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Zuo XN, He Y, Betzel RF, Colcombe S, Sporns O, Milham MP. Human Connectomics across the Life Span. Trends Cogn Sci 2016; 21:32-45. [PMID: 27865786 DOI: 10.1016/j.tics.2016.10.005] [Citation(s) in RCA: 129] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Revised: 10/10/2016] [Accepted: 10/17/2016] [Indexed: 01/19/2023]
Abstract
Connectomics has enhanced our understanding of neurocognitive development and decline by the integration of network sciences into studies across different stages of the human life span. However, these studies commonly occurred independently, missing the opportunity to test integrated models of the dynamical brain organization across the entire life span. In this review article, we survey empirical findings in life-span connectomics and propose a generative framework for computationally modeling the connectome over the human life span. This framework highlights initial findings that across the life span, the human connectome gradually shifts from an 'anatomically driven' organization to one that is more 'topological'. Finally, we consider recent advances that are promising to provide an integrative and systems perspective of human brain plasticity as well as underscore the pitfalls and challenges.
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Affiliation(s)
- Xi-Nian Zuo
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Magnetic Resonance Imaging Research Center, Institute of Psychology, Beijing, China; Lifespan Connectomics and Behavior Team, Institute of Psychology, Beijing, China; Key Laboratory for Brain and Education Sciences, Guangxi Teachers Education University, Nanning, Guangxi, China; Center for Longevity Research, Guangxi Teachers Education University, Nanning, Guangxi, China.
| | - Ye He
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Magnetic Resonance Imaging Research Center, Institute of Psychology, Beijing, China; Lifespan Connectomics and Behavior Team, Institute of Psychology, Beijing, China; Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Richard F Betzel
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Stan Colcombe
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, SC, USA
| | - Olaf Sporns
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Michael P Milham
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, SC, USA; Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
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52
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Davison EN, Turner BO, Schlesinger KJ, Miller MB, Grafton ST, Bassett DS, Carlson JM. Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan. PLoS Comput Biol 2016; 12:e1005178. [PMID: 27880785 PMCID: PMC5120784 DOI: 10.1371/journal.pcbi.1005178] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Accepted: 10/03/2016] [Indexed: 11/18/2022] Open
Abstract
Individual differences in brain functional networks may be related to complex personal identifiers, including health, age, and ability. Dynamic network theory has been used to identify properties of dynamic brain function from fMRI data, but the majority of analyses and findings remain at the level of the group. Here, we apply hypergraph analysis, a method from dynamic network theory, to quantify individual differences in brain functional dynamics. Using a summary metric derived from the hypergraph formalism-hypergraph cardinality-we investigate individual variations in two separate, complementary data sets. The first data set ("multi-task") consists of 77 individuals engaging in four consecutive cognitive tasks. We observe that hypergraph cardinality exhibits variation across individuals while remaining consistent within individuals between tasks; moreover, the analysis of one of the memory tasks revealed a marginally significant correspondence between hypergraph cardinality and age. This finding motivated a similar analysis of the second data set ("age-memory"), in which 95 individuals, aged 18-75, performed a memory task with a similar structure to the multi-task memory task. With the increased age range in the age-memory data set, the correlation between hypergraph cardinality and age correspondence becomes significant. We discuss these results in the context of the well-known finding linking age with network structure, and suggest that hypergraph analysis should serve as a useful tool in furthering our understanding of the dynamic network structure of the brain.
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Affiliation(s)
- Elizabeth N. Davison
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America
| | - Benjamin O. Turner
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, Santa Barbara, California, United States of America
| | - Kimberly J. Schlesinger
- Department of Physics, University of California, Santa Barbara, Santa Barbara, California, United States of America
| | - Michael B. Miller
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, Santa Barbara, California, United States of America
| | - Scott T. Grafton
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, Santa Barbara, California, United States of America
| | - Danielle S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Jean M. Carlson
- Department of Physics, University of California, Santa Barbara, Santa Barbara, California, United States of America
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53
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Marrelec G, Messé A, Giron A, Rudrauf D. Functional Connectivity's Degenerate View of Brain Computation. PLoS Comput Biol 2016; 12:e1005031. [PMID: 27736900 PMCID: PMC5063374 DOI: 10.1371/journal.pcbi.1005031] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Accepted: 06/26/2016] [Indexed: 01/20/2023] Open
Abstract
Brain computation relies on effective interactions between ensembles of neurons. In neuroimaging, measures of functional connectivity (FC) aim at statistically quantifying such interactions, often to study normal or pathological cognition. Their capacity to reflect a meaningful variety of patterns as expected from neural computation in relation to cognitive processes remains debated. The relative weights of time-varying local neurophysiological dynamics versus static structural connectivity (SC) in the generation of FC as measured remains unsettled. Empirical evidence features mixed results: from little to significant FC variability and correlation with cognitive functions, within and between participants. We used a unified approach combining multivariate analysis, bootstrap and computational modeling to characterize the potential variety of patterns of FC and SC both qualitatively and quantitatively. Empirical data and simulations from generative models with different dynamical behaviors demonstrated, largely irrespective of FC metrics, that a linear subspace with dimension one or two could explain much of the variability across patterns of FC. On the contrary, the variability across BOLD time-courses could not be reduced to such a small subspace. FC appeared to strongly reflect SC and to be partly governed by a Gaussian process. The main differences between simulated and empirical data related to limitations of DWI-based SC estimation (and SC itself could then be estimated from FC). Above and beyond the limited dynamical range of the BOLD signal itself, measures of FC may offer a degenerate representation of brain interactions, with limited access to the underlying complexity. They feature an invariant common core, reflecting the channel capacity of the network as conditioned by SC, with a limited, though perhaps meaningful residual variability.
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Affiliation(s)
- Guillaume Marrelec
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d’imagerie biomédicale (LIB), Paris, France
| | - Arnaud Messé
- Department of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
| | - Alain Giron
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d’imagerie biomédicale (LIB), Paris, France
| | - David Rudrauf
- Grenoble Institute of Neuroscience, INSERM-UJF-CHU, Grenoble, France
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54
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Grady C, Sarraf S, Saverino C, Campbell K. Age differences in the functional interactions among the default, frontoparietal control, and dorsal attention networks. Neurobiol Aging 2016; 41:159-172. [DOI: 10.1016/j.neurobiolaging.2016.02.020] [Citation(s) in RCA: 141] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Revised: 01/25/2016] [Accepted: 02/20/2016] [Indexed: 02/02/2023]
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Cao M, Huang H, Peng Y, Dong Q, He Y. Toward Developmental Connectomics of the Human Brain. Front Neuroanat 2016; 10:25. [PMID: 27064378 PMCID: PMC4814555 DOI: 10.3389/fnana.2016.00025] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2015] [Accepted: 02/29/2016] [Indexed: 12/23/2022] Open
Abstract
Imaging connectomics based on graph theory has become an effective and unique methodological framework for studying structural and functional connectivity patterns of the developing brain. Normal brain development is characterized by continuous and significant network evolution throughout infancy, childhood, and adolescence, following specific maturational patterns. Disruption of these normal changes is associated with neuropsychiatric developmental disorders, such as autism spectrum disorders or attention-deficit hyperactivity disorder. In this review, we focused on the recent progresses regarding typical and atypical development of human brain networks from birth to early adulthood, using a connectomic approach. Specifically, by the time of birth, structural networks already exhibit adult-like organization, with global efficient small-world and modular structures, as well as hub regions and rich-clubs acting as communication backbones. During development, the structure networks are fine-tuned, with increased global integration and robustness and decreased local segregation, as well as the strengthening of the hubs. In parallel, functional networks undergo more dramatic changes during maturation, with both increased integration and segregation during development, as brain hubs shift from primary regions to high order functioning regions, and the organization of modules transitions from a local anatomical emphasis to a more distributed architecture. These findings suggest that structural networks develop earlier than functional networks; meanwhile functional networks demonstrate more dramatic maturational changes with the evolution of structural networks serving as the anatomical backbone. In this review, we also highlighted topologically disorganized characteristics in structural and functional brain networks in several major developmental neuropsychiatric disorders (e.g., autism spectrum disorders, attention-deficit hyperactivity disorder and developmental dyslexia). Collectively, we showed that delineation of the brain network from a connectomics perspective offers a unique and refreshing view of both normal development and neuropsychiatric disorders.
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Affiliation(s)
- Miao Cao
- State Key Laboratory of Cognitive Neuroscience and Learning and International Data Group/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
| | - Hao Huang
- Department of Radiology, Children's Hospital of PhiladelphiaPhiladelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of PennsylvaniaPhiladelphia, PA, USA
| | - Yun Peng
- Department of Radiology, Beijing Children's Hospital Affiliated to Capital Medical University Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning and International Data Group/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning and International Data Group/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
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56
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Lin L, Jin C, Fu Z, Zhang B, Bin G, Wu S. Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 125:8-17. [PMID: 26718834 DOI: 10.1016/j.cmpb.2015.11.012] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2015] [Revised: 11/24/2015] [Accepted: 11/24/2015] [Indexed: 06/05/2023]
Abstract
Brain ageing is followed by changes of the connectivity of white matter (WM) and changes of the grey matter (GM) concentration. Neurodegenerative disease is more vulnerable to an accelerated brain ageing, which is associated with prospective cognitive decline and disease severity. Accurate detection of accelerated ageing based on brain network analysis has a great potential for early interventions designed to hinder atypical brain changes. To capture the brain ageing, we proposed a novel computational approach for modeling the 112 normal older subjects (aged 50-79 years) brain age by connectivity analyses of networks of the brain. Our proposed method applied principal component analysis (PCA) to reduce the redundancy in network topological parameters. Back propagation artificial neural network (BPANN) improved by hybrid genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithm is established to model the relation among principal components (PCs) and brain age. The predicted brain age is strongly correlated with chronological age (r=0.8). The model has mean absolute error (MAE) of 4.29 years. Therefore, we believe the method can provide a possible way to quantitatively describe the typical and atypical network organization of human brain and serve as a biomarker for presymptomatic detection of neurodegenerative diseases in the future.
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Affiliation(s)
- Lan Lin
- Biomedical Engineering Department, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
| | - Cong Jin
- Biomedical Engineering Department, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
| | - Zhenrong Fu
- Biomedical Engineering Department, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
| | - Baiwen Zhang
- Biomedical Engineering Department, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
| | - Guangyu Bin
- Biomedical Engineering Department, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
| | - Shuicai Wu
- Biomedical Engineering Department, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
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57
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Sato JR, Salum GA, Gadelha A, Crossley N, Vieira G, Manfro GG, Zugman A, Picon FA, Pan PM, Hoexter MQ, Anés M, Moura LM, Del'Aquilla MAG, Amaro E, McGuire P, Lacerda ALT, Rohde LA, Miguel EC, Jackowski AP, Bressan RA. Default mode network maturation and psychopathology in children and adolescents. J Child Psychol Psychiatry 2016; 57:55-64. [PMID: 26111611 DOI: 10.1111/jcpp.12444] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/13/2015] [Indexed: 11/29/2022]
Abstract
BACKGROUND The human default mode (DMN) is involved in a wide array of mental disorders. Current knowledge suggests that mental health disorders may reflect deviant trajectories of brain maturation. METHOD We studied 654 children using functional magnetic resonance imaging (fMRI) scans under a resting-state protocol. A machine-learning method was used to obtain age predictions of children based on the average coefficient of fractional amplitude of low frequency fluctuations (fALFFs) of the DMN, a measure of spontaneous local activity. The chronological ages of the children and fALFF measures from regions of this network, the response and predictor variables were considered respectively in a Gaussian Process Regression. Subsequently, we computed a network maturation status index for each subject (actual age minus predicted). We then evaluated the association between this maturation index and psychopathology scores on the Child Behavior Checklist (CBCL). RESULTS Our hypothesis was that the maturation status of the DMN would be negatively associated with psychopathology. Consistent with previous studies, fALFF significantly predicted the age of participants (p < .001). Furthermore, as expected, we found an association between the DMN maturation status (precocious vs. delayed) and general psychopathology scores (p = .011). CONCLUSIONS Our findings suggest that child psychopathology seems to be associated with delayed maturation of the DMN. This delay in the neurodevelopmental trajectory may offer interesting insights into the pathophysiology of mental health disorders.
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Affiliation(s)
- João Ricardo Sato
- Center of Mathematics Computation and Cognition, Universidade Federal do ABC, Santo Andre, Brazil.,Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de Sao Paulo (UNIFESP), Sao Paulo, Brazil.,Department of Radiology, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents, CNPq, Sao Paulo, Brazil
| | - Giovanni Abrahão Salum
- National Institute of Developmental Psychiatry for Children and Adolescents, CNPq, Sao Paulo, Brazil.,Department of Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Ary Gadelha
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de Sao Paulo (UNIFESP), Sao Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents, CNPq, Sao Paulo, Brazil
| | - Nicolas Crossley
- Institute of Psychiatry, King's College London, London, United Kingdom.,Institute for Biological and Medical Engineering, Faculties of Engineering, Medicine and Biological Sciences, P. Catholic University of Chile, Santiago, Chile
| | - Gilson Vieira
- Department of Radiology, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil.,Bioinformatics Program, Institute of Mathematics and Statistics, University of Sao Paulo, Sao Paulo, Brazil
| | - Gisele Gus Manfro
- National Institute of Developmental Psychiatry for Children and Adolescents, CNPq, Sao Paulo, Brazil.,Department of Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - André Zugman
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de Sao Paulo (UNIFESP), Sao Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents, CNPq, Sao Paulo, Brazil
| | - Felipe Almeida Picon
- National Institute of Developmental Psychiatry for Children and Adolescents, CNPq, Sao Paulo, Brazil.,Department of Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Pedro Mario Pan
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de Sao Paulo (UNIFESP), Sao Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents, CNPq, Sao Paulo, Brazil
| | - Marcelo Queiroz Hoexter
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de Sao Paulo (UNIFESP), Sao Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents, CNPq, Sao Paulo, Brazil.,Department of Psychiatry, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil
| | - Mauricio Anés
- National Institute of Developmental Psychiatry for Children and Adolescents, CNPq, Sao Paulo, Brazil.,Department of Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Luciana Monteiro Moura
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de Sao Paulo (UNIFESP), Sao Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents, CNPq, Sao Paulo, Brazil
| | - Marco Antonio Gomes Del'Aquilla
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de Sao Paulo (UNIFESP), Sao Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents, CNPq, Sao Paulo, Brazil
| | - Edson Amaro
- Department of Radiology, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil
| | - Philip McGuire
- Institute of Psychiatry, King's College London, London, United Kingdom
| | - Acioly Luiz Tavares Lacerda
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de Sao Paulo (UNIFESP), Sao Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents, CNPq, Sao Paulo, Brazil
| | - Luis Augusto Rohde
- National Institute of Developmental Psychiatry for Children and Adolescents, CNPq, Sao Paulo, Brazil.,Department of Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Euripedes Constantino Miguel
- National Institute of Developmental Psychiatry for Children and Adolescents, CNPq, Sao Paulo, Brazil.,Department of Psychiatry, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil
| | - Andrea Parolin Jackowski
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de Sao Paulo (UNIFESP), Sao Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents, CNPq, Sao Paulo, Brazil
| | - Rodrigo Affonseca Bressan
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de Sao Paulo (UNIFESP), Sao Paulo, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents, CNPq, Sao Paulo, Brazil
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59
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Zhao T, Cao M, Niu H, Zuo X, Evans A, He Y, Dong Q, Shu N. Age-related changes in the topological organization of the white matter structural connectome across the human lifespan. Hum Brain Mapp 2015; 36:3777-92. [PMID: 26173024 PMCID: PMC6869038 DOI: 10.1002/hbm.22877] [Citation(s) in RCA: 121] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2014] [Revised: 05/08/2015] [Accepted: 06/01/2015] [Indexed: 12/20/2022] Open
Abstract
Lifespan is a dynamic process with remarkable changes in brain structure and function. Previous neuroimaging studies have indicated age-related microstructural changes in specific white matter tracts during development and aging. However, the age-related alterations in the topological architecture of the white matter structural connectome across the human lifespan remain largely unknown. Here, a cohort of 113 healthy individuals (ages 9-85) with both diffusion and structural MRI acquisitions were examined. For each participant, the high-resolution white matter structural networks were constructed by deterministic fiber tractography among 1024 parcellation units and were quantified with graph theoretical analyses. The global network properties, including network strength, cost, topological efficiency, and robustness, followed an inverted U-shaped trajectory with a peak age around the third decade. The brain areas with the most significantly nonlinear changes were located in the prefrontal and temporal cortices. Different brain regions exhibited heterogeneous trajectories: the posterior cingulate and lateral temporal cortices displayed prolonged maturation/degeneration compared with the prefrontal cortices. Rich-club organization was evident across the lifespan, whereas hub integration decreased linearly with age, especially accompanied by the loss of frontal hubs and their connections. Additionally, age-related changes in structural connections were predominantly located within and between the prefrontal and temporal modules. Finally, based on the graph metrics of structural connectome, accurate predictions of individual age were obtained (r = 0.77). Together, the data indicated a dynamic topological organization of the brain structural connectome across human lifespan, which may provide possible structural substrates underlying functional and cognitive changes with age.
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Affiliation(s)
- Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijing100875China
- Center for Collaboration and Innovation in Brain and Learning SciencesBeijing Normal UniversityBeijing100875China
| | - Miao Cao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijing100875China
- Center for Collaboration and Innovation in Brain and Learning SciencesBeijing Normal UniversityBeijing100875China
| | - Haijing Niu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijing100875China
- Center for Collaboration and Innovation in Brain and Learning SciencesBeijing Normal UniversityBeijing100875China
| | - Xi‐Nian Zuo
- Center for Collaboration and Innovation in Brain and Learning SciencesBeijing Normal UniversityBeijing100875China
- Key Laboratory of Behavioral ScienceInstitute of Psychology Chinese Academy of SciencesBeijing100101China
- Laboratory for Functional Connectome and DevelopmentInstitute of Psychology Chinese Academy of SciencesBeijing100101China
- Magnetic Resonance Imaging Research Center, Institute of Psychology Chinese Academy of SciencesBeijing100101China
| | - Alan Evans
- McConnell Brain Imaging CenterMontreal Neurological Institute, McGill UniversityMontrealQuebecH3A 2B4Canada
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijing100875China
- Center for Collaboration and Innovation in Brain and Learning SciencesBeijing Normal UniversityBeijing100875China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijing100875China
- Center for Collaboration and Innovation in Brain and Learning SciencesBeijing Normal UniversityBeijing100875China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijing100875China
- Center for Collaboration and Innovation in Brain and Learning SciencesBeijing Normal UniversityBeijing100875China
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60
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Tremblay P, Deschamps I. Structural brain aging and speech production: a surface-based brain morphometry study. Brain Struct Funct 2015; 221:3275-99. [PMID: 26336952 DOI: 10.1007/s00429-015-1100-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2014] [Accepted: 08/27/2015] [Indexed: 11/30/2022]
Abstract
While there has been a growing number of studies examining the neurofunctional correlates of speech production over the past decade, the neurostructural correlates of this immensely important human behaviour remain less well understood, despite the fact that previous studies have established links between brain structure and behaviour, including speech and language. In the present study, we thus examined, for the first time, the relationship between surface-based cortical thickness (CT) and three different behavioural indexes of sublexical speech production: response duration, reaction times and articulatory accuracy, in healthy young and older adults during the production of simple and complex meaningless sequences of syllables (e.g., /pa-pa-pa/ vs. /pa-ta-ka/). The results show that each behavioural speech measure was sensitive to the complexity of the sequences, as indicated by slower reaction times, longer response durations and decreased articulatory accuracy in both groups for the complex sequences. Older adults produced longer speech responses, particularly during the production of complex sequence. Unique age-independent and age-dependent relationships between brain structure and each of these behavioural measures were found in several cortical and subcortical regions known for their involvement in speech production, including the bilateral anterior insula, the left primary motor area, the rostral supramarginal gyrus, the right inferior frontal sulcus, the bilateral putamen and caudate, and in some region less typically associated with speech production, such as the posterior cingulate cortex.
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Affiliation(s)
- Pascale Tremblay
- Centre de Recherche de l'Institut Universitaire en Santé Mentale de Québec, Quebec, QC, Canada. .,Département de Réadaptation, Faculté de Médecine, Université Laval, Quebec, QC, Canada. .,Département de Rehabilitation, Université Laval, Office 4462, 1050 avenue de la Médecine, Quebec, QC, G1V 0A6, Canada.
| | - Isabelle Deschamps
- Centre de Recherche de l'Institut Universitaire en Santé Mentale de Québec, Quebec, QC, Canada.,Département de Réadaptation, Faculté de Médecine, Université Laval, Quebec, QC, Canada
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Qin J, Chen SG, Hu D, Zeng LL, Fan YM, Chen XP, Shen H. Predicting individual brain maturity using dynamic functional connectivity. Front Hum Neurosci 2015; 9:418. [PMID: 26236224 PMCID: PMC4503925 DOI: 10.3389/fnhum.2015.00418] [Citation(s) in RCA: 73] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2015] [Accepted: 07/06/2015] [Indexed: 01/27/2023] Open
Abstract
Neuroimaging-based functional connectivity (FC) analyses have revealed significant developmental trends in specific intrinsic connectivity networks linked to cognitive and behavioral maturation. However, knowledge of how brain functional maturation is associated with FC dynamics at rest is limited. Here, we examined age-related differences in the temporal variability of FC dynamics with data publicly released by the Nathan Kline Institute (NKI; n = 183, ages 7-30) and showed that dynamic inter-region interactions can be used to accurately predict individual brain maturity across development. Furthermore, we identified a significant age-dependent trend underlying dynamic inter-network FC, including increasing variability of the connections between the visual network, default mode network (DMN) and cerebellum as well as within the cerebellum and DMN and decreasing variability within the cerebellum and between the cerebellum and DMN as well as the cingulo-opercular network. Overall, the results suggested significant developmental changes in dynamic inter-network interaction, which may shed new light on the functional organization of typical developmental brains.
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Affiliation(s)
- Jian Qin
- College of Mechatronics and Automation, National University of Defense Technology, Changsha China
| | - Shan-Guang Chen
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing China
| | - Dewen Hu
- College of Mechatronics and Automation, National University of Defense Technology, Changsha China
| | - Ling-Li Zeng
- College of Mechatronics and Automation, National University of Defense Technology, Changsha China
| | - Yi-Ming Fan
- College of Mechatronics and Automation, National University of Defense Technology, Changsha China
| | - Xiao-Ping Chen
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing China
| | - Hui Shen
- College of Mechatronics and Automation, National University of Defense Technology, Changsha China
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62
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Perry A, Wen W, Lord A, Thalamuthu A, Roberts G, Mitchell PB, Sachdev PS, Breakspear M. The organisation of the elderly connectome. Neuroimage 2015; 114:414-26. [DOI: 10.1016/j.neuroimage.2015.04.009] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Revised: 03/23/2015] [Accepted: 04/03/2015] [Indexed: 12/13/2022] Open
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63
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Qiu A, Lee A, Tan M, Chung MK. Manifold learning on brain functional networks in aging. Med Image Anal 2015; 20:52-60. [PMID: 25476411 DOI: 10.1016/j.media.2014.10.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2013] [Revised: 08/05/2014] [Accepted: 10/21/2014] [Indexed: 01/24/2023]
Affiliation(s)
- Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore; Clinical Imaging Research Center, National University of Singapore, Singapore.
| | - Annie Lee
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Mingzhen Tan
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Moo K Chung
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA
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64
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Otte WM, van Diessen E, Paul S, Ramaswamy R, Subramanyam Rallabandi VP, Stam CJ, Roy PK. Aging alterations in whole-brain networks during adulthood mapped with the minimum spanning tree indices: the interplay of density, connectivity cost and life-time trajectory. Neuroimage 2015; 109:171-89. [PMID: 25585021 DOI: 10.1016/j.neuroimage.2015.01.011] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Revised: 01/02/2015] [Accepted: 01/05/2015] [Indexed: 01/21/2023] Open
Abstract
The organizational network changes in the human brain across the lifespan have been mapped using functional and structural connectivity data. Brain network changes provide valuable insights into the processes underlying senescence. Nonetheless, the altered network density in the elderly severely compromises the usefulness of network analysis to study the aging brain. We successfully circumvented this problem by focusing on the critical structural network backbone, using a robust tree representation. Whole-brain networks' minimum spanning trees were determined in a dataset of diffusion-weighted images from 382 healthy subjects, ranging in age from 20.2 to 86.2 years. Tree-based metrics were compared with classical network metrics. In contrast to the tree-based metrics, classical metrics were highly influenced by age-related changes in network density. Tree-based metrics showed linear and non-linear correlation across adulthood and are in close accordance with results from previous histopathological characterizations of the changes in white matter integrity in the aging brain.
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Affiliation(s)
- Willem M Otte
- Biomedical MR Imaging and Spectroscopy Group, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands; Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Eric van Diessen
- Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Subhadip Paul
- National Neuroimaging Facility, National Brain Research Centre, Manesar 122051, Haryana, India
| | - Rajiv Ramaswamy
- National Neuroimaging Facility, National Brain Research Centre, Manesar 122051, Haryana, India
| | | | - Cornelis J Stam
- Department of Clinical Neurophysiology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Prasun K Roy
- Computational Neuroscience Division, National Brain Research Centre, Manesar 122051, Haryana, India; Clinical & Translational Neuroscience Unit, National Brain Research Centre, General Hospital Campus, Gurgaon 122001, Haryana, India.
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65
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Grady CL, St-Laurent M, Burianová H. Age differences in brain activity related to unsuccessful declarative memory retrieval. Brain Res 2014; 1612:30-47. [PMID: 25541365 DOI: 10.1016/j.brainres.2014.12.036] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Revised: 12/02/2014] [Accepted: 12/16/2014] [Indexed: 11/19/2022]
Abstract
Although memory recall is known to be reduced with normal aging, little is known about the patterns of brain activity that accompany these recall failures. By assessing faulty memory, we can identify the brain regions engaged during retrieval attempts in the absence of successful memory and determine the impact of aging on this functional activity. We used functional magnetic resonance imaging to examine age differences in brain activity associated with memory failure in three memory retrieval tasks: autobiographical (AM), episodic (EM) and semantic (SM). Compared to successful memory retrieval, both age groups showed more activity when they failed to recall a memory in regions consistent with the salience network (SLN), a brain network also associated with non-memory errors. Both groups also showed strong functional coupling among SLN regions during incorrect trials and in intrinsic patterns of functional connectivity. In comparison to young adults, older adults demonstrated (1) less activity within the SLN during unsuccessful AM trials; (2) weaker intrinsic functional connectivity between SLN nodes and dorsolateral prefrontal cortex; and (3) less differentiation of SLN functional connectivity during incorrect trials across memory conditions. These results suggest that the SLN is engaged during recall failures, as it is for non-memory errors, which may be because errors in general have particular salience for adapting behavior. In older adults, the dedifferentiation of functional connectivity within the SLN across memory conditions and the reduction of functional coupling between it and prefrontal cortex may indicate poorer inter-network communication and less flexible use of cognitive control processes, either while retrieval is attempted or when monitoring takes place after retrieval has failed. This article is part of a Special Issue entitled SI: Memory & Aging.
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Affiliation(s)
- Cheryl L Grady
- Rotman Research Institute at Baycrest, 3560 Bathurst St, Toronto, Ontario, Canada M6A2E1; Departments of Psychiatry and Psychology, University of Toronto, Ontario, Canada M5S 3G3.
| | - Marie St-Laurent
- Rotman Research Institute at Baycrest, 3560 Bathurst St, Toronto, Ontario, Canada M6A2E1
| | - Hana Burianová
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland 4072, Australia
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66
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Betzel RF, Byrge L, He Y, Goñi J, Zuo XN, Sporns O. Changes in structural and functional connectivity among resting-state networks across the human lifespan. Neuroimage 2014; 102 Pt 2:345-57. [DOI: 10.1016/j.neuroimage.2014.07.067] [Citation(s) in RCA: 542] [Impact Index Per Article: 54.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2014] [Revised: 06/30/2014] [Accepted: 07/31/2014] [Indexed: 01/21/2023] Open
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67
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Cao W, Luo C, Zhu B, Zhang D, Dong L, Gong J, Gong D, He H, Tu S, Yin W, Li J, Chen H, Yao D. Resting-state functional connectivity in anterior cingulate cortex in normal aging. Front Aging Neurosci 2014; 6:280. [PMID: 25400578 PMCID: PMC4212807 DOI: 10.3389/fnagi.2014.00280] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2014] [Accepted: 09/25/2014] [Indexed: 12/19/2022] Open
Abstract
Growing evidence suggests that normal aging is associated with cognitive decline and well-maintained emotional well-being. The anterior cingulate cortex (ACC) is an important brain region involved in emotional and cognitive processing. We investigated resting-state functional connectivity (FC) of two ACC subregions in 30 healthy older adults vs. 33 healthy younger adults, by parcellating into rostral (rACC) and dorsal (dACC) ACC based on clustering of FC profiles. Compared with younger adults, older adults demonstrated greater connection between rACC and anterior insula, suggesting that older adults recruit more proximal dACC brain regions connected with insula to maintain a salient response. Older adults also demonstrated increased FC between rACC and superior temporal gyrus and inferior frontal gyrus, decreased integration between rACC and default mode, and decreased dACC-hippocampal and dACC-thalamic connectivity. These altered FCs reflected rACC and dACC reorganization, and might be related to well emotion regulation and cognitive decline in older adults. Our findings provide further insight into potential functional substrates of emotional and cognitive alterations in the aging brain.
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Affiliation(s)
- Weifang Cao
- The Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China Chengdu, China
| | - Cheng Luo
- The Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China Chengdu, China
| | - Bin Zhu
- The Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China Chengdu, China
| | - Dan Zhang
- The Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China Chengdu, China
| | - Li Dong
- The Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China Chengdu, China
| | - Jinnan Gong
- The Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China Chengdu, China
| | - Diankun Gong
- The Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China Chengdu, China
| | - Hui He
- The Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China Chengdu, China
| | - Shipeng Tu
- The Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China Chengdu, China
| | - Wenjie Yin
- Radiology Department, Chengdu First People's Hospital Chengdu, China
| | - Jianfu Li
- The Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China Chengdu, China
| | - Huafu Chen
- The Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China Chengdu, China
| | - Dezhong Yao
- The Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China Chengdu, China
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68
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Selective vulnerability related to aging in large-scale resting brain networks. PLoS One 2014; 9:e108807. [PMID: 25271846 PMCID: PMC4182761 DOI: 10.1371/journal.pone.0108807] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2014] [Accepted: 09/04/2014] [Indexed: 01/20/2023] Open
Abstract
Normal aging is associated with cognitive decline. Evidence indicates that large-scale brain networks are affected by aging; however, it has not been established whether aging has equivalent effects on specific large-scale networks. In the present study, 40 healthy subjects including 22 older (aged 60–80 years) and 18 younger (aged 22–33 years) adults underwent resting-state functional MRI scanning. Four canonical resting-state networks, including the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN) and salience network, were extracted, and the functional connectivities in these canonical networks were compared between the younger and older groups. We found distinct, disruptive alterations present in the large-scale aging-related resting brain networks: the ECN was affected the most, followed by the DAN. However, the DMN and salience networks showed limited functional connectivity disruption. The visual network served as a control and was similarly preserved in both groups. Our findings suggest that the aged brain is characterized by selective vulnerability in large-scale brain networks. These results could help improve our understanding of the mechanism of degeneration in the aging brain. Additional work is warranted to determine whether selective alterations in the intrinsic networks are related to impairments in behavioral performance.
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69
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Abstract
Resting state functional magnetic imaging (fMRI) is a novel means to examine functional brain networks. It allows investigators to identify functional networks defined by distinct, spontaneous signal fluctuations. Resting state functional connectivity (RSFC) studies examining child and adolescent psychiatric disorders are being published with increasing frequency, despite concerns about the impact of motion on findings. Here we review important RSFC findings on typical brain development and recent publications of child and adolescent psychiatric disorders. We close with a summary of the major findings and current strengths and limitations of RSFC studies.
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70
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Marsolais Y, Perlbarg V, Benali H, Joanette Y. Age-related changes in functional network connectivity associated with high levels of verbal fluency performance. Cortex 2014; 58:123-38. [PMID: 25014614 DOI: 10.1016/j.cortex.2014.05.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2013] [Revised: 04/04/2014] [Accepted: 05/15/2014] [Indexed: 02/02/2023]
Abstract
The relative preservation of receptive language abilities in older adults has been associated with adaptive changes in cerebral activation patterns, which have been suggested to be task-load dependent. However, the effects of aging and task demands on the functional integration of neural networks contributing to speech production abilities remain largely unexplored. In the present functional neuroimaging study, data-driven spatial independent component analysis and hierarchical measures of integration were used to explore age-related changes in functional connectivity among cortical areas contributing to semantic, orthographic, and automated word fluency tasks in healthy young and older adults, as well as to assess the effect of age and task demands on the functional integration of a verbal fluency network. The results showed that the functional integration of speech production networks decreases with age, while at the same time this has a marginal effect on behavioral outcomes in high-performing older adults. Moreover, a significant task demand/age interaction was found in functional connectivity within the anterior and posterior subnetworks of the verbal fluency network. These results suggest that local changes in functional integration among cortical areas supporting lexical speech production are modulated by age and task demands.
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Affiliation(s)
- Yannick Marsolais
- Department of Psychology, Université de Montréal, Montréal, Québec, Canada; Centre de recherche, Institut universitaire de gériatrie de Montréal, Montréal, Québec, Canada
| | - Vincent Perlbarg
- LINeM, Inserm, Université de Montréal, Montréal, Québec, Canada; U678, Inserm, Laboratoire d'Imagerie Fonctionnelle, Paris, France; UMR-S, U678, UPMC, Université Pierre et Marie Curie, Faculté de médecine Pitié-Salpêtrière, Paris, France
| | - Habib Benali
- LINeM, Inserm, Université de Montréal, Montréal, Québec, Canada; U678, Inserm, Laboratoire d'Imagerie Fonctionnelle, Paris, France; UMR-S, U678, UPMC, Université Pierre et Marie Curie, Faculté de médecine Pitié-Salpêtrière, Paris, France
| | - Yves Joanette
- Centre de recherche, Institut universitaire de gériatrie de Montréal, Montréal, Québec, Canada; Faculty of Medicine, Université de Montréal, Montréal, Québec, Canada.
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71
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Cao M, Wang JH, Dai ZJ, Cao XY, Jiang LL, Fan FM, Song XW, Xia MR, Shu N, Dong Q, Milham MP, Castellanos FX, Zuo XN, He Y. Topological organization of the human brain functional connectome across the lifespan. Dev Cogn Neurosci 2014; 7:76-93. [PMID: 24333927 PMCID: PMC6987957 DOI: 10.1016/j.dcn.2013.11.004] [Citation(s) in RCA: 306] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2013] [Revised: 11/18/2013] [Accepted: 11/19/2013] [Indexed: 12/14/2022] Open
Abstract
Human brain function undergoes complex transformations across the lifespan. We employed resting-state functional MRI and graph-theory approaches to systematically chart the lifespan trajectory of the topological organization of human whole-brain functional networks in 126 healthy individuals ranging in age from 7 to 85 years. Brain networks were constructed by computing Pearson's correlations in blood-oxygenation-level-dependent temporal fluctuations among 1024 parcellation units followed by graph-based network analyses. We observed that the human brain functional connectome exhibited highly preserved non-random modular and rich club organization over the entire age range studied. Further quantitative analyses revealed linear decreases in modularity and inverted-U shaped trajectories of local efficiency and rich club architecture. Regionally heterogeneous age effects were mainly located in several hubs (e.g., default network, dorsal attention regions). Finally, we observed inverse trajectories of long- and short-distance functional connections, indicating that the reorganization of connectivity concentrates and distributes the brain's functional networks. Our results demonstrate topological changes in the whole-brain functional connectome across nearly the entire human lifespan, providing insights into the neural substrates underlying individual variations in behavior and cognition. These results have important implications for disease connectomics because they provide a baseline for evaluating network impairments in age-related neuropsychiatric disorders.
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Affiliation(s)
- Miao Cao
- State Key Laboratory of Cognitive Neuroscience and Learning & International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
| | - Jin-Hui Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou 310015, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 310015, China
| | - Zheng-Jia Dai
- State Key Laboratory of Cognitive Neuroscience and Learning & International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
| | - Xiao-Yan Cao
- Key Laboratory of Behavioral Science, Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou 310015, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 310015, China
| | - Li-Li Jiang
- Key Laboratory of Behavioral Science, Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Feng-Mei Fan
- Key Laboratory of Behavioral Science, Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Psychiatry Research Center, Beijing Huilongguan Hospital, Beijing 100096, China
| | - Xiao-Wei Song
- Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Ming-Rui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning & International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA
| | - F Xavier Castellanos
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA; Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, New York University Langone Medical Center, New York, NY 10016, USA
| | - Xi-Nian Zuo
- Key Laboratory of Behavioral Science, Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
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72
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Johnson EL, Munro SE, Bunge SA. Development of Neural Networks Supporting Goal-Directed Behavior. MINNESOTA SYMPOSIA ON CHILD PSYCHOLOGY 2013. [DOI: 10.1002/9781118732373.ch2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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73
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Collin G, van den Heuvel MP. The ontogeny of the human connectome: development and dynamic changes of brain connectivity across the life span. Neuroscientist 2013; 19:616-28. [PMID: 24047610 DOI: 10.1177/1073858413503712] [Citation(s) in RCA: 111] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The human brain comprises distributed cortical regions that are structurally and functionally connected into a network that is known as the human connectome. Elaborate developmental processes starting in utero herald connectome genesis, with dynamic changes in its architecture continuing throughout life. Connectome changes during development, maturation, and aging may be governed by a set of biological rules or algorithms, forming and shaping the macroscopic architecture of the brain's wiring network. To explore the presence of developmental patterns indicative of such rules, this review considers insights from studies on the cellular and the systems level into macroscopic connectome genesis and dynamics across the life span. We observe that in parallel with synaptogenesis, macroscopic connectome formation and transformation is characterized by an initial overgrowth and subsequent elimination of cortico-cortical axonal projections. Furthermore, dynamic changes in connectome organization throughout the life span are suggested to follow an inverted U-shaped pattern, with an increasingly integrated topology during development, a plateau lasting for the majority of adulthood and an increasingly localized topology in late life. Elucidating developmental patterns in brain connectivity is crucial for our understanding of the human connectome and how it may give rise to brain function, including the occurrence of brain network disorders across the life span.
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Affiliation(s)
- Guusje Collin
- 1Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
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74
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Satterthwaite TD, Wolf DH, Ruparel K, Erus G, Elliott MA, Eickhoff SB, Gennatas ED, Jackson C, Prabhakaran K, Smith A, Hakonarson H, Verma R, Davatzikos C, Gur RE, Gur RC. Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth. Neuroimage 2013; 83:45-57. [PMID: 23792981 DOI: 10.1016/j.neuroimage.2013.06.045] [Citation(s) in RCA: 183] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2013] [Revised: 06/10/2013] [Accepted: 06/12/2013] [Indexed: 10/26/2022] Open
Abstract
Several independent studies have demonstrated that small amounts of in-scanner motion systematically bias estimates of resting-state functional connectivity. This confound is of particular importance for studies of neurodevelopment in youth because motion is strongly related to subject age during this period. Critically, the effects of motion on connectivity mimic major findings in neurodevelopmental research, specifically an age-related strengthening of distant connections and weakening of short-range connections. Here, in a sample of 780 subjects ages 8-22, we re-evaluate patterns of change in functional connectivity during adolescent development after rigorously controlling for the confounding influences of motion at both the subject and group levels. We find that motion artifact inflates both overall estimates of age-related change as well as specific distance-related changes in connectivity. When motion is more fully accounted for, the prevalence of age-related change as well as the strength of distance-related effects is substantially reduced. However, age-related changes remain highly significant. In contrast, motion artifact tends to obscure age-related changes in connectivity associated with segregation of functional brain modules; improved preprocessing techniques allow greater sensitivity to detect increased within-module connectivity occurring with development. Finally, we show that subject's age can still be accurately estimated from the multivariate pattern of functional connectivity even while controlling for motion. Taken together, these results indicate that while motion artifact has a marked and heterogeneous impact on estimates of connectivity change during adolescence, functional connectivity remains a valuable phenotype for the study of neurodevelopment.
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75
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Wu K, Taki Y, Sato K, Qi H, Kawashima R, Fukuda H. A longitudinal study of structural brain network changes with normal aging. Front Hum Neurosci 2013; 7:113. [PMID: 23565087 PMCID: PMC3615182 DOI: 10.3389/fnhum.2013.00113] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2012] [Accepted: 03/15/2013] [Indexed: 12/30/2022] Open
Abstract
The aim of this study was to investigate age-related changes in the topological organization of structural brain networks by applying a longitudinal design over 6 years. Structural brain networks were derived from measurements of regional gray matter volume and were constructed in age-specific groups from baseline and follow-up scans. The structural brain networks showed economical small-world properties, providing high global and local efficiency for parallel information processing at low connection costs. In the analysis of the global network properties, the local and global efficiency of the baseline scan were significantly lower compared to the follow-up scan. Moreover, the annual rate of change in local and global efficiency showed a positive and negative quadratic correlation with the baseline age, respectively; both curvilinear correlations peaked at approximately the age of 50. In the analysis of the regional nodal properties, significant negative correlations between the annual rate of change in nodal strength and the baseline age were found in the brain regions primarily involved in the visual and motor/control systems, whereas significant positive quadratic correlations were found in the brain regions predominately associated with the default-mode, attention, and memory systems. The results of the longitudinal study are consistent with the findings of our previous cross-sectional study: the structural brain networks develop into a fast distribution from young to middle age (approximately 50 years old) and eventually became a fast localization in the old age. Our findings elucidate the network topology of structural brain networks and its longitudinal changes, thus enhancing the understanding of the underlying physiology of normal aging in the human brain.
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Affiliation(s)
- Kai Wu
- Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University Sendai, Japan ; Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology Guangzhou, China
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