allowed higher intra-modality inter-subject spatial normalization precision as well as higher inter-modality intra-subject spatial matching of older adult T1w and DTI data compared to other available templates. Consequently, MIITRA-0.5mm templates allowed detection of smaller inter-group differences for older adult data compared to other templates. The MIITRA-0.5mm templates were also shown to be most representative of the brain of non-demented older adults compared to other templates with submillimeter resolution. The new templates constructed in this work constitute two of the final products of the MIITRA atlas project and are anticipated to have important implications for the sensitivity and precision of studies on older adults.
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Affiliation(s)
- Yingjuan Wu
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
| | - Abdur Raquib Ridwan
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
| | - Mohammad Rakeen Niaz
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States.
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Van Hoornweder S, Nuyts M, Frieske J, Verstraelen S, Meesen RLJ, Caulfield KA. Outcome measures for electric field modeling in tES and TMS: A systematic review and large-scale modeling study. Neuroimage 2023; 281:120379. [PMID: 37716590 PMCID: PMC11008458 DOI: 10.1016/j.neuroimage.2023.120379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/18/2023] [Accepted: 09/13/2023] [Indexed: 09/18/2023] Open
Abstract
BACKGROUND Electric field (E-field) modeling is a potent tool to estimate the amount of transcranial magnetic and electrical stimulation (TMS and tES, respectively) that reaches the cortex and to address the variable behavioral effects observed in the field. However, outcome measures used to quantify E-fields vary considerably and a thorough comparison is missing. OBJECTIVES This two-part study aimed to examine the different outcome measures used to report on tES and TMS induced E-fields, including volume- and surface-level gray matter, region of interest (ROI), whole brain, geometrical, structural, and percentile-based approaches. The study aimed to guide future research in informed selection of appropriate outcome measures. METHODS Three electronic databases were searched for tES and/or TMS studies quantifying E-fields. The identified outcome measures were compared across volume- and surface-level E-field data in ten tES and TMS modalities targeting two common targets in 100 healthy individuals. RESULTS In the systematic review, we extracted 308 outcome measures from 202 studies that adopted either a gray matter volume-level (n = 197) or surface-level (n = 111) approach. Volume-level results focused on E-field magnitude, while surface-level data encompassed E-field magnitude (n = 64) and normal/tangential E-field components (n = 47). E-fields were extracted in ROIs, such as brain structures and shapes (spheres, hexahedra and cylinders), or the whole brain. Percentiles or mean values were mostly used to quantify E-fields. Our modeling study, which involved 1,000 E-field models and > 1,000,000 extracted E-field values, revealed that different outcome measures yielded distinct E-field values, analyzed different brain regions, and did not always exhibit strong correlations in the same within-subject E-field model. CONCLUSIONS Outcome measure selection significantly impacts the locations and intensities of extracted E-field data in both tES and TMS E-field models. The suitability of different outcome measures depends on the target region, TMS/tES modality, individual anatomy, the analyzed E-field component and the research question. To enhance the quality, rigor, and reproducibility in the E-field modeling domain, we suggest standard reporting practices across studies and provide four recommendations.
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Affiliation(s)
- Sybren Van Hoornweder
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, University of Hasselt, Diepenbeek, Belgium.
| | - Marten Nuyts
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, University of Hasselt, Diepenbeek, Belgium
| | - Joana Frieske
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, University of Hasselt, Diepenbeek, Belgium; Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Stefanie Verstraelen
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, University of Hasselt, Diepenbeek, Belgium
| | - Raf L J Meesen
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, University of Hasselt, Diepenbeek, Belgium; Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Kevin A Caulfield
- Brain Stimulation Laboratory, Department of Psychiatry, Medical University of South Carolina, Charleston, SC, United States.
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Chad JA, Sochen N, Chen JJ, Pasternak O. Implications of fitting a two-compartment model in single-shell diffusion MRI. Phys Med Biol 2023; 68:10.1088/1361-6560/ad0216. [PMID: 37816373 PMCID: PMC10929942 DOI: 10.1088/1361-6560/ad0216] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 10/10/2023] [Indexed: 10/12/2023]
Abstract
It is becoming increasingly common for studies to fit single-shell diffusion MRI data to a two-compartment model, which comprises a hindered cellular compartment and a freely diffusing isotropic compartment. These studies consistently find that the fraction of the isotropic compartment (f) is sensitive to white matter (WM) conditions and pathologies, although the actual biological source of changes infhas not been validated. In this work we put aside the biological interpretation offand study the sensitivity implications of fitting single-shell data to a two-compartment model. We identify a nonlinear transformation between the one-compartment model (diffusion tensor imaging, DTI) and a two-compartment model in which the mean diffusivities of both compartments are effectively fixed. While the analytic relationship implies that fitting this two-compartment model does not offer any more information than DTI, it explains why metrics derived from a two-compartment model can exhibit enhanced sensitivity over DTI to certain types of WM processes, such as age-related WM differences. The sensitivity enhancement should not be viewed as a substitute for acquiring multi-shell data. Rather, the results of this study provide insight into the consequences of choosing a two-compartment model when only single-shell data is available.
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Affiliation(s)
- Jordan A. Chad
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Nir Sochen
- School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel
- School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - J Jean Chen
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Ofer Pasternak
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
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Raven EP, Veraart J, Kievit RA, Genc S, Ward IL, Hall J, Cunningham A, Doherty J, van den Bree MBM, Jones DK. In vivo evidence of microstructural hypo-connectivity of brain white matter in 22q11.2 deletion syndrome. Mol Psychiatry 2023; 28:4342-4352. [PMID: 37495890 PMCID: PMC7615578 DOI: 10.1038/s41380-023-02178-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 06/26/2023] [Accepted: 07/03/2023] [Indexed: 07/28/2023]
Abstract
22q11.2 deletion syndrome, or 22q11.2DS, is a genetic syndrome associated with high rates of schizophrenia and autism spectrum disorders, in addition to widespread structural and functional abnormalities throughout the brain. Experimental animal models have identified neuronal connectivity deficits, e.g., decreased axonal length and complexity of axonal branching, as a primary mechanism underlying atypical brain development in 22q11.2DS. However, it is still unclear whether deficits in axonal morphology can also be observed in people with 22q11.2DS. Here, we provide an unparalleled in vivo characterization of white matter microstructure in participants with 22q11.2DS (12-15 years) and those undergoing typical development (8-18 years) using a customized magnetic resonance imaging scanner which is sensitive to axonal morphology. A rich array of diffusion MRI metrics are extracted to present microstructural profiles of typical and atypical white matter development, and provide new evidence of connectivity differences in individuals with 22q11.2DS. A recent, large-scale consortium study of 22q11.2DS identified higher diffusion anisotropy and reduced overall diffusion mobility of water as hallmark microstructural alterations of white matter in individuals across a wide age range (6-52 years). We observed similar findings across the white matter tracts included in this study, in addition to identifying deficits in axonal morphology. This, in combination with reduced tract volume measurements, supports the hypothesis that abnormal microstructural connectivity in 22q11.2DS may be mediated by densely packed axons with disproportionately small diameters. Our findings provide insight into the in vivo white matter phenotype of 22q11.2DS, and promote the continued investigation of shared features in neurodevelopmental and psychiatric disorders.
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Affiliation(s)
- Erika P Raven
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA.
| | - Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Rogier A Kievit
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Sila Genc
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Neuroscience Advanced Clinical Imaging Service (NACIS), Department of Neurosurgery, The Royal Children's Hospital, Parkville, VIC, Australia
| | - Isobel L Ward
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Jessica Hall
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Adam Cunningham
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Joanne Doherty
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Marianne B M van den Bree
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- Neuroscience and Mental Health Innovation Institute, Cardiff University, Cardiff, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
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Archer DB, Schilling K, Shashikumar N, Jasodanand V, Moore EE, Pechman KR, Bilgel M, Beason‐Held LL, An Y, Shafer A, Ferrucci L, Risacher SL, Gifford KA, Landman BA, Jefferson AL, Saykin AJ, Resnick SM, Hohman TJ. Leveraging longitudinal diffusion MRI data to quantify differences in white matter microstructural decline in normal and abnormal aging. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12468. [PMID: 37780863 PMCID: PMC10540270 DOI: 10.1002/dad2.12468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 06/27/2023] [Accepted: 07/05/2023] [Indexed: 10/03/2023]
Abstract
Introduction It is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging. Methods Diffusion MRI data from several well-established longitudinal cohorts of aging (Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], Vanderbilt Memory & Aging Project [VMAP]) were free-water corrected and harmonized. This dataset included 1723 participants (age at baseline: 72.8 ± 8.87 years, 49.5% male) and 4605 imaging sessions (follow-up time: 2.97 ± 2.09 years, follow-up range: 1-13 years, mean number of visits: 4.42 ± 1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed. Results While we found a global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging. Conclusions There is a prevalent role of white matter microstructural decline in aging, and future large-scale studies in this area may further refine our understanding of the underlying neurodegenerative processes. HIGHLIGHTS Longitudinal data were free-water corrected and harmonized.Global effects of white matter decline were seen in normal and abnormal aging.The free-water metric was most vulnerable to abnormal aging.Cingulum free-water was the most vulnerable to abnormal aging.
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Affiliation(s)
- Derek B. Archer
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Kurt Schilling
- Vanderbilt University Institute of Imaging ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Radiology & Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Niranjana Shashikumar
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Varuna Jasodanand
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Elizabeth E. Moore
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Kimberly R. Pechman
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Murat Bilgel
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Lori L. Beason‐Held
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Yang An
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Andrea Shafer
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology BranchNational Institute on AgingBaltimoreMDUSA
| | - Shannon L. Risacher
- Indiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
| | - Katherine A. Gifford
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Bennett A. Landman
- Vanderbilt University Institute of Imaging ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Radiology & Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Biomedical EngineeringVanderbilt UniversityNashvilleTennesseeUSA
- Department of Electrical and Computer EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Angela L. Jefferson
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Andrew J. Saykin
- Indiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
| | - Susan M. Resnick
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
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Pieciak T, París G, Beck D, Maximov II, Tristán-Vega A, de Luis-García R, Westlye LT, Aja-Fernández S. Spherical means-based free-water volume fraction from diffusion MRI increases non-linearly with age in the white matter of the healthy human brain. Neuroimage 2023; 279:120324. [PMID: 37574122 DOI: 10.1016/j.neuroimage.2023.120324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 08/15/2023] Open
Abstract
The term free-water volume fraction (FWVF) refers to the signal fraction that could be found as the cerebrospinal fluid of the brain, which has been demonstrated as a sensitive measure that correlates with cognitive performance and various neuropathological processes. It can be quantified by properly fitting the isotropic component of the magnetic resonance (MR) signal in diffusion-sensitized sequences. Using N=287 healthy subjects (178F/109M) aged 25-94, this study examines in detail the evolution of the FWVF obtained with the spherical means technique from multi-shell acquisitions in the human brain white matter across the adult lifespan, which has been previously reported to exhibit a positive trend when estimated from single-shell data using the bi-tensor signal representation. We found evidence of a noticeably non-linear gain after the sixth decade of life, with a region-specific variate and varying change rate of the spherical means-based multi-shell FWVF parameter with age, at the same time, a heteroskedastic pattern across the adult lifespan is suggested. On the other hand, the FW corrected diffusion tensor imaging (DTI) leads to a region-dependent flattened age-related evolution of the mean diffusivity (MD) and fractional anisotropy (FA), along with a considerable reduction in their variability, as compared to the studies conducted over the standard (single-component) DTI. This way, our study provides a new perspective on the trajectory-based assessment of the brain and explains the conceivable reason for the variations observed in FA and MD parameters across the lifespan with previous studies under the standard diffusion tensor imaging.
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Affiliation(s)
- Tomasz Pieciak
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain.
| | - Guillem París
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Dani Beck
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway. https://twitter.com/_DaniBeck
| | - Ivan I Maximov
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Antonio Tristán-Vega
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Rodrigo de Luis-García
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway. https://twitter.com/larswestlye
| | - Santiago Aja-Fernández
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain. https://twitter.com/SantiagoAjaFer1
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Schilling KG, Chad JA, Chamberland M, Nozais V, Rheault F, Archer D, Li M, Gao Y, Cai L, Del'Acqua F, Newton A, Moyer D, Gore JC, Lebel C, Landman BA. White matter tract microstructure, macrostructure, and associated cortical gray matter morphology across the lifespan. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.25.559330. [PMID: 37808645 PMCID: PMC10557619 DOI: 10.1101/2023.09.25.559330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Characterizing how, when and where the human brain changes across the lifespan is fundamental to our understanding of developmental processes of childhood and adolescence, degenerative processes of aging, and divergence from normal patterns in disease and disorders. We aimed to provide detailed descriptions of white matter pathways across the lifespan by thoroughly characterizing white matter microstructure, white matter macrostructure, and morphology of the cortex associated with white matter pathways. We analyzed 4 large, high-quality, publicly-available datasets comprising 2789 total imaging sessions, and participants ranging from 0 to 100 years old, using advanced tractography and diffusion modeling. We first find that all microstructural, macrostructural, and cortical features of white matter bundles show unique lifespan trajectories, with rates and timing of development and degradation that vary across pathways - describing differences between types of pathways and locations in the brain, and developmental milestones of maturation of each feature. Second, we show cross-sectional relationships between different features that may help elucidate biological changes occurring during different stages of the lifespan. Third, we show unique trajectories of age-associations across features. Finally, we find that age associations during development are strongly related to those during aging. Overall, this study reports normative data for several features of white matter pathways of the human brain that will be useful for studying normal and abnormal white matter development and degeneration.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jordan A Chad
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, ON, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Maxime Chamberland
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Francois Rheault
- Medical Imaging and Neuroinformatic (MINi) Lab, Department of Computer Science, University of Sherbrooke, Canada
| | - Derek Archer
- Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Muwei Li
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Leon Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Flavio Del'Acqua
- NatbrainLab, Department of Forensics and Neurodevelopmental Sciences, King's College London, London UK
| | - Allen Newton
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel Moyer
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - John C Gore
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Catherine Lebel
- Alberta Children's Hospital Research Institute (ACHRI), Calgary, AB, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Bennett A Landman
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
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38
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Robitzsch A. Estimating Local Structural Equation Models. J Intell 2023; 11:175. [PMID: 37754904 PMCID: PMC10532278 DOI: 10.3390/jintelligence11090175] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/16/2023] [Accepted: 08/22/2023] [Indexed: 09/28/2023] Open
Abstract
Local structural equation models (LSEM) are structural equation models that study model parameters as a function of a moderator. This article reviews and extends LSEM estimation methods and discusses the implementation in the R package sirt. In previous studies, LSEM was fitted as a sequence of models separately evaluated as each value of the moderator variables. In this article, a joint estimation approach is proposed that is a simultaneous estimation method across all moderator values and also allows some model parameters to be invariant with respect to the moderator. Moreover, sufficient details on the main estimation functions in the R package sirt are provided. The practical implementation of LSEM is demonstrated using illustrative datasets and an empirical example. Moreover, two simulation studies investigate the statistical properties of parameter estimation and significance testing in LSEM.
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Affiliation(s)
- Alexander Robitzsch
- IPN–Leibniz Institute for Science and Mathematics Education, Olshausenstraße 62, 24118 Kiel, Germany;
- Centre for International Student Assessment (ZIB), Olshausenstraße 62, 24118 Kiel, Germany
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Gao Y, Zhao Y, Li M, Lawless RD, Schilling KG, Xu L, Shafer AT, Beason-Held LL, Resnick SM, Rogers BP, Ding Z, Anderson AW, Landman BA, Gore JC. Functional alterations in bipartite network of white and grey matters during aging. Neuroimage 2023; 278:120277. [PMID: 37473978 PMCID: PMC10529380 DOI: 10.1016/j.neuroimage.2023.120277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/23/2023] [Accepted: 07/11/2023] [Indexed: 07/22/2023] Open
Abstract
The effects of normal aging on functional connectivity (FC) within various brain networks of gray matter (GM) have been well-documented. However, the age effects on the networks of FC between white matter (WM) and GM, namely WM-GM FC, remains unclear. Evaluating crucial properties, such as global efficiency (GE), for a WM-GM FC network poses a challenge due to the absence of closed triangle paths which are essential for assessing network properties in traditional graph models. In this study, we propose a bipartite graph model to characterize the WM-GM FC network and quantify these challenging network properties. Leveraging this model, we assessed the WM-GM FC network properties at multiple scales across 1,462 cognitively normal subjects aged 22-96 years from three repositories (ADNI, BLSA and OASIS-3) and investigated the age effects on these properties throughout adulthood and during late adulthood (age ≥70 years). Our findings reveal that (1) heterogeneous alterations occurred in region-specific WM-GM FC over the adulthood and decline predominated during late adulthood; (2) the FC density of WM bundles engaged in memory, executive function and processing speed declined with age over adulthood, particularly in later years; and (3) the GE of attention, default, somatomotor, frontoparietal and limbic networks reduced with age over adulthood, and GE of visual network declined during late adulthood. These findings provide unpresented insights into multi-scale alterations in networks of WM-GM functional synchronizations during normal aging. Furthermore, our bipartite graph model offers an extendable framework for quantifying WM-engaged networks, which may contribute to a wide range of neuroscience research.
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Affiliation(s)
- Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Richard D Lawless
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
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Warrier V, Stauffer EM, Huang QQ, Wigdor EM, Slob EAW, Seidlitz J, Ronan L, Valk SL, Mallard TT, Grotzinger AD, Romero-Garcia R, Baron-Cohen S, Geschwind DH, Lancaster MA, Murray GK, Gandal MJ, Alexander-Bloch A, Won H, Martin HC, Bullmore ET, Bethlehem RAI. Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes. Nat Genet 2023; 55:1483-1493. [PMID: 37592024 PMCID: PMC10600728 DOI: 10.1038/s41588-023-01475-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 07/13/2023] [Indexed: 08/19/2023]
Abstract
Our understanding of the genetics of the human cerebral cortex is limited both in terms of the diversity and the anatomical granularity of brain structural phenotypes. Here we conducted a genome-wide association meta-analysis of 13 structural and diffusion magnetic resonance imaging-derived cortical phenotypes, measured globally and at 180 bilaterally averaged regions in 36,663 individuals and identified 4,349 experiment-wide significant loci. These phenotypes include cortical thickness, surface area, gray matter volume, measures of folding, neurite density and water diffusion. We identified four genetic latent structures and causal relationships between surface area and some measures of cortical folding. These latent structures partly relate to different underlying gene expression trajectories during development and are enriched for different cell types. We also identified differential enrichment for neurodevelopmental and constrained genes and demonstrate that common genetic variants associated with cortical expansion are associated with cephalic disorders. Finally, we identified complex interphenotype and inter-regional genetic relationships among the 13 phenotypes, reflecting the developmental differences among them. Together, these analyses identify distinct genetic organizational principles of the cortex and their correlates with neurodevelopment.
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Affiliation(s)
- Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Department of Psychology, University of Cambridge, Cambridge, UK.
| | | | | | | | - Eric A W Slob
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Lisa Ronan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Sofie L Valk
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Andrew D Grotzinger
- Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Rafael Romero-Garcia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Dpto. de Fisiología Médica y Biofísica, Seville, Spain
| | - Simon Baron-Cohen
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Daniel H Geschwind
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Program in Neurogenetics, Department of Neurology, University of California, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Jane and TerrySemel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Institute of Precision Health, University of California, Los Angeles, CA, USA
| | - Madeline A Lancaster
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge, UK
| | - Graham K Murray
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Trust, Cambridge, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Michael J Gandal
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Aaron Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Hyejung Won
- Department of Genetics and the Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Trust, Cambridge, UK
| | - Richard A I Bethlehem
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Department of Psychology, University of Cambridge, Cambridge, UK.
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Bjerkan J, Lancaster G, Meglič B, Kobal J, Crawford TJ, McClintock PVE, Stefanovska A. Aging affects the phase coherence between spontaneous oscillations in brain oxygenation and neural activity. Brain Res Bull 2023; 201:110704. [PMID: 37451471 DOI: 10.1016/j.brainresbull.2023.110704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/03/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023]
Abstract
The risk of neurodegenerative disorders increases with age, due to reduced vascular nutrition and impaired neural function. However, the interactions between cardiovascular dynamics and neural activity, and how these interactions evolve in healthy aging, are not well understood. Here, the interactions are studied by assessment of the phase coherence between spontaneous oscillations in cerebral oxygenation measured by fNIRS, the electrical activity of the brain measured by EEG, and cardiovascular functions extracted from ECG and respiration effort, all simultaneously recorded. Signals measured at rest in 21 younger participants (31.1 ± 6.9 years) and 24 older participants (64.9 ± 6.9 years) were analysed by wavelet transform, wavelet phase coherence and ridge extraction for frequencies between 0.007 and 4 Hz. Coherence between the neural and oxygenation oscillations at ∼ 0.1 Hz is significantly reduced in the older adults in 46/176 fNIRS-EEG probe combinations. This reduction in coherence cannot be accounted for in terms of reduced power, thus indicating that neurovascular interactions change with age. The approach presented promises a noninvasive means of evaluating the efficiency of the neurovascular unit in aging and disease.
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Affiliation(s)
- Juliane Bjerkan
- Lancaster University, Department of Physics, LA1 4YB, Lancaster, United Kingdom
| | - Gemma Lancaster
- Lancaster University, Department of Physics, LA1 4YB, Lancaster, United Kingdom
| | - Bernard Meglič
- University of Ljubljana Medical Centre, Department of Neurology, 1525, Ljubljana, Slovenia
| | - Jan Kobal
- University of Ljubljana Medical Centre, Department of Neurology, 1525, Ljubljana, Slovenia
| | - Trevor J Crawford
- Lancaster University, Department of Psychology, LA1 4YF, Lancaster, United Kingdom
| | | | - Aneta Stefanovska
- Lancaster University, Department of Physics, LA1 4YB, Lancaster, United Kingdom.
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42
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Joo SW, Jo YT, Ahn S, Choi YJ, Choi W, Kim SK, Joe S, Lee J. Structural impairment in superficial and deep white matter in schizophrenia. Acta Neuropsychiatr 2023:1-10. [PMID: 37620164 DOI: 10.1017/neu.2023.44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
OBJECTIVE Although disconnectivity among brain regions has been one of the main hypotheses for schizophrenia, the superficial white matter (SWM) has received less attention in schizophrenia research than the deep white matter (DWM) owing to the challenge of consistent reconstruction across subjects. METHODS We obtained the diffusion magnetic resonance imaging (dMRI) data of 223 healthy controls and 143 patients with schizophrenia. After harmonising the raw dMRIs from three different studies, we performed whole-brain two-tensor tractography and fibre clustering on the tractography data. We compared the fractional anisotropy (FA) of white matter tracts between healthy controls and patients with schizophrenia. Spearman's rho was adopted for the associations with clinical symptoms measured by the Positive and Negative Syndrome Scale (PANSS). The Bonferroni correction was used to adjust multiple testing. RESULTS Among the 33 DWM and 8 SWM tracts, patients with schizophrenia had a lower FA in 14 DWM and 4 SWM tracts than healthy controls, with small effect sizes. In the patient group, the FA deviations of the corticospinal and superficial-occipital tracts were negatively correlated with the PANSS negative score; however, this correlation was not evident after adjusting for multiple testing. CONCLUSION We observed the structural impairments of both the DWM and SWM tracts in patients with schizophrenia. The SWM could be a potential target of interest in future research on neural biomarkers for schizophrenia.
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Affiliation(s)
- Sung Woo Joo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Tak Jo
- Department of Psychiatry, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea
| | - Soojin Ahn
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Jae Choi
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Woohyeok Choi
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sang Kyoung Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Soohyun Joe
- Brain Laboratory, Department of Psychiatry, University of California San Diego, School of Medicine, San Diego, CA, USA
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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43
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James SN, Manning EN, Storey M, Nicholas JM, Coath W, Keuss SE, Cash DM, Lane CA, Parker T, Keshavan A, Buchanan SM, Wagen A, Harris M, Malone I, Lu K, Needham LP, Street R, Thomas D, Dickson J, Murray-Smith H, Wong A, Freiberger T, Crutch SJ, Fox NC, Richards M, Barkhof F, Sudre CH, Barnes J, Schott JM. Neuroimaging, clinical and life course correlates of normal-appearing white matter integrity in 70-year-olds. Brain Commun 2023; 5:fcad225. [PMID: 37680671 PMCID: PMC10481255 DOI: 10.1093/braincomms/fcad225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 05/30/2023] [Accepted: 08/17/2023] [Indexed: 09/09/2023] Open
Abstract
We investigate associations between normal-appearing white matter microstructural integrity in cognitively normal ∼70-year-olds and concurrently measured brain health and cognition, demographics, genetics and life course cardiovascular health. Participants born in the same week in March 1946 (British 1946 birth cohort) underwent PET-MRI around age 70. Mean standardized normal-appearing white matter integrity metrics (fractional anisotropy, mean diffusivity, neurite density index and orientation dispersion index) were derived from diffusion MRI. Linear regression was used to test associations between normal-appearing white matter metrics and (i) concurrent measures, including whole brain volume, white matter hyperintensity volume, PET amyloid and cognition; (ii) the influence of demographic and genetic predictors, including sex, childhood cognition, education, socio-economic position and genetic risk for Alzheimer's disease (APOE-ɛ4); (iii) systolic and diastolic blood pressure and cardiovascular health (Framingham Heart Study Cardiovascular Risk Score) across adulthood. Sex interactions were tested. Statistical significance included false discovery rate correction (5%). Three hundred and sixty-two participants met inclusion criteria (mean age 70, 49% female). Higher white matter hyperintensity volume was associated with lower fractional anisotropy [b = -0.09 (95% confidence interval: -0.11, -0.06), P < 0.01], neurite density index [b = -0.17 (-0.22, -0.12), P < 0.01] and higher mean diffusivity [b = 0.14 (-0.10, -0.17), P < 0.01]; amyloid (in men) was associated with lower fractional anisotropy [b = -0.04 (-0.08, -0.01), P = 0.03)] and higher mean diffusivity [b = 0.06 (0.01, 0.11), P = 0.02]. Framingham Heart Study Cardiovascular Risk Score in later-life (age 69) was associated with normal-appearing white matter {lower fractional anisotropy [b = -0.06 (-0.09, -0.02) P < 0.01], neurite density index [b = -0.10 (-0.17, -0.03), P < 0.01] and higher mean diffusivity [b = 0.09 (0.04, 0.14), P < 0.01]}. Significant sex interactions (P < 0.05) emerged for midlife cardiovascular health (age 53) and normal-appearing white matter at 70: marginal effect plots demonstrated, in women only, normal-appearing white matter was associated with higher midlife Framingham Heart Study Cardiovascular Risk Score (lower fractional anisotropy and neurite density index), midlife systolic (lower fractional anisotropy, neurite density index and higher mean diffusivity) and diastolic (lower fractional anisotropy and neurite density index) blood pressure and greater blood pressure change between 43 and 53 years (lower fractional anisotropy and neurite density index), independently of white matter hyperintensity volume. In summary, poorer normal-appearing white matter microstructural integrity in ∼70-year-olds was associated with measures of cerebral small vessel disease, amyloid (in males) and later-life cardiovascular health, demonstrating how normal-appearing white matter can provide additional information to overt white matter disease. Our findings further show that greater 'midlife' cardiovascular risk and higher blood pressure were associated with poorer normal-appearing white matter microstructural integrity in females only, suggesting that women's brains may be more susceptible to the effects of midlife blood pressure and cardiovascular health.
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Affiliation(s)
- Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Emily N Manning
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Mathew Storey
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jennifer M Nicholas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah E Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Thomas Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ashvini Keshavan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah M Buchanan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Aaron Wagen
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Mathew Harris
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ian Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Kirsty Lu
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Louisa P Needham
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
| | - Rebecca Street
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - David Thomas
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - John Dickson
- Institute of Nuclear Medicine, University College London Hospitals Foundation Trust, London, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
| | - Tamar Freiberger
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sebastian J Crutch
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
| | - Frederik Barkhof
- Centre for Medical Image Computing, University College London, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
- School of Biomedical Engineering, King’s College, London, UK
| | - Josephine Barnes
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jonathan M Schott
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
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Alsameen MH, Gong Z, Qian W, Kiely M, Triebswetter C, Bergeron CM, Cortina LE, Faulkner ME, Laporte JP, Bouhrara M. C-NODDI: a constrained NODDI model for axonal density and orientation determinations in cerebral white matter. Front Neurol 2023; 14:1205426. [PMID: 37602266 PMCID: PMC10435293 DOI: 10.3389/fneur.2023.1205426] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/14/2023] [Indexed: 08/22/2023] Open
Abstract
Purpose Neurite orientation dispersion and density imaging (NODDI) provides measures of neurite density and dispersion through computation of the neurite density index (NDI) and the orientation dispersion index (ODI). However, NODDI overestimates the cerebrospinal fluid water fraction in white matter (WM) and provides physiologically unrealistic high NDI values. Furthermore, derived NDI values are echo-time (TE)-dependent. In this work, we propose a modification of NODDI, named constrained NODDI (C-NODDI), for NDI and ODI mapping in WM. Methods Using NODDI and C-NODDI, we investigated age-related alterations in WM in a cohort of 58 cognitively unimpaired adults. Further, NDI values derived using NODDI or C-NODDI were correlated with the neurofilament light chain (NfL) concentration levels, a plasma biomarker of axonal degeneration. Finally, we investigated the TE dependence of NODDI or C-NODDI derived NDI and ODI. Results ODI derived values using both approaches were virtually identical, exhibiting constant trends with age. Further, our results indicated a quadratic relationship between NDI and age suggesting that axonal maturation continues until middle age followed by a decrease. This quadratic association was notably significant in several WM regions using C-NODDI, while limited to a few regions using NODDI. Further, C-NODDI-NDI values exhibited a stronger correlation with NfL concentration levels as compared to NODDI-NDI, with lower NDI values corresponding to higher levels of NfL. Finally, we confirmed the previous finding that NDI estimation using NODDI was dependent on TE, while NDI derived values using C-NODDI exhibited lower sensitivity to TE in WM. Conclusion C-NODDI provides a complementary method to NODDI for determination of NDI in white matter.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
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45
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Yoder KK, Chumin EJ, Mustafi SM, Kolleck KA, Halcomb ME, Hile KL, Plawecki MH, O'Connor SJ, Dzemidzic M, Wu YC. Effects of acute alcohol exposure and chronic alcohol use on neurite orientation dispersion and density imaging (NODDI) parameters. Psychopharmacology (Berl) 2023; 240:1465-1472. [PMID: 37209164 PMCID: PMC10594986 DOI: 10.1007/s00213-023-06380-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 04/28/2023] [Indexed: 05/22/2023]
Abstract
RATIONALE Little is known about how acute and chronic alcohol exposure may alter the in vivo membrane properties of neurons. OBJECTIVES We employed neurite orientation dispersion and density imaging (NODDI) to examine acute and chronic effects of alcohol exposure on neurite density. METHODS Twenty-one healthy social drinkers (CON) and thirteen nontreatment-seeking individuals with alcohol use disorder (AUD) underwent a baseline multi-shell diffusion magnetic resonance imaging (dMRI) scan. A subset (10 CON, 5 AUD) received dMRI during intravenous infusions of saline and alcohol during dMRI. NODDI parametric images included orientation dispersion (OD), isotropic volume fraction (ISOVF), and corrected intracellular volume fraction (cICVF). Diffusion tensor imaging metrics of fractional anisotropy and mean, axial, and radial diffusivity (FA, MD, AD, RD) were also computed. Average parameter values were extracted from white matter (WM) tracts defined by the Johns Hopkins University atlas. RESULTS There were group differences in FA, RD, MD, OD, and cICVF, primarily in the corpus callosum. Both saline and alcohol had effects on AD and cICVF in WM tracts proximal to the striatum, cingulate, and thalamus. This is the first work to indicate that acute fluid infusions may alter WM properties, which are conventionally believed to be insensitive to acute pharmacological challenges. It also suggests that the NODDI approach may be sensitive to transient changes in WM. The next steps should include determining if the effect on neurite density differs with solute or osmolality, or both, and translational studies to assess how alcohol and osmolality affect the efficiency of neurotransmission.
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Affiliation(s)
- Karmen K Yoder
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W. 16th St., GH Ste. 4100, Indianapolis, IN, 46202, USA.
- Center for Neuroimaging, Indiana Institute of Biomedical Imaging, Indiana University School of Medicine, 355 W. 16th St., GH Ste. 4100, Indianapolis, IN, 46202, USA.
- Stark Neurosciences Research Institute, Indiana University School of Medicine, 320 W. 15th Street, Ste. 414, Indianapolis, IN, 46202, USA.
| | - Evgeny J Chumin
- Center for Neuroimaging, Indiana Institute of Biomedical Imaging, Indiana University School of Medicine, 355 W. 16th St., GH Ste. 4100, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, 320 W. 15th Street, Ste. 414, Indianapolis, IN, 46202, USA
- Department of Psychological and Brain Sciences, Indiana University, 1101 E 10th St, IN, 47405, Bloomington, USA
- Indiana University Network Science Institute, Indiana University, 1015 E 11th St, Bloomington, IN, 47408, USA
| | - Sourajit M Mustafi
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W. 16th St., GH Ste. 4100, Indianapolis, IN, 46202, USA
- Center for Neuroimaging, Indiana Institute of Biomedical Imaging, Indiana University School of Medicine, 355 W. 16th St., GH Ste. 4100, Indianapolis, IN, 46202, USA
| | - Kelly A Kolleck
- Indiana University School of Medicine, 340 W. 10th St., Indianapolis, IN, 46202, USA
| | - Meredith E Halcomb
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W. 16th St., GH Ste. 4100, Indianapolis, IN, 46202, USA
- Center for Neuroimaging, Indiana Institute of Biomedical Imaging, Indiana University School of Medicine, 355 W. 16th St., GH Ste. 4100, Indianapolis, IN, 46202, USA
| | - Karen L Hile
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W. 16th St., GH Ste. 4100, Indianapolis, IN, 46202, USA
- Center for Neuroimaging, Indiana Institute of Biomedical Imaging, Indiana University School of Medicine, 355 W. 16th St., GH Ste. 4100, Indianapolis, IN, 46202, USA
| | - Martin H Plawecki
- Department of Psychiatry, Indiana University School of Medicine, 355 W. 16th St., GH Ste. 4800, Indianapolis, IN, 46202, USA
| | - Sean J O'Connor
- Department of Psychiatry, Indiana University School of Medicine, 355 W. 16th St., GH Ste. 4800, Indianapolis, IN, 46202, USA
| | - Mario Dzemidzic
- Department of Neurology, Indiana University School of Medicine, 355 W. 16th St., GH Ste. 4700, Indianapolis, IN, 46202, USA
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W. 16th St., GH Ste. 4100, Indianapolis, IN, 46202, USA
- Center for Neuroimaging, Indiana Institute of Biomedical Imaging, Indiana University School of Medicine, 355 W. 16th St., GH Ste. 4100, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, 320 W. 15th Street, Ste. 414, Indianapolis, IN, 46202, USA
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Rasooli A, Adab HZ, Van Ruitenbeek P, Weerasekera A, Chalavi S, Cuypers K, Levin O, Dhollander T, Peeters R, Sunaert S, Mantini D, Swinnen SP. White matter and neurochemical mechanisms underlying age-related differences in motor processing speed. iScience 2023; 26:106794. [PMID: 37255665 PMCID: PMC10225899 DOI: 10.1016/j.isci.2023.106794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 01/11/2023] [Accepted: 04/27/2023] [Indexed: 06/01/2023] Open
Abstract
Aging is associated with changes in the central nervous system and leads to reduced life quality. Here, we investigated the age-related differences in the CNS underlying motor performance deficits using magnetic resonance spectroscopy and diffusion MRI. MRS measured N-acetyl aspartate (NAA), choline (Cho), and creatine (Cr) concentrations in the sensorimotor and occipital cortex, whereas dMRI quantified apparent fiber density (FD) in the same voxels to evaluate white matter microstructural organization. We found that aging was associated with increased reaction time and reduced FD and NAA concentration in the sensorimotor voxel. Both FD and NAA mediated the association between age and reaction time. The NAA concentration was found to mediate the association between age and FD in the sensorimotor voxel. We propose that the age-related decrease in NAA concentration may result in reduced axonal fiber density in the sensorimotor cortex which may ultimately account for the response slowness of older participants.
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Affiliation(s)
- Amirhossein Rasooli
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Hamed Zivari Adab
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Peter Van Ruitenbeek
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD Maastricht, the Netherlands
| | - Akila Weerasekera
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sima Chalavi
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Koen Cuypers
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
- REVAL Rehabilitation Research Center, Hasselt University, Diepenbeek, Belgium
| | - Oron Levin
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Thijs Dhollander
- Murdoch Children’s Research Institute, Melbourne, VIC, Australia
| | - Ronald Peeters
- KU Leuven, Department of Imaging and Pathology, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Stefan Sunaert
- KU Leuven, Department of Imaging and Pathology, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Dante Mantini
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Stephan P. Swinnen
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
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47
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Korbmacher M, Gurholt TP, de Lange AMG, van der Meer D, Beck D, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Bio-psycho-social factors' associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants. Front Psychol 2023; 14:1117732. [PMID: 37359862 PMCID: PMC10288151 DOI: 10.3389/fpsyg.2023.1117732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 04/27/2023] [Indexed: 06/28/2023] Open
Abstract
Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age (N = 35,749, 44.6-82.8 years of age). Bio-psycho-social factors could uniquely explain a small proportion of the brain age variance, in a similar pattern across diffusion approaches: cognitive scores, life satisfaction, health and lifestyle factors adding to the variance explained, but not socio-demographics. Consistent brain age associations across models were found for waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception. Furthermore, we found large variability in sex and ethnicity group differences in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors, and to observe bio-psycho-social factor interactions' influence on brain age in future studies.
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Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
| | - Tiril P. Gurholt
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Ann-Marie G. de Lange
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Dani Beck
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
| | - Arvid Lundervold
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I. Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
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Kruper J, Benson NC, Caffarra S, Owen J, Wu Y, Lee AY, Lee CS, Yeatman JD, Rokem A. Optic radiations representing different eccentricities age differently. Hum Brain Mapp 2023; 44:3123-3135. [PMID: 36896869 PMCID: PMC10171550 DOI: 10.1002/hbm.26267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 01/10/2023] [Accepted: 02/16/2023] [Indexed: 03/11/2023] Open
Abstract
The neural pathways that carry information from the foveal, macular, and peripheral visual fields have distinct biological properties. The optic radiations (OR) carry foveal and peripheral information from the thalamus to the primary visual cortex (V1) through adjacent but separate pathways in the white matter. Here, we perform white matter tractometry using pyAFQ on a large sample of diffusion MRI (dMRI) data from subjects with healthy vision in the U.K. Biobank dataset (UKBB; N = 5382; age 45-81). We use pyAFQ to characterize white matter tissue properties in parts of the OR that transmit information about the foveal, macular, and peripheral visual fields, and to characterize the changes in these tissue properties with age. We find that (1) independent of age there is higher fractional anisotropy, lower mean diffusivity, and higher mean kurtosis in the foveal and macular OR than in peripheral OR, consistent with denser, more organized nerve fiber populations in foveal/parafoveal pathways, and (2) age is associated with increased diffusivity and decreased anisotropy and kurtosis, consistent with decreased density and tissue organization with aging. However, anisotropy in foveal OR decreases faster with age than in peripheral OR, while diffusivity increases faster in peripheral OR, suggesting foveal/peri-foveal OR and peripheral OR differ in how they age.
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Affiliation(s)
- John Kruper
- Department of PsychologyUniversity of WashingtonSeattleWashingtonUSA
- eScience InstituteUniversity of WashingtonSeattleWashingtonUSA
| | - Noah C. Benson
- eScience InstituteUniversity of WashingtonSeattleWashingtonUSA
| | - Sendy Caffarra
- Graduate School of Education, Stanford University and Division of Developmental‐Behavioral Pediatrics, Stanford University School of MedicineStanford UniversityStanfordCaliforniaUSA
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | - Julia Owen
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Yue Wu
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Aaron Y. Lee
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Cecilia S. Lee
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Jason D. Yeatman
- Graduate School of Education, Stanford University and Division of Developmental‐Behavioral Pediatrics, Stanford University School of MedicineStanford UniversityStanfordCaliforniaUSA
| | - Ariel Rokem
- Department of PsychologyUniversity of WashingtonSeattleWashingtonUSA
- eScience InstituteUniversity of WashingtonSeattleWashingtonUSA
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49
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Fürtjes AE, Arathimos R, Coleman JRI, Cole JH, Cox SR, Deary IJ, de la Fuente J, Madole JW, Tucker‐Drob EM, Ritchie SJ. General dimensions of human brain morphometry inferred from genome-wide association data. Hum Brain Mapp 2023; 44:3311-3323. [PMID: 36987996 PMCID: PMC10171533 DOI: 10.1002/hbm.26283] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 02/01/2023] [Accepted: 03/08/2023] [Indexed: 03/30/2023] Open
Abstract
Understanding the neurodegenerative mechanisms underlying cognitive decline in the general population may facilitate early detection of adverse health outcomes in late life. This study investigates genetic links between brain morphometry, ageing and cognitive ability. We develop Genomic Principal Components Analysis (Genomic PCA) to model general dimensions of brain-wide morphometry at the level of their underlying genetic architecture. Genomic PCA is applied to genome-wide association data for 83 brain-wide volumes (36,778 UK Biobank participants) and we extract genomic principal components (PCs) to capture global dimensions of genetic covariance across brain regions (unlike ancestral PCs that index genetic similarity between participants). Using linkage disequilibrium score regression, we estimate genetic overlap between those general brain dimensions and cognitive ageing. The first genetic PCs underlying the morphometric organisation of 83 brain-wide regions accounted for substantial genetic variance (R2 = 40%) with the pattern of component loadings corresponding closely to those obtained from phenotypic analyses. Genetically more central regions to overall brain structure - specifically frontal and parietal volumes thought to be part of the central executive network - tended to be somewhat more susceptible towards age (r = -0.27). We demonstrate the moderate genetic overlap between the first PC underlying each of several structural brain networks and general cognitive ability (rg = 0.17-0.21), which was not specific to a particular subset of the canonical networks examined. We provide a multivariate framework integrating covariance across multiple brain regions and the genome, revealing moderate shared genetic etiology between brain-wide morphometry and cognitive ageing.
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Affiliation(s)
- Anna E. Fürtjes
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
| | - Ryan Arathimos
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
- National Institutes for Health Research Maudsley Biomedical Research CentreSouth London and Maudsley NHS TrustLondonSE5 8AFUK
| | - Jonathan R. I. Coleman
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
- National Institutes for Health Research Maudsley Biomedical Research CentreSouth London and Maudsley NHS TrustLondonSE5 8AFUK
| | - James H. Cole
- Department of NeuroimageingInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonWC1V 6LJUK
- Dementia Research CentreInstitute of Neurology, University College LondonLondonWC1N 3BGUK
| | - Simon R. Cox
- Department of PsychologyThe University of EdinburghEdinburghEH8 9JZUK
- Lothian Birth CohortsUniversity of EdinburghEdinburghEH8 9JZUK
| | - Ian J. Deary
- Department of PsychologyThe University of EdinburghEdinburghEH8 9JZUK
- Lothian Birth CohortsUniversity of EdinburghEdinburghEH8 9JZUK
| | - Javier de la Fuente
- Department of PsychologyUniversity of Texas at AustinAustinTexas78712‐1043USA
- Population Research Center and Center on Ageing and Population SciencesUniversity of Texas at AustinAustinTexas78712‐1043USA
| | - James W. Madole
- Department of PsychologyUniversity of Texas at AustinAustinTexas78712‐1043USA
| | - Elliot M. Tucker‐Drob
- Department of PsychologyUniversity of Texas at AustinAustinTexas78712‐1043USA
- Population Research Center and Center on Ageing and Population SciencesUniversity of Texas at AustinAustinTexas78712‐1043USA
| | - Stuart J. Ritchie
- Social, Genetic and Developmental Psychiatry (SGDP) CentreInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonSE5 8AFUK
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50
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Bang M, Kim B, Lee KS, Choi TK, Lee SH. Long-term benefits of mindfulness on white matter tracts underlying the cortical midline structures in panic disorder: A 2-year longitudinal study. Psychiatry Clin Neurosci 2023; 77:355-364. [PMID: 36917206 DOI: 10.1111/pcn.13544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 03/08/2023] [Accepted: 03/10/2023] [Indexed: 03/16/2023]
Abstract
AIMS We aimed to examine the long-term benefits of mindfulness-based cognitive therapy (MBCT) on white matter plasticity in the cortical midline structures (CMS) for a period of 2 years in patients with panic disorder and the relationships between white matter changes in the CMS and severity of state and trait symptoms. METHODS Seventy-one participants were enrolled and underwent diffusion tensor imaging at baseline and after 2 years (26 who received MBCT as an adjunct to pharmacotherapy [MBCT+PT], 20 treated with pharmacotherapy alone [PT-alone], and 25 healthy controls [HCs]). The severity of symptoms and fractional anisotropy (FA) in white matter regions underlying the CMS were assessed at baseline and 2-year follow-up. RESULTS The MBCT+PT group showed better outcomes after 2 years than the PT-alone group. The groups showed different FA changes: the MBCT+PT group showed decreased FA in the left anterior cingulate cortex (ACC); the PT-alone group showed increased FA in the bilateral dorsomedial prefrontal cortex, posterior cingulate cortex (PCC), and precuneus. Decreased white matter FA in the ACC, PCC, and precuneus was associated with improvements in the severity of state and trait symptoms in patients with panic disorder. CONCLUSION Alleviation of excessive white matter connectivity in the CMS after MBCT leads to improvements in clinical symptoms and trait vulnerability in patients with panic disorder. Our study provides new evidence for the long-term benefits of MBCT on white matter plasticity and its clinical applicability as a robust treatment for panic disorder.
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Affiliation(s)
- Minji Bang
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Borah Kim
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Kang Soo Lee
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Tai Kiu Choi
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Sang-Hyuk Lee
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
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