1
|
Paus T. Development and Maturation of the Human Brain, from Infancy to Adolescence. Curr Top Behav Neurosci 2024. [PMID: 39138744 DOI: 10.1007/7854_2024_514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
This chapter describes basic principles and key findings regarding the development and maturation of the human brain, the former referring to the pre-natal and early post-natal periods and the latter concerning childhood and adolescence. In both cases, we focus on brain structure as revealed in vivo with multi-modal magnetic resonance imaging (MRI). We begin with a few numbers about the human brain and its cellular composition and a brief overview of a number of MRI-based metrics used to characterize age-related variations in grey and white matter. We then proceed with synthesizing current knowledge about developmental and maturational changes in the cerebral cortex (its thickness, surface area, and intra-cortical myelination) and the underlying white matter (volume and structural properties). To facilitate biological interpretations of MRI-derived metrics, we introduce the concept of virtual histology. We conclude the chapter with a few notes about future directions in the study of factors shaping the human brain from conception onwards.
Collapse
Affiliation(s)
- Tomáš Paus
- Departments of Psychiatry and Neuroscience, Faculty of Medicine and Centre Hospitalier Universitaire, University of Montréal, Montreal, QC, Canada.
| |
Collapse
|
2
|
Zhang Z, Wei W, Wang S, Li M, Li X, Li X, Wang Q, Yu H, Zhang Y, Guo W, Ma X, Zhao L, Deng W, Sham PC, Sun Y, Li T. Dynamic structure-function coupling across three major psychiatric disorders. Psychol Med 2024; 54:1629-1640. [PMID: 38084608 DOI: 10.1017/s0033291723003525] [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] [Indexed: 05/29/2024]
Abstract
BACKGROUND Convergent evidence has suggested atypical relationships between brain structure and function in major psychiatric disorders, yet how the abnormal patterns coincide and/or differ across different disorders remains largely unknown. Here, we aim to investigate the common and/or unique dynamic structure-function coupling patterns across major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ). METHODS We quantified the dynamic structure-function coupling in 452 patients with psychiatric disorders (MDD/BD/SZ = 166/168/118) and 205 unaffected controls at three distinct brain network levels, such as global, meso-, and local levels. We also correlated dynamic structure-function coupling with the topological features of functional networks to examine how the structure-function relationship facilitates brain information communication over time. RESULTS The dynamic structure-function coupling is preserved for the three disorders at the global network level. Similar abnormalities in the rich-club organization are found in two distinct functional configuration states at the meso-level and are associated with the disease severity of MDD, BD, and SZ. At the local level, shared and unique alterations are observed in the brain regions involving the visual, cognitive control, and default mode networks. In addition, the relationships between structure-function coupling and the topological features of functional networks are altered in a manner indicative of state specificity. CONCLUSIONS These findings suggest both transdiagnostic and illness-specific alterations in the dynamic structure-function relationship of large-scale brain networks across MDD, BD, and SZ, providing new insights and potential biomarkers into the neurodevelopmental basis underlying the behavioral and cognitive deficits observed in these disorders.
Collapse
Affiliation(s)
- Zhe Zhang
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- School of Physics, Hangzhou Normal University, Hangzhou, China
- Institute of Brain Science, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China
| | - Wei Wei
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Sujie Wang
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
| | - Mingli Li
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaojing Li
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Xiaoyu Li
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, China
| | - Hua Yu
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Yamin Zhang
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Wanjun Guo
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Xiaohong Ma
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, China
| | - Wei Deng
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Pak C Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China
| | - Yu Sun
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Li
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| |
Collapse
|
3
|
Liang X, Sun L, Liao X, Lei T, Xia M, Duan D, Zeng Z, Li Q, Xu Z, Men W, Wang Y, Tan S, Gao JH, Qin S, Tao S, Dong Q, Zhao T, He Y. Structural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence. Nat Commun 2024; 15:784. [PMID: 38278807 PMCID: PMC10817914 DOI: 10.1038/s41467-024-44863-6] [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: 05/15/2023] [Accepted: 01/08/2024] [Indexed: 01/28/2024] Open
Abstract
Cortical thinning is an important hallmark of the maturation of brain morphology during childhood and adolescence. However, the connectome-based wiring mechanism that underlies cortical maturation remains unclear. Here, we show cortical thinning patterns primarily located in the lateral frontal and parietal heteromodal nodes during childhood and adolescence, which are structurally constrained by white matter network architecture and are particularly represented using a network-based diffusion model. Furthermore, connectome-based constraints are regionally heterogeneous, with the largest constraints residing in frontoparietal nodes, and are associated with gene expression signatures of microstructural neurodevelopmental events. These results are highly reproducible in another independent dataset. These findings advance our understanding of network-level mechanisms and the associated genetic basis that underlies the maturational process of cortical morphology during childhood and adolescence.
Collapse
Affiliation(s)
- Xinyuan Liang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Tianyuan Lei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Dingna Duan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Zilong Zeng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Zhilei Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, 100871, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, 100096, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, 100871, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
| |
Collapse
|
4
|
Delaney SW, Xerxa Y, Muetzel RL, White T, Haneuse S, Ressler KJ, Tiemeier H, Kubzansky LD. Long-term associations between early-life family functioning and preadolescent white matter microstructure. Psychol Med 2023; 53:4528-4538. [PMID: 35611817 PMCID: PMC10388303 DOI: 10.1017/s0033291722001404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 04/14/2022] [Accepted: 04/27/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Causes of childhood behavior problems remain poorly understood. Enriched family environments and corresponding brain development may reduce the risk of their onset, but research investigating white matter neurodevelopmental pathways explaining associations between the family environment and behavior remains limited. We hypothesized that more positive prenatal and mid-childhood family functioning - a measure of a family's problem solving and supportive capacity - would be associated with two markers of preadolescent white matter neurodevelopment related to reduced behavior problems: higher global fractional anisotropy (FA) and lower global mean diffusivity (MD). METHODS Data are from 2727 families in the Generation R Study, the Netherlands. Mothers reported family functioning (McMaster Family Assessment Device, range 1-4, higher scores indicate healthier functioning) prenatally and in mid-childhood (mean age 6.1 years). In preadolescence (mean age 10.1), the study collected diffusion-weighted scans. We computed standardized global MD and FA values by averaging metrics from 27 white matter tracts, and we fit linear models adjusting for possible confounders to examine global and tract-specific outcomes. RESULTS Prenatal and mid-childhood family functioning scores were moderately correlated, r = 0.38. However, only prenatal family functioning - and not mid-childhood functioning - was associated with higher global FA and lower global MD in preadolescence in fully adjusted models: βglobal FA = 0.11 (95% CI 0.00, 0.21) and βglobal MD = -0.15 (95% CI -0.28, -0.03) per one-unit increase in functioning score. Sensitivity and tract-specific analyses supported these global findings. CONCLUSIONS These results suggest high-functioning prenatal or perinatal family environments may confer lasting white matter neurodevelopmental benefits into preadolescence.
Collapse
Affiliation(s)
- Scott W. Delaney
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA USA
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
- Lee Kum Sheung Center for Health and Happiness, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Yllza Xerxa
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
- The Generation R Study Group, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Ryan L. Muetzel
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Sebastien Haneuse
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Kerry J. Ressler
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA USA
| | - Henning Tiemeier
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA USA
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Laura D. Kubzansky
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA USA
- Lee Kum Sheung Center for Health and Happiness, Harvard T.H. Chan School of Public Health, Boston, MA USA
| |
Collapse
|
5
|
Wu D, Li X. Graph propagation network captures individual specificity of the relationship between functional and structural connectivity. Hum Brain Mapp 2023; 44:3885-3896. [PMID: 37186004 PMCID: PMC10203799 DOI: 10.1002/hbm.26320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 04/06/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
Functional connectivity (FC) network characterizes the functional interactions between brain regions and is considered to root in the underlying structural connectivity (SC) network. If this is the case, individual variations in SC should cause corresponding individual variations in FC. However, divergences exist in the correspondence between direct SC and FC and researchers still cannot capture individual differences in FC via direct SC. As brain regions may interact through multi-hop indirect SC pathways, we conceived that one can capture the individual specific SC-FC relationship via incorporating indirect SC pathways appropriately. In this study, we designed graph propagation network (GPN) that models the information propagation between brain regions based on the SC network. Effects of interactions through multi-hop SC pathways naturally emerge from the multilayer information propagation in GPN. We predicted the individual differences in FC network based on SC network via multilayer GPN and results indicate that multilayer GPN incorporating effects of multi-hop indirect SCs greatly enhances the ability to predict individual FC network. Furthermore, the SC-FC relationship evaluated via the prediction accuracy is negatively correlated with the functional gradient, suggesting that the SC-FC relationship gradually uncouples along the functional hierarchy spanning from unimodal to transmodal cortex. We also revealed important intermediate brain regions along multi-hop SC pathways involving in the individual SC-FC relationship. These results suggest that multilayer GPN can serve as a method to establish individual SC-FC relationship at the macroneuroimaging level.
Collapse
Affiliation(s)
- Dongya Wu
- School of Information Science and TechnologyNorthwest UniversityXi'anChina
| | - Xin Li
- School of MathematicsNorthwest UniversityXi'anChina
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Chen Y, Wang Y, Song Z, Fan Y, Gao T, Tang X. Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review. Ageing Res Rev 2023; 87:101911. [PMID: 36931328 DOI: 10.1016/j.arr.2023.101911] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 03/01/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023]
Abstract
Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d-AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.
Collapse
Affiliation(s)
- Yu Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yifei Wang
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Zeyu Song
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Tianxin Gao
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
| | - Xiaoying Tang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China; School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
| |
Collapse
|
8
|
White Matter Injury: An Emerging Potential Target for Treatment after Subarachnoid Hemorrhage. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2023; 2023:3842493. [PMID: 36798684 PMCID: PMC9928519 DOI: 10.1155/2023/3842493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 12/20/2022] [Accepted: 01/04/2023] [Indexed: 02/10/2023]
Abstract
Subarachnoid hemorrhage (SAH) refers to vascular brain injury mainly from a ruptured aneurysm, which has a high lifetime risk and imposes a substantial burden on patients, families, and society. Previous studies on SAH mainly focused on neurons in gray matter (GM). However, according to literature reports in recent years, in-depth research on the mechanism of white matter (WM) is of great significance to injury and recovery after SAH. In terms of functional recovery after SAH, all kinds of cells in the central nervous system (CNS) should be protected. In other words, it is necessary to protect not only GM but also WM, not only neurons but also glial cells and axons, and not only for the lesion itself but also for the prevention and treatment of remote damage. Clarifying the mechanism of white matter injury (WMI) and repair after SAH is of great importance. Therefore, this present review systematically summarizes the current research on WMI after SAH, which might provide therapeutic targets for treatment after SAH.
Collapse
|
9
|
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.
Collapse
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.
| |
Collapse
|
10
|
Parsons N, Ugon J, Morgan K, Shelyag S, Hocking A, Chan SY, Poudel G, Domìnguez D JF, Caeyenberghs K. Structural-Functional Connectivity Bandwidth of the Human Brain. Neuroimage 2022; 263:119659. [PMID: 36191756 DOI: 10.1016/j.neuroimage.2022.119659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 09/25/2022] [Accepted: 09/29/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND The human brain is a complex network that seamlessly manifests behaviour and cognition. This network comprises neurons that directly, or indirectly mediate communication between brain regions. Here, we show how multilayer/multiplex network analysis provides a suitable framework to uncover the throughput of structural connectivity (SC) to mediate information transfer-giving rise to functional connectivity (FC). METHOD We implemented a novel method to reconcile SC and FC using diffusion and resting-state functional MRI connectivity data from 484 subjects (272 females, 212 males; age = 29.15 ± 3.47) from the Human Connectome Project. First, we counted the number of direct and indirect structural paths that mediate FC. FC nodes with indirect SC paths were then weighted according to their least restrictive SC path. We refer to this as SC-FC Bandwidth. We then mapped paths with the highest SC-FC Bandwidth across 7 canonical resting-state networks. FINDINGS We found that most pairs of FC nodes were connected by SC paths of length two and three (SC paths of length >5 were virtually non-existent). Direct SC-FC connections accounted for only 10% of all SC-FC connections. The majority of FC nodes without a direct SC path were mediated by a proportion of two (44%) or three SC path lengths (39%). Only a small proportion of FC nodes were mediated by SC path lengths of four (5%). We found high-bandwidth direct SC-FC connections show dense intra- and sparse inter-network connectivity, with a bilateral, anteroposterior distribution. High bandwidth SC-FC triangles have a right superomedial distribution within the somatomotor network. High-bandwidth SC-FC quads have a superoposterior distribution within the default mode network. CONCLUSION Our method allows the measurement of indirect SC-FC using undirected, weighted graphs derived from multimodal MRI data in order to map the location and throughput of SC to mediate FC. An extension of this work may be to explore how SC-FC Bandwidth changes over time, relates to cognition/behavior, and if this measure reflects a marker of neurological injury or psychiatric disorders.
Collapse
Affiliation(s)
- Nicholas Parsons
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, VIC, Australia.
| | - 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
| | - Su Yuan Chan
- School of Information Technology, Faculty of Science Engineering & Built Environment, Deakin University, Melbourne, VIC, Australia
| | - Govinda Poudel
- School of Information Technology, Faculty of Science Engineering & Built Environment, Deakin University, Melbourne, VIC, Australia
| | - Juan F Domìnguez D
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, VIC, Australia
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, VIC, Australia; Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia
| |
Collapse
|
11
|
Tsuzuki D, Taga G, Watanabe H, Homae F. Individual variability in the nonlinear development of the corpus callosum during infancy and toddlerhood: a longitudinal MRI analysis. Brain Struct Funct 2022; 227:1995-2013. [PMID: 35396953 DOI: 10.1007/s00429-022-02485-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 03/22/2022] [Indexed: 11/29/2022]
Abstract
The human brain spends several years bootstrapping itself through intrinsic and extrinsic modulation, thus gradually developing both spatial organization and functions. Based on previous studies on developmental patterns and inter-individual variability of the corpus callosum (CC), we hypothesized that inherent variations of CC shape among infants emerge, depending on the position within the CC, along the developmental timeline. Here we used longitudinal magnetic resonance imaging data from infancy to toddlerhood and investigated the area, thickness, and shape of the midsagittal plane of the CC by applying multilevel modeling. The shape characteristics were extracted using the Procrustes method. We found nonlinearity, region-dependency, and inter-individual variability, as well as intra-individual consistencies, in CC development. Overall, the growth rate is faster in the first year than in the second year, and the trajectory differs between infants; the direction of CC formation in individual infants was determined within six months and maintained to two years. The anterior and posterior subregions increase in area and thickness faster than other subregions. Moreover, we clarified that the growth rate of the middle part of the CC is faster in the second year than in the first year in some individuals. Since the division of regions exhibiting different tendencies coincides with previously reported divisions based on the diameter of axons that make up the region, our results suggest that subregion-dependent individual variability occurs due to the increase in the diameter of the axon caliber, myelination partly due to experience and axon elimination during the early developmental period.
Collapse
Affiliation(s)
- Daisuke Tsuzuki
- Department of Language Sciences, Tokyo Metropolitan University, Hachioji, Tokyo, 192-0397, Japan. .,Graduate School of Education, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.
| | - Gentaro Taga
- Graduate School of Education, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Hama Watanabe
- Graduate School of Education, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Fumitaka Homae
- Department of Language Sciences, Tokyo Metropolitan University, Hachioji, Tokyo, 192-0397, Japan.,Research Center for Language, Brain and Genetics, Tokyo Metropolitan University, Hachioji, Tokyo, 192-0397, Japan
| |
Collapse
|
12
|
Duffau H. White Matter Tracts and Diffuse Lower-Grade Gliomas: The Pivotal Role of Myelin Plasticity in the Tumor Pathogenesis, Infiltration Patterns, Functional Consequences and Therapeutic Management. Front Oncol 2022; 12:855587. [PMID: 35311104 PMCID: PMC8924360 DOI: 10.3389/fonc.2022.855587] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 02/14/2022] [Indexed: 12/18/2022] Open
Abstract
For many decades, interactions between diffuse lower-grade glioma (LGG) and brain connectome were neglected. However, the neoplasm progression is intimately linked to its environment, especially the white matter (WM) tracts and their myelin status. First, while the etiopathogenesis of LGG is unclear, this tumor seems to appear during the adolescence, and it is mostly located within anterior and associative cerebral areas. Because these structures correspond to those which were myelinated later in the brain maturation process, WM myelination could play a role in the development of LGG. Second, WM fibers and the myelin characteristics also participate in LGG diffusion, since glioma cells migrate along the subcortical pathways, especially when exhibiting a demyelinated phenotype, which may result in a large invasion of the parenchyma. Third, such a migratory pattern can induce functional (neurological, cognitive and behavioral) disturbances, because myelinated WM tracts represent the main limitation of neuroplastic potential. These parameters are critical for tailoring an individualized therapeutic strategy, both (i) regarding the timing of active treatment(s) which must be proposed earlier, before a too wide glioma infiltration along the WM bundles, (ii) and regarding the anatomic extent of surgical resection and irradiation, which should take account of the subcortical connectivity. Therefore, the new science of connectomics must be integrated in LGG management, based upon an improved understanding of the interplay across glioma dissemination within WM and reactional neural networks reconfiguration, in order to optimize long-term oncological and functional outcomes. To this end, mechanisms of activity-dependent myelin plasticity should be better investigated.
Collapse
Affiliation(s)
- Hugues Duffau
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, Montpellier, France.,Team "Plasticity of Central Nervous System, Stem Cells and Glial Tumors", Institute of Functional Genomics, National Institute for Health and Medical Research (INSERM) U1191, University of Montpellier, Montpellier, France
| |
Collapse
|
13
|
Veale T, Malone IB, Poole T, Parker TD, Slattery CF, Paterson RW, Foulkes AJM, Thomas DL, Schott JM, Zhang H, Fox NC, Cash DM. Loss and dispersion of superficial white matter in Alzheimer's disease: a diffusion MRI study. Brain Commun 2021; 3:fcab272. [PMID: 34859218 PMCID: PMC8633427 DOI: 10.1093/braincomms/fcab272] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 09/24/2021] [Accepted: 10/18/2021] [Indexed: 11/22/2022] Open
Abstract
Pathological cerebral white matter changes in Alzheimer's disease have been shown using diffusion tensor imaging. Superficial white matter changes are relatively understudied despite their importance in cortico-cortical connections. Measuring superficial white matter degeneration using diffusion tensor imaging is challenging due to its complex organizational structure and proximity to the cortex. To overcome this, we investigated diffusion MRI changes in young-onset Alzheimer's disease using standard diffusion tensor imaging and Neurite Orientation Dispersion and Density Imaging to distinguish between disease-related changes that are degenerative (e.g. loss of myelinated fibres) and organizational (e.g. increased fibre dispersion). Twenty-nine young-onset Alzheimer's disease patients and 22 healthy controls had both single-shell and multi-shell diffusion MRI. We calculated fractional anisotropy, mean diffusivity, neurite density index, orientation dispersion index and tissue fraction (1-free water fraction). Diffusion metrics were sampled in 15 a priori regions of interest at four points along the cortical profile: cortical grey matter, grey/white boundary, superficial white matter (1 mm below grey/white boundary) and superficial/deeper white matter (2 mm below grey/white boundary). To estimate cross-sectional group differences, we used average marginal effects from linear mixed effect models of participants' diffusion metrics along the cortical profile. The superficial white matter of young-onset Alzheimer's disease individuals had lower neurite density index compared to controls in five regions (superior and inferior parietal, precuneus, entorhinal and parahippocampus) (all P < 0.05), and higher orientation dispersion index in three regions (fusiform, entorhinal and parahippocampus) (all P < 0.05). Young-onset Alzheimer's disease individuals had lower fractional anisotropy in the entorhinal and parahippocampus regions (both P < 0.05) and higher fractional anisotropy within the postcentral region (P < 0.05). Mean diffusivity was higher in the young-onset Alzheimer's disease group in the parahippocampal region (P < 0.05) and lower in the postcentral, precentral and superior temporal regions (all P < 0.05). In the overlying grey matter, disease-related changes were largely consistent with superficial white matter findings when using neurite density index and fractional anisotropy, but appeared at odds with orientation dispersion and mean diffusivity. Tissue fraction was significantly lower across all grey matter regions in young-onset Alzheimer's disease individuals (all P < 0.001) but group differences reduced in magnitude and coverage when moving towards the superficial white matter. These results show that microstructural changes occur within superficial white matter and along the cortical profile in individuals with young-onset Alzheimer's disease. Lower neurite density and higher orientation dispersion suggests underlying fibres undergo neurodegeneration and organizational changes, two effects previously indiscernible using standard diffusion tensor metrics in superficial white matter.
Collapse
Affiliation(s)
- Thomas Veale
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| | - Ian B Malone
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Teresa Poole
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Thomas D Parker
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Catherine F Slattery
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Ross W Paterson
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| | - Alexander J M Foulkes
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - David L Thomas
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - Jonathan M Schott
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Hui Zhang
- Department of Computer Science and Centre for Medical Image Computing, UCL, London, UK
| | - Nick C Fox
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| | - David M Cash
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| |
Collapse
|
14
|
Norbom LB, Ferschmann L, Parker N, Agartz I, Andreassen OA, Paus T, Westlye LT, Tamnes CK. New insights into the dynamic development of the cerebral cortex in childhood and adolescence: Integrating macro- and microstructural MRI findings. Prog Neurobiol 2021; 204:102109. [PMID: 34147583 DOI: 10.1016/j.pneurobio.2021.102109] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 04/26/2021] [Accepted: 06/15/2021] [Indexed: 12/11/2022]
Abstract
Through dynamic transactional processes between genetic and environmental factors, childhood and adolescence involve reorganization and optimization of the cerebral cortex. The cortex and its development plays a crucial role for prototypical human cognitive abilities. At the same time, many common mental disorders appear during these critical phases of neurodevelopment. Magnetic resonance imaging (MRI) can indirectly capture several multifaceted changes of cortical macro- and microstructure, of high relevance to further our understanding of the neural foundation of cognition and mental health. Great progress has been made recently in mapping the typical development of cortical morphology. Moreover, newer less explored MRI signal intensity and specialized quantitative T2 measures have been applied to assess microstructural cortical development. We review recent findings of typical postnatal macro- and microstructural development of the cerebral cortex from early childhood to young adulthood. We cover studies of cortical volume, thickness, area, gyrification, T1-weighted (T1w) tissue contrasts such a grey/white matter contrast, T1w/T2w ratio, magnetization transfer and myelin water fraction. Finally, we integrate imaging studies with cortical gene expression findings to further our understanding of the underlying neurobiology of the developmental changes, bridging the gap between ex vivo histological- and in vivo MRI studies.
Collapse
Affiliation(s)
- Linn B Norbom
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.
| | - Lia Ferschmann
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway
| | - Nadine Parker
- Institute of Medical Science, University of Toronto, Ontario, Canada
| | - Ingrid Agartz
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; K.G Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Norway
| | - Ole A Andreassen
- K.G Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Tomáš Paus
- ECOGENE-21, Chicoutimi, Quebec, Canada; Department of Psychology and Psychiatry, University of Toronto, Ontario, Canada; Department of Psychiatry and Centre hospitalier universitaire Sainte-Justine, University of Montreal, Canada
| | - Lars T Westlye
- K.G Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway
| | - Christian K Tamnes
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.
| |
Collapse
|
15
|
Liao Z, Patel Y, Khairullah A, Parker N, Paus T. Pubertal Testosterone and the Structure of the Cerebral Cortex in Young Men. Cereb Cortex 2021; 31:2812-2821. [PMID: 33429422 DOI: 10.1093/cercor/bhaa389] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/27/2020] [Accepted: 12/02/2020] [Indexed: 11/12/2022] Open
Abstract
Adolescence is a period of brain maturation that may involve a second wave of organizational effects of sex steroids on the brain. Rodent studies suggest that, overall, organizational effects of gonadal steroid hormones decrease from the prenatal/perinatal period to adulthood. Here we used multimodal magnetic resonance imaging to investigate whether 1) testosterone exposure during adolescence (9-17 years) correlates with the structure of cerebral cortex in young men (n = 216, 19 years of age); 2) this relationship is modulated by the timing of testosterone surge during puberty. Our results showed that pubertal testosterone correlates with structural properties of the cerebral cortex, as captured by principal component analysis of T1 and T2 relaxation times, myelin water fraction, magnetization transfer ratio, fractional anisotropy and mean diffusivity. Many of the correlations between pubertal testosterone and the cortical structure were stronger in individuals with earlier (vs. later) testosterone surge. We also demonstrated that the strength of the relationship between pubertal testosterone and cortical structure across the cerebral cortex varies as a function of inter-regional profiles of gene expression specific to dendrites, axonal cytoskeleton, and myelin. This finding suggests that the cellular substrate underlying the relationships between pubertal testosterone and cerebral cortex involves both dendritic arbor and axon.
Collapse
Affiliation(s)
- Zhijie Liao
- Department of Psychology, University of Toronto, Toronto, ON M5S3G3, Canada
| | - Yash Patel
- Institute of Medical Sciences, University of Toronto, Toronto, ON M5S3G3, Canada
| | - Ammar Khairullah
- Department of Psychiatry, University of Toronto, Toronto, ON M5S3G3, Canada
| | - Nadine Parker
- Institute of Medical Sciences, University of Toronto, Toronto, ON M5S3G3, Canada
| | - Tomas Paus
- Department of Psychology, University of Toronto, Toronto, ON M5S3G3, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON M5S3G3, Canada
| |
Collapse
|
16
|
Sargurupremraj M, Suzuki H, Jian X, Sarnowski C, Evans TE, Bis JC, Eiriksdottir G, Sakaue S, Terzikhan N, Habes M, Zhao W, Armstrong NJ, Hofer E, Yanek LR, Hagenaars SP, Kumar RB, van den Akker EB, McWhirter RE, Trompet S, Mishra A, Saba Y, Satizabal CL, Beaudet G, Petit L, Tsuchida A, Zago L, Schilling S, Sigurdsson S, Gottesman RF, Lewis CE, Aggarwal NT, Lopez OL, Smith JA, Valdés Hernández MC, van der Grond J, Wright MJ, Knol MJ, Dörr M, Thomson RJ, Bordes C, Le Grand Q, Duperron MG, Smith AV, Knopman DS, Schreiner PJ, Evans DA, Rotter JI, Beiser AS, Maniega SM, Beekman M, Trollor J, Stott DJ, Vernooij MW, Wittfeld K, Niessen WJ, Soumaré A, Boerwinkle E, Sidney S, Turner ST, Davies G, Thalamuthu A, Völker U, van Buchem MA, Bryan RN, Dupuis J, Bastin ME, Ames D, Teumer A, Amouyel P, Kwok JB, Bülow R, Deary IJ, Schofield PR, Brodaty H, Jiang J, Tabara Y, Setoh K, Miyamoto S, Yoshida K, Nagata M, Kamatani Y, Matsuda F, Psaty BM, Bennett DA, De Jager PL, Mosley TH, Sachdev PS, Schmidt R, Warren HR, Evangelou E, Trégouët DA, Ikram MA, Wen W, DeCarli C, Srikanth VK, Jukema JW, Slagboom EP, Kardia SLR, Okada Y, Mazoyer B, Wardlaw JM, Nyquist PA, Mather KA, Grabe HJ, Schmidt H, Van Duijn CM, Gudnason V, Longstreth WT, Launer LJ, Lathrop M, Seshadri S, Tzourio C, Adams HH, Matthews PM, Fornage M, Debette S. Cerebral small vessel disease genomics and its implications across the lifespan. Nat Commun 2020; 11:6285. [PMID: 33293549 PMCID: PMC7722866 DOI: 10.1038/s41467-020-19111-2] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Accepted: 09/10/2020] [Indexed: 12/14/2022] Open
Abstract
White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.
Collapse
Affiliation(s)
- Muralidharan Sargurupremraj
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
| | - Hideaki Suzuki
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo, Aoba, Sendai, 980-8573, Japan
- Department of Cardiovascular Medicine, Tohoku University Hospital, 1-1, Seiryo, Aoba, Sendai, 980-8574, Japan
- Department of Brain Sciences, Imperial College London, London, W12 0NN, UK
| | - Xueqiu Jian
- University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX, 77030, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, 78229, USA
| | - Chloé Sarnowski
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Tavia E Evans
- Department of Clinical Genetics, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
- Department of Radiology & Nuclear Medicine, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, 98101, USA
| | | | - Saori Sakaue
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo, 113-0033, Japan
| | - Natalie Terzikhan
- Department of Epidemiology, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, 78229, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Community Medicine, University Medicine Greifswald, 17475, Greifswald, Germany
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109-2029, USA
| | - Nicola J Armstrong
- Mathematics and Statistics, Murdoch University, Murdoch, WA, 6150, Australia
| | - Edith Hofer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, 8036, Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036, Graz, Austria
| | - Lisa R Yanek
- GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Saskia P Hagenaars
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Social Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
| | - Rajan B Kumar
- Department of Public Health Sciences, University of California at Davis, Davis, CA, 95616, USA
| | - Erik B van den Akker
- Section of Molecular Epidemiology, Biomedical Sciences, Leiden university Medical Center, 2333 ZA, Leiden, The Netherlands
- Pattern Recognition & Bioinformatics, Delft University of Technology, Delft, NL, 2629 HS, USA
- Leiden Computational Biology Centre, Leiden University Medical Centre, 2333 ZA, Leiden, The Netherlands
| | - Rebekah E McWhirter
- Centre for Law and Genetics, Faculty of Law, University of Tasmania, Hobart, TAS, 7005, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, 7000, Australia
| | - Stella Trompet
- Department of Internal Medicine, section of gerontology and geriatrics, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
- Department of Cardiology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Aniket Mishra
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
| | - Yasaman Saba
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
- Gottfried Schatz Research Center, Department of Molecular Biology and Biochemistry, Medical University of Graz, 8010, Graz, Austria
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, 78229, USA
- Boston University and the NHLBI's Framingham Heart Study, Boston, MA, 02215, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Gregory Beaudet
- University of Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
| | - Laurent Petit
- University of Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
| | - Ami Tsuchida
- University of Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
| | - Laure Zago
- University of Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
| | - Sabrina Schilling
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
| | | | | | - Cora E Lewis
- University of Alabama at Birmingham School of Medicine, Birmingham, AL, 35233, USA
| | - Neelum T Aggarwal
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Oscar L Lopez
- Departments of Neurology and Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109-2029, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 48104, USA
| | - Maria C Valdés Hernández
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
- Row Fogo Centre for Ageing and The Brain, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Jeroen van der Grond
- Department of Radiology, Leiden University medical Center, 2333 ZA, Leiden, The Netherlands
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, St Lucia, QLD, 4072, Australia
- Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Maria J Knol
- Department of Epidemiology, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
| | - Marcus Dörr
- Department of Internal Medicine B, University Medicine Greifswald, 17475, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, 17475, Greifswald, Germany
| | - Russell J Thomson
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, 7000, Australia
- Centre for Research in Mathematics and Data Science, Western Sydney University, Penrith, NSW, 2751, Australia
| | - Constance Bordes
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
| | - Quentin Le Grand
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
| | - Marie-Gabrielle Duperron
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
| | | | | | - Pamela J Schreiner
- University of Minnesota School of Public Health, Minneapolis, MN, 55455, USA
| | - Denis A Evans
- Department of Internal Medicine, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Pediatrics at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Alexa S Beiser
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
- Boston University and the NHLBI's Framingham Heart Study, Boston, MA, 02215, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Susana Muñoz Maniega
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
| | - Marian Beekman
- Section of Molecular Epidemiology, Biomedical Sciences, Leiden university Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Julian Trollor
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, 2052, Australia
- Department of Developmental Disability Neuropsychiatry, School of Psychiatry, University of New South Wales, Sydney, NSW, 2052, Australia
| | - David J Stott
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Meike W Vernooij
- Department of Radiology & Nuclear Medicine, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
| | - Katharina Wittfeld
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, 17489, Greifswald, Germany
| | - Wiro J Niessen
- Department of Radiology & Nuclear Medicine, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
- Faculty of Applied Sciences, Delft University of Technology, Delft, NL, 2629 HS, USA
| | - Aicha Soumaré
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
| | - Eric Boerwinkle
- University of Texas Health Science Center at Houston School of Public Health, Houston, TX, 77030, USA
| | - Stephen Sidney
- Kaiser Permanente Division of Research, Oakland, CA, 94612, USA
| | - Stephen T Turner
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, 55905, USA
| | - Gail Davies
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036, Graz, Austria
- Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Anbupalam Thalamuthu
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Pediatrics at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, 17475, Greifswald, Germany
| | - Mark A van Buchem
- Row Fogo Centre for Ageing and The Brain, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - R Nick Bryan
- The University of Texas at Austin Dell Medical School, Austin, TX, 78712, USA
| | - Josée Dupuis
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, 78229, USA
- Department of Cardiology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Mark E Bastin
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036, Graz, Austria
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 48104, USA
| | - David Ames
- National Ageing Research Institute Royal Melbourne Hospital, Parkville, VIC, 3052, Australia
- Academic Unit for Psychiatry of Old Age, University of Melbourne, St George's Hospital, Kew, VIC, 3101, Australia
| | - Alexander Teumer
- Department of Epidemiology, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
- Department of Internal Medicine B, University Medicine Greifswald, 17475, Greifswald, Germany
| | - Philippe Amouyel
- Inserm U1167, 59000, Lille, France
- Department of Epidemiology and Public Health, Pasteur Institute of Lille, 59000, Lille, France
| | - John B Kwok
- Brain and Mind Centre - The University of Sydney, Camperdown, NSW, 2050, Australia
- School of Medical Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Robin Bülow
- Department of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, 17489, Greifswald, Germany
| | - Ian J Deary
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036, Graz, Austria
- Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Peter R Schofield
- School of Medical Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
- Neuroscience Research Australia, Randwick, NSW, 2031, Australia
| | - Henry Brodaty
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Pediatrics at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
- Dementia Centre for Research Collaboration, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Jiyang Jiang
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Pediatrics at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan
| | - Kazuya Setoh
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan
| | - Susumu Miyamoto
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan
| | - Kazumichi Yoshida
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan
| | - Manabu Nagata
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan
| | - Bruce M Psaty
- Departments of Epidemiology, Medicine and Health Services, University of Washington, Seattle, WA, 98195, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, 10032, USA
- Program in Population and Medical Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Thomas H Mosley
- Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Perminder S Sachdev
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Pediatrics at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, 2031, Australia
| | - Reinhold Schmidt
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109-2029, USA
| | - Helen R Warren
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, E1 4NS, UK
- National Institute for Health Research Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Evangelos Evangelou
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, SW7 2AZ, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Mpizani, 455 00, Greece
| | - David-Alexandre Trégouët
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
| | - Mohammad A Ikram
- Department of Epidemiology, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Charles DeCarli
- Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, CA, 95817, USA
| | - Velandai K Srikanth
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, 7000, Australia
- Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Eline P Slagboom
- Section of Molecular Epidemiology, Biomedical Sciences, Leiden university Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109-2029, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, 565-0871, Osaka, Japan
| | - Bernard Mazoyer
- University of Bordeaux, IMN, UMR 5293, 33000, Bordeaux, France
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK
- Row Fogo Centre for Ageing and The Brain, University of Edinburgh, Edinburgh, EH8 9JZ, UK
- MRC UK Dementia Research Institute at the University of Edinburgh, Edinburgh, EH8 9YL, UK
| | - Paul A Nyquist
- Department of Neurology, Johns Hopkins School of Medicine, Baltimone, MD, 21205, USA
- General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, 2052, Australia
- Neuroscience Research Australia, Randwick, NSW, 2031, Australia
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, 17475, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, 17475, Greifswald, Germany
| | - Helena Schmidt
- Gottfried Schatz Research Center, Department of Molecular Biology and Biochemistry, Medical University of Graz, 8010, Graz, Austria
| | - Cornelia M Van Duijn
- Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
| | - Vilmundur Gudnason
- Icelandic Heart Association, IS-201, Kópavogur, Iceland
- University of Iceland, Faculty of Medicine, 101, Reykjavík, Iceland
| | - William T Longstreth
- Departments of Neurology and Epidemiology, University of Washington, Seattle, WA, 98104-2420, USA
| | - Lenore J Launer
- Laboratory of Epidemiology, Demography, and Biometry, National Institute of Aging, The National Institutes of Health, Bethesda, MD, 20892, USA
- Intramural Research Program/National Institute on Aging/National Institutes of Health, Bethesda, MD, 20892, USA
| | - Mark Lathrop
- University of McGill Genome Center, Montreal, QC, H3A 0G1, Canada
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, 78229, USA
- Boston University and the NHLBI's Framingham Heart Study, Boston, MA, 02215, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Christophe Tzourio
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
- CHU de Bordeaux, Pole de santé publique, Service d'information médicale, 33000, Bordeaux, France
| | - Hieab H Adams
- Department of Clinical Genetics, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
- Department of Radiology & Nuclear Medicine, Erasmus MC, 3015 GE, Rotterdam, The Netherlands
| | - Paul M Matthews
- Department of Brain Sciences, Imperial College London, London, W12 0NN, UK
- UK Dementia Research Institute, London, WC1E 6BT, UK
- Data Science Institute, Imperial College London, London, SW7 2AZ, UK
| | - Myriam Fornage
- University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX, 77030, USA.
| | - Stéphanie Debette
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France.
- Department of Neurology, Boston University School of Medicine, Boston, MA, 02118, USA.
- Department of Neurology, CHU de Bordeaux, 33000, Bordeaux, France.
| |
Collapse
|
17
|
Grange P. Topology of the mesoscale connectome of the mouse brain. COMPUTATIONAL AND MATHEMATICAL BIOPHYSICS 2020. [DOI: 10.1515/cmb-2020-0106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
The wiring diagram of the mouse brain has recently been mapped at a mesoscopic scale in the Allen Mouse Brain Connectivity Atlas. Axonal projections from brain regions were traced using green fluoresent proteins. The resulting data were registered to a common three-dimensional reference space. They yielded a matrix of connection strengths between 213 brain regions. Global features such as closed loops formed by connections of similar intensity can be inferred using tools from persistent homology. We map the wiring diagram of the mouse brain to a simplicial complex (filtered by connection strengths). We work out generators of the first homology group. Some regions, including nucleus accumbens, are connected to the entire brain by loops, whereas no region has non-zero connection strength to all brain regions. Thousands of loops go through the isocortex, the striatum and the thalamus. On the other hand, medulla is the only major brain compartment that contains more than 100 loops.
Collapse
Affiliation(s)
- Pascal Grange
- Xi’an Jiaotong-Liverpool University , Department of Physics , Suzhou , China
| |
Collapse
|
18
|
Choy SW, Bagarinao E, Watanabe H, Ho ETW, Maesawa S, Mori D, Hara K, Kawabata K, Yoneyama N, Ohdake R, Imai K, Masuda M, Yokoi T, Ogura A, Taoka T, Koyama S, Tanabe HC, Katsuno M, Wakabayashi T, Kuzuya M, Hoshiyama M, Isoda H, Naganawa S, Ozaki N, Sobue G. Changes in white matter fiber density and morphology across the adult lifespan: A cross-sectional fixel-based analysis. Hum Brain Mapp 2020; 41:3198-3211. [PMID: 32304267 PMCID: PMC7375080 DOI: 10.1002/hbm.25008] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 03/27/2020] [Accepted: 04/01/2020] [Indexed: 12/13/2022] Open
Abstract
White matter (WM) fiber bundles change dynamically with age. These changes could be driven by alterations in axonal diameter, axonal density, and myelin content. In this study, we applied a novel fixel‐based analysis (FBA) framework to examine these changes throughout the adult lifespan. Using diffusion‐weighted images from a cohort of 293 healthy volunteers (89 males/204 females) from ages 21 to 86 years old, we performed FBA to analyze age‐related changes in microscopic fiber density (FD) and macroscopic fiber morphology (fiber cross section [FC]). Our results showed significant and widespread age‐related alterations in FD and FC across the whole brain. Interestingly, some fiber bundles such as the anterior thalamic radiation, corpus callosum, and superior longitudinal fasciculus only showed significant negative relationship with age in FD values, but not in FC. On the other hand, some segments of the cerebello‐thalamo‐cortical pathway only showed significant negative relationship with age in FC, but not in FD. Analysis at the tract‐level also showed that major fiber tract groups predominantly distributed in the frontal lobe (cingulum, forceps minor) exhibited greater vulnerability to the aging process than the others. Differences in FC and the combined measure of FD and cross section values observed between sexes were mostly driven by differences in brain sizes although male participants tended to exhibit steeper negative linear relationship with age in FD as compared to female participants. Overall, these findings provide further insights into the structural changes the brain's WM undergoes due to the aging process.
Collapse
Affiliation(s)
- Shao Wei Choy
- Center for Intelligent Signal and Imaging Research, Universiti Teknologi Petronas, Seri Iskandar, Perak, Malaysia
| | | | - Hirohisa Watanabe
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan.,Department of Neurology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan.,Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Eric Tatt Wei Ho
- Center for Intelligent Signal and Imaging Research, Universiti Teknologi Petronas, Seri Iskandar, Perak, Malaysia
| | - Satoshi Maesawa
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan.,Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Daisuke Mori
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
| | - Kazuhiro Hara
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Kazuya Kawabata
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Noritaka Yoneyama
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Reiko Ohdake
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Kazunori Imai
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Michihito Masuda
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takamasa Yokoi
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Aya Ogura
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Toshiaki Taoka
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Shuji Koyama
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
| | - Hiroki C Tanabe
- Department of Cognitive and Psychological Sciences, Graduate School of Informatics, Nagoya University, Nagoya, Aichi, Japan
| | - Masahisa Katsuno
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Toshihiko Wakabayashi
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Masafumi Kuzuya
- Department of Community Healthcare and Geriatrics, Nagoya University Graduate School of Medicine and Institute of Innovation for Future Society, Nagoya University, Nagoya, Aichi, Japan
| | - Minoru Hoshiyama
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
| | - Haruo Isoda
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Norio Ozaki
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan.,Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Gen Sobue
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
| |
Collapse
|
19
|
Shafiei G, Markello RD, Makowski C, Talpalaru A, Kirschner M, Devenyi GA, Guma E, Hagmann P, Cashman NR, Lepage M, Chakravarty MM, Dagher A, Mišić B. Spatial Patterning of Tissue Volume Loss in Schizophrenia Reflects Brain Network Architecture. Biol Psychiatry 2020; 87:727-735. [PMID: 31837746 DOI: 10.1016/j.biopsych.2019.09.031] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 09/04/2019] [Accepted: 09/30/2019] [Indexed: 12/25/2022]
Abstract
BACKGROUND There is growing recognition that connectome architecture shapes cortical and subcortical gray matter atrophy across a spectrum of neurological and psychiatric diseases. Whether connectivity contributes to tissue volume loss in schizophrenia in the same manner remains unknown. METHODS Here, we relate tissue volume loss in patients with schizophrenia to patterns of structural and functional connectivity. Gray matter deformation was estimated in a sample of 133 individuals with chronic schizophrenia (48 women, mean age 34.7 ± 12.9 years) and 113 control subjects (64 women, mean age 23.5 ± 8.4 years). Deformation-based morphometry was used to estimate cortical and subcortical gray matter deformation from T1-weighted magnetic resonance images. Structural and functional connectivity patterns were derived from an independent sample of 70 healthy participants using diffusion spectrum imaging and resting-state functional magnetic resonance imaging. RESULTS We found that regional deformation is correlated with the deformation of structurally and functionally connected neighbors. Distributed deformation patterns are circumscribed by specific functional systems (the ventral attention network) and cytoarchitectonic classes (limbic class), with an epicenter in the anterior cingulate cortex. CONCLUSIONS Altogether, the present study demonstrates that brain tissue volume loss in schizophrenia is conditioned by structural and functional connectivity, accounting for 25% to 35% of regional variance in deformation.
Collapse
Affiliation(s)
- Golia Shafiei
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Ross D Markello
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Carolina Makowski
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Alexandra Talpalaru
- Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada; Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada
| | - Matthias Kirschner
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Gabriel A Devenyi
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada
| | - Elisa Guma
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada; Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Neil R Cashman
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Martin Lepage
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada
| | - M Mallar Chakravarty
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada; Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada.
| | - Bratislav Mišić
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada.
| |
Collapse
|
20
|
Ibrahim C, Le Foll B, French L. Transcriptomic Characterization of the Human Insular Cortex and Claustrum. Front Neuroanat 2019; 13:94. [PMID: 31827426 PMCID: PMC6890825 DOI: 10.3389/fnana.2019.00094] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 10/31/2019] [Indexed: 12/13/2022] Open
Abstract
The insular cortex has been linked to a multitude of functions. In contrast, the nearby claustrum is a densely connected subcortical region with unclear function. To view the insula-claustrum region from the molecular perspective we analyzed the transcriptomic profile of these areas in six adult and four fetal human brains. We identified marker genes with specific expression and performed transcriptome-wide tests for enrichment of biological processes, molecular functions, and cellular components. In addition, specific insular and claustral expression of genes pertaining to diseases, addiction, and depression was tested. At the anatomical level, we used brain-wide analyses to determine the specificity of our results and to determine the transcriptomic similarity of the insula-claustrum region. We found UCMA to be the most significantly enriched gene in the insular cortex and confirmed specific expression of NR4A2, NTNG2, and LXN in the claustrum. Furthermore, the insula was found to have enriched expression of genes associated with mood disorders, learning, cardiac muscle contraction, oxygen transport, glutamate and dopamine signaling. Specific expression in the claustrum was enriched for genes pertaining to human immunodeficiency virus (HIV), severe intellectual disability, epileptic encephalopathy, intracellular transport, spine development, and macroautophagy. We tested for enrichment of genes related to addiction and depression, but they were generally not highly specific to the insula-claustrum region. Exceptions include high insular expression of genes linked to cocaine abuse and genes associated with ever smoking in the claustrum. Brain-wide, we find that markers of the adult claustrum are most specifically expressed in the fetal and adult insula. Altogether, our results provide a novel molecular perspective on the unique properties of the insula and claustrum.
Collapse
Affiliation(s)
- Christine Ibrahim
- Translational Addiction Research Laboratory, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | - Bernard Le Foll
- Translational Addiction Research Laboratory, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
- Addictions Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
- Division of Brain and Therapeutics, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Leon French
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Division of Brain and Therapeutics, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| |
Collapse
|
21
|
Abstract
The white matter architecture of brain networks promotes synchrony among neuronal populations, giving rise to richly patterned functional networks. Relating structure and function is a fundamental question for systems neuroscience, but the nature of the relationship is unknown. Here we examine the possibility that structure–function relationships are not uniform in the brain. We find that structure and function are closely aligned in unimodal cortex (primary sensory and motor regions), but diverge in transmodal cortex (default mode and salience networks). The divergence between structure and function closely follows representational and cytoarchitectonic hierarchies, reflecting a macroscale gradient. Our findings suggest structure and function are not uniformly related, but gradually decouple in parallel to this macroscale gradient. The white matter architecture of the brain imparts a distinct signature on neuronal coactivation patterns. Interregional projections promote synchrony among distant neuronal populations, giving rise to richly patterned functional networks. A variety of statistical, communication, and biophysical models have been proposed to study the relationship between brain structure and function, but the link is not yet known. In the present report we seek to relate the structural and functional connection profiles of individual brain areas. We apply a simple multilinear model that incorporates information about spatial proximity, routing, and diffusion between brain regions to predict their functional connectivity. We find that structure–function relationships vary markedly across the neocortex. Structure and function correspond closely in unimodal, primary sensory, and motor regions, but diverge in transmodal cortex, particularly the default mode and salience networks. The divergence between structure and function systematically follows functional and cytoarchitectonic hierarchies. Altogether, the present results demonstrate that structural and functional networks do not align uniformly across the brain, but gradually uncouple in higher-order polysensory areas.
Collapse
|
22
|
MR g-ratio-weighted connectome analysis in patients with multiple sclerosis. Sci Rep 2019; 9:13522. [PMID: 31534143 PMCID: PMC6751178 DOI: 10.1038/s41598-019-50025-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 09/05/2019] [Indexed: 12/16/2022] Open
Abstract
Multiple sclerosis (MS) is a brain network disconnection syndrome. Although the brain network topology in MS has been evaluated using diffusion MRI tractography, the mechanism underlying disconnection in the disorder remains unclear. In this study, we evaluated the brain network topology in MS using connectomes with connectivity strengths based on the ratio of the inner to outer myelinated axon diameter (i.e., g-ratio), thereby providing enhanced sensitivity to demyelination compared with the conventional measures of connectivity. We mapped g-ratio-based connectomes in 14 patients with MS and compared them with those of 14 age- and sex-matched healthy controls. For comparison, probabilistic tractography was also used to map connectomes based on the number of streamlines (NOS). We found that g-ratio- and NOS-based connectomes comprised significant connectivity reductions in patients with MS, predominantly in the motor, somatosensory, visual, and limbic regions. However, only the g-ratio-based connectome enabled detection of significant increases in nodal strength in patients with MS. Finally, we found that the g-ratio-weighted nodal strength in motor, visual, and limbic regions significantly correlated with inter-individual variation in measures of disease severity. The g-ratio-based connectome can serve as a sensitive biomarker for diagnosing and monitoring disease progression.
Collapse
|
23
|
Jensen SKG, Pangelinan M, Björnholm L, Klasnja A, Leemans A, Drakesmith M, Evans CJ, Barker ED, Paus T. Associations between prenatal, childhood, and adolescent stress and variations in white-matter properties in young men. Neuroimage 2018; 182:389-397. [PMID: 29066395 PMCID: PMC5911246 DOI: 10.1016/j.neuroimage.2017.10.033] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Revised: 08/30/2017] [Accepted: 10/16/2017] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVE Previous studies have shown that both pre- and post-natal adversities, the latter including exposures to stress during childhood and adolescence, explain variation in structural properties of white matter (WM) in the brain. While previous studies have examined effects of independent stress exposures within one developmental period, such as childhood, we examine effects of stress across development using data from a prospective longitudinal study. More specifically, we ask how stressful events during prenatal development, childhood, and adolescence relate to variation in WM properties in early adulthood in young men recruited from a birth cohort. METHOD Using data from 393 mother-son pairs from a community-based birth cohort from England (Avon Longitudinal Study of Parents and Children), we examined how stressful life events relate to variation in different structural properties of WM in the corpus callosum and across the whole brain in early adulthood in men aged 18-21 years. We distinguish between stress occurring during three developmental periods: a) prenatal maternal stress, b) postnatal stress within the first four years of life, c) stress during adolescence (age 12-16 years). To obtain a comprehensive quantification of variation in WM, we assess structural properties of WM using four different measures, namely fractional anisotropy (FA), mean diffusivity (MD), magnetization transfer ratio (MTR) and myelin water fraction (MWF). RESULTS The developmental model shows that prenatal stress is associated with lower MTR and MWF in the genu and/or splenium of the corpus callosum, and with lower MTR in global (lobar) WM. Stress during early childhood is associated with higher MTR in the splenium, and stress during adolescence is associated with higher MTR in the genu and lower MD in the splenium. We see no associations between postnatal stress and variation in global (lobar) WM. CONCLUSIONS The current study found evidence for independent effects of stress on WM properties during distinct neurodevelopmental periods. We speculate that these independent effects are due to differences in the developmental processes unfolding at different developmental time points. We suggest that associations between prenatal stress and WM properties may relate to abnormalities in neurogenesis, affecting the number and density of axons, while postnatal stress may interfere with processes related to myelination or radial growth of axons. Potential consequences of prenatal glucocorticoid exposure should be considered in obstetric care.
Collapse
Affiliation(s)
- Sarah K G Jensen
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, United Kingdom; Department of Pediatrics, Boston Children's Hospital, Boston, MA USA; Harvard Medical School, Boston, MA, USA
| | | | | | - Anja Klasnja
- Rotman Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mark Drakesmith
- Neuroscience and Mental Health Research Institute, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - C J Evans
- CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Edward D Barker
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, United Kingdom
| | - Tomáš Paus
- Rotman Research Institute, University of Toronto, Toronto, Ontario, Canada; Departments of Psychology and Psychiatry, University of Toronto, Toronto, Canada; Child Mind Institute, New York, USA.
| |
Collapse
|
24
|
Genome-wide association study of 23,500 individuals identifies 7 loci associated with brain ventricular volume. Nat Commun 2018; 9:3945. [PMID: 30258056 PMCID: PMC6158214 DOI: 10.1038/s41467-018-06234-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Accepted: 08/08/2018] [Indexed: 01/28/2023] Open
Abstract
The volume of the lateral ventricles (LV) increases with age and their abnormal enlargement is a key feature of several neurological and psychiatric diseases. Although lateral ventricular volume is heritable, a comprehensive investigation of its genetic determinants is lacking. In this meta-analysis of genome-wide association studies of 23,533 healthy middle-aged to elderly individuals from 26 population-based cohorts, we identify 7 genetic loci associated with LV volume. These loci map to chromosomes 3q28, 7p22.3, 10p12.31, 11q23.1, 12q23.3, 16q24.2, and 22q13.1 and implicate pathways related to tau pathology, S1P signaling, and cytoskeleton organization. We also report a significant genetic overlap between the thalamus and LV volumes (ρgenetic = −0.59, p-value = 3.14 × 10−6), suggesting that these brain structures may share a common biology. These genetic associations of LV volume provide insights into brain morphology. An increase in the volume of the brain lateral ventricles is a sign of normal aging, but can also be associated with neurological and psychiatric disorders. Here, Vojinovic et al. identify seven genetic loci in a GWA study for ventricular volume in 23,500 individuals and find correlation with thalamus volume.
Collapse
|
25
|
Longoni G, Brown RA, MomayyezSiahkal P, Elliott C, Narayanan S, Bar-Or A, Marrie RA, Yeh EA, Filippi M, Banwell B, Arnold DL. White matter changes in paediatric multiple sclerosis and monophasic demyelinating disorders. Brain 2017; 140:1300-1315. [PMID: 28334875 DOI: 10.1093/brain/awx041] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Accepted: 01/16/2017] [Indexed: 12/30/2022] Open
Abstract
See Hacohen et al. (doi:10.1093/awx075) for a scientific commentary on this article. Most children who experience an acquired demyelinating syndrome of the central nervous system will have a monophasic disease course, with no further clinical or radiological symptoms. A subset will be diagnosed with multiple sclerosis, a life-long disorder. Using linear mixed effects models we examined longitudinal diffusion properties of normal-appearing white matter in 505 serial scans of 132 paediatric participants with acquired demyelinating syndromes followed for a median of 4.4 years, many from first clinical presentation, and 106 scans of 80 healthy paediatric participants. Fifty-three participants with demyelinating syndromes eventually received a diagnosis of paediatric-onset multiple sclerosis. Diffusion tensor imaging measures properties of water diffusion through tissue, which normally becomes increasingly restricted and anisotropic in the brain during childhood and adolescence, as fibre bundles develop and myelinate. In the healthy paediatric participants, our data demonstrate the expected trajectory of more restricted and anisotropic white matter diffusivity with increasing age. However, in participants with multiple sclerosis, fractional anisotropy decreased and mean diffusivity of non-lesional, normal-appearing white matter progressively increased after clinical presentation, suggesting not only a failure of age-expected white matter development but also a progressive loss of tissue integrity. Surprisingly, patients with monophasic disease failed to show age-expected changes in diffusion parameters in normal-appearing white matter, although they did not show progressive loss of integrity over time. Further analysis demonstrated that participants with monophasic disease experienced different post-onset trajectories in normal-appearing white matter depending on their presenting phenotype: those with acute disseminated encephalomyelitis demonstrated abnormal trajectories of diffusion parameters compared to healthy paediatric participants, as did patients with non-acute disseminated encephalomyelitis presentations associated with lesions in the brain at onset. Patients with monofocal syndromes such as optic neuritis, transverse myelitis, or isolated brainstem syndromes in whom multifocal brain lesions were absent, showed trajectories more closely approximating normal-appearing white matter development. Our findings also suggest the existence of sexual dimorphism in the effects of demyelinating syndromes on normal-appearing white matter development. Overall, we demonstrate failure of white matter maturational changes and progressive loss of white matter integrity in paediatric-onset multiple sclerosis, but also show that even a single demyelinating attack-when associated with white matter lesions in the brain-negatively impacts subsequent normal-appearing white matter development.
Collapse
Affiliation(s)
- Giulia Longoni
- Neuroimaging Research Unit, Institute of Experimental Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
- Department of Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Robert A Brown
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Parya MomayyezSiahkal
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Colm Elliott
- Centre for Intelligent Machines, Department of Electrical and Computer Engineering, Faculty of Engineering, McGill University, Montreal, QC, Canada
| | - Sridar Narayanan
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Amit Bar-Or
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Ruth Ann Marrie
- Department of Internal Medicine, University of Manitoba, Winnipeg, Canada
| | - E Ann Yeh
- Department of Pediatrics, University of Toronto; Division of Neurology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
- Department of Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Brenda Banwell
- Division of Neurology, The Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Douglas L Arnold
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| |
Collapse
|
26
|
Imaging microstructure in the living human brain: A viewpoint. Neuroimage 2017; 182:3-7. [PMID: 29024791 DOI: 10.1016/j.neuroimage.2017.10.013] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Revised: 09/01/2017] [Accepted: 10/06/2017] [Indexed: 01/06/2023] Open
Abstract
This special issue summarizes an impressive body of work concerned with in vivo imaging of brain microstructure. Collectively, papers included here demonstrate the power of multi-modal magnetic resonance imaging (MRI) for mapping various structural properties of brain tissue. In this introduction, I provide a user's perspective vis-à-vis motivations for these efforts, review briefly the cellular composition of grey and white matter in the human brain, and provide a few examples of how we can bridge the gap between ex vivo and in vivo datasets to facilitate interpretation of studies measuring brain macro- and microstructure.
Collapse
|
27
|
Mancini M, Giulietti G, Dowell N, Spanò B, Harrison N, Bozzali M, Cercignani M. Introducing axonal myelination in connectomics: A preliminary analysis of g-ratio distribution in healthy subjects. Neuroimage 2017; 182:351-359. [PMID: 28917698 DOI: 10.1016/j.neuroimage.2017.09.018] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 08/16/2017] [Accepted: 09/08/2017] [Indexed: 12/21/2022] Open
Abstract
Microstructural imaging and connectomics are two research areas that hold great potential for investigating brain structure and function. Combining these two approaches can lead to a better and more complete characterization of the brain as a network. The aim of this work is characterizing the connectome from a novel perspective using the myelination measure given by the g-ratio. The g-ratio is the ratio of the inner to the outer diameters of a myelinated axon, whose aggregated value can now be estimated in vivo using MRI. In two different datasets of healthy subjects, we reconstructed the structural connectome and then used the g-ratio estimated from diffusion and magnetization transfer data to characterize the network structure. Significant characteristics of g-ratio weighted graphs emerged. First, the g-ratio distribution across the edges of the graph did not show the power-law distribution observed using the number of streamlines as a weight. Second, connections involving regions related to motor and sensory functions were the highest in myelin content. We also observed significant differences in terms of the hub structure and the rich-club organization suggesting that connections involving hub regions present higher myelination than peripheral connections. Taken together, these findings offer a characterization of g-ratio distribution across the connectome in healthy subjects and lay the foundations for further investigating plasticity and pathology using a similar approach.
Collapse
Affiliation(s)
- Matteo Mancini
- Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy; Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK.
| | | | - Nicholas Dowell
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK
| | - Barbara Spanò
- Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy
| | - Neil Harrison
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK
| | - Marco Bozzali
- Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy; Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK
| | - Mara Cercignani
- Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy; Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK
| |
Collapse
|
28
|
Björnholm L, Nikkinen J, Kiviniemi V, Nordström T, Niemelä S, Drakesmith M, Evans JC, Pike GB, Veijola J, Paus T. Structural properties of the human corpus callosum: Multimodal assessment and sex differences. Neuroimage 2017; 152:108-118. [DOI: 10.1016/j.neuroimage.2017.02.056] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Revised: 02/15/2017] [Accepted: 02/21/2017] [Indexed: 11/17/2022] Open
|
29
|
Pausova Z, Paus T, Abrahamowicz M, Bernard M, Gaudet D, Leonard G, Peron M, Pike GB, Richer L, Séguin JR, Veillette S. Cohort Profile: The Saguenay Youth Study (SYS). Int J Epidemiol 2017; 46:e19. [PMID: 27018016 PMCID: PMC5837575 DOI: 10.1093/ije/dyw023] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/20/2016] [Indexed: 01/15/2023] Open
Abstract
The Saguenay Youth Study (SYS) is a two-generational study of adolescents and their parents (n = 1029 adolescents and 962 parents) aimed at investigating the aetiology, early stages and trans-generational trajectories of common cardiometabolic and brain diseases. The ultimate goal of this study is to identify effective means for increasing healthy life expectancy. The cohort was recruited from the genetic founder population of the Saguenay Lac St Jean region of Quebec, Canada. The participants underwent extensive (15-h) phenotyping, including an hour-long recording of beat-by-beat blood pressure, magnetic resonance imaging of the brain and abdomen, and serum lipidomic profiling with LC-ESI-MS. All participants have been genome-wide genotyped (with ∼ 8 M imputed single nucleotide polymorphisms) and a subset of them (144 adolescents and their 288 parents) has been genome-wide epityped (whole blood DNA, Infinium HumanMethylation450K BeadChip). These assessments are complemented by a detailed evaluation of each participant in a number of domains, including cognition, mental health and substance use, diet, physical activity and sleep, and family environment. The data collection took place during 2003-12 in adolescents (full) and their parents (partial), and during 2012-15 in parents (full). All data are available upon request.
Collapse
Affiliation(s)
- Zdenka Pausova
- Hospital for Sick Children and Departments of Physiology and Nutritional Science
| | - Tomas Paus
- Rotman Research Institute and Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada
- Child Mind Institute, New York, NY, USA
| | - Michal Abrahamowicz
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Manon Bernard
- Hospital for Sick Children and Departments of Physiology and Nutritional Science
| | - Daniel Gaudet
- Community Genomic Centre, Université de Montréal, Chicoutimi, QC, Canada
| | - Gabriel Leonard
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Michel Peron
- Department of Human Sciences, Université du Québec à Chicoutimi, Chicoutimi, QC, Canada
| | - G Bruce Pike
- Hotchkiss Brain Institute, University of Calgary, Calgary, BC, Canada
| | - Louis Richer
- Department of Health Sciences, Université du Québec à Chicoutimi, Chicoutimi, QC, Canada and
| | - Jean R Séguin
- Sainte-Justine Hospital Research Center and Department of Psychiatry, Université de Montréal, Montreal, QC, Canada
| | - Suzanne Veillette
- Department of Human Sciences, Université du Québec à Chicoutimi, Chicoutimi, QC, Canada
| |
Collapse
|
30
|
Nelson EE. Learning through the ages: How the brain adapts to the social world across development. COGNITIVE DEVELOPMENT 2017. [DOI: 10.1016/j.cogdev.2017.02.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|
31
|
Vanhauwaert R, Kuenen S, Masius R, Bademosi A, Manetsberger J, Schoovaerts N, Bounti L, Gontcharenko S, Swerts J, Vilain S, Picillo M, Barone P, Munshi ST, de Vrij FM, Kushner SA, Gounko NV, Mandemakers W, Bonifati V, Meunier FA, Soukup SF, Verstreken P. The SAC1 domain in synaptojanin is required for autophagosome maturation at presynaptic terminals. EMBO J 2017; 36:1392-1411. [PMID: 28331029 DOI: 10.15252/embj.201695773] [Citation(s) in RCA: 164] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Revised: 02/25/2017] [Accepted: 03/01/2017] [Indexed: 11/09/2022] Open
Abstract
Presynaptic terminals are metabolically active and accrue damage through continuous vesicle cycling. How synapses locally regulate protein homeostasis is poorly understood. We show that the presynaptic lipid phosphatase synaptojanin is required for macroautophagy, and this role is inhibited by the Parkinson's disease mutation R258Q. Synaptojanin drives synaptic endocytosis by dephosphorylating PI(4,5)P2, but this function appears normal in SynaptojaninRQ knock-in flies. Instead, R258Q affects the synaptojanin SAC1 domain that dephosphorylates PI(3)P and PI(3,5)P2, two lipids found in autophagosomal membranes. Using advanced imaging, we show that SynaptojaninRQ mutants accumulate the PI(3)P/PI(3,5)P2-binding protein Atg18a on nascent synaptic autophagosomes, blocking autophagosome maturation at fly synapses and in neurites of human patient induced pluripotent stem cell-derived neurons. Additionally, we observe neurodegeneration, including dopaminergic neuron loss, in SynaptojaninRQ flies. Thus, synaptojanin is essential for macroautophagy within presynaptic terminals, coupling protein turnover with synaptic vesicle cycling and linking presynaptic-specific autophagy defects to Parkinson's disease.
Collapse
Affiliation(s)
- Roeland Vanhauwaert
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, Leuven Institute for Neurodegenerative Disease (LIND), KU Leuven, Leuven, Belgium
| | - Sabine Kuenen
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, Leuven Institute for Neurodegenerative Disease (LIND), KU Leuven, Leuven, Belgium
| | - Roy Masius
- Department of Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands
| | - Adekunle Bademosi
- Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, Qld, Australia
| | - Julia Manetsberger
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, Leuven Institute for Neurodegenerative Disease (LIND), KU Leuven, Leuven, Belgium
| | - Nils Schoovaerts
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, Leuven Institute for Neurodegenerative Disease (LIND), KU Leuven, Leuven, Belgium
| | - Laura Bounti
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, Leuven Institute for Neurodegenerative Disease (LIND), KU Leuven, Leuven, Belgium
| | - Serguei Gontcharenko
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, Leuven Institute for Neurodegenerative Disease (LIND), KU Leuven, Leuven, Belgium
| | - Jef Swerts
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, Leuven Institute for Neurodegenerative Disease (LIND), KU Leuven, Leuven, Belgium
| | - Sven Vilain
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, Leuven Institute for Neurodegenerative Disease (LIND), KU Leuven, Leuven, Belgium
| | - Marina Picillo
- Department of Medicine and Surgery, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Salerno, Italy
| | - Paolo Barone
- Department of Medicine and Surgery, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Salerno, Italy
| | | | - Femke Ms de Vrij
- Department of Psychiatry, Erasmus MC, Rotterdam, The Netherlands
| | - Steven A Kushner
- Department of Psychiatry, Erasmus MC, Rotterdam, The Netherlands
| | - Natalia V Gounko
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, Leuven Institute for Neurodegenerative Disease (LIND), KU Leuven, Leuven, Belgium.,Electron Microscopy Platform, VIB Bio-Imaging Core, Leuven, Belgium
| | - Wim Mandemakers
- Department of Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands
| | - Vincenzo Bonifati
- Department of Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands
| | - Frederic A Meunier
- Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, Qld, Australia
| | - Sandra-Fausia Soukup
- VIB Center for Brain & Disease Research, Leuven, Belgium .,Department of Human Genetics, Leuven Institute for Neurodegenerative Disease (LIND), KU Leuven, Leuven, Belgium
| | - Patrik Verstreken
- VIB Center for Brain & Disease Research, Leuven, Belgium .,Department of Human Genetics, Leuven Institute for Neurodegenerative Disease (LIND), KU Leuven, Leuven, Belgium
| |
Collapse
|
32
|
Gudi V, Gai L, Herder V, Tejedor LS, Kipp M, Amor S, Sühs KW, Hansmann F, Beineke A, Baumgärtner W, Stangel M, Skripuletz T. Synaptophysin Is a Reliable Marker for Axonal Damage. J Neuropathol Exp Neurol 2017; 76:109-125. [PMID: 28177496 DOI: 10.1093/jnen/nlw114] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Indexed: 11/13/2022] Open
Abstract
Synaptophysin is an abundant membrane protein of synaptic vesicles. The objective of this study was to determine the utility of identifying synaptophysin accumulations (spheroids/ovoids/bulbs) in CNS white matter as an immunohistochemical marker of axonal damage in demyelinating and neuroinflammatory conditions. We studied the cuprizone toxicity and Theiler’s murine encephalomyelitis virus (TMEV) infection models of demyelination and analyzed CNS tissue from patients with multiple sclerosis (MS). Synaptophysin colocalized with the amyloid precursor protein (APP), a well-known marker of axonal damage. In the cuprizone model, numerous pathological synaptophysin/APP-positive spheroids/ovoids were identified in the corpus callosum at the onset of demyelination; the extent of synaptophysin/APP-positive vesicle aggregates correlated with identified reactive microglia; during late and chronic demyelination, the majority of synaptophysin/APP-positive spheroids/ovoids resolved but a few remained, indicating persistent axonal damage; in the remyelination phase, scattered large synaptophysin/APP-positive bulbs persisted. In the TMEV model, only a few large- to medium-sized synaptophysin/APP-positive bulbs were found in demyelinated areas. In MS patient tissue samples, the bulbs appeared exclusively at the inflammatory edges of lesions. In conclusion, our data suggest that synaptophysin as a reliable marker of axonal damage in the CNS in inflammatory/demyelinating conditions.
Collapse
Affiliation(s)
- Viktoria Gudi
- Clinical Neuroimmunology and Neurochemistry, Department of Neurology, Hannover Medical School, Hannover, Germany
| | - Lijie Gai
- Clinical Neuroimmunology and Neurochemistry, Department of Neurology, Hannover Medical School, Hannover, Germany
| | - Vanessa Herder
- Department of Pathology, University of Veterinary Medicine Hannover, Hannover, Germany.,Center for Systems Neuroscience, Hannover, Germany
| | - Laura Salinas Tejedor
- Clinical Neuroimmunology and Neurochemistry, Department of Neurology, Hannover Medical School, Hannover, Germany.,Center for Systems Neuroscience, Hannover, Germany
| | - Markus Kipp
- Department of Anatomy II, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Sandra Amor
- Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands
| | - Kurt-Wolfram Sühs
- Clinical Neuroimmunology and Neurochemistry, Department of Neurology, Hannover Medical School, Hannover, Germany
| | - Florian Hansmann
- Center for Systems Neuroscience, Hannover, Germany.,Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands
| | - Andreas Beineke
- Center for Systems Neuroscience, Hannover, Germany.,Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands
| | - Wolfgang Baumgärtner
- Department of Pathology, University of Veterinary Medicine Hannover, Hannover, Germany
| | - Martin Stangel
- Clinical Neuroimmunology and Neurochemistry, Department of Neurology, Hannover Medical School, Hannover, Germany.,Center for Systems Neuroscience, Hannover, Germany
| | - Thomas Skripuletz
- Clinical Neuroimmunology and Neurochemistry, Department of Neurology, Hannover Medical School, Hannover, Germany
| |
Collapse
|
33
|
Paus T. Population neuroscience. HANDBOOK OF CLINICAL NEUROLOGY 2016; 138:17-37. [PMID: 27637950 DOI: 10.1016/b978-0-12-802973-2.00002-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Population neuroscience endeavors to identify influences shaping the human brain from conception onwards, thus generating knowledge relevant for building and maintaining brain health throughout the life span. This can be achieved by studying large samples of participants drawn from the general population and evaluated with state-of-the-art tools for assessing (a) genes and their regulation; (b) external and internal environments; and (c) brain properties. This chapter reviews the three elements of population neuroscience (principles, tools, innovations, limitations), and discusses future directions in this field.
Collapse
Affiliation(s)
- T Paus
- Rotman Research Institute and Departments of Psychology and Psychiatry, University of Toronto, Toronto; Canada and Child Mind Institute, New York, NY, USA.
| |
Collapse
|
34
|
Bakker G, Caan MWA, Schluter RS, Bloemen OJN, da Silva-Alves F, de Koning MB, Boot E, Vingerhoets WAM, Nieman DH, de Haan L, Booij J, van Amelsvoort TAMJ. Distinct white-matter aberrations in 22q11.2 deletion syndrome and patients at ultra-high risk for psychosis. Psychol Med 2016; 46:2299-2311. [PMID: 27193339 DOI: 10.1017/s0033291716000970] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Patients with a deletion at chromosome 22q11.2 (22q11DS) have 30% lifetime risk of developing a psychosis. People fulfilling clinical criteria for ultra-high risk (UHR) for psychosis have 30% risk of developing a psychosis within 2 years. Both high-risk groups show white-matter (WM) abnormalities in microstructure and volume compared to healthy controls (HC), which have been related to psychotic symptoms. Comparisons of WM pathology between these two groups may specify WM markers related to genetic and clinical risk factors. METHOD Fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) were assessed using diffusion tensor magnetic resonance imaging (MRI), and WM volume with structural MRI, in 23 UHR patients, 21 22q11DS patients, and 33 HC. RESULTS Compared to UHR patients 22q11DS patients had (1) lower AD and RD in corpus callosum (CC), cortical fasciculi, and anterior thalamic radiation (ATR), (2) higher FA in CC and ATR, and (3) lower occipital and superior temporal gyrus WM volume. Compared to HC, 22q11DS patients had (1) lower AD and RD throughout cortical fasciculi and (2) higher FA in ATR, CC and inferior fronto-occipital fasciculus. Compared to HC, UHR patients had (1) higher mean MD, RD, and AD in CC, ATR and cortical fasciculi, (2) no differences in FA. CONCLUSIONS UHR and 22q11DS patients share a susceptibility for developing psychosis yet were characterized by distinct patterns of WM alterations relative to HC. While UHR patients were typified by signs suggestive of aberrant myelination, 22q11DS subjects showed signs suggestive of lower axonal integrity.
Collapse
Affiliation(s)
- G Bakker
- Department of Psychiatry & Psychology,University of Maastricht,The Netherlands
| | - M W A Caan
- Department of Radiology,Academic Medical Center, University of Amsterdam,Amsterdam,The Netherlands
| | - R S Schluter
- Department of Radiology,Academic Medical Center, University of Amsterdam,Amsterdam,The Netherlands
| | - O J N Bloemen
- Department of Psychiatry & Psychology,University of Maastricht,The Netherlands
| | - F da Silva-Alves
- Department of Psychiatry,Academic Medical Center, University of Amsterdam,Amsterdam,The Netherlands
| | - M B de Koning
- Department of Psychiatry,Academic Medical Center, University of Amsterdam,Amsterdam,The Netherlands
| | - E Boot
- Department of Nuclear Medicine,Academic Medical Center, University of Amsterdam,Amsterdam,The Netherlands
| | - W A M Vingerhoets
- Department of Psychiatry & Psychology,University of Maastricht,The Netherlands
| | - D H Nieman
- Department of Psychiatry,Academic Medical Center, University of Amsterdam,Amsterdam,The Netherlands
| | - L de Haan
- Department of Psychiatry,Academic Medical Center, University of Amsterdam,Amsterdam,The Netherlands
| | - J Booij
- Department of Nuclear Medicine,Academic Medical Center, University of Amsterdam,Amsterdam,The Netherlands
| | | |
Collapse
|
35
|
van Ewijk H, Noordermeer SDS, Heslenfeld DJ, Luman M, Hartman CA, Hoekstra PJ, Faraone SV, Franke B, Buitelaar JK, Oosterlaan J. The influence of comorbid oppositional defiant disorder on white matter microstructure in attention-deficit/hyperactivity disorder. Eur Child Adolesc Psychiatry 2016; 25:701-10. [PMID: 26507746 PMCID: PMC4932146 DOI: 10.1007/s00787-015-0784-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Accepted: 10/07/2015] [Indexed: 12/22/2022]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) are highly comorbid disorders. ADHD has been associated with altered white matter (WM) microstructure, though the literature is inconsistent, which may be due to differences in the in- or exclusion of participants with comorbid ODD. WM abnormalities in ODD are still poorly understood, and it is unclear whether comorbid ODD in ADHD may have confounded the current ADHD literature. Diffusion Tensor Imaging (DTI) was used to compare fractional anisotropy (FA) and mean diffusivity (MD) between ADHD patients with (n = 42) and without (n = 117) comorbid ODD. All participants were between 8-25 years and groups did not differ in mean age or gender. Follow-up analyses were conducted to examine the role of antisocial behaviour (conduct problems) on FA and MD values in both groups. Comorbid ODD in ADHD was associated with lower FA in left frontotemporal WM, which appeared independent of ADHD symptoms. FA was negatively associated with antisocial behaviour in ADHD + ODD, but not in ADHD-only. Comorbid ODD is associated with WM abnormalities in individuals with ADHD, which appears to be independent of ADHD symptoms. Altered WM microstructure in comorbid ODD may play a role in inconsistencies in the current DTI literature in ADHD. Altered development of these tracts may contribute to social-emotional and cognitive problems in children with oppositional and antisocial behaviour.
Collapse
Affiliation(s)
- Hanneke van Ewijk
- Department of Clinical Neuropsychology, Faculty of Psychology and Education, VU University Amsterdam, van der Boechorststraat 1, 1081 BT, Amsterdam, The Netherlands.
| | - Siri D S Noordermeer
- Department of Clinical Neuropsychology, Faculty of Psychology and Education, VU University Amsterdam, van der Boechorststraat 1, 1081 BT, Amsterdam, The Netherlands
| | - Dirk J Heslenfeld
- Department of Clinical Neuropsychology, Faculty of Psychology and Education, VU University Amsterdam, van der Boechorststraat 1, 1081 BT, Amsterdam, The Netherlands
- Department of Cognitive Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - Marjolein Luman
- Department of Clinical Neuropsychology, Faculty of Psychology and Education, VU University Amsterdam, van der Boechorststraat 1, 1081 BT, Amsterdam, The Netherlands
| | - Catharina A Hartman
- Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Pieter J Hoekstra
- Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Stephen V Faraone
- Departments of Psychiatry and of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, USA
| | - Barbara Franke
- Departments of Human Genetics and Psychiatry, Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Jan K Buitelaar
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Karakter, Child and Adolescent Psychiatry, University Center Nijmegen in Nijmegen, Nijmegen, The Netherlands
| | - J Oosterlaan
- Department of Clinical Neuropsychology, Faculty of Psychology and Education, VU University Amsterdam, van der Boechorststraat 1, 1081 BT, Amsterdam, The Netherlands
| |
Collapse
|
36
|
Spitzbarth I, Lempp C, Kegler K, Ulrich R, Kalkuhl A, Deschl U, Baumgärtner W, Seehusen F. Immunohistochemical and transcriptome analyses indicate complex breakdown of axonal transport mechanisms in canine distemper leukoencephalitis. Brain Behav 2016; 6:e00472. [PMID: 27247850 PMCID: PMC4864272 DOI: 10.1002/brb3.472] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Revised: 02/24/2016] [Accepted: 03/11/2016] [Indexed: 12/03/2022] Open
Abstract
INTRODUCTION CDV-DL (Canine distemper virus-induced demyelinating leukoencephalitis) represents a spontaneously occurring animal model for demyelinating disorders. Axonopathy represents a key pathomechanism in this disease; however, its underlying pathogenesis has not been addressed in detail so far. This study aimed at the characterization of axonal cytoskeletal, transport, and potential regenerative changes with a parallel focus upon Schwann cell remyelination. METHODS Immunohistochemistry of canine cerebellar tissue as well as a comparative analysis of genes from an independent microarray study were performed. RESULTS Increased axonal immunoreactivity for nonphosphorylated neurofilament was followed by loss of cytoskeletal and motor proteins. Interestingly, a subset of genes encoding for neurofilament subunits and motor proteins was up-regulated in the chronic stage compared to dogs with subacute CDV-DL. However, immunohistochemically, hints for axonal regeneration were restricted to up-regulated axonal positivity of hypoxia-inducible factor 1 alpha, while growth-associated protein 43, erythropoietin and its receptor were not or even down-regulated. Periaxin-positive structures, indicative of Schwann cell remyelination, were only detected within few advanced lesions. CONCLUSIONS The present findings demonstrate a complex sequence of axonal cytoskeletal breakdown mechanisms. Moreover, though sparse, this is the first report of Schwann cell remyelination in CDV-DL. Facilitation of these very limited endogenous regenerative responses represents an important topic for future research.
Collapse
Affiliation(s)
- Ingo Spitzbarth
- Department of Pathology University of Veterinary Medicine Hannover Foundation Bünteweg 17 30559 Hannover Germany; Center for Systems Neuroscience Bünteweg 2 30559 Hannover Germany
| | - Charlotte Lempp
- Department of Pathology University of Veterinary Medicine Hannover Foundation Bünteweg 17 30559 Hannover Germany
| | - Kristel Kegler
- Department of Pathology University of Veterinary Medicine Hannover Foundation Bünteweg 17 30559 Hannover Germany; Center for Systems Neuroscience Bünteweg 2 30559 Hannover Germany
| | - Reiner Ulrich
- Department of Pathology University of Veterinary Medicine Hannover Foundation Bünteweg 17 30559 Hannover Germany; Center for Systems Neuroscience Bünteweg 2 30559 Hannover Germany
| | - Arno Kalkuhl
- Department of Non-Clinical Drug Safety Boehringer Ingelheim Pharma GmbH & Co KG Biberach (Riß) Germany
| | - Ulrich Deschl
- Department of Non-Clinical Drug Safety Boehringer Ingelheim Pharma GmbH & Co KG Biberach (Riß) Germany
| | - Wolfgang Baumgärtner
- Department of Pathology University of Veterinary Medicine Hannover Foundation Bünteweg 17 30559 Hannover Germany; Center for Systems Neuroscience Bünteweg 2 30559 Hannover Germany
| | - Frauke Seehusen
- Department of Pathology University of Veterinary Medicine Hannover Foundation Bünteweg 17 30559 Hannover Germany
| |
Collapse
|
37
|
Wang Y, Liu G, Hong D, Chen F, Ji X, Cao G. White matter injury in ischemic stroke. Prog Neurobiol 2016; 141:45-60. [PMID: 27090751 PMCID: PMC5677601 DOI: 10.1016/j.pneurobio.2016.04.005] [Citation(s) in RCA: 192] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2016] [Revised: 04/01/2016] [Accepted: 04/10/2016] [Indexed: 02/06/2023]
Abstract
Stroke is one of the major causes of disability and mortality worldwide. It is well known that ischemic stroke can cause gray matter injury. However, stroke also elicits profound white matter injury, a risk factor for higher stroke incidence and poor neurological outcomes. The majority of damage caused by stroke is located in subcortical regions and, remarkably, white matter occupies nearly half of the average infarct volume. Indeed, white matter is exquisitely vulnerable to ischemia and is often injured more severely than gray matter. Clinical symptoms related to white matter injury include cognitive dysfunction, emotional disorders, sensorimotor impairments, as well as urinary incontinence and pain, all of which are closely associated with destruction and remodeling of white matter connectivity. White matter injury can be noninvasively detected by MRI, which provides a three-dimensional assessment of its morphology, metabolism, and function. There is an urgent need for novel white matter therapies, as currently available strategies are limited to preclinical animal studies. Optimal protection against ischemic stroke will need to encompass the fortification of both gray and white matter. In this review, we discuss white matter injury after ischemic stroke, focusing on clinical features and tools, such as imaging, manifestation, and potential treatments. We also briefly discuss the pathophysiology of WMI and future research directions.
Collapse
Affiliation(s)
- Yuan Wang
- Department of Neurology, Xuanwu Hospital, Capital University of Medicine, Beijing 100053, China
| | - Gang Liu
- Department of Neurology, Xuanwu Hospital, Capital University of Medicine, Beijing 100053, China
| | - Dandan Hong
- Department of Bioengineering, University of Pittsburgh School of Engineering, United States
| | - Fenghua Chen
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, United States
| | - Xunming Ji
- Department of Neurosurgery, Xuanwu Hospital, Capital University of Medicine, Beijing 100053, China.
| | - Guodong Cao
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, United States; Geriatric Research Education and Clinical Centers, VA Pittsburgh Healthcare System, Pittsburgh, PA 15240, United States.
| |
Collapse
|
38
|
Shao L, Golbaz K, Honer WG, Beasley CL. Deficits in axon-associated proteins in prefrontal white matter in bipolar disorder but not schizophrenia. Bipolar Disord 2016; 18:342-51. [PMID: 27218831 DOI: 10.1111/bdi.12395] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Revised: 01/25/2016] [Accepted: 02/26/2016] [Indexed: 12/24/2022]
Abstract
OBJECTIVES Brain imaging studies have implicated white matter dysfunction in the pathophysiology of both bipolar disorder (BD) and schizophrenia (SCZ). However, the contribution of axons to white matter pathology in these disorders is not yet understood. Maintenance of neuronal function is dependent on the active transport of biological material, including synaptic proteins, along the axon. In this study, the expression of six proteins associated with axonal transport of synaptic cargoes was quantified in postmortem samples of prefrontal white matter in subjects with BD, those with SCZ, and matched controls, as a measure of axonal dysfunction in these disorders. METHODS Levels of the microtubule-associated proteins β-tubulin and microtubule-associated protein 6 (MAP6), the motor and accessory proteins kinesin-1 and disrupted-in-schizophrenia 1 (DISC1), and the synaptic cargoes synaptotagmin and synaptosomal-associated protein-25 (SNAP-25) were quantified in white matter adjacent to the dorsolateral prefrontal cortex in subjects with BD (n = 34), subjects with SCZ (n = 35), and non-psychiatric controls (n = 35) using immunoblotting and an enzyme-linked immunosorbent assay (ELISA). RESULTS Protein expression of β-tubulin, kinesin-1, DISC1, synaptotagmin, and SNAP-25 was significantly lower in subjects with BD compared to controls. Levels of axon-associated proteins were also lower in subjects with SCZ, but failed to reach statistical significance. CONCLUSIONS These data provide evidence for deficits in axon-associated proteins in prefrontal white matter in BD. Findings are suggestive of decreased axonal density or dysregulation of axonal function in this disorder.
Collapse
Affiliation(s)
- Li Shao
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Khashayar Golbaz
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - William G Honer
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Clare L Beasley
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| |
Collapse
|
39
|
Bender AR, Prindle JJ, Brandmaier AM, Raz N. White matter and memory in healthy adults: Coupled changes over two years. Neuroimage 2016; 131:193-204. [PMID: 26545457 PMCID: PMC4848116 DOI: 10.1016/j.neuroimage.2015.10.085] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2015] [Revised: 10/06/2015] [Accepted: 10/29/2015] [Indexed: 11/16/2022] Open
Abstract
Numerous cross-sectional studies have used diffusion tensor imaging (DTI) to link age-related differences in white matter (WM) anisotropy and concomitant decrements in cognitive ability. Due to a dearth of longitudinal evidence, the relationship between changes in diffusion properties of WM and cognitive performance remains unclear. Here we examine the relationship between two-year changes in WM organization and cognitive performance in healthy adults (N=96, age range at baseline=18-79 years). We used latent change score models (LCSM) to evaluate changes in age-sensitive cognitive abilities - fluid intelligence and associative memory. WM changes were assessed by fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) in WM regions that are considered part of established memory networks and exhibited individual differences in change. In modeling change, we postulated reciprocal paths between baseline measures and change factors, within and between WM and cognition domains, and accounted for individual differences in baseline age. Although baseline cross-sectional memory performance was positively associated with FA and negatively with RD, longitudinal effects told an altogether different story. Independent of age, longitudinal improvements in associative memory were significantly associated with linear reductions in FA and increases in RD. The present findings demonstrate the sensitivity of DTI-derived indices to changes in the brain and cognition and affirm the importance of longitudinal models for evaluating brain-cognition relations.
Collapse
Affiliation(s)
- Andrew R Bender
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.
| | - John J Prindle
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Andreas M Brandmaier
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Naftali Raz
- Institute of Gerontology & Department of Psychology, Wayne State University, Detroit, MI, USA
| |
Collapse
|
40
|
Bender AR, Völkle MC, Raz N. Differential aging of cerebral white matter in middle-aged and older adults: A seven-year follow-up. Neuroimage 2016; 125:74-83. [PMID: 26481675 PMCID: PMC4691398 DOI: 10.1016/j.neuroimage.2015.10.030] [Citation(s) in RCA: 86] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Revised: 09/30/2015] [Accepted: 10/12/2015] [Indexed: 11/22/2022] Open
Abstract
The few extant reports of longitudinal white matter (WM) changes in healthy aging, using diffusion tensor imaging (DTI), reveal substantial differences in change across brain regions and DTI indices. According to the "last-in-first-out" hypothesis of brain aging late-developing WM tracts may be particularly vulnerable to advanced age. To test this hypothesis we compared age-related changes in association, commissural and projection WM fiber regions using a skeletonized, region of interest DTI approach. Using linear mixed effect models, we evaluated the influences of age and vascular risk at baseline on seven-year changes in three indices of WM integrity and organization (axial diffusivity, AD, radial diffusivity, RD, and fractional anisotropy, FA) in healthy middle-aged and older adults (mean age=65.4, SD=9.0years). Association fibers showed the most pronounced declines over time. Advanced age was associated with greater longitudinal changes in RD and FA, independent of fiber type. Furthermore, older age was associated with longitudinal RD increases in late-developing, but not early-developing projection fibers. These findings demonstrate the increased vulnerability of later developing WM regions and support the "last-in-first-out" hypothesis of brain aging.
Collapse
Affiliation(s)
- Andrew R Bender
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Germany.
| | - Manuel C Völkle
- Department of Psychology, Humboldt University, Max Planck Institute for Human Development, Germany; Center for Lifespan Psychology, Max Planck Institute for Human Development, Germany
| | - Naftali Raz
- Institute of Gerontology & Department of Psychology, Wayne State University, USA
| |
Collapse
|
41
|
French L, Gray C, Leonard G, Perron M, Pike GB, Richer L, Séguin JR, Veillette S, Evans CJ, Artiges E, Banaschewski T, Bokde AWL, Bromberg U, Bruehl R, Buchel C, Cattrell A, Conrod PJ, Flor H, Frouin V, Gallinat J, Garavan H, Gowland P, Heinz A, Lemaitre H, Martinot JL, Nees F, Orfanos DP, Pangelinan MM, Poustka L, Rietschel M, Smolka MN, Walter H, Whelan R, Timpson NJ, Schumann G, Smith GD, Pausova Z, Paus T. Early Cannabis Use, Polygenic Risk Score for Schizophrenia and Brain Maturation in Adolescence. JAMA Psychiatry 2015; 72:1002-11. [PMID: 26308966 PMCID: PMC5075969 DOI: 10.1001/jamapsychiatry.2015.1131] [Citation(s) in RCA: 123] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
IMPORTANCE Cannabis use during adolescence is known to increase the risk for schizophrenia in men. Sex differences in the dynamics of brain maturation during adolescence may be of particular importance with regard to vulnerability of the male brain to cannabis exposure. OBJECTIVE To evaluate whether the association between cannabis use and cortical maturation in adolescents is moderated by a polygenic risk score for schizophrenia. DESIGN, SETTING, AND PARTICIPANTS Observation of 3 population-based samples included initial analysis in 1024 adolescents of both sexes from the Canadian Saguenay Youth Study (SYS) and follow-up in 426 adolescents of both sexes from the IMAGEN Study from 8 European cities and 504 male youth from the Avon Longitudinal Study of Parents and Children (ALSPAC) based in England. A total of 1577 participants (aged 12-21 years; 899 [57.0%] male) had (1) information about cannabis use; (2) imaging studies of the brain; and (3) a polygenic risk score for schizophrenia across 108 genetic loci identified by the Psychiatric Genomics Consortium. Data analysis was performed from March 1 through December 31, 2014. MAIN OUTCOMES AND MEASURES Cortical thickness derived from T1-weighted magnetic resonance images. Linear regression tests were used to assess the relationships between cannabis use, cortical thickness, and risk score. RESULTS Across the 3 samples of 1574 participants, a negative association was observed between cannabis use in early adolescence and cortical thickness in male participants with a high polygenic risk score. This observation was not the case for low-risk male participants or for the low- or high-risk female participants. Thus, in SYS male participants, cannabis use interacted with risk score vis-à-vis cortical thickness (P = .009); higher scores were associated with lower thickness only in males who used cannabis. Similarly, in the IMAGEN male participants, cannabis use interacted with increased risk score vis-à-vis a change in decreasing cortical thickness from 14.5 to 18.5 years of age (t137 = -2.36; P = .02). Finally, in the ALSPAC high-risk group of male participants, those who used cannabis most frequently (≥61 occasions) had lower cortical thickness than those who never used cannabis (difference in cortical thickness, 0.07 [95% CI, 0.01-0.12]; P = .02) and those with light use (<5 occasions) (difference in cortical thickness, 0.11 [95% CI, 0.03-0.18]; P = .004). CONCLUSIONS AND RELEVANCE Cannabis use in early adolescence moderates the association between the genetic risk for schizophrenia and cortical maturation among male individuals. This finding implicates processes underlying cortical maturation in mediating the link between cannabis use and liability to schizophrenia.
Collapse
Affiliation(s)
- Leon French
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
| | - Courtney Gray
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
| | - Gabriel Leonard
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Michel Perron
- Groupe d'Étude des Conditions de vie et des Besoins de la Population, Cégep de Jonquiere, Jonquiere, Saguenay, Quebec, Canada 4Department of Human Sciences, University of Quebec in Chicoutimi, Chicoutimi, Quebec, Canada
| | - G Bruce Pike
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada6Department of Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
| | - Louis Richer
- Department of Health Sciences, University of Quebec in Chicoutimi, Chicoutimi, Quebec, Canada
| | - Jean R Séguin
- Department of Psychiatry and Centre de Recherche du Centre Hospitalier Universitaire Ste-Justine, University de Montréal, Montreal, Quebec, Canada
| | - Suzanne Veillette
- Groupe d'Étude des Conditions de vie et des Besoins de la Population, Cégep de Jonquiere, Jonquiere, Saguenay, Quebec, Canada 4Department of Human Sciences, University of Quebec in Chicoutimi, Chicoutimi, Quebec, Canada
| | - C John Evans
- School of Psychology, Cardiff University, Cardiff, Wales
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Medicale (INSERM), Unité Mixte de Recherche (UMR) 1000, Research Unit Imaging and Psychiatry, Commissariat à l'Énergie Atomique (CEA), Direction des Sciences du Vivant, Institut d'Imagerie Biomédicale, Serv
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Arun W L Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neurosciences, Trinity College, Dublin, Ireland
| | - Uli Bromberg
- Institut für Systemische Neurowissenschaften, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | | | - Christian Buchel
- Institut für Systemische Neurowissenschaften, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Anna Cattrell
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England20Medical Research Council-Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London
| | - Patricia J Conrod
- Department of Psychiatry and Centre de Recherche du Centre Hospitalier Universitaire Ste-Justine, University de Montréal, Montreal, Quebec, Canada19Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | | | - Jurgen Gallinat
- Institut für Systemische Neurowissenschaften, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont, Burlington24Department of Psychology, University of Vermont, Burlington
| | - Penny Gowland
- School of Physics and Astronomy, University of Nottingham, Nottingham, England
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Herve Lemaitre
- Institut National de la Santé et de la Recherche Medicale (INSERM), Unité Mixte de Recherche (UMR) 1000, Research Unit Imaging and Psychiatry, Commissariat à l'Énergie Atomique (CEA), Direction des Sciences du Vivant, Institut d'Imagerie Biomédicale, Serv
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Medicale (INSERM), Unité Mixte de Recherche (UMR) 1000, Research Unit Imaging and Psychiatry, Commissariat à l'Énergie Atomique (CEA), Direction des Sciences du Vivant, Institut d'Imagerie Biomédicale, Serv
| | - Frauke Nees
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | | | | | - Luise Poustka
- Department of Child and Adolescent Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Marcella Rietschel
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Robert Whelan
- Department of Psychology, University College Dublin, Dublin, Ireland
| | - Nic J Timpson
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, England
| | - Gunter Schumann
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England20Medical Research Council-Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, England
| | - Zdenka Pausova
- Department of Physiology and Experimental Medicine, Hospital for Sick Children, University of Toronto, Ontario, Canada31Department of Physiology, University of Toronto, Ontario, Canada32Department of Nutritional Sciences, University of Toronto, Ontario, C
| | - Tomáš Paus
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada33Department of Psychology, University of Toronto, Ontario, Canada34Department of Psychiatry, University of Toronto, Ontario, Canada35Child Mind Institute, New York, New York
| |
Collapse
|
42
|
Neuroimaging effects of prenatal alcohol exposure on the developing human brain: a magnetic resonance imaging review. Acta Neuropsychiatr 2015; 27:251-69. [PMID: 25780875 DOI: 10.1017/neu.2015.12] [Citation(s) in RCA: 117] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
OBJECTIVE This paper reviews the magnetic resonance imaging (MRI) literature on the effects of prenatal alcohol exposure on the developing human brain. METHOD A literature search was conducted through the following databases: PubMed, PsycINFO and Google Scholar. Combinations of the following search terms and keywords were used to identify relevant studies: 'alcohol', 'fetal alcohol spectrum disorders', 'fetal alcohol syndrome', 'FAS', 'FASD', 'MRI', 'DTI', 'MRS', 'neuroimaging', 'children' and 'infants'. RESULTS A total of 64 relevant articles were identified across all modalities. Overall, studies reported smaller total brain volume as well as smaller volume of both the white and grey matter in specific cortical regions. The most consistently reported structural MRI findings were alterations in the shape and volume of the corpus callosum, as well as smaller volume in the basal ganglia and hippocampi. The most consistent finding from diffusion tensor imaging studies was lower fractional anisotropy in the corpus callosum. Proton magnetic resonance spectroscopy studies are few to date, but showed altered neurometabolic profiles in the frontal and parietal cortex, thalamus and dentate nuclei. Resting-state functional MRI studies reported reduced functional connectivity between cortical and deep grey matter structures. Discussion There is a critical gap in the literature of MRI studies in alcohol-exposed children under 5 years of age across all MRI modalities. The dynamic nature of brain maturation and appreciation of the effects of alcohol exposure on the developing trajectory of the structural and functional network argue for the prioritisation of studies that include a longitudinal approach to understanding this spectrum of effects and potential therapeutic time points.
Collapse
|
43
|
A study of the effects of prenatal alcohol exposure on white matter microstructural integrity at birth. Acta Neuropsychiatr 2015; 27:197-205. [PMID: 26022619 PMCID: PMC6465963 DOI: 10.1017/neu.2015.35] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
BACKGROUND Neuroimaging studies have indicated that prenatal alcohol exposure is associated with alterations in the structure of specific brain regions in children. However, the temporal and regional specificity of such changes and their behavioural consequences are less known. Here we explore the integrity of regional white matter microstructure in infants with in utero exposure to alcohol, shortly after birth. METHODS Twenty-eight alcohol-exposed and 28 healthy unexposed infants were imaged using diffusion tensor imaging sequences to evaluate white matter integrity using validated tract-based spatial statistics analysis methods. Second, diffusion values were extracted for group comparisons by regions of interest. Differences in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity were compared between groups and associations with measures from the Dubowitz neonatal neurobehavioural assessment were examined. RESULTS Lower AD values (p<0.05) were observed in alcohol-exposed infants in the right superior longitudinal fasciculus compared with non-exposed infants. Altered FA and MD values in alcohol-exposed neonates in the right inferior cerebellar were associated with abnormal neonatal neurobehaviour. CONCLUSION These exploratory data suggest that prenatal alcohol exposure is associated with reduced white matter microstructural integrity even early in the neonatal period. The association with clinical measures reinforces the likely clinical significance of this finding. The location of the findings is remarkably consistent with previously reported studies of white matter structural deficits in older children with a diagnosis of foetal alcohol spectrum disorders.
Collapse
|
44
|
Pesaresi M, Soon-Shiong R, French L, Kaplan DR, Miller FD, Paus T. Axon diameter and axonal transport: In vivo and in vitro effects of androgens. Neuroimage 2015; 115:191-201. [PMID: 25956809 DOI: 10.1016/j.neuroimage.2015.04.048] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2015] [Revised: 04/23/2015] [Accepted: 04/24/2015] [Indexed: 01/05/2023] Open
Abstract
Testosterone is a sex hormone involved in brain maturation via multiple molecular mechanisms. Previous human studies described age-related changes in the overall volume and structural properties of white matter during male puberty. Based on this work, we have proposed that testosterone may induce a radial growth of the axon and, possibly, modulate axonal transport. In order to determine whether this is the case we have used two different experimental approaches. With electron microscopy, we have evaluated sex differences in the structural properties of axons in the corpus callosum (splenium) of young rats, and tested consequences of castration carried out after weaning. Then we examined in vitro the effect of the non-aromatizable androgen Mibolerone on the structure and bidirectional transport of wheat-germ agglutinin vesicles in the axons of cultured sympathetic neurons. With electron microscopy, we found robust sex differences in axonal diameter (males>females) and g ratio (males>females). Removal of endogenous testosterone by castration was associated with lower axon diameter and lower g ratio in castrated (vs. intact) males. In vitro, Mibolerone influenced the axonal transport in a time- and dose-dependent manner, and increased the axon caliber as compared with vehicle-treated neurons. These findings are consistent with the role of testosterone in shaping the axon by regulating its radial growth, as predicted by the initial human studies.
Collapse
Affiliation(s)
- M Pesaresi
- Rotman Research Institute, University of Toronto, 3560 Bathurst Street, Toronto, Ontario M6A 2E1, Canada
| | - R Soon-Shiong
- Rotman Research Institute, University of Toronto, 3560 Bathurst Street, Toronto, Ontario M6A 2E1, Canada
| | - L French
- Rotman Research Institute, University of Toronto, 3560 Bathurst Street, Toronto, Ontario M6A 2E1, Canada
| | - D R Kaplan
- Program in Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
| | - F D Miller
- Program in Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
| | - T Paus
- Rotman Research Institute, University of Toronto, 3560 Bathurst Street, Toronto, Ontario M6A 2E1, Canada.
| |
Collapse
|
45
|
Yamada S, Takahashi S, Ukai S, Tsuji T, Iwatani J, Tsuda K, Kita A, Sakamoto Y, Yamamoto M, Terada M, Shinosaki K. Microstructural abnormalities in anterior callosal fibers and their relationship with cognitive function in major depressive disorder and bipolar disorder: a tract-specific analysis study. J Affect Disord 2015; 174:542-8. [PMID: 25556672 DOI: 10.1016/j.jad.2014.12.022] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Accepted: 12/04/2014] [Indexed: 01/23/2023]
Abstract
BACKGROUND The corpus callosum modulates interhemispheric communication and cognitive processes. It has been suggested that white matter abnormalities in the corpus callosum are related to the pathophysiology of major depressive disorder (MDD) and bipolar disorder (BD). The aim of this study was to examine microstructural abnormalities in callosal fibers separated by their connection to functional brain regions and determine the relationship of these abnormalities with cognitive function in MDD and BD. METHODS The subjects were 18 patients with MDD, 20 patients with BD, and 21 healthy controls. The callosal fibers were divided into 6 segments based on their cortical projection using tract-specific analysis of diffusion tensor imaging. We examined differences in the fractional anisotropy (FA) of callosal fibers in six segments among the three subject groups and examined the correlation between the FA in each segment and cognitive performance in the 3 groups. RESULTS The FA of anterior callosal fibers were reduced significantly in the MDD and BD groups compared to those in the HC group, and the FA of anterior callosal fibers correlated significantly with the raw scores of the digit sequencing task and symbol coding in the MDD group. LIMITATIONS The patients were medicated at the time of scanning, and the MDD and BD groups were not matched for symptom severity. CONCLUSIONS Our results suggest that MDD and BD have similar microstructural abnormalities in anterior callosal fibers connecting bilateral frontal cortices, and these abnormalities may be related to impairment of working memory and attention in MDD.
Collapse
Affiliation(s)
- Shinichi Yamada
- Department of Neuropsychiatry, Wakayama Medical University, Wakayama, Japan.
| | - Shun Takahashi
- Department of Neuropsychiatry, Wakayama Medical University, Wakayama, Japan
| | - Satoshi Ukai
- Department of Neuropsychiatry, Wakayama Medical University, Wakayama, Japan
| | - Tomikimi Tsuji
- Department of Neuropsychiatry, Wakayama Medical University, Wakayama, Japan
| | - Jun Iwatani
- Department of Neuropsychiatry, Wakayama Medical University, Wakayama, Japan
| | - Kumi Tsuda
- Department of Neuropsychiatry, Wakayama Medical University, Wakayama, Japan
| | - Akira Kita
- Department of Neuropsychiatry, Wakayama Medical University, Wakayama, Japan
| | - Yuka Sakamoto
- Department of Neuropsychiatry, Wakayama Medical University, Wakayama, Japan
| | - Masahiro Yamamoto
- Department of Neuropsychiatry, Wakayama Medical University, Wakayama, Japan
| | | | - Kazuhiro Shinosaki
- Department of Neuropsychiatry, Wakayama Medical University, Wakayama, Japan
| |
Collapse
|
46
|
Keshavan MS, Giedd J, Lau JYF, Lewis DA, Paus T. Changes in the adolescent brain and the pathophysiology of psychotic disorders. Lancet Psychiatry 2014; 1:549-58. [PMID: 26361314 DOI: 10.1016/s2215-0366(14)00081-9] [Citation(s) in RCA: 132] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2014] [Accepted: 05/23/2014] [Indexed: 10/24/2022]
Abstract
Adolescence is a time of extensive neuroanatomical, functional, and chemical reorganisation of the brain which parallels substantial maturational changes in cognition and affect regulation. This period is characterised by stabilisation of synapses to diminish redundancy and increase efficiency of neural function, fine-tuning of excitatory and inhibitory neurotransmitter systems, beginning of integration between late maturing and early maturing brain structures, and development of effective connections. In effect, these so-called moving parts create a state of dynamic change that might underlie adolescent behaviours. Imbalances or changes in timing of these developmental processes clearly increase the risk for psychiatric disorders. Genetic, environmental, and epigenetic factors that shape brain development and hormonal changes that affect stress reactivity could be reasons why some, but not all, adolescents are at a heightened risk of developing a psychopathological disorder. In this Series paper, we assess the neurobiology of the changing adolescent brain, implications of this knowledge, and future research in major psychiatric disorders, particularly for psychotic disorders.
Collapse
Affiliation(s)
- Matcheri S Keshavan
- Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Western Psychiatric Institute and Clinic, Pittsburgh, PA, USA.
| | - Jay Giedd
- Brain Imaging Section, Child Psychiatry Branch, NIMH, Bethesda, MD, USA
| | | | - David A Lewis
- Department of Psychiatry, Western Psychiatric Institute and Clinic, Pittsburgh, PA, USA
| | - Tomáš Paus
- Rotman Research Institute and Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
47
|
Unraveling the secrets of white matter--bridging the gap between cellular, animal and human imaging studies. Neuroscience 2014; 276:2-13. [PMID: 25003711 PMCID: PMC4155933 DOI: 10.1016/j.neuroscience.2014.06.058] [Citation(s) in RCA: 105] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2014] [Accepted: 06/25/2014] [Indexed: 12/20/2022]
Abstract
The CNS white matter makes up about half of the human brain, and with advances in human imaging it is increasingly becoming clear that changes in the white matter play a major role in shaping human behavior and learning. However, the mechanisms underlying these white matter changes remain poorly understood. Within this special issue of Neuroscience on white matter, recent advances in our knowledge of the function of white matter, from the molecular level to human imaging, are reviewed. Collaboration between fields is essential to understand the function of the white matter, but due to differences in methods and field-specific 'language', communication is often hindered. In this review, we try to address this hindrance by introducing the methods and providing a basic background to myelin biology and human imaging as a prelude to the other reviews within this special issue.
Collapse
|