A longitudinal study of structural brain network changes with normal aging

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.

[1]  Alan C. Evans,et al.  Developmental changes in organization of structural brain networks. , 2013, Cerebral cortex.

[2]  Alan C. Evans,et al.  Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. , 2007, Cerebral cortex.

[3]  Yong Liu,et al.  Disrupted Small-World Brain Networks in Moderate Alzheimer's Disease: A Resting-State fMRI Study , 2012, PloS one.

[4]  S. Petersen,et al.  Concepts and principles in the analysis of brain networks , 2011, Annals of the New York Academy of Sciences.

[5]  Daniel L. Kimmel,et al.  Neuroimaging insights into network-based neurodegeneration. , 2012, Current opinion in neurology.

[6]  K. Worsley,et al.  Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load. , 2009, Brain : a journal of neurology.

[7]  Suzanne E. Welcome,et al.  Mapping cortical change across the human life span , 2003, Nature Neuroscience.

[8]  S M Hadi Hosseini,et al.  Altered small-world properties of gray matter networks in breast cancer , 2012, BMC Neurology.

[9]  Mingrui Xia,et al.  Magnetic Resonance Imaging and Graph Theoretical Analysis of Complex Brain Networks in Neuropsychiatric Disorders , 2011, Brain Connect..

[10]  Yong He,et al.  Disrupted small-world networks in schizophrenia. , 2008, Brain : a journal of neurology.

[11]  Karl J. Friston,et al.  Structural Covariance in the Human Cortex , 2005, The Journal of Neuroscience.

[12]  M. Catani,et al.  The rises and falls of disconnection syndromes. , 2005, Brain : a journal of neurology.

[13]  Edward T. Bullmore,et al.  Efficiency and Cost of Economical Brain Functional Networks , 2007, PLoS Comput. Biol..

[14]  Yong He,et al.  Discrete Neuroanatomical Networks Are Associated with Specific Cognitive Abilities in Old Age , 2011, The Journal of Neuroscience.

[15]  Phillip J. Holcomb,et al.  The impact of visual acuity on age-related differences in neural markers of early visual processing , 2013, NeuroImage.

[16]  Willem Jan van der Veen,et al.  Age and gender , 2004 .

[17]  D. Shen,et al.  Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features , 2012, Neurobiology of Aging.

[18]  S. Kinomura,et al.  Correlation between degree of white matter hyperintensities and global gray matter volume decline rate , 2011, Neuroradiology.

[19]  Mary E. Meyerand,et al.  Support vector machine classification and characterization of age-related reorganization of functional brain networks , 2012, NeuroImage.

[20]  C. Owsley.  Aging and vision , 2011, Vision Research.

[21]  Liang Wang,et al.  Parcellation‐dependent small‐world brain functional networks: A resting‐state fMRI study , 2009, Human brain mapping.

[22]  V Latora,et al.  Efficient behavior of small-world networks. , 2001, Physical review letters.

[23]  Y. Stern,et al.  Age-Related Changes in Task Related Functional Network Connectivity , 2012, PloS one.

[24]  T. Elbert,et al.  Development of large-scale functional networks over the lifespan , 2012, Neurobiology of Aging.

[25]  Yong He,et al.  Topologically Convergent and Divergent Structural Connectivity Patterns between Patients with Remitted Geriatric Depression and Amnestic Mild Cognitive Impairment , 2012, The Journal of Neuroscience.

[26]  Edward T. Bullmore,et al.  Age-related changes in modular organization of human brain functional networks , 2009, NeuroImage.

[27]  Olaf Sporns,et al.  THE HUMAN CONNECTOME: A COMPLEX NETWORK , 2011, Schizophrenia Research.

[28]  R. N. Spreng,et al.  Default network modulation and large-scale network interactivity in healthy young and old adults. , 2012, Cerebral cortex.

[29]  Yong He,et al.  Graph theoretical modeling of brain connectivity. , 2010, Current opinion in neurology.

[30]  Jean-François Gagnon,et al.  The impact of aging on gray matter structural covariance networks , 2012, NeuroImage.

[31]  C. Rice,et al.  Age‐related changes in motor unit function , 1997, Muscle & nerve.

[32]  Yong He,et al.  Disrupted Functional Brain Connectome in Individuals at Risk for Alzheimer's Disease , 2013, Biological Psychiatry.

[33]  Janice E. Knoefel,et al.  Clinical neurology of aging, 2nd ed. , 1994 .

[34]  E. Bullmore,et al.  A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs , 2006, The Journal of Neuroscience.

[35]  Danielle S Bassett,et al.  Cognitive fitness of cost-efficient brain functional networks , 2009, Proceedings of the National Academy of Sciences.

[36]  Yong He,et al.  Graph theoretical analysis of human brain structural networks , 2011, Reviews in the neurosciences.

[37]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[38]  C. J. Stam,et al.  Functional connectivity patterns of human magnetoencephalographic recordings: a ‘small-world’ network? , 2004, Neuroscience Letters.

[39]  Liang Wang,et al.  Altered small‐world brain functional networks in children with attention‐deficit/hyperactivity disorder , 2009, Human brain mapping.

[40]  Kazunori Sato,et al.  Neuroanatomical database of normal Japanese brains , 2003, Neural Networks.

[41]  Alan C. Evans,et al.  Structural Insights into Aberrant Topological Patterns of Large-Scale Cortical Networks in Alzheimer's Disease , 2008, The Journal of Neuroscience.

[42]  Janice E. Knoefel,et al.  Clinical neurology of aging , 1985 .

[43]  H. Akaike.  A new look at the statistical model identification , 1974 .

[44]  Joseph A Maldjian,et al.  Precentral gyrus discrepancy in electronic versions of the Talairach atlas , 2004, NeuroImage.

[45]  G. Bartzokis,et al.  Age-related changes in frontal and temporal lobe volumes in men: a magnetic resonance imaging study. , 2001, Archives of general psychiatry.

[46]  Olaf Sporns,et al.  From simple graphs to the connectome: Networks in neuroimaging , 2012, NeuroImage.

[47]  Hiroshi Fukuda,et al.  Age‐related changes in topological organization of structural brain networks in healthy individuals , 2012, Human brain mapping.

[48]  Denise C. Park,et al.  The adaptive brain: aging and neurocognitive scaffolding. , 2009, Annual review of psychology.

[49]  Alan C. Evans,et al.  Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex , 2012, NeuroImage.

[50]  A. Meyer-Lindenberg,et al.  5-HTTLPR polymorphism impacts human cingulate-amygdala interactions: a genetic susceptibility mechanism for depression , 2005, Nature Neuroscience.

[51]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[52]  Hiroshi Fukuda,et al.  A longitudinal study of gray matter volume decline with age and modifying factors , 2011, Neurobiology of Aging.

[53]  Benjamin Thyreau,et al.  A longitudinal study of the relationship between personality traits and the annual rate of volume changes in regional gray matter in healthy adults , 2013, Human brain mapping.

[54]  R. Krampe.  Aging, expertise and fine motor movement , 2002, Neuroscience & Biobehavioral Reviews.

[55]  Linda Geerligs,et al.  Reduced specificity of functional connectivity in the aging brain during task performance , 2014, Human brain mapping.

[56]  Hiroshi Fukuda,et al.  The Overlapping Community Structure of Structural Brain Network in Young Healthy Individuals , 2011, PloS one.

[57]  K. Sneppen,et al.  Specificity and Stability in Topology of Protein Networks , 2002, Science.

[58]  Yong He,et al.  Mapping the Alzheimer’s Brain with Connectomics , 2012, Front. Psychiatry.

[59]  Benjamin Thyreau,et al.  A longitudinal study of age‐ and gender‐related annual rate of volume changes in regional gray matter in healthy adults , 2013, Human brain mapping.

[60]  Edward T. Bullmore,et al.  SYSTEMS NEUROSCIENCE Original Research Article , 2009 .

[61]  C. Grady.  The cognitive neuroscience of ageing , 2012, Nature Reviews Neuroscience.

[62]  C. Stam,et al.  Small-world networks and functional connectivity in Alzheimer's disease. , 2006, Cerebral cortex.

[63]  M. Hallett,et al.  Aging influence on functional connectivity of the motor network in the resting state , 2007, Neuroscience Letters.

[64]  J L Lancaster,et al.  Automated Talairach Atlas labels for functional brain mapping , 2000, Human brain mapping.

[65]  E. Bullmore,et al.  Human brain networks in health and disease , 2009, Current opinion in neurology.

[66]  John Suckling,et al.  Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[67]  Olaf Sporns,et al.  The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..

[68]  Shanbao Tong,et al.  Reorganization of Brain Networks in Aging and Age-related Diseases. , 2012, Aging and disease.

[69]  Yong He,et al.  Age-related changes in topological patterns of large-scale brain functional networks during memory encoding and recognition , 2010, NeuroImage.

[70]  Gretel Sanabria-Diaz,et al.  Surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks , 2010, NeuroImage.

[71]  J. Gabrieli,et al.  Insights into the ageing mind: a view from cognitive neuroscience , 2004, Nature Reviews Neuroscience.

[72]  B. Gandevia,et al.  DECLARATION OF HELSINKI. , 1964, The Medical journal of Australia.

[73]  Danielle S Bassett,et al.  Brain graphs: graphical models of the human brain connectome. , 2011, Annual review of clinical psychology.

[74]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[75]  Paul J. Laurienti,et al.  An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets , 2003, NeuroImage.

[76]  E. Bullmore,et al.  Hierarchical Organization of Human Cortical Networks in Health and Schizophrenia , 2008, The Journal of Neuroscience.

[77]  Yong He,et al.  Changing topological patterns in normal aging using large-scale structural networks , 2012, Neurobiology of Aging.

[78]  P. D. Spear,et al.  Neural bases of visual deficits during aging , 1993, Vision Research.

[79]  Alan C. Evans,et al.  Age- and Gender-Related Differences in the Cortical Anatomical Network , 2009, The Journal of Neuroscience.

[80]  Justin L. Vincent,et al.  Disruption of Large-Scale Brain Systems in Advanced Aging , 2007, Neuron.

[81]  D. Hu,et al.  Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI , 2012, PloS one.

[82]  F. Schmitt,et al.  Critical decline in fine motor hand movements in human aging. , 1999, Neurology.

[83]  Edward T. Bullmore,et al.  A simple view of the brain through a frequency-specific functional connectivity measure , 2008, NeuroImage.

[84]  Kazunori Sato,et al.  Correlation between baseline regional gray matter volume and global gray matter volume decline rate , 2011, NeuroImage.

[85]  M. Mesulam,et al.  From sensation to cognition. , 1998, Brain : a journal of neurology.

[86]  Olaf Sporns,et al.  The small world of the cerebral cortex , 2007, Neuroinformatics.

[87]  Jonas Persson,et al.  Structure-function correlates of cognitive decline in aging. , 2006, Cerebral cortex.

[88]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[89]  M. Torrens.  Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268 , 1990 .

[90]  Yong He,et al.  Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data , 2011, PloS one.

[91]  Edward T. Bullmore,et al.  Whole-brain anatomical networks: Does the choice of nodes matter? , 2010, NeuroImage.

[92]  C. Jack,et al.  Longitudinal MRI atrophy biomarkers: Relationship to conversion in the ADNI cohort , 2010, Neurobiology of Aging.

[93]  Kaustubh Supekar,et al.  Systems Neuroscience Review Article , 2011 .

[94]  Jonathan D. Power,et al.  Functional Brain Networks Develop from a “Local to Distributed” Organization , 2009, PLoS Comput. Biol..