1
|
Li J, Wang Q, Li K, Yao L, Guo X. Tracking Age-Related Topological Changes in Individual Brain Morphological Networks Across the Human Lifespan. J Magn Reson Imaging 2024; 59:1841-1851. [PMID: 37702277 DOI: 10.1002/jmri.28984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/15/2023] [Accepted: 08/15/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUND Many studies have shown topological alterations associated with age in population-based brain morphological networks. However, it is not clear how individual brain morphological networks change with age across the lifespan. PURPOSE To characterize age-related topological changes in individual networks and investigate the relationships between individual- and group-based brain networks at the nodal, modular, and connectome levels. STUDY TYPE Retrospective analysis. POPULATION One hundred seventy-nine healthy subjects (108 males and 71 females), aged 6-85 years with a median age of 32 years and an inter-quartile range (IQR) of 26 years. FIELD STRENGTH/SEQUENCE T1-weighted images using the magnetization-prepared rapid gradient echo (MPRAGE) sequences. ASSESSMENT Two nodal-level indicators (nodal similarity and node matching), five modular-level indicators (modularity, intra/inter-module similarity, adjusted mutual information [AMI], and module variation), and five connectome-level indicators (global efficiency, characteristic path length, clustering coefficient, local efficiency, and individual contribution) were calculated in brain morphological networks. Regression models for different indicators were built to examine their lifetime trajectory patterns. STATISTICAL TESTS Single-sample t-test, Mantel's test, Pearson correlation coefficient. A P value <0.05 was considered statistically significant. RESULTS Among 68 nodes, 34 nodes showed significant age-related patterns (all P < 0.05, FDR-corrected) in nodal similarity, including linear decline and quadratic trends. The lifespan trajectory of the connectome-level topological attributes of the individual networks presented U-shaped or inverse U-shaped trends with age. Between the individual- and group-based brain networks, the average nodal similarity was 0.67 and the average AMI of module partitions was 0.57. DATA CONCLUSION The lifespan trajectories of the nodal similarity mainly followed linear decreasing and nonlinear trends, whereas the modularity and the global topological attributes exhibited nonlinear patterns. There was a high degree of consistency at both nodal similarity and modular division between the individual and group networks. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 1.
Collapse
Affiliation(s)
- Jingming Li
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Qian Wang
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Ke Li
- Strategic Support Force Medical Center, Beijing, China
| | - Li Yao
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Xiaojuan Guo
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| |
Collapse
|
2
|
Niu J, Jiao Q, Cui D, Dou R, Guo Y, Yu G, Zhang X, Sun F, Qiu J, Dong L, Cao W. Age-associated cortical similarity networks correlate with cell type-specific transcriptional signatures. Cereb Cortex 2024; 34:bhad454. [PMID: 38037843 DOI: 10.1093/cercor/bhad454] [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: 09/25/2023] [Accepted: 11/07/2023] [Indexed: 12/02/2023] Open
Abstract
Human brain structure shows heterogeneous patterns of change across adults aging and is associated with cognition. However, the relationship between cortical structural changes during aging and gene transcription signatures remains unclear. Here, using structural magnetic resonance imaging data of two separate cohorts of healthy participants from the Cambridge Centre for Aging and Neuroscience (n = 454, 18-87 years) and Dallas Lifespan Brain Study (n = 304, 20-89 years) and a transcriptome dataset, we investigated the link between cortical morphometric similarity network and brain-wide gene transcription. In two cohorts, we found reproducible morphometric similarity network change patterns of decreased morphological similarity with age in cognitive related areas (mainly located in superior frontal and temporal cortices), and increased morphological similarity in sensorimotor related areas (postcentral and lateral occipital cortices). Changes in morphometric similarity network showed significant spatial correlation with the expression of age-related genes that enriched to synaptic-related biological processes, synaptic abnormalities likely accounting for cognitive decline. Transcription changes in astrocytes, microglia, and neuronal cells interpreted most of the age-related morphometric similarity network changes, which suggest potential intervention and therapeutic targets for cognitive decline. Taken together, by linking gene transcription signatures to cortical morphometric similarity network, our findings might provide molecular and cellular substrates for cortical structural changes related to cognitive decline across adults aging.
Collapse
Affiliation(s)
- Jinpeng Niu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Qing Jiao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Dong Cui
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Ruhai Dou
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Yongxin Guo
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Guanghui Yu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Xiaotong Zhang
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Fengzhu Sun
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Jianfeng Qiu
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Weifang Cao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| |
Collapse
|
3
|
González-García N, Buimer EEL, Moreno-López L, Sallie SN, Váša F, Lim S, Romero-Garcia R, Scheuplein M, Whitaker KJ, Jones PB, Dolan RJ, Fonagy P, Goodyer I, Bullmore ET, van Harmelen AL. Resilient functioning is associated with altered structural brain network topology in adolescents exposed to childhood adversity. Dev Psychopathol 2023; 35:2253-2263. [PMID: 37493043 DOI: 10.1017/s0954579423000901] [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] [Indexed: 07/27/2023]
Abstract
Childhood adversity is one of the strongest predictors of adolescent mental illness. Therefore, it is critical that the mechanisms that aid resilient functioning in individuals exposed to childhood adversity are better understood. Here, we examined whether resilient functioning was related to structural brain network topology. We quantified resilient functioning at the individual level as psychosocial functioning adjusted for the severity of childhood adversity in a large sample of adolescents (N = 2406, aged 14-24). Next, we examined nodal degree (the number of connections that brain regions have in a network) using brain-wide cortical thickness measures in a representative subset (N = 275) using a sliding window approach. We found that higher resilient functioning was associated with lower nodal degree of multiple regions including the dorsolateral prefrontal cortex, the medial prefrontal cortex, and the posterior superior temporal sulcus (z > 1.645). During adolescence, decreases in nodal degree are thought to reflect a normative developmental process that is part of the extensive remodeling of structural brain network topology. Prior findings in this sample showed that decreased nodal degree was associated with age, as such our findings of negative associations between nodal degree and resilient functioning may therefore potentially resemble a more mature structural network configuration in individuals with higher resilient functioning.
Collapse
Affiliation(s)
- Nadia González-García
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Laboratory of Neurosciences, Hospital Infantil de México Federico Gómez, México City, Mexico
| | - Elizabeth E L Buimer
- Institute of Education and Child Studies, Leiden University, Leiden, The Netherlands
| | | | | | - František Váša
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Sol Lim
- Public health and Primary Care, Cardiovascular Epidemiology Unit (CEU), University of Cambridge, Cambridge, UK
| | - Rafael Romero-Garcia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Dpto. de Fisiología Médica y Biofísica. Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla, Sevilla, Spain
| | - Maximilian Scheuplein
- Institute of Education and Child Studies, Leiden University, Leiden, The Netherlands
| | | | - Peter B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Raymond J Dolan
- Wellcome Trust Center for Neuroimaging, University College London, London, UK
| | - Peter Fonagy
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Ian Goodyer
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | | | - Anne-Laura van Harmelen
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Institute of Education and Child Studies, Leiden University, Leiden, The Netherlands
| |
Collapse
|
4
|
Su S, Chen Y, Qian L, Dai Y, Yan Z, Lin L, Zhang H, Liu M, Zhao J, Yang Z. Evaluation of individual-based morphological brain network alterations in children with attention-deficit/hyperactivity disorder: a multi-method investigation. Eur Child Adolesc Psychiatry 2023; 32:2281-2289. [PMID: 36056264 DOI: 10.1007/s00787-022-02072-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 08/19/2022] [Indexed: 11/03/2022]
Abstract
To investigate the topological organization of individual-based morphological brain networks (MBNs) in attention-deficit/hyperactivity disorder (ADHD) children with different methods. A total of 60 ADHD children and 60 typically developing (TD) controls matched for age and gender were enrolled. Each participant underwent a structural 3D T1-weighted scan. Based on the inter-regional morphological similarity of GM regions, Kullback-Leibler-based similarity (KLS), Multivariate Euclidean Distance (MED), and Tijms's method were used to construct individual-based MBNs, respectively. The between-group difference of global and nodal network topological profiles was estimated, and partial correlation analysis was used for further analysis. According to KLS and MED-based network, ADHD showed a decreased global efficiency (Eglob) and increased characteristic path length (Lp) compared to the TD group, while Tijms's method-based network showed no between-group difference in global and nodal profiles. Nodal profiles were significantly decreased in the bilateral caudate, and nodal efficiency of the bilateral caudate was negatively correlated with clinical symptom severity of ADHD (P < 0.05, FDR-corrected) by the KLS-based network. Nodal betweenness was significantly decreased in the left inferior occipital gyrus and correlated with clinical symptom severity of ADHD (P < 0.05, FDR-corrected) by the MED-based network. ADHD was found to have a significantly less integrated organization and a shift to a "weaker small-worldness" pattern, while abnormal nodal profiles were mainly in the corpus striatum and default-mode networks. Our study highlights the crucial role of abnormal morphological connectivity patterns in understanding the brain maturational effects in ADHD and enriching the insights into MBNs at an individual level.
Collapse
Affiliation(s)
- Shu Su
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Yingqian Chen
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Long Qian
- MR Research, GE Healthcare, Beijing, China
| | - Yan Dai
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Zi Yan
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Liping Lin
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Hongyu Zhang
- Department of Pediatric, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Meina Liu
- Department of Pediatric, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jing Zhao
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
| | - Zhiyun Yang
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
| |
Collapse
|
5
|
Clifford KP, Miles AE, Prevot TD, Misquitta KA, Ellegood J, Lerch JP, Sibille E, Nikolova YS, Banasr M. Brain structure and working memory adaptations associated with maturation and aging in mice. Front Aging Neurosci 2023; 15:1195748. [PMID: 37484693 PMCID: PMC10359104 DOI: 10.3389/fnagi.2023.1195748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 06/13/2023] [Indexed: 07/25/2023] Open
Abstract
Introduction As the population skews toward older age, elucidating mechanisms underlying human brain aging becomes imperative. Structural MRI has facilitated non-invasive investigation of lifespan brain morphology changes, yet this domain remains uncharacterized in rodents despite increasing use as models of disordered human brain aging. Methods Young (2m, n = 10), middle-age (10m, n = 10) and old (22m, n = 9) mice were utilized for maturational (young vs. middle-age) and aging-related (middle-age vs. old mice) comparisons. Regional brain volume was averaged across hemispheres and reduced to 32 brain regions. Pairwise group differences in regional volume were tested using general linear models, with total brain volume as a covariate. Sample-wide associations between regional brain volume and Y-maze performance were assessed using logistic regression, residualized for total brain volume. Both analyses corrected for multiple comparisons. Structural covariance networks were generated using the R package "igraph." Group differences in network centrality (degree), integration (mean distance), and segregation (transitivity, modularity) were tested across network densities (5-40%), using 5,000 (1,000 for degree) permutations with significance criteria of p < 0.05 at ≥5 consecutive density thresholds. Results Widespread significant maturational changes in volume occurred in 18 brain regions, including considerable loss in isocortex regions and increases in brainstem regions and white matter tracts. The aging-related comparison yielded 6 significant changes in brain volume, including further loss in isocortex regions and increases in white matter tracts. No significant volume changes were observed across either comparison for subcortical regions. Additionally, smaller volume of the anterior cingulate area (χ2 = 2.325, pBH = 0.044) and larger volume of the hippocampal formation (χ2 = -2.180, pBH = 0.044) were associated with poorer cognitive performance. Maturational network comparisons yielded significant degree changes in 9 regions, but no aging-related changes, aligning with network stabilization trends in humans. Maturational decline in modularity occurred (24-29% density), mirroring human trends of decreased segregation in young adulthood, while mean distance and transitivity remained stable. Conclusion/Implications These findings offer a foundational account of age effects on brain volume, structural brain networks, and working memory in mice, informing future work in facilitating translation between rodent models and human brain aging.
Collapse
Affiliation(s)
- Kevan P. Clifford
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Amy E. Miles
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Thomas D. Prevot
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Keith A. Misquitta
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Departments of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | - Jacob Ellegood
- Mouse Imaging Centre (MICe), Hospital for Sick Children, Toronto, ON, Canada
| | - Jason P. Lerch
- Mouse Imaging Centre (MICe), Hospital for Sick Children, Toronto, ON, Canada
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Etienne Sibille
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Departments of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | - Yuliya S. Nikolova
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Mounira Banasr
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Departments of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
6
|
Zeighami Y, Dadar M, Daoust J, Pelletier M, Biertho L, Bouvet-Bouchard L, Fulton S, Tchernof A, Dagher A, Richard D, Evans A, Michaud A. Impact of Weight Loss on Brain Age: Improved Brain Health Following Bariatric Surgery. Neuroimage 2022; 259:119415. [PMID: 35760293 DOI: 10.1016/j.neuroimage.2022.119415] [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: 12/10/2021] [Revised: 06/17/2022] [Accepted: 06/23/2022] [Indexed: 10/17/2022] Open
Abstract
Individuals living with obesity tend to have increased brain age, reflecting poorer brain health likely due to grey and white matter atrophy related to obesity. However, it is unclear if older brain age associated with obesity can be reversed following weight loss and cardiometabolic health improvement. The aim of this study was to assess the impact of weight loss and cardiometabolic improvement following bariatric surgery on brain health, as measured by change in brain age estimated based on voxel-based morphometry (VBM) measurements. We used three distinct datasets to perform this study: 1) CamCAN dataset to train the brain age prediction model, 2) Human Connectome Project (HCP) dataset to investigate whether individuals with obesity have greater brain age than individuals with normal weight, and 3) pre-surgery, as well as 4, 12, and 24 month post-surgery data from participants (n=87, age: 44.0±9.2 years, BMI: 43.9±4.2 kg/m2) who underwent a bariatric surgery to investigate whether weight loss and cardiometabolic improvement as a result of bariatric surgery lowers the brain age. As expected, our results from the HCP dataset showed a higher brain age for individuals with obesity compared to individuals with normal weight (T-value = 7.08, p-value < 0.0001). We also found significant improvement in brain health, indicated by a decrease of 2.9 and 5.6 years in adjusted delta age at 12 and 24 months following bariatric surgery compared to baseline (p-value < 0.0005 for both). While the overall effect seemed to be driven by a global change across all brain regions and not from a specific region, our exploratory analysis showed lower delta age in certain brain regions (mainly in somatomotor, visual, and ventral attention networks) at 24 months. This reduced age was also associated with post-surgery improvements in BMI, systolic/diastolic blood pressure, and HOMA-IR (T-valueBMI=4.29, T-valueSBP=4.67, T-valueDBP=4.12, T-valueHOMA-IR=3.16, all p-values < 0.05). In conclusion, these results suggest that obesity-related brain health abnormalities (as measured by delta age) might be reversed by bariatric surgery-induced weight loss and widespread improvements in cardiometabolic alterations.
Collapse
Affiliation(s)
- Yashar Zeighami
- Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, Canada; Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, Canada.
| | - Mahsa Dadar
- Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, Canada
| | - Justine Daoust
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Mélissa Pelletier
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Laurent Biertho
- Département de chirurgie générale, Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Léonie Bouvet-Bouchard
- Département de chirurgie générale, Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Stephanie Fulton
- Centre de Recherche du CHUM, Department of Nutrition, Université de Montréal, Montreal Diabetes Research Center, Montreal, QC, Canada
| | - André Tchernof
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Alain Dagher
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - Denis Richard
- Département de chirurgie générale, Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada
| | - Alan Evans
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - Andréanne Michaud
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, Canada.
| |
Collapse
|
7
|
Wang P, Li W, Zhu H, Liu X, Yu T, Zhang D, Zhang Y. Reorganization of the Brain Structural Covariance Network in Ischemic Moyamoya Disease Revealed by Graph Theoretical Analysis. Front Aging Neurosci 2022; 14:788661. [PMID: 35721027 PMCID: PMC9201423 DOI: 10.3389/fnagi.2022.788661] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveIschemic moyamoya (MMD) disease could alter the cerebral structure, but little is known about the topological organization of the structural covariance network (SCN). This study employed structural magnetic resonance imaging and graph theory to evaluate SCN reorganization in ischemic MMD patients.MethodForty-nine stroke-free ischemic MMD patients and 49 well-matched healthy controls (HCs) were examined by T1-MPRAGE imaging. Structural images were pre-processed using the Computational Anatomy Toolbox 12 (CAT 12) based on the diffeomorphic anatomical registration through exponentiated lie (DARTEL) algorithm and both the global and regional SCN parameters were calculated and compared using the Graph Analysis Toolbox (GAT).ResultsMost of the important metrics of global network organization, including characteristic path length (Lp), clustering coefficient (Cp), assortativity, local efficiency, and transitivity, were significantly reduced in MMD patients compared with HCs. In addition, the regional betweenness centrality (BC) values of the bilateral medial orbitofrontal cortices were significantly lower in MMD patients than in HCs after false discovery rate (FDR) correction for multiple comparisons. The BC was also reduced in the left medial superior frontal gyrus and hippocampus, and increased in the bilateral middle cingulate gyri of patients, but these differences were not significant after FDR correlation. No differences in network resilience were detected by targeted attack analysis or random failure analysis.ConclusionsBoth global and regional properties of the SCN are altered in MMD, even in the absence of major stroke or hemorrhagic damage. Patients exhibit a less optimal and more randomized SCN than HCs, and the nodal BC of the bilateral medial orbitofrontal cortices is severely reduced. These changes may account for the cognitive impairments in MMD patients.
Collapse
Affiliation(s)
- Peijing Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Wenjie Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Huan Zhu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Xingju Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Tao Yu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Dong Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Yan Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
- *Correspondence: Yan Zhang,
| |
Collapse
|
8
|
Mareckova K, Miles A, Liao Z, Andryskova L, Brazdil M, Paus T, Nikolova YS. Prenatal stress and its association with amygdala-related structural covariance patterns in youth. Neuroimage Clin 2022; 34:102976. [PMID: 35316668 PMCID: PMC8938327 DOI: 10.1016/j.nicl.2022.102976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 02/05/2022] [Accepted: 02/26/2022] [Indexed: 11/27/2022]
Abstract
Prenatal exposure to stress predicts amygdala degree centrality in young adulthood. High (vs. low) stress group showed lower structural covariance degree of amygdala. These effects were particularly significant in men. Global network parameters did not drive these effects.
Background Prenatal stress influences brain development and mood disorder vulnerability. Brain structural covariance network (SCN) properties based on inter-regional volumetric correlations may reflect developmentally-mediated shared plasticity among regions. Childhood trauma is associated with amygdala-centric SCN reorganization patterns, however, the impact of prenatal stress on SCN properties remains unknown. Methods The study included participants from the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) with archival prenatal stress data and structural MRI acquired in young adulthood (age 23–24). SCNs were constructed based on Freesurfer-extracted volumes of 7 subcortical and 34 cortical regions. We compared amygdala degree centrality, a measure of hubness, between those exposed to high vs. low (median split) prenatal stress, defined by maternal reports of stressful life events during the first (n = 93, 57% female) and second (n = 125, 54% female) half of pregnancy. Group differences were tested across network density thresholds (5–40%) using 10,000 permutations, with sex and intracranial volume as covariates, followed by sex-specific analyses. Finally, we sought to replicate our results in an independent all-male sample (n = 450, age 18–20) from the Avon Longitudinal Study of Parents and Children (ALSPAC). Results The high-stress during the first half of pregnancy ELSPAC group showed lower amygdala degree particularly in men, who demonstrated this difference at 10 consecutive thresholds, with no significant differences in global network properties. At the lowest significant density threshold, amygdala volume was positively correlated with hippocampus, putamen, rostral anterior and posterior cingulate, transverse temporal, and pericalcarine cortex in the low-stress (p(FDR) < 0.027), but not the high-stress (p(FDR) > 0.882) group. Although amygdala degree was nominally lower across thresholds in the high-stress ALSPAC group, these results were not significant. Conclusion Unlike childhood trauma, prenatal stress may shift SCN towards a less amygdala-centric SCN pattern, particularly in men. These findings did not replicate in an all-male ALSPAC sample, possibly due to the sample’s younger age and lower prenatal stress exposure.
Collapse
Affiliation(s)
- Klara Mareckova
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada; Brain and Mind Research, Central European Institute of Technology, Masaryk University, Brno, Czech Republic.
| | - Amy Miles
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
| | - Zhijie Liao
- Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Lenka Andryskova
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Milan Brazdil
- Brain and Mind Research, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Tomas Paus
- Department of Psychology, University of Toronto, Toronto, ON, Canada; Departments of Psychiatry and Neuroscience and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Yuliya S Nikolova
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada.
| |
Collapse
|
9
|
Structural covariance changes in major cortico-basal ganglia and thalamic networks in amyloid-positive patients with white matter hyperintensities. Neurobiol Aging 2022; 117:117-127. [DOI: 10.1016/j.neurobiolaging.2022.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 05/19/2022] [Accepted: 05/23/2022] [Indexed: 11/23/2022]
|
10
|
Tuerk C, Dégeilh F, Catroppa C, Anderson V, Beauchamp MH. Pediatric Moderate-Severe Traumatic Brain Injury and Gray Matter Structural Covariance Networks: A Preliminary Longitudinal Investigation. Dev Neurosci 2021; 43:335-347. [PMID: 34515088 DOI: 10.1159/000518752] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 07/23/2021] [Indexed: 11/19/2022] Open
Abstract
Pediatric traumatic brain injury (TBI) is prevalent and can disrupt ongoing brain maturation. However, the long-term consequences of pediatric TBI on the brain's network architecture are poorly understood. Structural covariance networks (SCN), based on anatomical correlations between brain regions, may provide important insights into brain topology following TBI. Changes in global SCN (default-mode network [DMN], central executive network [CEN], and salience network [SN]) were compared sub-acutely (<90 days) and in the long-term (approximately 12-24 months) after pediatric moderate-severe TBI (n = 16), and compared to typically developing children assessed concurrently (n = 15). Gray matter (GM) volumes from selected seeds (DMN: right angular gyrus [rAG], CEN: right dorsolateral prefrontal cortex [rDLPFC], SN: right anterior insula) were extracted from T1-weighted images at both timepoints. No group differences were found sub-acutely; at the second timepoint, the TBI group showed significantly reduced structural covariance within the DMN seeded from the rAG and the (1) right middle frontal gyrus, (2) left superior frontal gyrus, and (3) left fusiform gyrus. Reduced structural covariance was also found within the CEN, that is, between the rDLPFC and the (1) calcarine sulcus, and (2) right occipital gyrus. In addition, injury severity was positively associated with GM volumes in the identified CEN regions. Over time, there were no significant changes in SCN in either group. The findings, albeit preliminary, suggest for the first time a long-term effect of pediatric TBI on SCN. SCN may be a complementary approach to characterize the global effect of TBI on the developing brain. Future work needs to further examine how disruptions of these networks relate to behavioral and cognitive difficulties.
Collapse
Affiliation(s)
- Carola Tuerk
- Department of Psychology, University of Montreal, Montreal, Québec, Canada,
| | - Fanny Dégeilh
- Department of Psychology, University of Montreal, Montreal, Québec, Canada.,Sainte-Justine Hospital Research Center, Montreal, Québec, Canada
| | - Cathy Catroppa
- Murdoch Children's Research Institute, The Royal Children's Hospital, Melbourne, Victoria, Australia.,Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia
| | - Vicki Anderson
- Murdoch Children's Research Institute, The Royal Children's Hospital, Melbourne, Victoria, Australia.,Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia
| | - Miriam H Beauchamp
- Department of Psychology, University of Montreal, Montreal, Québec, Canada.,Sainte-Justine Hospital Research Center, Montreal, Québec, Canada
| |
Collapse
|
11
|
Zoltowski AR, Lyu I, Failla M, Mash LE, Dunham K, Feldman JI, Woynaroski TG, Wallace MT, Barquero LA, Nguyen TQ, Cutting LE, Kang H, Landman BA, Cascio CJ. Cortical Morphology in Autism: Findings from a Cortical Shape-Adaptive Approach to Local Gyrification Indexing. Cereb Cortex 2021; 31:5188-5205. [PMID: 34195789 DOI: 10.1093/cercor/bhab151] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 04/09/2021] [Accepted: 05/04/2021] [Indexed: 11/14/2022] Open
Abstract
It has been challenging to elucidate the differences in brain structure that underlie behavioral features of autism. Prior studies have begun to identify patterns of changes in autism across multiple structural indices, including cortical thickness, local gyrification, and sulcal depth. However, common approaches to local gyrification indexing used in prior studies have been limited by low spatial resolution relative to functional brain topography. In this study, we analyze the aforementioned structural indices, utilizing a new method of local gyrification indexing that quantifies this index adaptively in relation to specific sulci/gyri, improving interpretation with respect to functional organization. Our sample included n = 115 autistic and n = 254 neurotypical participants aged 5-54, and we investigated structural patterns by group, age, and autism-related behaviors. Differing structural patterns by group emerged in many regions, with age moderating group differences particularly in frontal and limbic regions. There were also several regions, particularly in sensory areas, in which one or more of the structural indices of interest either positively or negatively covaried with autism-related behaviors. Given the advantages of this approach, future studies may benefit from its application in hypothesis-driven examinations of specific brain regions and/or longitudinal studies to assess brain development in autism.
Collapse
Affiliation(s)
- Alisa R Zoltowski
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA
| | - Ilwoo Lyu
- Department of Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, South Korea
| | - Michelle Failla
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA.,College of Nursing, Ohio State University, Columbus, OH 43210, USA
| | - Lisa E Mash
- San Diego Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, CA 92120, USA
| | - Kacie Dunham
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, TN 37232, USA
| | - Jacob I Feldman
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.,Frist Center for Autism and Innovation, Vanderbilt University, Nashville, TN 37212, USA
| | - Tiffany G Woynaroski
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.,Frist Center for Autism and Innovation, Vanderbilt University, Nashville, TN 37212, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Mark T Wallace
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA.,Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.,Frist Center for Autism and Innovation, Vanderbilt University, Nashville, TN 37212, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Pharmacology, Vanderbilt University, Nashville, TN 37232, USA.,Department of Psychology and Human Development, Vanderbilt University, Nashville, TN 37203, USA
| | - Laura A Barquero
- Department of Psychology and Human Development, Vanderbilt University, Nashville, TN 37203, USA
| | - Tin Q Nguyen
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Special Education, Vanderbilt University, Nashville, TN 37203, USA
| | - Laurie E Cutting
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Psychology and Human Development, Vanderbilt University, Nashville, TN 37203, USA.,Department of Special Education, Vanderbilt University, Nashville, TN 37203, USA.,Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Hakmook Kang
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Bennett A Landman
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA
| | - Carissa J Cascio
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA.,Frist Center for Autism and Innovation, Vanderbilt University, Nashville, TN 37212, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Psychology and Human Development, Vanderbilt University, Nashville, TN 37203, USA
| |
Collapse
|
12
|
Vandewouw MM, Hunt BAE, Ziolkowski J, Taylor MJ. The developing relations between networks of cortical myelin and neurophysiological connectivity. Neuroimage 2021; 237:118142. [PMID: 33951516 DOI: 10.1016/j.neuroimage.2021.118142] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 04/11/2021] [Accepted: 04/14/2021] [Indexed: 01/01/2023] Open
Abstract
Recent work identified that patterns of distributed brain regions sharing similar myeloarchitecture are related to underlying functional connectivity, demonstrating cortical myelin's plasticity to changes in functional demand. However, the changing relations between functional and structural architecture throughout child and adulthood are poorly understood. We show that structural covariance connectivity (T1-weighted/T2-weighted ratio) and functional connectivity (magnetoencephalography) exhibit nonlinear developmental changes. We then show significant relations between structural and functional connectivity, which have shared and distinct characteristics dependent on the neural oscillatory frequency. Increases in structure-function coupling are visible during the protracted myelination observed throughout childhood and adolescence and are followed by decreases near the onset of adulthood. Our work lays the foundation for understanding the mechanisms by which myeloarchitecture supports brain function, enabling future investigations into how clinical populations may deviate from normative patterns.
Collapse
Affiliation(s)
- Marlee M Vandewouw
- Department of Diagnostic Imaging, Hospital for Sick Children, 555 University Ave., Toronto, ON M5G 0A4, Canada; Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto M5G 0A4, Canada; Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON M4G 1R8, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto M5S 3G9, Canada.
| | - Benjamin A E Hunt
- Department of Diagnostic Imaging, Hospital for Sick Children, 555 University Ave., Toronto, ON M5G 0A4, Canada; Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto M5G 0A4, Canada
| | - Justine Ziolkowski
- Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto M5G 0A4, Canada
| | - Margot J Taylor
- Department of Diagnostic Imaging, Hospital for Sick Children, 555 University Ave., Toronto, ON M5G 0A4, Canada; Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto M5G 0A4, Canada; Department of Psychology, University of Toronto, Toronto M5G 0A4 Canada; Department of Medical Imaging, University of Toronto, Toronto M5G 0A4, Canada
| |
Collapse
|
13
|
Yun JY, Kim YK. Phenotype Network and Brain Structural Covariance Network of Major Depression. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1305:3-18. [PMID: 33834391 DOI: 10.1007/978-981-33-6044-0_1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Phenotype networks enable clinicians to elucidate the patterns of coexistence and interactions among the clinical symptoms, negative cognitive styles , neurocognitive performance, and environmental factors in major depressive disorder (MDD). Results of phenotype network approach could be used in finding the target symptoms as these are tightly connected or associated with many other phenomena within the phenotype network of MDD specifically when comorbid psychiatric disorder(s) is/are present. Further, by comparing the differential patterns of phenotype networks before and after the treatment, changing or enduring patterns of associations among the clinical phenomena in MDD have been deciphered.Brain structural covariance networks describe the inter-regional co-varying patterns of brain morphologies, and overlapping findings have been reported between the brain structural covariance network and coordinated trajectories of brain development and maturation. Intra-individual brain structural covariance illustrates the degrees of similarities among the different brain regions for how much the values of brain morphological features are deviated from those of healthy controls. Inter-individual brain structural covariance reflects the degrees of concordance among the different brain regions for the inter-individual distribution of brain morphologic values. Estimation of the graph metrics for these brain structural covariance networks uncovers the organizational profile of brain morphological variations in the whole brain and the regional distribution of brain hubs.
Collapse
Affiliation(s)
- Je-Yeon Yun
- Seoul National University Hospital, Seoul, Republic of Korea. .,Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Yong-Ku Kim
- Department of Psychiatry, Korea University Ansan Hospital, College of Medicine, Ansan, Republic of Korea
| |
Collapse
|
14
|
Zheng W, Zhao Z, Zhang Z, Liu T, Zhang Y, Fan J, Wu D. Developmental pattern of the cortical topology in high-functioning individuals with autism spectrum disorder. Hum Brain Mapp 2020; 42:660-675. [PMID: 33085836 PMCID: PMC7814766 DOI: 10.1002/hbm.25251] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 09/24/2020] [Accepted: 10/07/2020] [Indexed: 12/15/2022] Open
Abstract
A number of studies have indicated alterations of brain morphology in individuals with autism spectrum disorder (ASD); however, how ASD influences the topological organization of the brain cortex at different developmental stages is not yet well characterized. In this study, we used structural images of 492 high‐functioning participants in the Autism Brain Imaging Data Exchange database acquired from 17 international imaging centers, including 75 autistic children (age 7–11 years), 91 adolescents with ASD (age 12–17 years), and 80 young adults with ASD (age 18–29 years), and 246 typically developing controls (TDCs) that were age, gender, handedness, and full‐scale IQ matched. Cortical thickness (CT) and surface area (SA) were extracted and the covariance between cortical regions across participants were treated as a network to examine developmental patterns of the cortical topological organization at different stages. A center‐paired resampling strategy was developed to control the center bias during the permutation test. Compared with the TDCs, network of SA (but not CT) of individuals with ASD showed reduced small‐worldness in childhood, and the network hubs were reorganized in the adulthood such that hubs inclined to connect with nonhub nodes and demonstrated more dispersed spatial distribution. Furthermore, the SA network of the ASD cohort exhibited increased segregation of the inferior parietal lobule and prefrontal cortex, and insular‐opercular cortex in all three age groups, resulting in the emergence of two unique modules in the autistic brain. Our findings suggested that individuals with ASD may undergo remarkable remodeling of the cortical topology from childhood to adulthood, which may be associated with the altered social and cognitive functions in ASD.
Collapse
Affiliation(s)
- Weihao Zheng
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Zhe Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Tingting Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Jin Fan
- Department of Psychology, Queens College, The City University of New York, New York, New York, USA
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| |
Collapse
|
15
|
Miotto EC, Balardin JB, Martin MDGM, Polanczyk GV, Savage CR, Miguel EC, Batistuzzo MC. Effects of semantic categorization strategy training on episodic memory in children and adolescents. PLoS One 2020; 15:e0228866. [PMID: 32069310 PMCID: PMC7028277 DOI: 10.1371/journal.pone.0228866] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Accepted: 01/25/2020] [Indexed: 11/18/2022] Open
Abstract
Episodic memory is the ability to learn, store and recall new information. The prefrontal cortex (PFC) is a crucial area engaged in this ability. Cognitive training has been demonstrated to improve episodic memory in adults and older subjects. However, there are no studies examining the effects of cognitive training on episodic memory encoding in typically developing children and adolescents. This study investigated the behavioral effects and neural correlates of semantic categorization strategy training in children and adolescents during verbal episodic memory encoding using functional magnetic resonance imaging (fMRI). Participants with age range: 7-18 years were scanned before and after semantic categorization training during encoding of word lists. Results showed improved memory performance in adolescents, but not in children. Deactivation of the anterior medial PFC/anterior cingulate and higher activation of the right anterior and lateral orbital gyri, right frontal pole and right middle frontal gyrus activation were found after training in adolescents when compared to children. These findings suggest different maturational paths of brain regions, especially in the PFC, and deactivation of default mode network areas, which are involved in successful memory and executive processes in the developing brain.
Collapse
Affiliation(s)
- Eliane C. Miotto
- Department of Neurology, Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo (HC-FMUSP), Sao Paulo, SP, Brazil
- * E-mail:
| | - Joana B. Balardin
- Department of Neurology, Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo (HC-FMUSP), Sao Paulo, SP, Brazil
- Brain Institute, Hospital Israelita Albert Einstein, Sao Paulo, SP, Brazil
| | | | | | - Cary R. Savage
- Banner Alzheimer’s Institute, Phoenix, AZ, United States of America
| | | | - Marcelo C. Batistuzzo
- Department of Neurology, Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo (HC-FMUSP), Sao Paulo, SP, Brazil
- Department of Psychiatry, HC-FMUSP, Sao Paulo, SP, Brazil
| |
Collapse
|
16
|
Yun JY, Boedhoe PSW, Vriend C, Jahanshad N, Abe Y, Ameis SH, Anticevic A, Arnold PD, Batistuzzo MC, Benedetti F, Beucke JC, Bollettini I, Bose A, Brem S, Calvo A, Cheng Y, Cho KIK, Ciullo V, Dallaspezia S, Denys D, Feusner JD, Fouche JP, Giménez M, Gruner P, Hibar DP, Hoexter MQ, Hu H, Huyser C, Ikari K, Kathmann N, Kaufmann C, Koch K, Lazaro L, Lochner C, Marques P, Marsh R, Martínez-Zalacaín I, Mataix-Cols D, Menchón JM, Minuzzi L, Morgado P, Moreira P, Nakamae T, Nakao T, Narayanaswamy JC, Nurmi EL, O'Neill J, Piacentini J, Piras F, Piras F, Reddy YCJ, Sato JR, Simpson HB, Soreni N, Soriano-Mas C, Spalletta G, Stevens MC, Szeszko PR, Tolin DF, Venkatasubramanian G, Walitza S, Wang Z, van Wingen GA, Xu J, Xu X, Zhao Q, Thompson PM, Stein DJ, van den Heuvel OA, Kwon JS. Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium. Brain 2020; 143:684-700. [PMID: 32040561 PMCID: PMC7009583 DOI: 10.1093/brain/awaa001] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 11/24/2019] [Accepted: 11/26/2019] [Indexed: 12/13/2022] Open
Abstract
Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P < 0.0001), lower modularity (P < 0.0001), and lower small-worldness (P = 0.017). Detection of community membership emphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed) centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicative of altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated with OCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of this study, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariance networks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometry in OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularly in cingulate and orbitofrontal regions.
Collapse
Affiliation(s)
- Je-Yeon Yun
- Seoul National University Hospital, Seoul, Republic of Korea
- Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Premika S W Boedhoe
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Chris Vriend
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Yoshinari Abe
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Stephanie H Ameis
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health, Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, Canada
- Centre for Brain and Mental Health, The Hospital for Sick Children, Toronto, Canada
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Paul D Arnold
- Mathison Centre for Mental Health Research and Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Marcelo C Batistuzzo
- Departamento e Instituto de Psiquiatria do Hospital das Clinicas, IPQ HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, SP, Brazil
| | - Francesco Benedetti
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Jan C Beucke
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Irene Bollettini
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Anushree Bose
- Obsessive-Compulsive Disorder (OCD) Clinic Department of Psychiatry National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Silvia Brem
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Anna Calvo
- Magnetic Resonance Image Core Facility, IDIBAPS (Institut d'Investigacions Biomèdiques August Pi i Sunyer), Barcelona, Spain
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Kang Ik K Cho
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea
| | - Valentina Ciullo
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Sara Dallaspezia
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Damiaan Denys
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Jamie D Feusner
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Jean-Paul Fouche
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - Mònica Giménez
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Health Institute, Barcelona, Spain
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Patricia Gruner
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Derrek P Hibar
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Marcelo Q Hoexter
- Departamento e Instituto de Psiquiatria do Hospital das Clinicas, IPQ HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, SP, Brazil
| | - Hao Hu
- Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine, PR China
| | - Chaim Huyser
- De Bascule, Academic Center for Child and Adolescent Psychiatry, Amsterdam, The Netherlands
- Amsterdam UMC, Department of Child and Adolescent Psychiatry, Amsterdam, The Netherlands
| | - Keisuke Ikari
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, Japan
| | - Norbert Kathmann
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Christian Kaufmann
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Kathrin Koch
- Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Germany
- TUM-Neuroimaging Center (TUM-NIC) of Klinikum rechts der Isar, Technische Universität München, Germany
| | - Luisa Lazaro
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neurosciences, Hospital Clínic Universitari, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Medicine, University of Barcelona, Barcelona, Spain
- Centro de Investigación Biomèdica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
| | - Christine Lochner
- SAMRC Unit on Anxiety and Stress Disorders, Department of Psychiatry, University of Stellenbosch, South Africa
| | - Paulo Marques
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
| | - Rachel Marsh
- Columbia University Medical College, Columbia University, New York, NY, USA
- The New York State Psychiatric Institute, New York, NY, USA
| | - Ignacio Martínez-Zalacaín
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- Department of Clinical Sciences, University of Barcelona, Spain
| | - David Mataix-Cols
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
| | - José M Menchón
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- Centro de Investigación Biomèdica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
- Department of Clinical Sciences, University of Barcelona, Spain
| | - Luciano Minuzzi
- McMaster University, Department of Psychiatry and Behavioural Neurosciences, Hamilton, Ontario, Canada
| | - Pedro Morgado
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- Clinical Academic Center-Braga, Braga, Portugal
- ICVS-3Bs PT Government Associate Laboratory, Braga, Portugal
| | - Pedro Moreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- Clinical Academic Center-Braga, Braga, Portugal
- ICVS-3Bs PT Government Associate Laboratory, Braga, Portugal
| | - Takashi Nakamae
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Tomohiro Nakao
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Janardhanan C Narayanaswamy
- Obsessive-Compulsive Disorder (OCD) Clinic Department of Psychiatry National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Erika L Nurmi
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Joseph O'Neill
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Division of Child and Adolescent Psychiatry, University of California, Los Angeles, CA, USA
| | - John Piacentini
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Division of Child and Adolescent Psychiatry, University of California, Los Angeles, CA, USA
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Federica Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Y C Janardhan Reddy
- Obsessive-Compulsive Disorder (OCD) Clinic Department of Psychiatry National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Joao R Sato
- Center for Mathematics, Computing and Cognition, Universidade Federal do ABC, Santo Andre, Brazil
| | - H Blair Simpson
- Columbia University Medical College, Columbia University, New York, NY, USA
- Center for OCD and Related Disorders, New York State Psychiatric Institute, New York, NY, USA
| | - Noam Soreni
- Pediatric OCD Consultation Service, Anxiety Treatment and Research Center, St. Joseph's HealthCare, Hamilton, Ontario, Canada
| | - Carles Soriano-Mas
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- Centro de Investigación Biomèdica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
- Department of Psychobiology and Methodology of Health Sciences, Universitat Autònoma de Barcelona, Spain
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
- Beth K. and Stuart C. Yudofsky Division of Neuropsychiatry, Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas, USA
| | - Michael C Stevens
- Yale University School of Medicine, New Haven, Connecticut, USA
- Clinical Neuroscience and Development Laboratory, Olin Neuropsychiatry Research Center, Hartford, Connecticut, USA
| | - Philip R Szeszko
- Icahn School of Medicine at Mount Sinai, New York, USA
- James J. Peters VA Medical Center, Bronx, New York, USA
| | - David F Tolin
- Yale University School of Medicine, New Haven, Connecticut, USA
- Institute of Living/Hartford Hospital, Hartford, Connecticut, USA
| | - Ganesan Venkatasubramanian
- Obsessive-Compulsive Disorder (OCD) Clinic Department of Psychiatry National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Susanne Walitza
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Zhen Wang
- Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine, PR China
- Shanghai Key Laboratory of Psychotic Disorders, PR China
| | - Guido A van Wingen
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Jian Xu
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, PR China
| | - Xiufeng Xu
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, PR China
| | - Qing Zhao
- Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine, PR China
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Dan J Stein
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - Odile A van den Heuvel
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| |
Collapse
|
17
|
Aboud KS, Huo Y, Kang H, Ealey A, Resnick SM, Landman BA, Cutting LE. Structural covariance across the lifespan: Brain development and aging through the lens of inter-network relationships. Hum Brain Mapp 2018; 40:125-136. [PMID: 30368995 DOI: 10.1002/hbm.24359] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Revised: 08/03/2018] [Accepted: 08/08/2018] [Indexed: 12/12/2022] Open
Abstract
Recent studies have revealed that brain development is marked by morphological synchronization across brain regions. Regions with shared growth trajectories form structural covariance networks (SCNs) that not only map onto functionally identified cognitive systems, but also correlate with a range of cognitive abilities across the lifespan. Despite advances in within-network covariance examinations, few studies have examined lifetime patterns of structural relationships across known SCNs. In the current study, we used a big-data framework and a novel application of covariate-adjusted restricted cubic spline regression to identify volumetric network trajectories and covariance patterns across 13 networks (n = 5,019, ages = 7-90). Our findings revealed that typical development and aging are marked by significant shifts in the degree that networks preferentially coordinate with one another (i.e., modularity). Specifically, childhood showed higher modularity of networks compared to adolescence, reflecting a shift over development from segregation to desegregation of inter-network relationships. The shift from young to middle adulthood was marked by a significant decrease in inter-network modularity and organization, which continued into older adulthood, potentially reflecting changes in brain organizational efficiency with age. This study is the first to characterize brain development and aging in terms of inter-network structural covariance across the lifespan.
Collapse
Affiliation(s)
- Katherine S Aboud
- Department of Special Education, Vanderbilt Brain Institute, Vanderbilt University, Nashville, Tennessee
| | - Yuankai Huo
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee
| | - Ashley Ealey
- Department of Neuroscience, Agnes Scott College, Decatur, Georgia
| | | | - Bennett A Landman
- Departments of Electrical Engineering and Computer Science, Biomedical Engineering, Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee
| | - Laurie E Cutting
- Departments of Special Education, Psychology, Radiology, Pediatrics, Institute of Imaging Sciences, Vanderbilt University, Nashville, Tennessee
| |
Collapse
|