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Gutiérrez-de Pablo V, Poza J, Maturana-Candelas A, Rodríguez-González V, Tola-Arribas MÁ, Cano M, Hoshi H, Shigihara Y, Hornero R, Gómez C. Exploring the disruptions of the neurophysiological organization in Alzheimer's disease: An integrative approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108197. [PMID: 38688139 DOI: 10.1016/j.cmpb.2024.108197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 12/20/2023] [Accepted: 04/21/2024] [Indexed: 05/02/2024]
Abstract
BACKGROUND AND OBJECTIVE Alzheimer's disease (AD) is a neurological disorder that impairs brain functions associated with cognition, memory, and behavior. Noninvasive neurophysiological techniques like magnetoencephalography (MEG) and electroencephalography (EEG) have shown promise in reflecting brain changes related to AD. These techniques are usually assessed at two levels: local activation (spectral, nonlinear, and dynamic properties) and global synchronization (functional connectivity, frequency-dependent network, and multiplex network organization characteristics). Nonetheless, the understanding of the organization formed by the existing relationships between these levels, henceforth named neurophysiological organization, remains unexplored. This work aims to assess the alterations AD causes in the resting-state neurophysiological organization. METHODS To that end, three datasets from healthy controls (HC) and patients with dementia due to AD were considered: MEG database (55 HC and 87 patients with AD), EEG1 database (51 HC and 100 patients with AD), and EEG2 database (45 HC and 82 patients with AD). To explore the alterations induced by AD in the relationships between several features extracted from M/EEG data, association networks (ANs) were computed. ANs are graphs, useful to quantify and visualize the intricate relationships between multiple features. RESULTS Our results suggested a disruption in the neurophysiological organization of patients with AD, exhibiting a greater inclination towards the local activation level; and a significant decrease in the complexity and diversity of the ANs (p-value ¡ 0.05, Mann-Whitney U-test, Bonferroni correction). This effect might be due to a shift of the neurophysiological organization towards more regular configurations, which may increase its vulnerability. Moreover, our findings support the crucial role played by the local activation level in maintaining the stability of the neurophysiological organization. Classification performance exhibited accuracy values of 83.91%, 73.68%, and 72.65% for MEG, EEG1, and EEG2 databases, respectively. CONCLUSION This study introduces a novel, valuable methodology able to integrate parameters characterize different properties of the brain activity and to explore the intricate organization of the neurophysiological organization at different levels. It was noted that AD increases susceptibility to changes in functional neural organization, suggesting a greater ease in the development of severe impairments. Therefore, ANs could facilitate a deeper comprehension of the complex interactions in brain function from a global standpoint.
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Affiliation(s)
- Víctor Gutiérrez-de Pablo
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain.
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Aarón Maturana-Candelas
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
| | - Víctor Rodríguez-González
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
| | - Miguel Ángel Tola-Arribas
- CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; Department of Neurology, Río Hortega University Hospital, Valladolid, Spain
| | - Mónica Cano
- Department of Clinical Neurophysiology, Río Hortega University Hospital, Valladolid, Spain
| | - Hideyuki Hoshi
- Precision Medicine Centre, Hokuto Hospital, Obihiro, Japan
| | | | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
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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.
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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
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Lee P, Chou K, Lee W, Peng L, Chen L, Lin C, Liang C, Chung C. Altered cerebellar and caudate gray-matter volumes and structural covariance networks preceding dual cognitive and mobility impairments in older people. Alzheimers Dement 2024; 20:2420-2433. [PMID: 38298159 PMCID: PMC11032519 DOI: 10.1002/alz.13714] [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: 09/12/2023] [Revised: 11/17/2023] [Accepted: 12/16/2023] [Indexed: 02/02/2024]
Abstract
INTRODUCTION The neuroanatomical changes driving both cognitive and mobility impairments, an emerging preclinical dementia syndrome, are not fully understood. We examined gray-matter volumes (GMVs) and structural covariance networks (SCNs) abnormalities in community-based older people preceding the conversion to physio-cognitive decline syndrome (PCDS). METHODS Voxel-wise brain GMV and established SCNs were compared between PCDS and non-PCDS converters. RESULTS The study included 343 individuals (60.2 ± 6.9 years, 49.6% men) with intact cognitive and mobility functions. Over an average 5.6-year follow-up, 116 transitioned to PCDS. Identified regions with abnormal GMVs in PCDS converters were over cerebellum and caudate, which served as seeds for SCNs establishment. Significant differences in cerebellum-based (to right frontal pole and left middle frontal gyrus) and caudate-based SCNs (to right caudate putamen, right planum temporale, left precentral gyrus, right postcentral gyrus, and left parietal operculum) between converters and nonconverters were observed. DISCUSSION This study reveals early neuroanatomic changes, emphasizing the cerebellum's role, in dual cognitive and mobility impairments. HIGHLIGHTS Neuroanatomic precursors of dual cognitive and mobility impairments are identified. Cerebellar GMV reductions and increased right caudate GMV precede the onset of PCDS. Altered cerebellum- and caudate-based SCNs drive PCDS transformation. This research establishes a foundation for understanding PCDS as a specific dementia syndrome.
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Affiliation(s)
- Pei‐Lin Lee
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Institute of NeuroscienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
| | - Kun‐Hsien Chou
- Institute of NeuroscienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Brain Research CenterNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
| | - Wei‐Ju Lee
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Department of Family MedicineTaipei Veterans General Hospital Yuanshan BranchYi‐LanTaiwan
| | - Li‐Ning Peng
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Center for Geriatric and GerontologyTaipei Veterans General HospitalTaipeiTaiwan
| | - Liang‐Kung Chen
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Center for Geriatric and GerontologyTaipei Veterans General HospitalTaipeiTaiwan
- Taipei Municipal Gan‐Dau Hospital (managed by Taipei Veterans General Hospital)TaipeiTaiwan
| | - Ching‐Po Lin
- Institute of NeuroscienceNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Brain Research CenterNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Department of Education and ResearchTaipei City HospitalTaipeiTaiwan
| | - Chih‐Kuang Liang
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Center for Geriatrics and GerontologyKaohsiung Veterans General HospitalKaohsiungTaiwan
- Division of NeurologyDepartment of Internal MedicineKaohsiung Veterans General HospitalKaohsiungTaiwan
| | - Chih‐Ping Chung
- Center for Healthy Longevity and Aging SciencesNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Department of NeurologyNeurological InstituteTaipei Veterans General HospitalTaipeiTaiwan
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Chung MK, Azizi T, Hanson JL, Alexander AL, Pollak SD, Davidson RJ. Altered topological structure of the brain white matter in maltreated children through topological data analysis. Netw Neurosci 2024; 8:355-376. [PMID: 38711544 PMCID: PMC11073548 DOI: 10.1162/netn_a_00355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 11/30/2023] [Indexed: 05/08/2024] Open
Abstract
Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging and diffusion tensor imaging. We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children with a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.
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Affiliation(s)
- Moo K. Chung
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI, USA
| | - Tahmineh Azizi
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI, USA
| | - Jamie L. Hanson
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andrew L. Alexander
- Department of Medical Physics, University of Wisconsin–Madison, Madison, WI, USA
| | - Seth D. Pollak
- Department of Psychology, University of Wisconsin–Madison, Madison, WI, USA
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Eussen MJ, Jansen JF, Voncken TP, Debeij-Van Hall MH, Hendriksen JG, Vermeulen RJ, Klinkenberg S, Backes WH, Drenthen GS. Exploring the core network of the structural covariance network in childhood absence epilepsy. Heliyon 2023; 9:e22657. [PMID: 38107302 PMCID: PMC10724663 DOI: 10.1016/j.heliyon.2023.e22657] [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: 02/09/2023] [Revised: 10/04/2023] [Accepted: 11/16/2023] [Indexed: 12/19/2023] Open
Abstract
Childhood absence epilepsy (CAE) is a generalized pediatric epilepsy, which is generally considered to be a benign condition since most children become seizure-free before reaching adulthood. However, cognitive deficits and changes of brain morphological have been previously reported in CAE. These morphological changes, even if they might be very subtle, are not independent due to the underlying network structure and can be captured by the structural covariance network (SCN). In this study, SCNs were used to quantify the structural brain network for children with CAE as well as controls. Seventeen children with CAE (6-12y) and fifteen controls (6-12y) were included. To estimate the SCN, T1-weighted images were acquired and parcellated into 68 cortical regions. Graph measures characterizing the core network architecture, i.e. the assortativity and rich-club coefficient, were calculated for all individuals. Multivariable linear regression models, including age and sex as covariates, were used to assess differences between children with CAE and controls. Additionally, potential relations between the core network and cognitive performance was investigated. A lower assortativity (i.e. less efficiently organized core network organization) was found for children with CAE compared to controls. Moreover, better cognitive performance was found to relate to stronger assortative mixing pattern (i.e. more efficient core network structure). Rich-club coefficients did not differ between groups, nor relate to cognitions. The core network organization of the SCN in children with CAE tend to be less efficient organized compared to controls, and relates to cognitive performance, and therefore this study provides novel insights into the SCN organization in relation to CAE and cognition.
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Affiliation(s)
- Merel J.A. Eussen
- Department of Biomedical Technology, Eindhoven University of Technology, Eindhoven, the Netherlands
- Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Jacobus F.A. Jansen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Twan P.C. Voncken
- Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
- Epilepsy Center Kempenhaeghe, Heeze, the Netherlands
| | | | - Jos G.M. Hendriksen
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
- Epilepsy Center Kempenhaeghe, Heeze, the Netherlands
| | - R. Jeroen Vermeulen
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Sylvia Klinkenberg
- Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Walter H. Backes
- Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Gerhard S. Drenthen
- Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
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Chung MK, Azizi T, Hanson JL, Alexander AL, Davidson RJ, Pollak SD. Altered Topological Structure of the Brain White Matter in Maltreated Children through Topological Data Analysis. ARXIV 2023:arXiv:2304.05908v3. [PMID: 37090232 PMCID: PMC10120754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white-matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children to a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.
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Affiliation(s)
- Moo K. Chung
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA
| | - Tahmineh Azizi
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA
| | | | | | | | - Seth D. Pollak
- Department of Psychology, University of Wisconsin-Madison, USA
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Yang CC, Totzek JF, Lepage M, Lavigne KM. Sex differences in cognition and structural covariance-based morphometric connectivity: evidence from 28,000+ UK Biobank participants. Cereb Cortex 2023; 33:10341-10354. [PMID: 37557917 DOI: 10.1093/cercor/bhad286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 07/06/2023] [Accepted: 07/08/2023] [Indexed: 08/11/2023] Open
Abstract
There is robust evidence for sex differences in domain-specific cognition, where females typically show an advantage for verbal memory, whereas males tend to perform better in spatial memory. Sex differences in brain connectivity are well documented and may provide insight into these differences. In this study, we examined sex differences in cognition and structural covariance, as an index of morphometric connectivity, of a large healthy sample (n = 28,821) from the UK Biobank. Using T1-weighted magnetic resonance imaging scans and regional cortical thickness values, we applied jackknife bias estimation and graph theory to obtain subject-specific measures of structural covariance, hypothesizing that sex-related differences in brain network global efficiency, or overall covariance, would underlie cognitive differences. As predicted, females demonstrated better verbal memory and males showed a spatial memory advantage. Females also demonstrated faster processing speed, with no observed sex difference in executive functioning. Males showed higher global efficiency, as well as higher regional covariance (nodal strengths) in both hemispheres relative to females. Furthermore, higher global efficiency in males mediated sex differences in verbal memory and processing speed. Findings contribute to an improved understanding of how biological sex and differences in cognition are related to morphometric connectivity as derived from graph-theoretic methods.
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Affiliation(s)
- Crystal C Yang
- Department of Psychology, McGill University, Montréal, QC H4H 1R3, Canada
| | - Jana F Totzek
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, 6211 LK, Netherlands
- Department of Psychiatry, McGill University, Montréal, QC H4H 1R3, Canada
- Douglas Research Centre, Montréal, QC, H4H 1R3, Canada
| | - Martin Lepage
- Department of Psychology, McGill University, Montréal, QC H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montréal, QC H4H 1R3, Canada
- Douglas Research Centre, Montréal, QC, H4H 1R3, Canada
| | - Katie M Lavigne
- Department of Psychiatry, McGill University, Montréal, QC H4H 1R3, Canada
- Douglas Research Centre, Montréal, QC, H4H 1R3, Canada
- Montreal Neurological Institute-Hospital, McGill University, Montréal, QC H4H 1R3, Canada
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Saggar M, Bruno JL, Hall SS. Brief intensive social gaze training reorganizes functional brain connectivity in boys with fragile X syndrome. Cereb Cortex 2023; 33:5218-5227. [PMID: 36376964 PMCID: PMC10151883 DOI: 10.1093/cercor/bhac411] [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: 06/20/2022] [Revised: 09/19/2022] [Accepted: 09/20/2022] [Indexed: 11/16/2022] Open
Abstract
Boys with fragile X syndrome (FXS), the leading known genetic cause of autism spectrum disorder (ASD), demonstrate significant impairments in social gaze and associated weaknesses in communication, social interaction, and other areas of adaptive functioning. Little is known, however, concerning the impact of behavioral treatments for these behaviors on functional brain connectivity in this population. As part of a larger study, boys with FXS (mean age 13.23 ± 2.31 years) and comparison boys with ASD (mean age 12.15 ± 2.76 years) received resting-state functional magnetic resonance imaging scans prior to and following social gaze training administered by a trained behavior therapist in our laboratory. Network-agnostic connectome-based predictive modeling of pretreatment resting-state functional connectivity data revealed a set of positive (FXS > ASD) and negative (FXS < ASD) edges that differentiated the groups significantly and consistently across all folds of cross-validation. Following administration of the brief training, the FXS and ASD groups demonstrated reorganization of connectivity differences. The divergence in the spatial pattern of reorganization response, based on functional connectivity differences pretreatment, suggests a unique pattern of response to treatment in the FXS and ASD groups. These results provide further support for implementing targeted behavioral treatments to ameliorate syndrome-specific behavioral features in FXS.
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Affiliation(s)
- Manish Saggar
- Division of Interdisciplinary Brain Sciences, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, United States
| | - Jennifer L Bruno
- Division of Interdisciplinary Brain Sciences, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, United States
| | - Scott S Hall
- Division of Interdisciplinary Brain Sciences, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, United States
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Ma J, Hua XY, Zheng MX, Wu JJ, Huo BB, Xing XX, Gao X, Zhang H, Xu JG. Brain Metabolic Network Redistribution in Patients with White Matter Hyperintensities on MRI Analyzed with an Individualized Index Derived from 18F-FDG-PET/MRI. Korean J Radiol 2022; 23:986-997. [PMID: 36098344 PMCID: PMC9523232 DOI: 10.3348/kjr.2022.0320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE Whether metabolic redistribution occurs in patients with white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) is unknown. This study aimed 1) to propose a measure of the brain metabolic network for an individual patient and preliminarily apply it to identify impaired metabolic networks in patients with WMHs, and 2) to explore the clinical and imaging features of metabolic redistribution in patients with WMHs. MATERIALS AND METHODS This study included 50 patients with WMHs and 70 healthy controls (HCs) who underwent 18F-fluorodeoxyglucose-positron emission tomography/MRI. Various global property parameters according to graph theory and an individual parameter of brain metabolic network called "individual contribution index" were obtained. Parameter values were compared between the WMH and HC groups. The performance of the parameters in discriminating between the two groups was assessed using the area under the receiver operating characteristic curve (AUC). The correlation between the individual contribution index and Fazekas score was assessed, and the interaction between age and individual contribution index was determined. A generalized linear model was fitted with the individual contribution index as the dependent variable and the mean standardized uptake value (SUVmean) of nodes in the whole-brain network or seven classic functional networks as independent variables to determine their association. RESULTS The means ± standard deviations of the individual contribution index were (0.697 ± 10.9) × 10-3 and (0.0967 ± 0.0545) × 10-3 in the WMH and HC groups, respectively (p < 0.001). The AUC of the individual contribution index was 0.864 (95% confidence interval, 0.785-0.943). A positive correlation was identified between the individual contribution index and the Fazekas scores in patients with WMHs (r = 0.57, p < 0.001). Age and individual contribution index demonstrated a significant interaction effect on the Fazekas score. A significant direct association was observed between the individual contribution index and the SUVmean of the limbic network (p < 0.001). CONCLUSION The individual contribution index may demonstrate the redistribution of the brain metabolic network in patients with WMHs.
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Affiliation(s)
- Jie Ma
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Mou-Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jia-Jia Wu
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Bei-Bei Huo
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiang-Xin Xing
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Gao
- Panoramic Medical Imaging Diagnostic Center, Shanghai, China
| | - Han Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
| | - Jian-Guang Xu
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China.
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Garcia-Ramos C, Nair V, Maganti R, Mathis J, Conant LL, Prabhakaran V, Binder JR, Meyerand B, Hermann B, Struck AF. Network phenotypes and their clinical significance in temporal lobe epilepsy using machine learning applications to morphological and functional graph theory metrics. Sci Rep 2022; 12:14407. [PMID: 36002603 PMCID: PMC9402557 DOI: 10.1038/s41598-022-18495-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 08/12/2022] [Indexed: 02/08/2023] Open
Abstract
Machine learning analyses were performed on graph theory (GT) metrics extracted from brain functional and morphological data from temporal lobe epilepsy (TLE) patients in order to identify intrinsic network phenotypes and characterize their clinical significance. Participants were 97 TLE and 36 healthy controls from the Epilepsy Connectome Project. Each imaging modality (i.e., Resting-state functional Magnetic Resonance Imaging (RS-fMRI), and structural MRI) rendered 2 clusters: one comparable to controls and one deviating from controls. Participants were minimally overlapping across the identified clusters, suggesting that an abnormal functional GT phenotype did not necessarily mean an abnormal morphological GT phenotype for the same subject. Morphological clusters were associated with a significant difference in the estimated lifetime number of generalized tonic-clonic seizures and functional cluster membership was associated with age. Furthermore, controls exhibited significant correlations between functional GT metrics and cognition, while for TLE participants morphological GT metrics were linked to cognition, suggesting a dissociation between higher cognitive abilities and GT-derived network measures. Overall, these findings demonstrate the existence of clinically meaningful minimally overlapping phenotypes of morphological and functional GT networks. Functional network properties may underlie variance in cognition in healthy brains, but in the pathological state of epilepsy the cognitive limits might be primarily related to structural cerebral network properties.
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Affiliation(s)
- Camille Garcia-Ramos
- grid.14003.360000 0001 2167 3675Department of Medical Physics, University of Wisconsin-Madison, Madison, USA ,grid.14003.360000 0001 2167 3675Department of Neurology, University of Wisconsin-Madison, Madison, USA
| | - Veena Nair
- grid.14003.360000 0001 2167 3675Department of Radiology, University of Wisconsin-Madison, Madison, USA
| | - Rama Maganti
- grid.14003.360000 0001 2167 3675Department of Neurology, University of Wisconsin-Madison, Madison, USA
| | - Jedidiah Mathis
- grid.30760.320000 0001 2111 8460Department of Neurology, Medical College of Wisconsin, Milwaukee, USA
| | - Lisa L. Conant
- grid.14003.360000 0001 2167 3675Department of Neurology, University of Wisconsin-Madison, Madison, USA
| | - Vivek Prabhakaran
- grid.14003.360000 0001 2167 3675Department of Radiology, University of Wisconsin-Madison, Madison, USA
| | - Jeffrey R. Binder
- grid.30760.320000 0001 2111 8460Department of Neurology, Medical College of Wisconsin, Milwaukee, USA
| | - Beth Meyerand
- grid.14003.360000 0001 2167 3675Department of Medical Physics, University of Wisconsin-Madison, Madison, USA
| | - Bruce Hermann
- grid.14003.360000 0001 2167 3675Department of Neurology, University of Wisconsin-Madison, Madison, USA
| | - Aaron F. Struck
- grid.14003.360000 0001 2167 3675Department of Neurology, University of Wisconsin-Madison, Madison, USA ,grid.417123.20000 0004 0420 6882William S Middleton VA Hospital, Madison, WI USA
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11
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Sun L, Liang X, Duan D, Liu J, Chen Y, Wang X, Liao X, Xia M, Zhao T, He Y. Structural insight into the individual variability architecture of the functional brain connectome. Neuroimage 2022; 259:119387. [PMID: 35752416 DOI: 10.1016/j.neuroimage.2022.119387] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 06/11/2022] [Accepted: 06/13/2022] [Indexed: 11/15/2022] Open
Abstract
Human cognition and behaviors depend upon the brain's functional connectomes, which vary remarkably across individuals. However, whether and how the functional connectome individual variability architecture is structurally constrained remains largely unknown. Using tractography- and morphometry-based network models, we observed the spatial convergence of structural and functional connectome individual variability, with higher variability in heteromodal association regions and lower variability in primary regions. We demonstrated that functional variability is significantly predicted by a unifying structural variability pattern and that this prediction follows a primary-to-heteromodal hierarchical axis, with higher accuracy in primary regions and lower accuracy in heteromodal regions. We further decomposed group-level connectome variability patterns into individual unique contributions and uncovered the structural-functional correspondence that is associated with individual cognitive traits. These results advance our understanding of the structural basis of individual functional variability and suggest the importance of integrating multimodal connectome signatures for individual differences in cognition and behaviors.
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Affiliation(s)
- 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
| | - 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
| | - 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
| | - Jin Liu
- 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
| | - Yuhan Chen
- 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
| | - Xindi Wang
- 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
| | - 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
| | - 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.
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12
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Drenthen GS, Backes WH, Freeze WM, Jacobs HI, Verheggen IC, van Boxtel MP, Hoff EI, Verhey FR, Jansen JF. Rich-Club Connectivity of the Structural Covariance Network Relates to Memory Processes in Mild Cognitive Impairment and Alzheimer's Disease. J Alzheimers Dis 2022; 89:209-217. [PMID: 35871335 PMCID: PMC9484119 DOI: 10.3233/jad-220175] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/13/2022] [Indexed: 11/15/2022]
Abstract
BACKGROUND Though mediotemporal lobe volume changes are well-known features of Alzheimer's disease (AD), grey matter volume changes may be distributed throughout the brain. These distributed changes are not independent due to the underlying network structure and can be described in terms of a structural covariance network (SCN). OBJECTIVE To investigate how the cortical brain organization is altered in AD we studied the mutual connectivity of hubs in the SCN, i.e., the rich-club. METHODS To construct the SCNs, cortical thickness was obtained from structural MRI for 97 participants (normal cognition, n = 37; mild cognitive impairment, n = 41; Alzheimer-type dementia, n = 19). Subsequently, rich-club coefficients were calculated from the SCN, and related to memory performance and hippocampal volume using linear regression. RESULTS Lower rich-club connectivity was related to lower memory performance as well as lower hippocampal volume. CONCLUSION Therefore, this study provides novel evidence of reduced connectivity in hub areas in relation to AD-related cognitive impairments and atrophy.
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Affiliation(s)
- Gerhard S. Drenthen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Walter H. Backes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Whitney M. Freeze
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Psychiatry & Neuropsychology, Maastricht University, Maastricht, the Netherlands
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Heidi I.L. Jacobs
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Gordon Center for Medical Imaging Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Inge C.M. Verheggen
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Psychiatry & Neuropsychology, Maastricht University, Maastricht, the Netherlands
| | - Martin P.J. van Boxtel
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Psychiatry & Neuropsychology, Maastricht University, Maastricht, the Netherlands
| | - Erik I. Hoff
- Department of Neurology, Zuyderland Medical Centre Heerlen, Heerlen, the Netherlands
| | - Frans R. Verhey
- Department of Psychiatry & Neuropsychology, Maastricht University, Maastricht, the Netherlands
| | - Jacobus F.A. Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
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13
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Váša F, Hobday H, Stanyard RA, Daws RE, Giampietro V, O'Daly O, Lythgoe DJ, Seidlitz J, Skare S, Williams SCR, Marquand AF, Leech R, Cole JH. Rapid processing and quantitative evaluation of structural brain scans for adaptive multimodal imaging. Hum Brain Mapp 2021; 43:1749-1765. [PMID: 34953014 PMCID: PMC8886661 DOI: 10.1002/hbm.25755] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 11/02/2021] [Accepted: 11/21/2021] [Indexed: 12/17/2022] Open
Abstract
Current neuroimaging acquisition and processing approaches tend to be optimised for quality rather than speed. However, rapid acquisition and processing of neuroimaging data can lead to novel neuroimaging paradigms, such as adaptive acquisition, where rapidly processed data is used to inform subsequent image acquisition steps. Here we first evaluate the impact of several processing steps on the processing time and quality of registration of manually labelled T1 -weighted MRI scans. Subsequently, we apply the selected rapid processing pipeline both to rapidly acquired multicontrast EPImix scans of 95 participants (which include T1 -FLAIR, T2 , T2 *, T2 -FLAIR, DWI and ADC contrasts, acquired in ~1 min), as well as to slower, more standard single-contrast T1 -weighted scans of a subset of 66 participants. We quantify the correspondence between EPImix T1 -FLAIR and single-contrast T1 -weighted scans, using correlations between voxels and regions of interest across participants, measures of within- and between-participant identifiability as well as regional structural covariance networks. Furthermore, we explore the use of EPImix for the rapid construction of morphometric similarity networks. Finally, we quantify the reliability of EPImix-derived data using test-retest scans of 10 participants. Our results demonstrate that quantitative information can be derived from a neuroimaging scan acquired and processed within minutes, which could further be used to implement adaptive multimodal imaging and tailor neuroimaging examinations to individual patients.
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Affiliation(s)
- František Váša
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Harriet Hobday
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Ryan A Stanyard
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Department of Forensic & Developmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Richard E Daws
- The Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Brain Sciences, Imperial College London, London, UK
| | - Vincent Giampietro
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Owen O'Daly
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - David J Lythgoe
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Stefan Skare
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Steven C R Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Andre F Marquand
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands.,Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - Robert Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - James H Cole
- Department of Computer Science, Centre for Medical Image Computing, University College London, London, UK.,Dementia Research Centre, Institute of Neurology, University College London, London, UK
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14
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He C, Cortes JM, Kang X, Cao J, Chen H, Guo X, Wang R, Kong L, Huang X, Xiao J, Shan X, Feng R, Chen H, Duan X. Individual-based morphological brain network organization and its association with autistic symptoms in young children with autism spectrum disorder. Hum Brain Mapp 2021; 42:3282-3294. [PMID: 33934442 PMCID: PMC8193534 DOI: 10.1002/hbm.25434] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 03/04/2021] [Accepted: 03/25/2021] [Indexed: 01/01/2023] Open
Abstract
Individual-based morphological brain networks built from T1-weighted magnetic resonance imaging (MRI) reflect synchronous maturation intensities between anatomical regions at the individual level. Autism spectrum disorder (ASD) is a socio-cognitive and neurodevelopmental disorder with high neuroanatomical heterogeneity, but the specific patterns of morphological networks in ASD remain largely unexplored at the individual level. In this study, individual-based morphological networks were constructed by using high-resolution structural MRI data from 40 young children with ASD (age range: 2-8 years) and 38 age-, gender-, and handedness-matched typically developing children (TDC). Measurements were recorded as threefold. Results showed that compared with TDC, young children with ASD exhibited lower values of small-worldness (i.e., σ) of individual-level morphological brain networks, increased morphological connectivity in cortico-striatum-thalamic-cortical (CSTC) circuitry, and decreased morphological connectivity in the cortico-cortical network. In addition, morphological connectivity abnormalities can predict the severity of social communication deficits in young children with ASD, thus confirming an associational impact at the behavioral level. These findings suggest that the morphological brain network in the autistic developmental brain is inefficient in segregating and distributing information. The results also highlight the crucial role of abnormal morphological connectivity patterns in the socio-cognitive deficits of ASD and support the possible use of the aberrant developmental patterns of morphological brain networks in revealing new clinically-relevant biomarkers for ASD.
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Affiliation(s)
- Changchun He
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
| | - Jesus M. Cortes
- Computational Neuroimaging LaboratoryBiocruces‐Bizkaia Health Research InstituteBarakaldoSpain
- Ikerbasque: The Basque Foundation for ScienceBilbaoSpain
- Department of Cell Biology and HistologyUniversity of the Basque CountryLeioaSpain
| | - Xiaodong Kang
- Affiliated Sichuan Provincial Rehabilitation Hospital of Chengdu University of TCMSichuan Bayi Rehabilitation CenterChengduChina
| | - Jing Cao
- Affiliated Sichuan Provincial Rehabilitation Hospital of Chengdu University of TCMSichuan Bayi Rehabilitation CenterChengduChina
| | - Heng Chen
- School of MedicineMedical College of Guizhou UniversityGuiyangChina
| | - Xiaonan Guo
- School of Information Science and EngineeringYanshan UniversityQinhuangdaoChina
- Hebei Key Laboratory of information transmission and signal processingYanshan UniversityQinhuangdaoChina
| | - Ruishi Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
| | - Lingyin Kong
- Department of Biomedical Engineering, School of Material Science and EngineeringSouth China University of TechnologyGuangzhouChina
| | - Xinyue Huang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
| | - Jinming Xiao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
| | - Xiaolong Shan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
| | - Rui Feng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
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15
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Nadig A, Seidlitz J, McDermott CL, Liu S, Bethlehem R, Moore TM, Mallard TT, Clasen LS, Blumenthal JD, Lalonde F, Gur RC, Gur RE, Bullmore ET, Satterthwaite TD, Raznahan A. Morphological integration of the human brain across adolescence and adulthood. Proc Natl Acad Sci U S A 2021; 118:e2023860118. [PMID: 33811142 PMCID: PMC8040585 DOI: 10.1073/pnas.2023860118] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Brain structural covariance norms capture the coordination of neurodevelopmental programs between different brain regions. We develop and apply anatomical imbalance mapping (AIM), a method to measure and model individual deviations from these norms, to provide a lifespan map of morphological integration in the human cortex. In cross-sectional and longitudinal data, analysis of whole-brain average anatomical imbalance reveals a reproducible tightening of structural covariance by age 25 y, which loosens after the seventh decade of life. Anatomical imbalance change in development and in aging is greatest in the association cortex and least in the sensorimotor cortex. Finally, we show that interindividual variation in whole-brain average anatomical imbalance is positively correlated with a marker of human prenatal stress (birthweight disparity between monozygotic twins) and negatively correlated with general cognitive ability. This work provides methods and empirical insights to advance our understanding of coordinated anatomical organization of the human brain and its interindividual variation.
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Affiliation(s)
- Ajay Nadig
- Harvard/MIT MD-PhD Program, Harvard Medical School, Boston, MA, 02115;
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD, 20892
| | - Jakob Seidlitz
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD, 20892
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104
| | - Cassidy L McDermott
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD, 20892
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, 19104
| | - Siyuan Liu
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD, 20892
| | - Richard Bethlehem
- Department of Psychiatry, University of Cambridge, Cambridge CB2 1TN, United Kingdom
| | - Tyler M Moore
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104
| | - Travis T Mallard
- Department of Psychology, University of Texas at Austin, Austin, TX, 78712
| | - Liv S Clasen
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD, 20892
| | - Jonathan D Blumenthal
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD, 20892
| | - François Lalonde
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD, 20892
| | - Ruben C Gur
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104
| | - Raquel E Gur
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104
| | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge CB2 1TN, United Kingdom
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge CB2 1TN, United Kingdom
| | | | - Armin Raznahan
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD, 20892
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16
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Bruno JL, Hong DS, Lightbody AA, Hosseini SMH, Hallmayer J, Reiss AL. Glucocorticoid regulation and neuroanatomy in fragile x syndrome. J Psychiatr Res 2021; 134:81-88. [PMID: 33373777 PMCID: PMC8577316 DOI: 10.1016/j.jpsychires.2020.12.015] [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: 04/13/2020] [Revised: 10/18/2020] [Accepted: 12/09/2020] [Indexed: 10/22/2022]
Abstract
Fragile X syndrome (FXS) is the leading known inherited cause for intellectual disability. Due to mutations in the FMR1 gene, affected individuals are at risk for serious cognitive and behavioral symptoms and developmental disability. Clinical presentation varies considerably, and investigation of genetic factors not directly related to FMR1 may help better understand variability. The present study examined the BclI polymorphism of the glucocorticoid receptor gene NR3C1 in 43 individuals with FXS (28 females, age 16 to 25). Females with FXS who presented with one or more G alleles demonstrated attenuated symptoms of anxiety/depression (p = 0.038) and externalizing behaviors (p = 0.042) relative to individuals with the C/C allele. In the combined sample (males and females) structural neuroimaging data differentiated individuals with a G allele from those with the C/C genotype (p < 0.001). Key components of anxiety/fear neurocircuitry (amygdala, insula) contributed more (relative to other regions) to the model differentiating groups. These results indicate that GR polymorphisms are associated with an altered pattern of behavioral and brain development in FXS. This information is important for understanding and treating mood disorders and altered brain development among individuals with FXS. With further research, these findings could be informative for understanding anxiety and mood disorders more broadly.
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Affiliation(s)
- Jennifer L Bruno
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94304, USA.
| | - David S Hong
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94304, USA
| | - Amy A Lightbody
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94304, USA
| | - S M Hadi Hosseini
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94304, USA
| | - Joachim Hallmayer
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94304, USA
| | - Allan L Reiss
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94304, USA; Department of Radiology, Stanford University, Stanford, CA, 94304, USA; Department of Pediatrics, Stanford University, Stanford, CA, 94304, USA.
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17
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King DJ, Seri S, Beare R, Catroppa C, Anderson VA, Wood AG. Developmental divergence of structural brain networks as an indicator of future cognitive impairments in childhood brain injury: Executive functions. Dev Cogn Neurosci 2020; 42:100762. [PMID: 32072940 PMCID: PMC6996014 DOI: 10.1016/j.dcn.2020.100762] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 11/01/2019] [Accepted: 01/19/2020] [Indexed: 11/29/2022] Open
Abstract
Brain insults during childhood can perturb the already non-linear trajectory of typical brain maturation. The diffuse effects of injury can be modelled using structural covariance networks (SCN), which change as a function of neurodevelopment. However, SCNs are estimated at the group-level, limiting applicability to predicting individual-subject outcomes. This study aimed to measure the divergence of the brain networks in paediatric traumatic brain injury (pTBI) patients and controls, and investigate relationships with executive functioning (EF) at 24 months post-injury. T1-weighted MRI acquired acutely in 78 child survivors of pTBI and 33 controls underwent 3D-tissue segmentation to estimate cortical thickness (CT) across 68 atlas-based regions-of-interest (ROIs). Using an 'add-one-patient' approach, we estimate a developmental divergence index (DDI). Our approach adopts a novel analytic framework in which age-appropriate reference networks to calculate the DDI were generated from control participants from the ABIDE dataset using a sliding-window approach. Divergence from the age-appropriate SCN was related to reduced EF performance and an increase in behaviours related to executive dysfunctions. The DDI measure showed predictive value with regard to executive functions, highlighting that early imaging can assist in prognosis for cognition.
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Affiliation(s)
- Daniel J King
- School of Life and Health Sciences & Aston Neuroscience Institute, Aston University, Birmingham, B4 7ET, UK; Department of Clinical Neurophysiology, Birmingham Women's and Children's Hospital NHS Foundation Trust, UK
| | - Stefano Seri
- School of Life and Health Sciences & Aston Neuroscience Institute, Aston University, Birmingham, B4 7ET, UK; Department of Clinical Neurophysiology, Birmingham Women's and Children's Hospital NHS Foundation Trust, UK
| | - Richard Beare
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Monash University, Melbourne, Australia
| | - Cathy Catroppa
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Department of Psychology, Royal Children's Hospital, Melbourne, Australia
| | - Vicki A Anderson
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Department of Psychology, Royal Children's Hospital, Melbourne, Australia
| | - Amanda G Wood
- School of Life and Health Sciences & Aston Neuroscience Institute, Aston University, Birmingham, B4 7ET, UK; Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; School of Psychology, Faculty of Health, Melbourne Burwood Campus, Deakin University, Geelong, Victoria, Australia.
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18
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Garcia-Ramos C, Dabbs K, Lin JJ, Jones JE, Stafstrom CE, Hsu DA, Meyerand ME, Prabhakaran V, Hermann BP. Network analysis of prospective brain development in youth with benign epilepsy with centrotemporal spikes and its relationship to cognition. Epilepsia 2019; 60:1838-1848. [PMID: 31347155 PMCID: PMC7394051 DOI: 10.1111/epi.16290] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 06/25/2019] [Accepted: 06/25/2019] [Indexed: 12/26/2022]
Abstract
OBJECTIVE Benign epilepsy with centrotemporal spikes (BECTS) is the most common childhood idiopathic localization-related epilepsy syndrome. BECTS presents normal routine magnetic resonance imaging (MRI); however, quantitative analytic techniques have captured subtle cortical and subcortical magnetic resonance anomalies. Network science, including graph theory (GT) analyses, facilitates understanding of brain covariance patterns, potentially informing in important ways how this common self-limiting epilepsy syndrome may impact normal patterns of brain and cognitive development. METHODS GT analyses examined the developmental covariance among cortical and subcortical regions in children with new/recent onset BECTS (n = 19) and typically developing healthy controls (n = 22) who underwent high-resolution MRI and cognitive assessment at baseline and 2 years later. Global (transitivity, global efficiency, and modularity index [Q]) and regional measures (local efficiency and hubs) were investigated to characterize network development in each group. Associations between baseline-based GT measures and cognition at both time points addressed the implications of GT analyses for cognition and prospective cognitive development. Furthermore, an individual contribution measure was investigated, reflecting how important for cognition it is for BECTS to resemble the correlation matrices of controls. RESULTS Groups exhibited similar Q and overall network configuration, with BECTS presenting significantly higher transitivity and both global and local efficiency. Furthermore, both groups presented a similar number of hubs, with BECTS showing a higher number in temporal lobe regions compared to controls. The investigated measures were negatively associated with 2-year cognitive outcomes in BECTS. SIGNIFICANCE Children with BECTS present a higher-than-normal global developmental configuration compared to controls, along with divergence from normality in terms of regional configuration. Baseline GT measures demonstrate potential as a cognitive biomarker to predict cognitive outcome in BECTS 2 years after diagnosis. Similarities and differences in developmental network configurations and their implications for cognition and behavior across common epilepsy syndromes are of theoretical interest and clinical relevance.
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Affiliation(s)
- Camille Garcia-Ramos
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Kevin Dabbs
- Department of Neurology, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Jack J Lin
- Department of Neurology, University of California, Irvine, Irvine, California
| | - Jana E Jones
- Department of Neurology, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Carl E Stafstrom
- Department of Neurology, Johns Hopkins Medical School, Baltimore, Maryland
| | - David A Hsu
- Department of Neurology, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Mary Elizabeth Meyerand
- Department of Biomedical Engineering, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Vivek Prabhakaran
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin.,Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Bruce P Hermann
- Department of Neurology, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
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19
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Das A, Sexton D, Lainscsek C, Cash SS, Sejnowski TJ. Characterizing Brain Connectivity From Human Electrocorticography Recordings With Unobserved Inputs During Epileptic Seizures. Neural Comput 2019; 31:1271-1326. [PMID: 31113298 PMCID: PMC7155929 DOI: 10.1162/neco_a_01205] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Epilepsy is a neurological disorder characterized by the sudden occurrence of unprovoked seizures. There is extensive evidence of significantly altered brain connectivity during seizure periods in the human brain. Research on analyzing human brain functional connectivity during epileptic seizures has been limited predominantly to the use of the correlation method. However, spurious connectivity can be measured between two brain regions without having direct connection or interaction between them. Correlations can be due to the apparent interactions of the two brain regions resulting from common input from a third region, which may or may not be observed. Hence, researchers have recently proposed a sparse-plus-latent-regularized precision matrix (SLRPM) when there are unobserved or latent regions interacting with the observed regions. The SLRPM method yields partial correlations of the conditional statistics of the observed regions given the latent regions, thus identifying observed regions that are conditionally independent of both the observed and latent regions. We evaluate the performance of the methods using a spring-mass artificial network and assuming that some nodes cannot be observed, thus constituting the latent variables in the example. Several cases have been considered, including both sparse and dense connections, short-range and long-range connections, and a varying number of latent variables. The SLRPM method is then applied to estimate brain connectivity during epileptic seizures from human ECoG recordings. Seventy-four clinical seizures from five patients, all having complex partial epilepsy, were analyzed using SLRPM, and brain connectivity was quantified using modularity index, clustering coefficient, and eigenvector centrality. Furthermore, using a measure of latent inputs estimated by the SLRPM method, it was possible to automatically detect 72 of the 74 seizures with four false positives and find six seizures that were not marked manually.
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Affiliation(s)
- Anup Das
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, and Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, U.S.A.
| | - Daniel Sexton
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, U.S.A.
| | - Claudia Lainscsek
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, U.S.A.
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, and Harvard Medical School, Boston, MA 02115, U.S.A.
| | - Terrence J Sejnowski
- Division of Biological Sciences and Institute of Neural Computation, University of California, San Diego, La Jolla, CA 92093, and Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, CA 92037, U.S.A.
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20
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Paulus MP, Squeglia LM, Bagot K, Jacobus J, Kuplicki R, Breslin FJ, Bodurka J, Morris AS, Thompson WK, Bartsch H, Tapert SF. Screen media activity and brain structure in youth: Evidence for diverse structural correlation networks from the ABCD study. Neuroimage 2019; 185:140-153. [PMID: 30339913 PMCID: PMC6487868 DOI: 10.1016/j.neuroimage.2018.10.040] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Revised: 10/05/2018] [Accepted: 10/13/2018] [Indexed: 01/20/2023] Open
Abstract
The adolescent brain undergoes profound structural changes which is influenced by many factors. Screen media activity (SMA; e.g., watching television or videos, playing video games, or using social media) is a common recreational activity in children and adolescents; however, its effect on brain structure is not well understood. A multivariate approach with the first cross-sectional data release from the Adolescent Brain Cognitive Development (ABCD) study was used to test the maturational coupling hypothesis, i.e. the notion that coordinated patterns of structural change related to specific behaviors. Moreover, the utility of this approach was tested by determining the association between these structural correlation networks and psychopathology or cognition. ABCD participants with usable structural imaging and SMA data (N = 4277 of 4524) were subjected to a Group Factor Analysis (GFA) to identify latent variables that relate SMA to cortical thickness, sulcal depth, and gray matter volume. Subject scores from these latent variables were used in generalized linear mixed-effect models to investigate associations between SMA and internalizing and externalizing psychopathology, as well as fluid and crystalized intelligence. Four SMA-related GFAs explained 37% of the variance between SMA and structural brain indices. SMA-related GFAs correlated with brain areas that support homologous functions. Some but not all SMA-related factors corresponded with higher externalizing (Cohen's d effect size (ES) 0.06-0.1) but not internalizing psychopathology and lower crystalized (ES: 0.08-0.1) and fluid intelligence (ES: 0.04-0.09). Taken together, these findings support the notion of SMA related maturational coupling or structural correlation networks in the brain and provides evidence that individual differences of these networks have mixed consequences for psychopathology and cognitive performance.
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Affiliation(s)
- Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, OK, USA; University of California San Diego, Department of Psychiatry, USA.
| | - Lindsay M Squeglia
- Medical University of South Carolina, Department of Psychiatry and Behavioral Sciences, Addiction Sciences Division, USA
| | - Kara Bagot
- University of California San Diego, Department of Psychiatry, USA
| | - Joanna Jacobus
- University of California San Diego, Department of Psychiatry, USA
| | | | | | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Amanda Sheffield Morris
- Laureate Institute for Brain Research, Tulsa, OK, USA; Oklahoma State University, College of Human Development and Family Science, USA
| | - Wesley K Thompson
- University of California San Diego, Division of Biostatistics, Department of Family Medicine and Public Health, USA
| | - Hauke Bartsch
- University of California San Diego, Department of Radiology, USA
| | - Susan F Tapert
- University of California San Diego, Department of Psychiatry, USA
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21
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Sánchez-Catasús CA, Willemsen A, Boellaard R, Juarez-Orozco LE, Samper-Noa J, Aguila-Ruiz A, De Deyn PP, Dierckx R, Medina YI, Melie-Garcia L. Episodic memory in mild cognitive impairment inversely correlates with the global modularity of the cerebral blood flow network. Psychiatry Res Neuroimaging 2018; 282:73-81. [PMID: 30419408 DOI: 10.1016/j.pscychresns.2018.11.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 09/23/2018] [Accepted: 11/02/2018] [Indexed: 12/25/2022]
Abstract
Cerebral blood flow (CBF) SPECT is an interesting methodology to study brain connectivity in mild cognitive impairment (MCI) since it is accessible worldwide and can be used as a biomarker of neuronal injury in MCI. In CBF SPECT, connectivity is grounded in group-based correlation networks. Therefore, topological metrics derived from the CBF correlation network cannot be used to support diagnosis and prognosis individually. However, methods to extract the individual patient contribution to topological metrics of group-based correlation networks were developed although not yet applied to MCI patients. Here, we investigate whether the episodic memory of 24 amnestic MCI patients correlates with individual patient contributions to topological metrics of the CBF correlation network. We first compared topological metrics of the MCI group network with the network corresponding to 26 controls. Metrics that showed significant differences were then used for the individual patient contribution analysis. We found that the global network modularity was increased while global efficiency decreased in the MCI network compared to the control. Most importantly, we found that episodic memory inversely correlates with the patient contribution to the global network modularity, which highlights the potential of this approach to develop a CBF connectivity-based biomarker at the individual level.
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Affiliation(s)
- Carlos A Sánchez-Catasús
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, GZ 9713, the Netherlands; Center for Neurological Restoration (CIREN), Ave. 25, No. 15 805, Playa, La Habana 11300, Cuba.
| | - Antoon Willemsen
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, GZ 9713, the Netherlands
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, GZ 9713, the Netherlands
| | - Luis Eduardo Juarez-Orozco
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, GZ 9713, the Netherlands
| | - Juan Samper-Noa
- Hospital Carlos J. Finlay, Ave. 31, Playa, La Habana 11400, Cuba; Cuban Neuroscience Center, Ave. 25, No. 15007, Playa, La Habana 11600, Cuba
| | - Angel Aguila-Ruiz
- Center for Neurological Restoration (CIREN), Ave. 25, No. 15 805, Playa, La Habana 11300, Cuba
| | - Peter Paul De Deyn
- Department of Neurology and Alzheimer Research Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, GZ 9713, the Netherlands; University of Antwerp, Institute Born-Bunge, Laboratory of Neurochemistry and Behaviour, Universiteitsplein 1, Antwerpen BE-2610, Belgium
| | - Rudi Dierckx
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, GZ 9713, the Netherlands
| | - Yasser Iturria Medina
- Cuban Neuroscience Center, Ave. 25, No. 15007, Playa, La Habana 11600, Cuba; McConnell Brain Imaging Center, Montreal Neurological Institute, 3801 University Street, Montréal, Quebec H3A 2B4, Canada
| | - Lester Melie-Garcia
- Cuban Neuroscience Center, Ave. 25, No. 15007, Playa, La Habana 11600, Cuba; Laboratoire de Recherche en Neuroimagerie (LREN), Centre Hospitalier Universitaire Vaudois (CHUV), Mont-Paisible 16, Lausanne CH-1011, Switzerland
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22
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Drenthen GS, Backes WH, Rouhl RPW, Vlooswijk MCG, Majoie MHJM, Hofman PAM, Aldenkamp AP, Jansen JFA. Structural covariance networks relate to the severity of epilepsy with focal-onset seizures. NEUROIMAGE-CLINICAL 2018; 20:861-867. [PMID: 30278373 PMCID: PMC6169103 DOI: 10.1016/j.nicl.2018.09.023] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Revised: 08/31/2018] [Accepted: 09/25/2018] [Indexed: 12/01/2022]
Abstract
PURPOSE The brains of patients with epilepsy may exhibit various morphological abnormalities, which are often not directly visible on structural MR images, as they may be focally subtle or related to a more large-scale inconspicuous disorganization of brain structures. To explore the relation between structural brain organization and epilepsy characteristics, including severity and cognitive co-morbidity, we determined structural covariance networks (SCNs). SCNs represent interregional correlations of morphologic measures, for instance in terms of cortical thickness, between various large-scale distributed brain regions. METHODS Thirty-eight patients with focal seizures of all subtypes and 21 healthy controls underwent structural MRI, neurological, and IQ assessment. Cortical thickness was derived from the structural MRIs using FreeSurfer. Subsequently, SCNs were constructed on a group-level based on correlations of the cortical thicknesses between various brain regions. Individual SCNs for the epilepsy patients were extracted by adding the respective patient to the control group prior to the SCN construction (i.e. add-one-patient approach). Calculated network measures, i.e. path length, clustering coefficient and betweenness centrality were correlated with characteristics related to the severity of epilepsy, including seizure history and age at onset of epilepsy, and cognitive performance. RESULTS Stronger clustering in the individual SCN was associated with a higher number of focal to bilateral tonic-clonic seizures during life time, a younger age at onset, and lower cognitive performance. The path length of the individual SCN was not related to the severity of epilepsy or cognitive performance. Higher betweenness centrality of the left cuneus and lower betweenness centrality of the right rostral middle frontal gyrus were associated with increased drug load and younger age at onset, respectively. CONCLUSIONS These results indicate that the correlations between interregional variations of cortical thickness reflect disease characteristics or responses to the disease and deficits in patients with epilepsy with focal seizures.
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Affiliation(s)
- Gerhard S Drenthen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands; School for Mental Health and Neuroscience, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands; Academic Center for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze and Maastricht, the Netherlands; Department of Electrical Engineering, Eindhoven University of Technology, Groene Loper 19, Eindhoven, the Netherlands
| | - Walter H Backes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands; School for Mental Health and Neuroscience, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands
| | - Rob P W Rouhl
- School for Mental Health and Neuroscience, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands; Academic Center for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze and Maastricht, the Netherlands; Department of Neurology, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands
| | - Marielle C G Vlooswijk
- Academic Center for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze and Maastricht, the Netherlands; Department of Neurology, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands
| | - Marian H J M Majoie
- Academic Center for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze and Maastricht, the Netherlands
| | - Paul A M Hofman
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands
| | - Albert P Aldenkamp
- School for Mental Health and Neuroscience, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands; Academic Center for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze and Maastricht, the Netherlands; Department of Electrical Engineering, Eindhoven University of Technology, Groene Loper 19, Eindhoven, the Netherlands; Department of Behavioral Sciences, Epilepsy Center Kempenhaeghe, Sterkselseweg 65, Heeze, the Netherlands
| | - Jacobus F A Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands; School for Mental Health and Neuroscience, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands.
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23
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Váša F, Seidlitz J, Romero-Garcia R, Whitaker KJ, Rosenthal G, Vértes PE, Shinn M, Alexander-Bloch A, Fonagy P, Dolan RJ, Jones PB, Goodyer IM, Sporns O, Bullmore ET. Adolescent Tuning of Association Cortex in Human Structural Brain Networks. Cereb Cortex 2018; 28:281-294. [PMID: 29088339 PMCID: PMC5903415 DOI: 10.1093/cercor/bhx249] [Citation(s) in RCA: 158] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Indexed: 12/27/2022] Open
Abstract
Motivated by prior data on local cortical shrinkage and intracortical myelination, we predicted age-related changes in topological organization of cortical structural networks during adolescence. We estimated structural correlation from magnetic resonance imaging measures of cortical thickness at 308 regions in a sample of N = 297 healthy participants, aged 14–24 years. We used a novel sliding-window analysis to measure age-related changes in network attributes globally, locally and in the context of several community partitions of the network. We found that the strength of structural correlation generally decreased as a function of age. Association cortical regions demonstrated a sharp decrease in nodal degree (hubness) from 14 years, reaching a minimum at approximately 19 years, and then levelling off or even slightly increasing until 24 years. Greater and more prolonged age-related changes in degree of cortical regions within the brain network were associated with faster rates of adolescent cortical myelination and shrinkage. The brain regions that demonstrated the greatest age-related changes were concentrated within prefrontal modules. We conclude that human adolescence is associated with biologically plausible changes in structural imaging markers of brain network organization, consistent with the concept of tuning or consolidating anatomical connectivity between frontal cortex and the rest of the connectome.
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Affiliation(s)
- František Váša
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Jakob Seidlitz
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK.,Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Rafael Romero-Garcia
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Kirstie J Whitaker
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK.,The Alan Turing Institute for Data Science, British Library, London NW1 2DB, UK
| | - Gideon Rosenthal
- Department of Brain and Cognitive Sciences, Ben-Gurion University of the Negev, PO Box 653, Beer-Sheva 8410501, Israel
| | - Petra E Vértes
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Maxwell Shinn
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Peter Fonagy
- Research Department of Clinical, Educational and Health Psychology, University College London, London WC1E 6BT, UK
| | - Raymond J Dolan
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London WC1N 3BG, UK.,Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London WC1B 5EH, UK
| | - Peter B Jones
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK.,Cambridgeshire & Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK
| | - Ian M Goodyer
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK.,Cambridgeshire & Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK
| | | | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Edward T Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK.,Cambridgeshire & Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK.,Immunology & Inflammation Therapeutic Area Unit, GlaxoSmithKline R&D, Stevenage SG1 2NY, UK
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24
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Yu K, Wang X, Li Q, Zhang X, Li X, Li S. Individual Morphological Brain Network Construction Based on Multivariate Euclidean Distances Between Brain Regions. Front Hum Neurosci 2018; 12:204. [PMID: 29887798 PMCID: PMC5981802 DOI: 10.3389/fnhum.2018.00204] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Accepted: 05/01/2018] [Indexed: 01/16/2023] Open
Abstract
Morphological brain network plays a key role in investigating abnormalities in neurological diseases such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, most of the morphological brain network construction methods only considered a single morphological feature. Each type of morphological feature has specific neurological and genetic underpinnings. A combination of morphological features has been proven to have better diagnostic performance compared with a single feature, which suggests that an individual morphological brain network based on multiple morphological features would be beneficial in disease diagnosis. Here, we proposed a novel method to construct individual morphological brain networks for two datasets by calculating the exponential function of multivariate Euclidean distance as the evaluation of similarity between two regions. The first dataset included 24 healthy subjects who were scanned twice within a 3-month period. The topological properties of these brain networks were analyzed and compared with previous studies that used different methods and modalities. Small world property was observed in all of the subjects, and the high reproducibility indicated the robustness of our method. The second dataset included 170 patients with MCI (86 stable MCI and 84 progressive MCI cases) and 169 normal controls (NC). The edge features extracted from the individual morphological brain networks were used to distinguish MCI from NC and separate MCI subgroups (progressive vs. stable) through the support vector machine in order to validate our method. The results showed that our method achieved an accuracy of 79.65% (MCI vs. NC) and 70.59% (stable MCI vs. progressive MCI) in a one-dimension situation. In a multiple-dimension situation, our method improved the classification performance with an accuracy of 80.53% (MCI vs. NC) and 77.06% (stable MCI vs. progressive MCI) compared with the method using a single feature. The results indicated that our method could effectively construct an individual morphological brain network based on multiple morphological features and could accurately discriminate MCI from NC and stable MCI from progressive MCI, and may provide a valuable tool for the investigation of individual morphological brain networks.
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Affiliation(s)
- Kaixin Yu
- School of Biological Science & Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Xuetong Wang
- School of Biological Science & Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Qiongling Li
- School of Biological Science & Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Xiaohui Zhang
- School of Biological Science & Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Xinwei Li
- School of Biological Science & Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Shuyu Li
- School of Biological Science & Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
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25
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van Veenendaal TM, Backes WH, Tse DHY, Scheenen TWJ, Klomp DW, Hofman PAM, Rouhl RPW, Vlooswijk MCG, Aldenkamp AP, Jansen JFA. High field imaging of large-scale neurotransmitter networks: Proof of concept and initial application to epilepsy. Neuroimage Clin 2018; 19:47-55. [PMID: 30035001 PMCID: PMC6051471 DOI: 10.1016/j.nicl.2018.04.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 03/22/2018] [Accepted: 04/01/2018] [Indexed: 01/05/2023]
Abstract
The brain can be considered a network, existing of multiple interconnected areas with various functions. MRI provides opportunities to map the large-scale network organization of the brain. We tap into the neurobiochemical dimension of these networks, as neuronal functioning and signal trafficking across distributed brain regions relies on the release and presence of neurotransmitters. Using high-field MR spectroscopic imaging at 7.0 T, we obtained a non-invasive snapshot of the spatial distribution of the neurotransmitters GABA and glutamate, and investigated interregional associations of these neurotransmitters. We demonstrate that interregional correlations of glutamate and GABA concentrations can be conceptualized as networks. Furthermore, patients with epilepsy display an increased number of glutamate and GABA connections and increased average strength of the GABA network. The increased glutamate and GABA connectivity in epilepsy might indicate a disrupted neurotransmitter balance. In addition to epilepsy, the 'neurotransmitter networks' concept might also provide new insights for other neurological diseases.
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Affiliation(s)
- Tamar M van Veenendaal
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center (MUMC+), The Netherlands; School for Mental Health and Neuroscience, Maastricht University, The Netherlands
| | - Walter H Backes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center (MUMC+), The Netherlands; School for Mental Health and Neuroscience, Maastricht University, The Netherlands
| | - Desmond H Y Tse
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center (MUMC+), The Netherlands; Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, The Netherlands
| | - Tom W J Scheenen
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Dennis W Klomp
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Paul A M Hofman
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center (MUMC+), The Netherlands; School for Mental Health and Neuroscience, Maastricht University, The Netherlands; Academic Center for Epileptology Kempenhaeghe/MUMC+, Heeze and Maastricht, The Netherlands
| | - Rob P W Rouhl
- School for Mental Health and Neuroscience, Maastricht University, The Netherlands; Academic Center for Epileptology Kempenhaeghe/MUMC+, Heeze and Maastricht, The Netherlands; Department of Neurology, Maastricht University Medical Center, The Netherlands
| | - Marielle C G Vlooswijk
- School for Mental Health and Neuroscience, Maastricht University, The Netherlands; Academic Center for Epileptology Kempenhaeghe/MUMC+, Heeze and Maastricht, The Netherlands; Department of Neurology, Maastricht University Medical Center, The Netherlands
| | - Albert P Aldenkamp
- School for Mental Health and Neuroscience, Maastricht University, The Netherlands; Academic Center for Epileptology Kempenhaeghe/MUMC+, Heeze and Maastricht, The Netherlands; Department of Neurology, Maastricht University Medical Center, The Netherlands
| | - Jacobus F A Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center (MUMC+), The Netherlands; School for Mental Health and Neuroscience, Maastricht University, The Netherlands.
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26
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Seghier ML, Price CJ. Interpreting and Utilising Intersubject Variability in Brain Function. Trends Cogn Sci 2018; 22:517-530. [PMID: 29609894 PMCID: PMC5962820 DOI: 10.1016/j.tics.2018.03.003] [Citation(s) in RCA: 143] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 01/30/2018] [Accepted: 03/07/2018] [Indexed: 11/30/2022]
Abstract
We consider between-subject variance in brain function as data rather than noise. We describe variability as a natural output of a noisy plastic system (the brain) where each subject embodies a particular parameterisation of that system. In this context, variability becomes an opportunity to: (i) better characterise typical versus atypical brain functions; (ii) reveal the different cognitive strategies and processing networks that can sustain similar tasks; and (iii) predict recovery capacity after brain damage by taking into account both damaged and spared processing pathways. This has many ramifications for understanding individual learning preferences and explaining the wide differences in human abilities and disabilities. Understanding variability boosts the translational potential of neuroimaging findings, in particular in clinical and educational neuroscience.
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Affiliation(s)
- Mohamed L Seghier
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education, PO Box 126662, Abu Dhabi, United Arab Emirates.
| | - Cathy J Price
- Wellcome Centre for Human Neuroimaging, University College London, Institute of Neurology, WC1N 3BG, London, UK.
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27
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Yao Z, Hu B, Chen X, Xie Y, Gutknecht J, Majoe D. Learning Metabolic Brain Networks in MCI and AD by Robustness and Leave-One-Out Analysis: An FDG-PET Study. Am J Alzheimers Dis Other Demen 2018; 33:42-54. [PMID: 28931302 PMCID: PMC10852436 DOI: 10.1177/1533317517731535] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2024]
Abstract
This study attempted to better understand the properties associated with the metabolic brain network in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Graph theory was employed to investigate the topological organization of metabolic brain network among 86 patients with MCI, 89 patients with AD, and 97 normal controls (NCs) using 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) data. The whole brain was divided into 82 areas by Brodmann atlas to construct networks. We found that MCI and AD showed a loss of small-world properties and topological aberrations, and MCI showed an intermediate measurement between NC and AD. The networks of MCI and AD were vulnerable to attacks resulting from the altered topological pattern. Furthermore, individual contributions were correlated with Mini-Mental State Examination and Clinical Dementia Rating. The present study indicated that the topological patterns of the metabolic networks were aberrant in patients with MCI and AD, which may be particularly helpful in uncovering the pathophysiology underlying the cognitive dysfunction in MCI and AD.
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Affiliation(s)
- Zhijun Yao
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, People’s Republic of China
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, People’s Republic of China
| | - Xuejiao Chen
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, People’s Republic of China
| | - Yuanwei Xie
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, People’s Republic of China
| | - Jürg Gutknecht
- Computer Systems Institute, ETH Zürich, Zürich, Switzerland
| | - Dennis Majoe
- Computer Systems Institute, ETH Zürich, Zürich, Switzerland
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28
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Westwater ML, Seidlitz J, Diederen KMJ, Fischer S, Thompson JC. Associations between cortical thickness, structural connectivity and severity of dimensional bulimia nervosa symptomatology. Psychiatry Res Neuroimaging 2018; 271:118-125. [PMID: 29150136 DOI: 10.1016/j.pscychresns.2017.11.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 09/18/2017] [Accepted: 11/10/2017] [Indexed: 01/11/2023]
Abstract
Bulimia nervosa (BN) is a psychiatric illness defined by preoccupation with body image (cognitive 'symptoms'), binge eating and compensatory behaviors. Although diagnosed BN has been related to grey matter alterations, characterization of brain structure in women with a range of BN symptoms has not been made. This study examined whether cortical thickness (CT) values scaled with severity of BN cognitions in 33 women with variable BN pathology. We then assessed global structural connectivity (SC) of CT to determine if individual differences in global SC relate to BN symptom severity. We used the Eating Disorder Examination Questionnaire (EDE-Q) as a continuous measure of BN symptom severity. EDE-Q score was negatively related to global CT and local CT in the left middle frontal gyrus, right superior frontal gyrus and bilateral orbitofrontal cortex (OFC) and temporoparietal regions. Moreover, cortical thinning was most pronounced in regions with high global connectivity. Finally, individual contributions to global SC at the group level related to EDE-Q score, where increased EDE-Q score correlated with reduced connectivity of the left OFC and middle temporal cortex and increased connectivity of the right superior parietal lobule. Findings represent the first evidence of cortical thinning that relates to cognitive BN symptoms.
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Affiliation(s)
- Margaret L Westwater
- Department of Psychiatry, University of Cambridge, Herchel Smith Building, Addenbrooke's Hospital, Cambridge CB2 0SZ, UK.
| | - Jakob Seidlitz
- Department of Psychiatry, University of Cambridge, Herchel Smith Building, Addenbrooke's Hospital, Cambridge CB2 0SZ, UK
| | - Kelly M J Diederen
- Department of Psychiatry, University of Cambridge, Herchel Smith Building, Addenbrooke's Hospital, Cambridge CB2 0SZ, UK
| | - Sarah Fischer
- Department of Psychology, George Mason University, Fairfax, VA 22030, USA
| | - James C Thompson
- Department of Psychology, George Mason University, Fairfax, VA 22030, USA
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29
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Seidlitz J, Váša F, Shinn M, Romero-Garcia R, Whitaker KJ, Vértes PE, Wagstyl K, Kirkpatrick Reardon P, Clasen L, Liu S, Messinger A, Leopold DA, Fonagy P, Dolan RJ, Jones PB, Goodyer IM, Raznahan A, Bullmore ET. Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation. Neuron 2017; 97:231-247.e7. [PMID: 29276055 DOI: 10.1016/j.neuron.2017.11.039] [Citation(s) in RCA: 229] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 10/05/2017] [Accepted: 11/22/2017] [Indexed: 12/22/2022]
Abstract
Macroscopic cortical networks are important for cognitive function, but it remains challenging to construct anatomically plausible individual structural connectomes from human neuroimaging. We introduce a new technique for cortical network mapping based on inter-regional similarity of multiple morphometric parameters measured using multimodal MRI. In three cohorts (two human, one macaque), we find that the resulting morphometric similarity networks (MSNs) have a complex topological organization comprising modules and high-degree hubs. Human MSN modules recapitulate known cortical cytoarchitectonic divisions, and greater inter-regional morphometric similarity was associated with stronger inter-regional co-expression of genes enriched for neuronal terms. Comparing macaque MSNs with tract-tracing data confirmed that morphometric similarity was related to axonal connectivity. Finally, variation in the degree of human MSN nodes accounted for about 40% of between-subject variability in IQ. Morphometric similarity mapping provides a novel, robust, and biologically plausible approach to understanding how human cortical networks underpin individual differences in psychological functions.
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Affiliation(s)
- Jakob Seidlitz
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK; Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA.
| | - František Váša
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | - Maxwell Shinn
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | | | - Kirstie J Whitaker
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | - Petra E Vértes
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | - Konrad Wagstyl
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | | | - Liv Clasen
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Siyuan Liu
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - David A Leopold
- Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda, MD 20892, USA; Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, Bethesda, MD 20892, USA
| | - Peter Fonagy
- Research Department of Clinical, Educational and Health Psychology, University College London, London WC1E 6BT, UK
| | - Raymond J Dolan
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London WC1N 3BG, UK; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK
| | - Peter B Jones
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK
| | - Ian M Goodyer
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK
| | | | - Armin Raznahan
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Edward T Bullmore
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK; ImmunoPsychiatry, Immuno-Inflammation Therapeutic Area Unit, GlaxoSmithKline R&D, Stevenage SG1 2NY, UK
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30
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Bruno JL, Hosseini SMH, Saggar M, Quintin EM, Raman MM, Reiss AL. Altered Brain Network Segregation in Fragile X Syndrome Revealed by Structural Connectomics. Cereb Cortex 2017; 27:2249-2259. [PMID: 27009247 DOI: 10.1093/cercor/bhw055] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Fragile X syndrome (FXS), the most common inherited cause of intellectual disability and autism spectrum disorder, is associated with significant behavioral, social, and neurocognitive deficits. Understanding structural brain network topology in FXS provides an important link between neurobiological and behavioral/cognitive symptoms of this disorder. We investigated the connectome via whole-brain structural networks created from group-level morphological correlations. Participants included 100 individuals: 50 with FXS and 50 with typical development, age 11-23 years. Results indicated alterations in topological properties of structural brain networks in individuals with FXS. Significantly reduced small-world index indicates a shift in the balance between network segregation and integration and significantly reduced clustering coefficient suggests that reduced local segregation shifted this balance. Caudate and amygdala were less interactive in the FXS network further highlighting the importance of subcortical region alterations in the neurobiological signature of FXS. Modularity analysis indicates that FXS and typically developing groups' networks decompose into different sets of interconnected sub networks, potentially indicative of aberrant local interconnectivity in individuals with FXS. These findings advance our understanding of the effects of fragile X mental retardation protein on large-scale brain networks and could be used to develop a connectome-level biological signature for FXS.
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Affiliation(s)
- Jennifer Lynn Bruno
- Department of Psychiatry, Center for Interdisciplinary Brain Sciences Research, Stanford, CA 94305-5795, USA
| | - S M Hadi Hosseini
- Department of Psychiatry, Center for Interdisciplinary Brain Sciences Research, Stanford, CA 94305-5795, USA
| | - Manish Saggar
- Department of Psychiatry, Center for Interdisciplinary Brain Sciences Research, Stanford, CA 94305-5795, USA
| | - Eve-Marie Quintin
- School and Applied Child Psychology Program, McGill University, Montreal, QC, CanadaH3A 1Y2
| | - Mira Michelle Raman
- Department of Psychiatry, Center for Interdisciplinary Brain Sciences Research, Stanford, CA 94305-5795, USA
| | - Allan L Reiss
- Department of Psychiatry, Center for Interdisciplinary Brain Sciences Research, Stanford, CA 94305-5795, USA.,Department of Radiology.,Department of Pediatrics, Stanford University, Stanford, CA 94305, USA
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31
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Sánchez-Catasús CA, Sanabria-Diaz G, Willemsen A, Martinez-Montes E, Samper-Noa J, Aguila-Ruiz A, Boellaard R, De Deyn PP, Dierckx RAJO, Melie-Garcia L. Subtle alterations in cerebrovascular reactivity in mild cognitive impairment detected by graph theoretical analysis and not by the standard approach. NEUROIMAGE-CLINICAL 2017; 15:151-160. [PMID: 28529871 PMCID: PMC5429238 DOI: 10.1016/j.nicl.2017.04.019] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 03/10/2017] [Accepted: 04/19/2017] [Indexed: 01/07/2023]
Abstract
There is growing support that cerebrovascular reactivity (CVR) in response to a vasodilatory challenge, also defined as the cerebrovascular reserve, is reduced in Alzheimer's disease dementia. However, this is less clear in patients with mild cognitive impairment (MCI). The current standard analysis may not reflect subtle abnormalities in CVR. In this study, we aimed to investigate vasodilatory-induced changes in the topology of the cerebral blood flow correlation (CBFcorr) network to study possible network-related CVR abnormalities in MCI. For this purpose, four CBFcorr networks were constructed: two using CBF SPECT data at baseline and under the vasodilatory challenge of acetazolamide (ACZ), obtained from a group of 26 MCI patients; and two equivalent networks from a group of 26 matched cognitively normal controls. The mean strength of association (SA) and clustering coefficient (C) were used to evaluate ACZ-induced changes on the topology of CBFcorr networks. We found that cognitively normal adults and MCI patients show different patterns of C and SA changes. The observed differences included the medial prefrontal cortices and inferior parietal lobe, which represent areas involved in MCI's cognitive dysfunction. In contrast, no substantial differences were detected by standard CVR analysis. These results suggest that graph theoretical analysis of ACZ-induced changes in the topology of the CBFcorr networks allows the identification of subtle network-related CVR alterations in MCI, which couldn't be detected by the standard approach. Subtle alterations in cerebrovascular reactivity in MCI by graph theoretical analysis. Graph theoretical analysis seems to be sensitive to subtle abnormalities. The standard approach could be insufficient for capturing subtle abnormal changes.
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Affiliation(s)
- Carlos A Sánchez-Catasús
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, The Netherlands; Department of Nuclear Medicine, Center for Neurological Restoration (CIREN), Havana, Cuba.
| | - Gretel Sanabria-Diaz
- Laboratoire de Recherche en Neuroimagerie (LREN), Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | - Antoon Willemsen
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, The Netherlands
| | | | - Juan Samper-Noa
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba; Hospital Carlos J. Finlay, Havana, Cuba
| | - Angel Aguila-Ruiz
- Department of Nuclear Medicine, Center for Neurological Restoration (CIREN), Havana, Cuba
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Peter P De Deyn
- Department of Neurology and Alzheimer Research Center, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Rudi A J O Dierckx
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Lester Melie-Garcia
- Laboratoire de Recherche en Neuroimagerie (LREN), Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
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32
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Kim HJ, Shin JH, Han CE, Kim HJ, Na DL, Seo SW, Seong JK. Using Individualized Brain Network for Analyzing Structural Covariance of the Cerebral Cortex in Alzheimer's Patients. Front Neurosci 2016; 10:394. [PMID: 27635121 PMCID: PMC5007703 DOI: 10.3389/fnins.2016.00394] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2016] [Accepted: 08/10/2016] [Indexed: 01/18/2023] Open
Abstract
Cortical thinning patterns in Alzheimer's disease (AD) have been widely reported through conventional regional analysis. In addition, the coordinated variance of cortical thickness in different brain regions has been investigated both at the individual and group network levels. In this study, we aim to investigate network architectural characteristics of a structural covariance network (SCN) in AD, and further to show that the structural covariance connectivity becomes disorganized across the brain regions in AD, while the normal control (NC) subjects maintain more clustered and consistent coordination in cortical atrophy variations. We generated SCNs directly from T1-weighted MR images of individual patients using surface-based cortical thickness data, with structural connectivity defined as similarity in cortical thickness within different brain regions. Individual SCNs were constructed using morphometric data from the Samsung Medical Center (SMC) dataset. The structural covariance connectivity showed higher clustering than randomly generated networks, as well as similar minimum path lengths, indicating that the SCNs are “small world.” There were significant difference between NC and AD group in characteristic path lengths (z = −2.97, p < 0.01) and small-worldness values (z = 4.05, p < 0.01). Clustering coefficients in AD was smaller than that of NC but there was no significant difference (z = 1.81, not significant). We further observed that the AD patients had significantly disrupted structural connectivity. We also show that the coordinated variance of cortical thickness is distributed more randomly from one region to other regions in AD patients when compared to NC subjects. Our proposed SCN may provide surface-based measures for understanding interaction between two brain regions with co-atrophy of the cerebral cortex due to normal aging or AD. We applied our method to the AD Neuroimaging Initiative (ADNI) data to show consistency in results with the SMC dataset.
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Affiliation(s)
- Hee-Jong Kim
- School of Biomedical Engineering, Korea UniversitySeoul, South Korea; Department of Bio-convergence Engineering, Korea UniversitySeoul, South Korea
| | - Jeong-Hyeon Shin
- School of Biomedical Engineering, Korea UniversitySeoul, South Korea; Department of Bio-convergence Engineering, Korea UniversitySeoul, South Korea
| | - Cheol E Han
- School of Biomedical Engineering, Korea UniversitySeoul, South Korea; Department of Bio-convergence Engineering, Korea UniversitySeoul, South Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of MedicineSeoul, South Korea; Neuroscience Center, Samsung Medical CenterSeoul, South Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of MedicineSeoul, South Korea; Neuroscience Center, Samsung Medical CenterSeoul, South Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of MedicineSeoul, South Korea; Neuroscience Center, Samsung Medical CenterSeoul, South Korea
| | - Joon-Kyung Seong
- School of Biomedical Engineering, Korea UniversitySeoul, South Korea; Department of Bio-convergence Engineering, Korea UniversitySeoul, South Korea
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