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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: 5] [Impact Index Per Article: 5.0] [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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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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58
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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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59
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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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60
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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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61
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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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62
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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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64
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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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Korbmacher M, de Lange AM, van der Meer D, Beck D, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Brain-wide associations between white matter and age highlight the role of fornix microstructure in brain ageing. Hum Brain Mapp 2023; 44:4101-4119. [PMID: 37195079 PMCID: PMC10258541 DOI: 10.1002/hbm.26333] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/16/2023] [Accepted: 04/26/2023] [Indexed: 05/18/2023] Open
Abstract
Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age (N = 35,749, 44.6-82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.
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Affiliation(s)
- Max Korbmacher
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- Mohn Medical Imaging and Visualisation Center (MMIV)BergenNorway
| | - Ann Marie de Lange
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- Department of PsychiatryUniversity of OxfordOxfordUK
- LREN, Centre for Research in Neurosciences–Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
| | - Dennis van der Meer
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- Faculty of Health, Medicine and Life SciencesMaastricht UniversityMaastrichtNetherlands
| | - Dani Beck
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- Department of Psychiatric Research, Diakonhjemmet HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Eli Eikefjord
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- Mohn Medical Imaging and Visualisation Center (MMIV)BergenNorway
| | - Arvid Lundervold
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- Mohn Medical Imaging and Visualisation Center (MMIV)BergenNorway
- Department of RadiologyHaukeland University HospitalBergenNorway
- Department of BiomedicineUniversity of BergenBergenNorway
| | - Ole A. Andreassen
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | - Lars T. Westlye
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | - Ivan I. Maximov
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- NORMENT Centre for Psychosis Research, Division of Mental Health and AddictionUniversity of Oslo and Oslo University HospitalOsloNorway
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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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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: 4] [Impact Index Per Article: 4.0] [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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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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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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van Heesewijk J, Steenwijk MD, Kreukels BPC, Veltman DJ, Bakker J, Burke SM. Alterations in the inferior fronto-occipital fasciculus - a specific neural correlate of gender incongruence? Psychol Med 2023; 53:3461-3470. [PMID: 35301969 PMCID: PMC10277722 DOI: 10.1017/s0033291721005547] [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: 02/18/2021] [Revised: 11/06/2021] [Accepted: 12/28/2021] [Indexed: 11/05/2022]
Abstract
BACKGROUND Increasing numbers of adolescents seek help for gender-identity questions. Consequently, requests for medical treatments, such as puberty suppression, are growing. However, studies investigating the neurobiological substrate of gender incongruence (when birth-assigned sex and gender identity do not align) are scarce, and knowledge about the effects of puberty suppression on the developing brain of transgender youth is limited. METHODS Here we cross-sectionally investigated sex and gender differences in regional fractional anisotropy (FA) as measured by diffusion MR imaging, and the impact of puberty on alterations in the white-matter organization of 35 treatment-naive prepubertal children and 41 adolescents with gender incongruence, receiving puberty suppression. The transgender groups were compared with 79 age-matched, treatment-naive cisgender (when sex and gender align) peers. RESULTS We found that transgender adolescents had lower FA in the bilateral inferior fronto-occipital fasciculus (IFOF), forceps major and corpus callosum than cisgender peers. In addition, average FA values of the right IFOF correlated negatively with adolescents' cumulative dosage of puberty suppressants received. Of note, prepubertal children also showed significant FA group differences in, again, the right IFOF and left cortico-spinal tract, but with the reverse pattern (transgender > cisgender) than was seen in adolescents. CONCLUSIONS Importantly, our results of lower FA (indexing less longitudinal organization, fiber coherence, and myelination) in the IFOF of gender-incongruent adolescents replicate prior findings in transgender adults, suggesting a salient neural correlate of gender incongruence. Findings highlight the complexity with which (pubertal) sex hormones impact white-matter development and add important insight into the neurobiological substrate associated with gender incongruence.
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Affiliation(s)
- Jason van Heesewijk
- Center of Expertise on Gender Dysphoria, Amsterdam University Medical Centers, location VUmc, De Boelelaan 1131, Amsterdam, Noord-Holland, Netherlands
| | - Martijn D. Steenwijk
- Center of Expertise on Gender Dysphoria, Amsterdam University Medical Centers, location VUmc, De Boelelaan 1131, Amsterdam, Noord-Holland, Netherlands
| | - Baudewijntje P. C. Kreukels
- Center of Expertise on Gender Dysphoria, Amsterdam University Medical Centers, location VUmc, De Boelelaan 1131, Amsterdam, Noord-Holland, Netherlands
| | - Dick J. Veltman
- Center of Expertise on Gender Dysphoria, Amsterdam University Medical Centers, location VUmc, De Boelelaan 1131, Amsterdam, Noord-Holland, Netherlands
| | - Julie Bakker
- Center of Expertise on Gender Dysphoria, Amsterdam University Medical Centers, location VUmc, De Boelelaan 1131, Amsterdam, Noord-Holland, Netherlands
| | - Sarah M. Burke
- Center of Expertise on Gender Dysphoria, Amsterdam University Medical Centers, location VUmc, De Boelelaan 1131, Amsterdam, Noord-Holland, Netherlands
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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. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.17.541182. [PMID: 37292885 PMCID: PMC10245725 DOI: 10.1101/2023.05.17.541182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
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 Neuroimaging Initiative (ADNI), Baltimore Longitudinal Study of Aging (BLSA), Vanderbilt Memory & Aging Project (VMAP)] was free-water corrected and harmonized. This dataset included 1,723 participants (age at baseline: 72.8±8.87 years, 49.5% male) and 4,605 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 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 was free-water corrected and harmonizedGlobal effects of white matter decline were seen in normal and abnormal agingThe free-water metric was most vulnerable to abnormal agingCingulum free-water was the most vulnerable to abnormal aging.
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Affiliation(s)
- Derek B Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kurt Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Niranjana Shashikumar
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Varuna Jasodanand
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Elizabeth E Moore
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Kimberly R Pechman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Murat Bilgel
- 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
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Andrea Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | | | - Shannon L Risacher
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Katherine A Gifford
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, 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 Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Angela L Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Andrew J Saykin
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
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Turrini S, Bevacqua N, Cataneo A, Chiappini E, Fiori F, Battaglia S, Romei V, Avenanti A. Neurophysiological Markers of Premotor-Motor Network Plasticity Predict Motor Performance in Young and Older Adults. Biomedicines 2023; 11:biomedicines11051464. [PMID: 37239135 DOI: 10.3390/biomedicines11051464] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/08/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
Aging is commonly associated with a decline in motor control and neural plasticity. Tuning cortico-cortical interactions between premotor and motor areas is essential for controlling fine manual movements. However, whether plasticity in premotor-motor circuits predicts hand motor abilities in young and elderly humans remains unclear. Here, we administered transcranial magnetic stimulation (TMS) over the ventral premotor cortex (PMv) and primary motor cortex (M1) using the cortico-cortical paired-associative stimulation (ccPAS) protocol to manipulate the strength of PMv-to-M1 connectivity in 14 young and 14 elderly healthy adults. We assessed changes in motor-evoked potentials (MEPs) during ccPAS as an index of PMv-M1 network plasticity. We tested whether the magnitude of MEP changes might predict interindividual differences in performance in two motor tasks that rely on premotor-motor circuits, i.e., the nine-hole pegboard test and a choice reaction task. Results show lower motor performance and decreased PMv-M1 network plasticity in elderly adults. Critically, the slope of MEP changes during ccPAS accurately predicted performance at the two tasks across age groups, with larger slopes (i.e., MEP increase) predicting better motor performance at baseline in both young and elderly participants. These findings suggest that physiological indices of PMv-M1 plasticity could provide a neurophysiological marker of fine motor control across age-groups.
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Affiliation(s)
- Sonia Turrini
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia "Renzo Canestriari", Campus di Cesena, Alma Mater Studiorum Università di Bologna, 47521 Cesena, Italy
- Precision Neuroscience & Neuromodulation Program, Gordon Center for Medical Imaging, Massachusetts General Hospital & Harvard Medical School, Boston, MA 02114, USA
| | - Naomi Bevacqua
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia "Renzo Canestriari", Campus di Cesena, Alma Mater Studiorum Università di Bologna, 47521 Cesena, Italy
- Dipartimento di Psicologia, Sapienza Università di Roma, 00185 Rome, Italy
| | - Antonio Cataneo
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia "Renzo Canestriari", Campus di Cesena, Alma Mater Studiorum Università di Bologna, 47521 Cesena, Italy
| | - Emilio Chiappini
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia "Renzo Canestriari", Campus di Cesena, Alma Mater Studiorum Università di Bologna, 47521 Cesena, Italy
- Institut für Klinische und Gesundheitspsychologie, Universität Wien, 1010 Vienna, Austria
| | - Francesca Fiori
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia "Renzo Canestriari", Campus di Cesena, Alma Mater Studiorum Università di Bologna, 47521 Cesena, Italy
- NeXT: Unità di Ricerca di Neurofisiologia e Neuroingegneria dell'Interazione Uomo-Tecnologia, Dipartimento di Medicina, Università Campus Bio-Medico, 00128 Rome, Italy
| | - Simone Battaglia
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia "Renzo Canestriari", Campus di Cesena, Alma Mater Studiorum Università di Bologna, 47521 Cesena, Italy
| | - Vincenzo Romei
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia "Renzo Canestriari", Campus di Cesena, Alma Mater Studiorum Università di Bologna, 47521 Cesena, Italy
| | - Alessio Avenanti
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia "Renzo Canestriari", Campus di Cesena, Alma Mater Studiorum Università di Bologna, 47521 Cesena, Italy
- Centro de Investigación en Neuropsicología y Neurociencias Cognitivas, Universidad Católica del Maule, Talca 346000, Chile
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Parsons N, Irimia A, Amgalan A, Ugon J, Morgan K, Shelyag S, Hocking A, Poudel G, Caeyenberghs K. Structural-functional connectivity bandwidth predicts processing speed in mild traumatic brain Injury: A multiplex network analysis. Neuroimage Clin 2023; 38:103428. [PMID: 37167841 PMCID: PMC10196722 DOI: 10.1016/j.nicl.2023.103428] [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: 01/10/2023] [Revised: 04/17/2023] [Accepted: 05/01/2023] [Indexed: 05/13/2023]
Abstract
An emerging body of work has revealed alterations in structural (SC) and functional (FC) brain connectivity following mild TBI (mTBI), with mixed findings. However, these studies seldom integrate complimentary neuroimaging modalities within a unified framework. Multilayer network analysis is an emerging technique to uncover how white matter organization enables functional communication. Using our novel graph metric (SC-FC Bandwidth), we quantified the information capacity of synchronous brain regions in 53 mild TBI patients (46 females; age mean = 40.2 years (y), σ = 16.7 (y), range: 18-79 (y). Diffusion MRI and resting state fMRI were administered at the acute and chronic post-injury intervals. Moreover, participants completed a cognitive task to measure processing speed (30 Seconds and Counting Task; 30-SACT). Processing speed was significantly increased at the chronic, relative to the acute post-injury intervals (p = <0.001). Nonlinear principal components of direct (t = -1.84, p = 0.06) and indirect SC-FC Bandwidth (t = 3.86, p = <0.001) predicted processing speed with a moderate effect size (R2 = 0.43, p < 0.001), while controlling for age. A subnetwork of interhemispheric edges with increased SC-FC Bandwidth was identified at the chronic, relative to the acute mTBI post-injury interval (pFDR = 0.05). Increased interhemispheric SC-FC Bandwidth of this network corresponded with improved processing speed at the chronic post-injury interval (partial r = 0.32, p = 0.02). Our findings revealed that mild TBI results in complex reorganization of brain connectivity optimized for maximum information flow, supporting improved cognitive performance as a compensatory mechanism. Moving forward, this measurement may complement clinical assessment as an objective marker of mTBI recovery.
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Affiliation(s)
- Nicholas Parsons
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, VIC, Australia; BrainCast Neurotechnologies, Australia; School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia.
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Anar Amgalan
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Julien Ugon
- School of Information Technology, Faculty of Science Engineering Built Environment, Deakin University, Melbourne, VIC, Australia
| | - Kerri Morgan
- School of Information Technology, Faculty of Science Engineering Built Environment, Deakin University, Melbourne, VIC, Australia
| | - Sergiy Shelyag
- School of Information Technology, Faculty of Science Engineering Built Environment, Deakin University, Melbourne, VIC, Australia
| | - Alex Hocking
- School of Information Technology, Faculty of Science Engineering Built Environment, Deakin University, Melbourne, VIC, Australia
| | - Govinda Poudel
- BrainCast Neurotechnologies, Australia; Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, VIC, Australia
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75
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Stellingwerff MD, Pouwels PJW, Roosendaal SD, Barkhof F, van der Knaap MS. Quantitative MRI in leukodystrophies. Neuroimage Clin 2023; 38:103427. [PMID: 37150021 PMCID: PMC10193020 DOI: 10.1016/j.nicl.2023.103427] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 05/09/2023]
Abstract
Leukodystrophies constitute a large and heterogeneous group of genetic diseases primarily affecting the white matter of the central nervous system. Different disorders target different white matter structural components. Leukodystrophies are most often progressive and fatal. In recent years, novel therapies are emerging and for an increasing number of leukodystrophies trials are being developed. Objective and quantitative metrics are needed to serve as outcome measures in trials. Quantitative MRI yields information on microstructural properties, such as myelin or axonal content and condition, and on the chemical composition of white matter, in a noninvasive fashion. By providing information on white matter microstructural involvement, quantitative MRI may contribute to the evaluation and monitoring of leukodystrophies. Many distinct MR techniques are available at different stages of development. While some are already clinically applicable, others are less far developed and have only or mainly been applied in healthy subjects. In this review, we explore the background, current status, potential and challenges of available quantitative MR techniques in the context of leukodystrophies.
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Affiliation(s)
- Menno D Stellingwerff
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Child Neurology, Emma Children's Hospital, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Petra J W Pouwels
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Stefan D Roosendaal
- Amsterdam UMC Location University of Amsterdam, Department of Radiology, Meibergdreef 9, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands; University College London, Institutes of Neurology and Healthcare Engineering, London, UK
| | - Marjo S van der Knaap
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Child Neurology, Emma Children's Hospital, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands; Vrije Universiteit Amsterdam, Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, De Boelelaan 1105, Amsterdam, the Netherlands.
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76
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Schilling KG, Archer D, Rheault F, Lyu I, Huo Y, Cai LY, Bunge SA, Weiner KS, Gore JC, Anderson AW, Landman BA. Superficial white matter across development, young adulthood, and aging: volume, thickness, and relationship with cortical features. Brain Struct Funct 2023; 228:1019-1031. [PMID: 37074446 PMCID: PMC10320929 DOI: 10.1007/s00429-023-02642-x] [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: 07/20/2022] [Accepted: 04/08/2023] [Indexed: 04/20/2023]
Abstract
Superficial white matter (SWM) represents a significantly understudied part of the human brain, despite comprising a large portion of brain volume and making up a majority of cortico-cortical white matter connections. Using multiple, high-quality datasets with large sample sizes (N = 2421, age range 5-100) in combination with methodological advances in tractography, we quantified features of SWM volume and thickness across the brain and across development, young adulthood, and aging. We had four primary aims: (1) characterize SWM thickness across brain regions (2) describe associations between SWM volume and age (3) describe associations between SWM thickness and age, and (4) quantify relationships between SWM thickness and cortical features. Our main findings are that (1) SWM thickness varies across the brain, with patterns robust across individuals and across the population at the region-level and vertex-level; (2) SWM volume shows unique volumetric trajectories with age that are distinct from gray matter and other white matter trajectories; (3) SWM thickness shows nonlinear cross-sectional changes across the lifespan that vary across regions; and (4) SWM thickness is associated with features of cortical thickness and curvature. For the first time, we show that SWM volume follows a similar trend as overall white matter volume, peaking at a similar time in adolescence, leveling off throughout adulthood, and decreasing with age thereafter. Notably, the relative fraction of total brain volume of SWM continuously increases with age, and consequently takes up a larger proportion of total white matter volume, unlike the other tissue types that decrease with respect to total brain volume. This study represents the first characterization of SWM features across the large portion of the lifespan and provides the background for characterizing normal aging and insight into the mechanisms associated with SWM development and decline.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.
| | - Derek Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Francois Rheault
- Department of Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Ilwoo Lyu
- Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Yuankai Huo
- Department of Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Silvia A Bunge
- Department of Psychology, University of California at Berkeley, Berkeley, USA
| | - Kevin S Weiner
- Department of Psychology, University of California at Berkeley, Berkeley, USA
- Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, USA
| | - John C Gore
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
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77
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Baranyi G, Buchanan CR, Conole EL, Backhouse EV, Maniega SM, Hernandez MV, 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. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.13.23288523. [PMID: 37131666 PMCID: PMC10153312 DOI: 10.1101/2023.04.13.23288523] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Neighbourhood disadvantage may be associated with brain health but the importance at different stages of the life course is poorly understood. Utilizing the Lothian Birth Cohort 1936, we explored the relationship between residential neighbourhood deprivation from birth to late adulthood, and global and regional neuroimaging measures at age 73. We found that residing in disadvantaged neighbourhoods in mid- to late adulthood was associated with smaller total brain (β=-0.06; SE=0.02; n=390) and grey matter volume (β=-0.11; SE=0.03; n=390), thinner cortex (β=-0.15; SE=0.06; n=379), and lower general white matter fractional anisotropy (β=-0.19; SE=0.06; n=388). Regional analysis identified affected focal cortical areas and specific white matter tracts. Among individuals belonging to lower occupational social classes, the brain-neighbourhood associations were stronger, with the impact of neighbourhood deprivation accumulating across the life course. Our findings suggest that living in deprived neighbourhoods is associated with adverse brain morphologies, with occupational social class adding to 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
- Edinburgh Imaging, The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh UK
| | - Maria Valdes Hernandez
- 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
- UK Dementia Research Institute Centre at 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
- Edinburgh Imaging, The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute Centre at 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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Trofimova O, Latypova A, DiDomenicantonio G, Lutti A, de Lange AMG, Kliegel M, Stringhini S, Marques-Vidal P, Vaucher J, Vollenweider P, Strippoli MPF, Preisig M, Kherif F, Draganski B. Topography of associations between cardiovascular risk factors and myelin loss in the ageing human brain. Commun Biol 2023; 6:392. [PMID: 37037939 PMCID: PMC10086032 DOI: 10.1038/s42003-023-04741-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 03/21/2023] [Indexed: 04/12/2023] Open
Abstract
Our knowledge of the mechanisms underlying the vulnerability of the brain's white matter microstructure to cardiovascular risk factors (CVRFs) is still limited. We used a quantitative magnetic resonance imaging (MRI) protocol in a single centre setting to investigate the cross-sectional association between CVRFs and brain tissue properties of white matter tracts in a large community-dwelling cohort (n = 1104, age range 46-87 years). Arterial hypertension was associated with lower myelin and axonal density MRI indices, paralleled by higher extracellular water content. Obesity showed similar associations, though with myelin difference only in male participants. Associations between CVRFs and white matter microstructure were observed predominantly in limbic and prefrontal tracts. Additional genetic, lifestyle and psychiatric factors did not modulate these results, but moderate-to-vigorous physical activity was linked to higher myelin content independently of CVRFs. Our findings complement previously described CVRF-related changes in brain water diffusion properties pointing towards myelin loss and neuroinflammation rather than neurodegeneration.
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Affiliation(s)
- Olga Trofimova
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Adeliya Latypova
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Giulia DiDomenicantonio
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ann-Marie G de Lange
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Matthias Kliegel
- Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Silvia Stringhini
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
- Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Julien Vaucher
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Marie-Pierre F Strippoli
- Center for Research in Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Martin Preisig
- Center for Research in Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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79
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Patel A, Chad JA, Chen JJ. Is adiposity associated with white matter microstructural health and intelligence differently in males and females? Obesity (Silver Spring) 2023; 31:1011-1023. [PMID: 36883598 DOI: 10.1002/oby.23686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 10/27/2022] [Accepted: 11/24/2022] [Indexed: 03/09/2023]
Abstract
OBJECTIVE The role of vascular risk factors in age-related brain degeneration has long been the subject of intense study, but the role of obesity remains understudied. Given known sex differences in fat storage and usage, this study investigates sex differences in the association between adiposity and white matter microstructural integrity, an important early marker of brain degeneration. METHODS This study assesses the associations between adiposity (abdominal fat ratio and liver proton density fat fraction) and brain health (measures of intelligence and white matter microstructure using diffusion-tensor imaging [DTI]) in a group of UK Biobank participants. RESULTS This study finds that intelligence and DTI metrics are indeed associated with adiposity differently in males and females. These sex differences are distinct from those in the associations of DTI metrics with age and blood pressure. CONCLUSIONS Taken together, these findings suggest that there are inherent sex-driven differences in how brain health is associated with obesity.
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Affiliation(s)
- Arjun Patel
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
| | - Jordan A Chad
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - J Jean Chen
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
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80
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Bouhrara M, Avram AV, Kiely M, Trivedi A, Benjamini D. Adult lifespan maturation and degeneration patterns in gray and white matter: A mean apparent propagator (MAP) MRI study. Neurobiol Aging 2023; 124:104-116. [PMID: 36641369 PMCID: PMC9985137 DOI: 10.1016/j.neurobiolaging.2022.12.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/24/2022] [Accepted: 12/27/2022] [Indexed: 01/02/2023]
Abstract
The relationship between brain microstructure and aging has been the subject of intense study, with diffusion MRI perhaps the most effective modality for elucidating these associations. Here, we used the mean apparent propagator (MAP)-MRI framework, which is suitable to characterize complex microstructure, to investigate age-related cerebral differences in a cohort of cognitively unimpaired participants and compared the results to those derived using diffusion tensor imaging. We studied MAP-MRI metrics, among them the non-Gaussianity (NG) and propagator anisotropy (PA), and established an opposing pattern in white matter of higher NG alongside lower PA among older adults, likely indicative of axonal degradation. In gray matter, however, these two indices were consistent with one another, and exhibited regional pattern heterogeneity compared to other microstructural parameters, which could indicate fewer neuronal projections across cortical layers along with an increased glial concentration. In addition, we report regional variations in the magnitude of age-related microstructural differences consistent with the posterior-anterior shift in aging paradigm. These results encourage further investigations in cognitive impairments and neurodegeneration.
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Affiliation(s)
- Mustapha Bouhrara
- Magnetic Resonance Physics of Aging and Dementia Unit, National Institute on Aging, NIH, Baltimore, MD 21224, USA.
| | - Alexandru V. Avram
- Section on Quantitative Imaging and Tissue Sciences,Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, USA,Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Matthew Kiely
- Magnetic Resonance Physics of Aging and Dementia Unit, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Aparna Trivedi
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD 21224, USA.
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81
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Takeuchi H, Kawashima R. Effects of television viewing on brain structures and risk of dementia in the elderly: Longitudinal analyses. Front Neurosci 2023; 17:984919. [PMID: 36968501 PMCID: PMC10030518 DOI: 10.3389/fnins.2023.984919] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 02/16/2023] [Indexed: 03/10/2023] Open
Abstract
IntroductionTV viewing in the elderly and in children is associated with subsequent greater decline of various cognitive functions including verbal working memory, but results of its association with subsequent risk of dementia were divided.MethodsIn this longitudinal cohort study of UK Biobank, we investigated the associations of TV viewing length with subsequent risk of dementia and longitudinal changes of brain structural measures after corrections of a wide range of potential confounders.ResultsOur results showed longer TV viewing was associated with increased risk of subsequent onset of dementia, as well as subsequent greater decline in intracellular volume fraction (ICVF) in the extensive areas of right lateral temporal cortex and the right medial temporal cortex, in the area around the left middle and inferior temporal cortex as well as the left fusiform gyrus, and the area adjacent to the left inferior frontal gyrus, and left insula.DiscussionThese results may suggest prolonged TV viewing was associated with decline in density of neurites (axon, dendrites) in areas particularly implicated in language, communication, and memory, which are altered in dementia.
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Affiliation(s)
- Hikaru Takeuchi
- Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- *Correspondence: Hikaru Takeuchi,
| | - Ryuta Kawashima
- Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- Smart Aging Research Center, Tohoku University, Sendai, Japan
- Department of Advanced Brain Science, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
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82
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Chen Z, Wu B, Li G, Zhou L, Zhang L, Liu J. Age and sex differentially shape brain networks in Parkinson's disease. CNS Neurosci Ther 2023. [PMID: 36890620 DOI: 10.1111/cns.14149] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 02/18/2023] [Accepted: 02/20/2023] [Indexed: 03/10/2023] Open
Abstract
AIMS Age and sex are important individual factors modifying the clinical symptoms of patients with Parkinson's disease (PD). Our goal is to evaluate the effects of age and sex on brain networks and clinical manifestations of PD patients. METHODS Parkinson's disease participants (n = 198) receiving functional magnetic resonance imaging from Parkinson's Progression Markers Initiative database were investigated. Participants were classified into lower quartile group (age rank: 0%~25%), interquartile group (age rank: 26%~75%), and upper quartile group (age rank: 76%~100%) according to their age quartiles to examine how age shapes brain network topology. The differences of brain network topological properties between male and female participants were also investigated. RESULTS Parkinson's disease patients in the upper quartile age group exhibited disrupted network topology of white matter networks and impaired integrity of white matter fibers compared to lower quartile age group. In contrast, sex preferentially shaped the small-world topology of gray matter covariance network. Differential network metrics mediated the effects of age and sex on cognitive function of PD patients. CONCLUSION Age and sex have diverse effects on brain structural networks and cognitive function of PD patients, highlighting their roles in the clinical management of PD.
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Affiliation(s)
- Zhichun Chen
- Department of Neurology and Institute of Neurology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Department of Neurology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Bin Wu
- Department of Neurology, Xuchang Central Hospital affiliated with Henan University of Science and Technology, Henan, China
| | - Guanglu Li
- Department of Neurology and Institute of Neurology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Liche Zhou
- Department of Neurology and Institute of Neurology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lina Zhang
- Department of Biostatistics, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Liu
- Department of Neurology and Institute of Neurology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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83
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Arnoldy L, Gauci S, Young LM, Marx W, Macpherson H, Pipingas A, Civier O, White DJ. The association of dietary and nutrient patterns on neurocognitive decline: A systematic review of MRI and PET studies. Ageing Res Rev 2023; 87:101892. [PMID: 36878405 DOI: 10.1016/j.arr.2023.101892] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 02/14/2023] [Accepted: 02/27/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND As the global population ages, there has been a growing incidence of neurodegenerative diseases such as Alzheimer's. More recently, studies exploring the relationship between dietary patterns and neuroimaging outcomes have received particular attention. This systematic literature review provides a structured overview of the association between dietary and nutrient patterns on neuroimaging outcomes and cognitive markers in middle-aged to older adults. A comprehensive literature search was conducted to find relevant articles published from 1999 to date using the following databases Ovid MEDLINE, Embase, PubMed, Scopus and Web of Science. The inclusion criteria for the articles comprised studies reporting on the association between dietary patterns and neuroimaging outcomes, which includes both specific pathological hallmarks of neurodegenerative diseases such as Aβ and tau and nonspecific markers such as structural MRI and glucose metabolism. The risk of bias was evaluated using the Quality Assessment tool from the National Heart, Lung, and Blood Institute of the National Institutes of Health. The results were then organized into a summary of results table, collated based on synthesis without meta-analysis. After conducting the search, 6050 records were extracted and screened for eligibility, with 107 eligible for full-text screening and 42 articles ultimately being included in this review. The results of the systematic review indicate that there is some evidence suggesting that healthy dietary and nutrient patterns were associated with neuroimaging measures, indicative of a protective influence on neurodegeneration and brain ageing. Conversely, unhealthy dietary and nutrient patterns showed evidence pointing to decreased brain volumes, poorer cognition and increased Aβ deposition. Future research should focus on sensitive neuroimaging acquisition and analysis methods, to study early neurodegenerative changes and identify critical periods for interventions and prevention. SYSTEMATIC REVIEW REGISTRATION PROSPERO registration no, CRD42020194444).
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Affiliation(s)
- Lizanne Arnoldy
- Centre of Human Psychopharmacology, Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia.
| | - Sarah Gauci
- Centre of Human Psychopharmacology, Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia; IMPACT - the Institute for Mental and Physical Health and Clinical Translation, Food & Mood Centre, School of Medicine, Deakin University, Geelong, Australia
| | - Lauren M Young
- Centre of Human Psychopharmacology, Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia; IMPACT - the Institute for Mental and Physical Health and Clinical Translation, Food & Mood Centre, School of Medicine, Deakin University, Geelong, Australia
| | - Wolfgang Marx
- IMPACT - the Institute for Mental and Physical Health and Clinical Translation, Food & Mood Centre, School of Medicine, Deakin University, Geelong, Australia
| | - Helen Macpherson
- Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, Australia
| | - Andrew Pipingas
- Centre of Human Psychopharmacology, Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia
| | - Oren Civier
- Swinburne Neuroimaging, Swinburne University, Melbourne, Australia
| | - David J White
- Centre of Human Psychopharmacology, Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia; Swinburne Neuroimaging, Swinburne University, Melbourne, Australia
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84
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Zanon Zotin MC, Yilmaz P, Sveikata L, Schoemaker D, van Veluw SJ, Etherton MR, Charidimou A, Greenberg SM, Duering M, Viswanathan A. Peak Width of Skeletonized Mean Diffusivity: A Neuroimaging Marker for White Matter Injury. Radiology 2023; 306:e212780. [PMID: 36692402 PMCID: PMC9968775 DOI: 10.1148/radiol.212780] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 10/01/2022] [Accepted: 10/14/2022] [Indexed: 01/25/2023]
Abstract
A leading cause of white matter (WM) injury in older individuals is cerebral small vessel disease (SVD). Cerebral SVD is the most prevalent vascular contributor to cognitive impairment and dementia. Therapeutic progress for cerebral SVD and other WM disorders depends on the development and validation of neuroimaging markers suitable as outcome measures in future interventional trials. Diffusion-tensor imaging (DTI) is one of the best-suited MRI techniques for assessing the extent of WM damage in the brain. But the optimal method to analyze individual DTI data remains hindered by labor-intensive and time-consuming processes. Peak width of skeletonized mean diffusivity (PSMD), a recently developed fast, fully automated DTI marker, was designed to quantify the WM damage secondary to cerebral SVD and reflect related cognitive impairment. Despite its promising results, knowledge about PSMD is still limited in the radiologic community. This focused review provides an overview of the technical details of PSMD while synthesizing the available data on its clinical and neuroimaging associations. From a critical expert viewpoint, the authors discuss the limitations of PSMD and its current validation status as a neuroimaging marker for vascular cognitive impairment. Finally, they point out the gaps to be addressed to further advance the field.
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Affiliation(s)
| | | | - Lukas Sveikata
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Dorothee Schoemaker
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Susanne J. van Veluw
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Mark R. Etherton
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Andreas Charidimou
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Steven M. Greenberg
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Marco Duering
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Anand Viswanathan
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
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85
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Srisaikaew P, Chad JA, Mahakkanukrauh P, Anderson ND, Chen JJ. Effect of sex on the APOE4-aging interaction in the white matter microstructure of cognitively normal older adults using diffusion-tensor MRI with orthogonal-tensor decomposition (DT-DOME). Front Neurosci 2023; 17:1049609. [PMID: 36908785 PMCID: PMC9992882 DOI: 10.3389/fnins.2023.1049609] [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: 09/20/2022] [Accepted: 02/06/2023] [Indexed: 02/24/2023] Open
Abstract
The influence of the apolipoprotein E ε4 allele (APOE4) on brain microstructure of cognitively normal older adults remains incompletely understood, in part due to heterogeneity within study populations. In this study, we examined white-matter microstructural integrity in cognitively normal older adults as a function of APOE4 carrier status using conventional diffusion-tensor imaging (DTI) and the novel orthogonal-tensor decomposition (DT-DOME), accounting for the effects of age and sex. Age associations with white-matter microstructure did not significantly depend on APOE4 status, but did differ between sexes, emphasizing the importance of accounting for sex differences in APOE research. Moreover, we found the DT-DOME to be more sensitive than conventional DTI metrics to such age-related and sex effects, especially in crossing WM fiber regions, and suggest their use in further investigation of white matter microstructure across the life span in health and disease.
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Affiliation(s)
- Patcharaporn Srisaikaew
- Ph.D. Program in Anatomy, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
- Department of Anatomy, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Jordan A. Chad
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Pasuk Mahakkanukrauh
- Department of Anatomy, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
- Excellence in Osteology Research and Training Center, Chiang Mai University, Chiang Mai, Thailand
| | - Nicole D. Anderson
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
- Department of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada
| | - J. Jean Chen
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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86
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Van Hoornweder S, Nuyts M, Frieske J, Verstraelen S, Meesen RLJ, Caulfield KA. A Systematic Review and Large-Scale tES and TMS Electric Field Modeling Study Reveals How Outcome Measure Selection Alters Results in a Person- and Montage-Specific Manner. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.22.529540. [PMID: 36865243 PMCID: PMC9980068 DOI: 10.1101/2023.02.22.529540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Background Electric field (E-field) modeling is a potent tool to examine the cortical effects of transcranial magnetic and electrical stimulation (TMS and tES, respectively) and to address the high variability in efficacy observed in the literature. However, outcome measures used to report E-field magnitude vary considerably and have not yet been compared in detail. Objectives The goal of this two-part study, encompassing a systematic review and modeling experiment, was to provide an overview of the different outcome measures used to report the magnitude of tES and TMS E-fields, and to conduct a direct comparison of these measures across different stimulation montages. Methods Three electronic databases were searched for tES and/or TMS studies reporting E-field magnitude. We extracted and discussed outcome measures in studies meeting the inclusion criteria. Additionally, outcome measures were compared via models of four common tES and two TMS modalities in 100 healthy younger adults. Results In the systematic review, we included 118 studies using 151 outcome measures related to E-field magnitude. Structural and spherical regions of interest (ROI) analyses and percentile-based whole-brain analyses were used most often. In the modeling analyses, we found that there was an average of only 6% overlap between ROI and percentile-based whole-brain analyses in the investigated volumes within the same person. The overlap between ROI and whole-brain percentiles was montage- and person-specific, with more focal montages such as 4Ã-1 and APPS-tES, and figure-of-eight TMS showing up to 73%, 60%, and 52% overlap between ROI and percentile approaches respectively. However, even in these cases, 27% or more of the analyzed volume still differed between outcome measures in every analyses. Conclusions The choice of outcome measures meaningfully alters the interpretation of tES and TMS E-field models. Well-considered outcome measure selection is imperative for accurate interpretation of results, valid between-study comparisons, and depends on stimulation focality and study goals. We formulated four recommendations to increase the quality and rigor of E-field modeling outcome measures. With these data and recommendations, we hope to guide future studies towards informed outcome measure selection, and improve the comparability of studies.
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87
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Zhao B, Li Y, Fan Z, Wu Z, Shu J, Yang X, Yang Y, Wang X, Li B, Wang X, Copana C, Yang Y, Lin J, Li Y, Stein JL, O'Brien JM, Li T, Zhu H. Eye-brain connections revealed by multimodal retinal and brain imaging genetics in the UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.16.23286035. [PMID: 36824893 PMCID: PMC9949187 DOI: 10.1101/2023.02.16.23286035] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
As an anatomical extension of the brain, the retina of the eye is synaptically connected to the visual cortex, establishing physiological connections between the eye and the brain. Despite the unique opportunity retinal structures offer for assessing brain disorders, less is known about their relationship to brain structure and function. Here we present a systematic cross-organ genetic architecture analysis of eye-brain connections using retina and brain imaging endophenotypes. Novel phenotypic and genetic links were identified between retinal imaging biomarkers and brain structure and function measures derived from multimodal magnetic resonance imaging (MRI), many of which were involved in the visual pathways, including the primary visual cortex. In 65 genomic regions, retinal imaging biomarkers shared genetic influences with brain diseases and complex traits, 18 showing more genetic overlaps with brain MRI traits. Mendelian randomization suggests that retinal structures have bidirectional genetic causal links with neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, cross-organ imaging genetics reveals a genetic basis for eye-brain connections, suggesting that the retinal images can elucidate genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zhenyi Wu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yilin Yang
- Department of Computer and Information Science and Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bingxuan Li
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Xiyao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Carlos Copana
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jinjie Lin
- Yale School of Management, Yale University, New Haven, CT 06511, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jason L. Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joan M. O'Brien
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn Medicine Center for Ophthalmic Genetics in Complex Diseases, PA, 19104, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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88
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Pillinger T, Osimo EF, de Marvao A, Shah M, Francis C, Huang J, D'Ambrosio E, Firth J, Nour MM, McCutcheon RA, Pardiñas AF, Matthews PM, O'Regan DP, Howes OD. Effect of polygenic risk for schizophrenia on cardiac structure and function: a UK Biobank observational study. Lancet Psychiatry 2023; 10:98-107. [PMID: 36632818 DOI: 10.1016/s2215-0366(22)00403-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 11/03/2022] [Accepted: 11/18/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND Cardiovascular disease is a major cause of excess mortality in people with schizophrenia. Several factors are responsible, including lifestyle and metabolic effects of antipsychotics. However, variations in cardiac structure and function are seen in people with schizophrenia in the absence of cardiovascular disease risk factors and after accounting for lifestyle and medication. Therefore, we aimed to explore whether shared genetic causes contribute to these cardiac variations. METHODS For this observational study, we used data from the UK Biobank and included White British or Irish individuals without diagnosed schizophrenia with variable polygenic risk scores for the condition. To test the association between polygenic risk score for schizophrenia and cardiac phenotype, we used principal component analysis and regression. Robust regression was then used to explore the association between the polygenic risk score for schizophrenia and individual cardiac phenotypes. We repeated analyses with fibro-inflammatory pathway-specific polygenic risk scores for schizophrenia. Last, we investigated genome-wide sharing of common variants between schizophrenia and cardiac phenotypes using linkage disequilibrium score regression. The primary outcome was principal component regression. FINDINGS Of 33 353 individuals recruited, 32 279 participants had complete cardiac MRI data and were included in the analysis, of whom 16 625 (51·5%) were female and 15 654 (48·5%) were male. 1074 participants were excluded on the basis of incomplete cardiac MRI data (for all phenotypes). A model regressing polygenic risk scores for schizophrenia onto the first five cardiac principal components of the principal components analysis was significant (F=5·09; p=0·00012). Principal component 1 captured a pattern of increased cardiac volumes, increased absolute peak diastolic strain rates, and reduced ejection fractions; polygenic risk scores for schizophrenia and principal component 1 were negatively associated (β=-0·01 [SE 0·003]; p=0·017). Similar to the principal component analysis results, for individual cardiac phenotypes, we observed negative associations between polygenic risk scores for schizophrenia and indexed right ventricular end-systolic volume (β=-0·14 [0·04]; p=0·0013, pFDR=0·015), indexed right ventricular end-diastolic volume (β=-0·17 [0·08]); p=0·025; pFDR=0·082), and absolute longitudinal peak diastolic strain rates (β=-0·01 [0·003]; p=0·0024, pFDR=0·015), and a positive association between polygenic risk scores for schizophrenia and right ventricular ejection fraction (β=0·09 [0·03]; p=0·0041, pFDR=0·015). Models examining the transforming growth factor-β (TGF-β)-specific and acute inflammation-specific polygenic risk scores for schizophrenia found significant associations with the first five principal components (F=2·62, p=0·022; F=2·54, p=0·026). Using linkage disequilibrium score regression, we observed genetic overlap with schizophrenia for right ventricular end-systolic volume and right ventricular ejection fraction (p=0·0090, p=0·0077). INTERPRETATION High polygenic risk scores for schizophrenia are associated with decreased cardiac volumes, increased ejection fractions, and decreased absolute peak diastolic strain rates. TGF-β and inflammatory pathways might be implicated, and there is evidence of genetic overlap for some cardiac phenotypes. Reduced absolute peak diastolic strain rates indicate increased myocardial stiffness and diastolic dysfunction, which increases risk of cardiac disease. Thus, genetic risk for schizophrenia is associated with cardiac structural changes that can worsen cardiac outcomes. Further work is required to determine whether these associations are specific to schizophrenia or are also seen in other psychiatric conditions. FUNDING National Institute for Health Research, Maudsley Charity, Wellcome Trust, Medical Research Council, Academy of Medical Sciences, Edmond J Safra Foundation, British Heart Foundation.
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Affiliation(s)
- Toby Pillinger
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London, UK; Psychiatric Imaging Group, Imperial College London, London, UK.
| | - Emanuele F Osimo
- Department of Psychiatry, University of Cambridge, Cambridge, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK; Psychiatric Imaging Group, Imperial College London, London, UK
| | - Antonio de Marvao
- British Heart Foundation Centre of Research Excellence, School of Cardiovascular Medicine and Sciences, King's College London, London, UK; Department of Women and Children's Health, King's College London, London, UK
| | - Mit Shah
- Computational Cardiac Imaging Group, Imperial College London, London, UK
| | - Catherine Francis
- MRC London Institute of Medical Sciences, Department of Cardiovascular Genetics and Genomics, National Heart and Lung Institute, Imperial College London, London, UK; Royal Brompton Hospital, Royal Brompton and Harefield NHS Foundation Trust, Uxbridge, UK
| | - Jian Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK; Singapore Institute for Clinical Sciences (SICS), the Agency for Science, Technology and Research (A*STAR), Singapore
| | - Enrico D'Ambrosio
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London, UK; Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari 'Aldo Moro', Italy
| | - Joseph Firth
- Division of Psychology and Mental Health, University of Manchester, and Greater Manchester Mental Health NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Matthew M Nour
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London, UK; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, and Wellcome Trust Centre for Human Neuroimaging, University College London, London, UK; Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Robert A McCutcheon
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London, UK; Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Antonio F Pardiñas
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Paul M Matthews
- Department of Brain Sciences and UK Dementia Research Institute Centre, Imperial College London, London, UK
| | - Declan P O'Regan
- Computational Cardiac Imaging Group, Imperial College London, London, UK
| | - Oliver D Howes
- Department of Psychological Medicine, King's College London, London, UK; Psychiatric Imaging Group, Imperial College London, London, UK; H Lundbeck A/S, St Albans, UK
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89
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Jin M, Cai SQ. Mechanisms Underlying Brain Aging Under Normal and Pathological Conditions. Neurosci Bull 2023; 39:303-314. [PMID: 36437436 PMCID: PMC9905409 DOI: 10.1007/s12264-022-00969-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 06/17/2022] [Indexed: 11/28/2022] Open
Abstract
Aging is a major risk factor for many human diseases, including cognitive impairment, which affects a large population of the elderly. In the past few decades, our understanding of the molecular and cellular mechanisms underlying the changes associated with aging and age-related diseases has expanded greatly, shedding light on the potential role of these changes in cognitive impairment. In this article, we review recent advances in understanding of the mechanisms underlying brain aging under normal and pathological conditions, compare their similarities and differences, discuss the causative and adaptive mechanisms of brain aging, and finally attempt to find some rules to guide us on how to promote healthy aging and prevent age-related diseases.
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Affiliation(s)
- Menglong Jin
- Institute of Neuroscience and State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shi-Qing Cai
- Institute of Neuroscience and State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China.
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90
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Lawrence KE, Abaryan Z, Laltoo E, Hernandez LM, Gandal MJ, McCracken JT, Thompson PM. White matter microstructure shows sex differences in late childhood: Evidence from 6797 children. Hum Brain Mapp 2023; 44:535-548. [PMID: 36177528 PMCID: PMC9842921 DOI: 10.1002/hbm.26079] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 07/29/2022] [Accepted: 08/19/2022] [Indexed: 02/01/2023] Open
Abstract
Sex differences in white matter microstructure have been robustly demonstrated in the adult brain using both conventional and advanced diffusion-weighted magnetic resonance imaging approaches. However, sex differences in white matter microstructure prior to adulthood remain poorly understood; previous developmental work focused on conventional microstructure metrics and yielded mixed results. Here, we rigorously characterized sex differences in white matter microstructure among over 6000 children from the Adolescent Brain Cognitive Development study who were between 9 and 10 years old. Microstructure was quantified using both the conventional model-diffusion tensor imaging (DTI)-and an advanced model, restriction spectrum imaging (RSI). DTI metrics included fractional anisotropy (FA) and mean, axial, and radial diffusivity (MD, AD, RD). RSI metrics included normalized isotropic, directional, and total intracellular diffusion (N0, ND, NT). We found significant and replicable sex differences in DTI or RSI microstructure metrics in every white matter region examined across the brain. Sex differences in FA were regionally specific. Across white matter regions, boys exhibited greater MD, AD, and RD than girls, on average. Girls displayed increased N0, ND, and NT compared to boys, on average, suggesting greater cell and neurite density in girls. Together, these robust and replicable findings provide an important foundation for understanding sex differences in health and disease.
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Affiliation(s)
- Katherine E. Lawrence
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Zvart Abaryan
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Emily Laltoo
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Leanna M. Hernandez
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Michael J. Gandal
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of MedicineUniversity of California Los AngelesLos AngelesCaliforniaUSA
- Department of Human Genetics, David Geffen School of MedicineUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - James T. McCracken
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
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91
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Radhakrishnan V, Gallea C, Valabregue R, Krishnan S, Kesavadas C, Thomas B, James P, Menon R, Kishore A. Cerebellar and basal ganglia structural connections in humans: Effect of aging and relation with memory and learning. Front Aging Neurosci 2023; 15:1019239. [PMID: 36776439 PMCID: PMC9908607 DOI: 10.3389/fnagi.2023.1019239] [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: 08/14/2022] [Accepted: 01/06/2023] [Indexed: 01/27/2023] Open
Abstract
Introduction The cerebellum and basal ganglia were initially considered anatomically distinct regions, each connected via thalamic relays which project to the same cerebral cortical targets, such as the motor cortex. In the last two decades, transneuronal viral transport studies in non-human primates showed bidirectional connections between the cerebellum and basal ganglia at the subcortical level, without involving the cerebral cortical motor areas. These findings have significant implications for our understanding of neurodevelopmental and neurodegenerative diseases. While these subcortical connections were established in smaller studies on humans, their evolution with natural aging is less understood. Methods In this study, we validated and expanded the previous findings of the structural connectivity within the cerebellum-basal ganglia subcortical network, in a larger dataset of 64 subjects, across different age ranges. Tractography and fixel-based analysis were performed on the 3 T diffusion-weighted dataset using Mrtrix3 software, considering fiber density and cross-section as indicators of axonal integrity. Tractography of the well-established cerebello-thalamo-cortical tract was conducted as a control. We tested the relationship between the structural white matter integrity of these connections with aging and with the performance in different domains of Addenbrooke's Cognitive Examination. Results Tractography analysis isolated connections from the dentate nucleus to the contralateral putamen via the thalamus, and reciprocal tracts from the subthalamic nucleus to the contralateral cerebellar cortex via the pontine nuclei. Control tracts of cerebello-thalamo-cortical tracts were also isolated, including associative cerebello-prefrontal tracts. A negative linear relationship was found between the fiber density of both the ascending and descending cerebellum-basal ganglia tracts and age. Considering the cognitive assessments, the fiber density values of cerebello-thalamo-putaminal tracts correlated with the registration/learning domain scores. In addition, the fiber density values of cerebello-frontal and subthalamo-cerebellar (Crus II) tracts correlated with the cognitive assessment scores from the memory domain. Conclusion We validated the structural connectivity within the cerebellum-basal ganglia reciprocal network, in a larger dataset of human subjects, across wider age range. The structural features of the subcortical cerebello-basal ganglia tracts in human subjects display age-related neurodegeneration. Individual morphological variability of cerebellar tracts to the striatum and prefrontal cortex was associated with different cognitive functions, suggesting a functional contribution of cerebellar tracts to cognitive decline with aging. This study offers new perspectives to consider the functional role of these pathways in motor learning and the pathophysiology of movement disorders involving the cerebellum and striatum.
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Affiliation(s)
- Vineeth Radhakrishnan
- Comprehensive Care Centre for Movement Disorders, Department of Neurology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Thiruvananthapuram, India
| | - Cecile Gallea
- INSERM, CNRS, Paris Brain Institute, Sorbonne Université, Paris, France
| | - Romain Valabregue
- INSERM, CNRS, Paris Brain Institute, Sorbonne Université, Paris, France
| | - Syam Krishnan
- Comprehensive Care Centre for Movement Disorders, Department of Neurology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Thiruvananthapuram, India
| | - Chandrasekharan Kesavadas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Thiruvananthapuram, India
| | - Bejoy Thomas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Thiruvananthapuram, India
| | - Praveen James
- Comprehensive Care Centre for Movement Disorders, Department of Neurology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Thiruvananthapuram, India
| | - Ramshekhar Menon
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
| | - Asha Kishore
- Comprehensive Care Centre for Movement Disorders, Department of Neurology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Thiruvananthapuram, India,Parkinson and Movement Disorder Centre, Department of Neurology, Aster Medcity, Kochi, India,*Correspondence: Asha Kishore, ✉
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92
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Diffusion Tensor Imaging in Amyotrophic Lateral Sclerosis: Machine Learning for Biomarker Development. Int J Mol Sci 2023; 24:ijms24031911. [PMID: 36768231 PMCID: PMC9915541 DOI: 10.3390/ijms24031911] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/11/2023] [Accepted: 01/16/2023] [Indexed: 01/21/2023] Open
Abstract
Diffusion tensor imaging (DTI) allows the in vivo imaging of pathological white matter alterations, either with unbiased voxel-wise or hypothesis-guided tract-based analysis. Alterations of diffusion metrics are indicative of the cerebral status of patients with amyotrophic lateral sclerosis (ALS) at the individual level. Using machine learning (ML) models to analyze complex and high-dimensional neuroimaging data sets, new opportunities for DTI-based biomarkers in ALS arise. This review aims to summarize how different ML models based on DTI parameters can be used for supervised diagnostic classifications and to provide individualized patient stratification with unsupervised approaches in ALS. To capture the whole spectrum of neuropathological signatures, DTI might be combined with additional modalities, such as structural T1w 3-D MRI in ML models. To further improve the power of ML in ALS and enable the application of deep learning models, standardized DTI protocols and multi-center collaborations are needed to validate multimodal DTI biomarkers. The application of ML models to multiparametric MRI/multimodal DTI-based data sets will enable a detailed assessment of neuropathological signatures in patients with ALS and the development of novel neuroimaging biomarkers that could be used in the clinical workup.
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93
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Pietrasik W, Cribben I, Olsen F, Malykhin N. Diffusion tensor imaging of superficial prefrontal white matter in healthy aging. Brain Res 2023; 1799:148152. [PMID: 36343726 DOI: 10.1016/j.brainres.2022.148152] [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: 06/06/2022] [Revised: 09/27/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022]
Abstract
The prefrontal cortex (PFC) is a heterogenous structure that is highly susceptible to the effects of aging. Few studies have investigated age effects on the superficial white matter (WM) contained within the PFC using in-vivo magnetic resonance imaging (MRI). This study used diffusion tensor imaging (DTI) tractography to examine the effects of age, sex, and intracranial volume (ICV) on superficial WM within specific PFC subregions, and to model the relationships with age using higher order polynomial regression modelling. PFC WM of 140 healthy individuals, aged 18-85, was segmented into medial and lateral orbitofrontal, medial prefrontal, and dorsolateral prefrontal subregions. Differences due to age in microstructural parameters such as fractional anisotropy (FA), axial and radial diffusivities, and macrostructural measures of tract volumes, fiber counts, average fiber lengths, and average number of fibers per voxel were examined. We found that most prefrontal subregions demonstrated age effects, with decreases in FA, tract volume, and fiber counts, and increases in all diffusivity measures. Age relationships were mostly non-linear, with higher order regressions chosen in most cases. Declines in PFC FA began at the onset of adulthood while the greatest changes in diffusivity and volume did not occur until middle age. The effects of age were most prominent in medial tracts while the lateral orbitofrontal tracts were less affected. Significant effects of sex and ICV were also observed in certain parameters. The patterns mostly followed myelination order, with late-myelinating prefrontal subregions experiencing earlier and more pronounced age effects, further supporting the frontal theory of aging.
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Affiliation(s)
- Wojciech Pietrasik
- Department of Biomedical Engineering, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada; Neuroscience and Mental Health Institute, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Ivor Cribben
- Neuroscience and Mental Health Institute, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada; Department of Accounting & Business Analytics, Alberta School of Business, University of Alberta, Edmonton, Alberta, Canada
| | - Fraser Olsen
- Department of Biomedical Engineering, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Nikolai Malykhin
- Neuroscience and Mental Health Institute, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada; Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada.
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94
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Stubbs JL, Jones AA, Wolfman D, Chan RCY, Vila-Rodriguez F, Vertinsky AT, Heran MK, Su W, Lang DJ, Field TS, Gicas KM, Woodward ML, Thornton AE, Barr AM, Leonova O, MacEwan W, Rauscher A, Honer WG, Panenka WJ. Differential age-associated brain atrophy and white matter changes among homeless and precariously housed individuals compared with the general population. BMJ Neurol Open 2023; 5:e000349. [PMID: 36660541 PMCID: PMC9843194 DOI: 10.1136/bmjno-2022-000349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 01/05/2023] [Indexed: 01/15/2023] Open
Abstract
Background Homeless or precariously housed individuals live with poor health and experience premature mortality compared with the general population, yet little is known about age-related brain changes among these individuals. We evaluated whether MRI measures of brain structure are differentially associated with age and selected risk factors among individuals who are homeless or precariously housed compared with a general population sample. Methods We compared T1-weighted and diffusion tensor imaging measures of brain macrostructure and white matter microstructure in a well-characterised sample of 312 precariously housed participants with a publicly available dataset of 382 participants recruited from the general population. We used piecewise and multiple linear regression to examine differential associations between MRI measures and between the samples, and to explore associations with risk factors in the precariously housed sample. Results Compared with the general population sample, older age in the precariously housed sample was associated with more whole-brain atrophy (β=-0.20, p=0.0029), lower whole-brain fractional anisotropy (β=-0.32, p<0.0001) and higher whole-brain mean diffusivity (β=0.69, p<0.0001). Several MRI measures had non-linear associations with age, with further adverse changes after age 35-40 in the precariously housed sample. History of traumatic brain injury, stimulant dependence and heroin dependence was associated with more atrophy or alterations in white matter diffusivity in the precariously housed sample. Conclusions Older age is associated with adverse MRI measures of brain structure among homeless and precariously housed individuals compared with the general population. Education, improvements in care provision and policy may help to reduce the health disparities experienced by these individuals.
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Affiliation(s)
- Jacob L Stubbs
- Department of Psychaitry, University of British Columbia, Vancouver, BC, Canada,British Columbia Mental Health and Substance Use Services Research Institute, Vancouver, BC, Canada
| | - Andrea A Jones
- Division of Neurology, University of British Columbia, Vancouver, BC, Canada
| | - Daniel Wolfman
- Department of Psychaitry, University of British Columbia, Vancouver, BC, Canada,British Columbia Mental Health and Substance Use Services Research Institute, Vancouver, BC, Canada
| | - Ryan C Y Chan
- Department of Psychaitry, University of British Columbia, Vancouver, BC, Canada,British Columbia Mental Health and Substance Use Services Research Institute, Vancouver, BC, Canada
| | - Fidel Vila-Rodriguez
- Department of Psychaitry, University of British Columbia, Vancouver, BC, Canada,Non-Invasive Neurostimulation Therapies Laboratory, University of British Columbia, Vancouver, BC, Canada
| | | | - Manraj K Heran
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Wayne Su
- Department of Psychaitry, University of British Columbia, Vancouver, BC, Canada,British Columbia Mental Health and Substance Use Services Research Institute, Vancouver, BC, Canada
| | - Donna J Lang
- British Columbia Mental Health and Substance Use Services Research Institute, Vancouver, BC, Canada,Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Thalia S Field
- Division of Neurology, University of British Columbia, Vancouver, BC, Canada
| | | | - Melissa L Woodward
- Department of Psychaitry, University of British Columbia, Vancouver, BC, Canada,British Columbia Mental Health and Substance Use Services Research Institute, Vancouver, BC, Canada
| | - Allen E Thornton
- British Columbia Mental Health and Substance Use Services Research Institute, Vancouver, BC, Canada,Department of Psychology, Simon Fraser University, Burnaby, BC, Canada
| | - Alasdair M Barr
- Department of Anesthesiology, Pharmacology, and Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - Olga Leonova
- Department of Psychaitry, University of British Columbia, Vancouver, BC, Canada
| | - William MacEwan
- Department of Psychaitry, University of British Columbia, Vancouver, BC, Canada
| | - Alexander Rauscher
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - William G Honer
- Department of Psychaitry, University of British Columbia, Vancouver, BC, Canada,British Columbia Mental Health and Substance Use Services Research Institute, Vancouver, BC, Canada
| | - William J Panenka
- Department of Psychaitry, University of British Columbia, Vancouver, BC, Canada,British Columbia Mental Health and Substance Use Services Research Institute, Vancouver, BC, Canada,British Columbia Neuropsychiatry Program, University of British Columbia, Vancouver, BC, Canada
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95
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Turrini S, Bevacqua N, Cataneo A, Chiappini E, Fiori F, Candidi M, Avenanti A. Transcranial cortico-cortical paired associative stimulation (ccPAS) over ventral premotor-motor pathways enhances action performance and corticomotor excitability in young adults more than in elderly adults. Front Aging Neurosci 2023; 15:1119508. [PMID: 36875707 PMCID: PMC9978108 DOI: 10.3389/fnagi.2023.1119508] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 01/30/2023] [Indexed: 02/18/2023] Open
Abstract
Transcranial magnetic stimulation (TMS) methods such as cortico-cortical paired associative stimulation (ccPAS) can increase the strength of functional connectivity between ventral premotor cortex (PMv) and primary motor cortex (M1) via spike timing-dependent plasticity (STDP), leading to enhanced motor functions in young adults. However, whether this STDP-inducing protocol is effective in the aging brain remains unclear. In two groups of young and elderly healthy adults, we evaluated manual dexterity with the 9-hole peg task before and after ccPAS of the left PMv-M1 circuit. We observed that ccPAS enhanced dexterity in young adults, and this effect was anticipated by a progressive increase in motor-evoked potentials (MEPs) during ccPAS administration. No similar effects were observed in elderly individuals or in a control task. Across age groups, we observed that the magnitude of MEP changes predicted larger behavioral improvements. These findings demonstrate that left PMv-to-M1 ccPAS induces functionally specific improvements in young adults' manual dexterity and an increase in corticomotor excitability, but altered plasticity prevents the effectiveness of ccPAS in the elderly.
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Affiliation(s)
- Sonia Turrini
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum Università di Bologna, Cesena, Italy.,Precision Neuroscience and Neuromodulation Program, Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Naomi Bevacqua
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum Università di Bologna, Cesena, Italy.,Dipartimento di Psicologia, Sapienza Università di Roma, Rome, Italy
| | - Antonio Cataneo
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum Università di Bologna, Cesena, Italy
| | - Emilio Chiappini
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum Università di Bologna, Cesena, Italy.,Department of Clinical and Health Psychology, University of Vienna, Vienna, Austria
| | - Francesca Fiori
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum Università di Bologna, Cesena, Italy.,Dipartimento di Medicina, NeXT: Unità di Ricerca di Neurofisiologia e Neuroingegneria dell'Interazione Uomo-Tecnologia, Rome, Italy
| | - Matteo Candidi
- Dipartimento di Psicologia, Sapienza Università di Roma, Rome, Italy
| | - Alessio Avenanti
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum Università di Bologna, Cesena, Italy.,Centro de Investigación en Neuropsicología y Neurociencias Cognitivas, Universidad Católica del Maule, Talca, Chile
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96
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Murray CJ, Vecchiarelli HA, Tremblay MÈ. Enhancing axonal myelination in seniors: A review exploring the potential impact cannabis has on myelination in the aged brain. Front Aging Neurosci 2023; 15:1119552. [PMID: 37032821 PMCID: PMC10073480 DOI: 10.3389/fnagi.2023.1119552] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 02/22/2023] [Indexed: 04/11/2023] Open
Abstract
Consumption of cannabis is on the rise as public opinion trends toward acceptance and its consequent legalization. Specifically, the senior population is one of the demographics increasing their use of cannabis the fastest, but research aimed at understanding cannabis' impact on the aged brain is still scarce. Aging is characterized by many brain changes that slowly alter cognitive ability. One process that is greatly impacted during aging is axonal myelination. The slow degradation and loss of myelin (i.e., demyelination) in the brain with age has been shown to associate with cognitive decline and, furthermore, is a common characteristic of numerous neurological diseases experienced in aging. It is currently not known what causes this age-dependent degradation, but it is likely due to numerous confounding factors (i.e., heightened inflammation, reduced blood flow, cellular senescence) that impact the many cells responsible for maintaining overall homeostasis and myelin integrity. Importantly, animal studies using non-human primates and rodents have also revealed demyelination with age, providing a reliable model for researchers to try and understand the cellular mechanisms at play. In rodents, cannabis was recently shown to modulate the myelination process. Furthermore, studies looking at the direct modulatory impact cannabis has on microglia, astrocytes and oligodendrocyte lineage cells hint at potential mechanisms to prevent some of the more damaging activities performed by these cells that contribute to demyelination in aging. However, research focusing on how cannabis impacts myelination in the aged brain is lacking. Therefore, this review will explore the evidence thus far accumulated to show how cannabis impacts myelination and will extrapolate what this knowledge may mean for the aged brain.
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Affiliation(s)
- Colin J. Murray
- Neuroscience Graduate Program, University of Victoria, Victoria, BC, Canada
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
- *Correspondence: Colin J. Murray,
| | | | - Marie-Ève Tremblay
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
- Départment de Médicine Moléculaire, Université Laval, Québec City, QC, Canada
- Axe Neurosciences, Center de Recherche du CHU de Québec, Université Laval, Québec City, QC, Canada
- Neurology and Neurosurgery Department, McGill University, Montréal, QC, Canada
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
- Centre for Advanced Materials and Related Technology (CAMTEC), University of Victoria, Victoria, BC, Canada
- Institute for Aging and Lifelong Health, University of Victoria, Victoria, BC, Canada
- Marie-Ève Tremblay,
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97
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Zhao S, Li Y, Shi Y, Li X. Cognitive Aging: How the Brain Ages? ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1419:9-21. [PMID: 37418203 DOI: 10.1007/978-981-99-1627-6_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
Cognitive aging refers to the cognitive changes or functional decline that comes with age. The relation between aging and functional declines involves various aspects of cognition, including memory, attention, processing speed, and executive function. In this chapter, we have introduced several dimensions about cognitive aging trajectories. Meanwhile, we have reviewed the history of the study of cognitive aging and expatiated two trends that are particularly noteworthy in the effort to elucidate the process of aging. One is that the differences between components of mental abilities have become gradually specified. The other one is a growing interest in the neural process, which relates changes in the brain structure to age-related changes in cognition. Lastly, as the basis of cognitive function, brain structures and functions change during aging, and these changes are reflected in a corresponding decline in cognitive function. We have discussed the patterns of reorganization of various structural and functional aging processes of the brain and their relationship with cognitive function.
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Affiliation(s)
- Shaokun Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China
| | - Yumeng Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China
| | - Yuqing Shi
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China
| | - Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China.
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China.
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98
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Williams CM, Peyre H, Ramus F. Brain volumes, thicknesses, and surface areas as mediators of genetic factors and childhood adversity on intelligence. Cereb Cortex 2022; 33:5885-5895. [PMID: 36533516 DOI: 10.1093/cercor/bhac468] [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: 09/15/2022] [Revised: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 12/23/2022] Open
Abstract
Although genetic and environmental factors influence general intelligence (g-factor), few studies examined the neuroanatomical measures mediating environmental and genetic effects on intelligence. Here, we investigate the brain volumes, cortical mean thicknesses, and cortical surface areas mediating the effects of the g-factor polygenic score (gPGS) and childhood adversity on the g-factor in the UK Biobank. We first examined the global and regional brain measures that contribute to the g-factor. Most regions contributed to the g-factor through global brain size. Parieto-frontal integration theory (P-FIT) regions were not more associated with the g-factor than non-PFIT regions. After adjusting for global brain size and regional associations, only a few regions predicted intelligence and were included in the mediation analyses. We conducted mediation analyses on global measures, regional volumes, mean thicknesses, and surface areas, separately. Total brain volume mediated 7.04% of the gPGS' effect on the g-factor and 2.50% of childhood adversity's effect on the g-factor. In comparison, the fraction of the gPGS and childhood adversity's effects mediated by individual regional volumes, surfaces, and mean thicknesses was 10-15 times smaller. Therefore, genetic and environmental effects on intelligence may be mediated to a larger extent by other brain properties.
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Affiliation(s)
- Camille M Williams
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, 29 rue d'ulm, 75005, Paris, France
| | - Hugo Peyre
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, 29 rue d'ulm, 75005, Paris, France
- INSERM UMR 1141, Paris Diderot University, 48 Bd Sérurier, 75019, Paris, France
- Department of Child and Adolescent Psychiatry, Robert Debré Hospital, APHP, 48 Bd Sérurier, 75019, Paris, France
| | - Franck Ramus
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, 29 rue d'ulm, 75005, Paris, France
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99
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Stumme J, Krämer C, Miller T, Schreiber J, Caspers S, Jockwitz C. Interrelating differences in structural and functional connectivity in the older adult's brain. Hum Brain Mapp 2022; 43:5543-5561. [PMID: 35916531 PMCID: PMC9704795 DOI: 10.1002/hbm.26030] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/11/2022] [Accepted: 07/15/2022] [Indexed: 01/15/2023] Open
Abstract
In the normal aging process, the functional connectome restructures and shows a shift from more segregated to more integrated brain networks, which manifests itself in highly different cognitive performances in older adults. Underpinnings of this reorganization are not fully understood, but may be related to age-related differences in structural connectivity, the underlying scaffold for information exchange between regions. The structure-function relationship might be a promising factor to understand the neurobiological sources of interindividual cognitive variability, but remain unclear in older adults. Here, we used diffusion weighted and resting-state functional magnetic resonance imaging as well as cognitive performance data of 573 older subjects from the 1000BRAINS cohort (55-85 years, 287 males) and performed a partial least square regression on 400 regional functional and structural connectivity (FC and SC, respectively) estimates comprising seven resting-state networks. Our aim was to identify FC and SC patterns that are, together with cognitive performance, characteristic of the older adults aging process. Results revealed three different aging profiles prevalent in older adults. FC was found to behave differently depending on the severity of age-related SC deteriorations. A functionally highly interconnected system is associated with a structural connectome that shows only minor age-related decreases. Because this connectivity profile was associated with the most severe age-related cognitive decline, a more interconnected FC system in older adults points to a process of dedifferentiation. Thus, functional network integration appears to increase primarily when SC begins to decline, but this does not appear to mitigate the decline in cognitive performance.
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Affiliation(s)
- Johanna Stumme
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany
- Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Camilla Krämer
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany
- Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Tatiana Miller
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany
- Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Jan Schreiber
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany
- Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany
- Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
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100
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Puybasset L, Perlbarg V, Unrug J, Cassereau D, Galanaud D, Torkomian G, Battisti V, Lefort M, Velly L, Degos V, Citerio G, Bayen É, Pelegrini-Issac M. Prognostic value of global deep white matter DTI metrics for 1-year outcome prediction in ICU traumatic brain injury patients: an MRI-COMA and CENTER-TBI combined study. Intensive Care Med 2022; 48:201-212. [PMID: 34904191 DOI: 10.1007/s00134-021-06583-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 11/11/2021] [Indexed: 12/17/2022]
Abstract
PURPOSE A reliable tool for outcome prognostication in severe traumatic brain injury (TBI) would improve intensive care unit (ICU) decision-making process by providing objective information to caregivers and family. This study aimed at designing a new classification score based on magnetic resonance (MR) diffusion metrics measured in the deep white matter between day 7 and day 35 after TBI to predict 1-year clinical outcome. METHODS Two multicenter cohorts (29 centers) were used. MRI-COMA cohort (NCT00577954) was split into MRI-COMA-Train (50 patients enrolled between 2006 and mid-2014) and MRI-COMA-Test (140 patients followed up in clinical routine from 2014) sub-cohorts. These latter patients were pooled with 56 ICU patients (enrolled from 2014 to 2020) from CENTER-TBI cohort (NCT02210221). Patients were dichotomised depending on their 1-year Glasgow outcome scale extended (GOSE) score: GOSE 1-3, unfavorable outcome (UFO); GOSE 4-8, favorable outcome (FO). A support vector classifier incorporating fractional anisotropy and mean diffusivity measured in deep white matter, and age at the time of injury was developed to predict whether the patients would be either UFO or FO. RESULTS The model achieved an area under the ROC curve of 0.93 on MRI-COMA-Train training dataset, and 49% sensitivity for 96.8% specificity in predicting UFO and 58.5% sensitivity for 97.1% specificity in predicting FO on the pooled MRI-COMA-Test and CENTER-TBI validation datasets. CONCLUSION The model successfully identified, with a specificity compatible with a personalized decision-making process in ICU, one in two patients who had an unfavorable outcome at 1 year after the injury, and two-thirds of the patients who experienced a favorable outcome.
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Affiliation(s)
- Louis Puybasset
- Neurosurgical Intensive Care Unit, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France.
- Laboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, Paris, France.
- Department of Anesthesiology and Intensive Care, Groupe Hospitalier Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, 47-83 Boulevard de l'Hôpital, 75013, Paris, France.
- Clinical Research Group 29, Sorbonne Université, Paris, France.
| | | | - Jean Unrug
- Neurosurgical Intensive Care Unit, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
- Laboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, Paris, France
| | - Didier Cassereau
- Laboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, Paris, France
| | - Damien Galanaud
- Laboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, Paris, France
- Department of Neuroradiology, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
| | - Grégory Torkomian
- Neurosurgical Intensive Care Unit, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
| | - Valentine Battisti
- Neurosurgical Intensive Care Unit, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
| | - Muriel Lefort
- Laboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, Paris, France
| | - Lionel Velly
- Department of Anesthesiology and Critical Care Medicine, University Hospital Timone, AP-HM, Aix Marseille University, Marseille, France
- CNRS, Institute of Neuroscience Timone, UMR7289, Aix Marseille University, Marseille, France
| | - Vincent Degos
- Clinical Research Group 29, Sorbonne Université, Paris, France
- Department of Anesthesia, Critical Care and Peri-Operative Medicine, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
- INSERM UMR 1141, Paris, France
| | - Guiseppe Citerio
- Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- Neurointensive Care Unit, Department of Emergency and Urgency, ASST-Monza, San Gerardo Hospital, Monza, Italy
| | - Éléonore Bayen
- Laboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, Paris, France
- Rehabilitation Unit, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France
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