Structure–function multi‐scale connectomics reveals a major role of the fronto‐striato‐thalamic circuit in brain aging

Physiological aging affects brain structure and function impacting morphology, connectivity, and performance. However, whether some brain connectivity metrics might reflect the age of an individual is still unclear. Here, we collected brain images from healthy participants (N = 155) ranging from 10 to 80 years to build functional (resting state) and structural (tractography) connectivity matrices, both data sets combined to obtain different connectivity features. We then calculated the brain connectome age—an age estimator resulting from a multi‐scale methodology applied to the structure–function connectome, and compared it to the chronological age (ChA). Our results were twofold. First, we found that aging widely affects the connectivity of multiple structures, such as anterior cingulate and medial prefrontal cortices, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus, and temporal pole. Second, we found that the connectivity between basal ganglia and thalamus to frontal areas, also known as the fronto‐striato‐thalamic (FST) circuit, makes the major contribution to age estimation. In conclusion, our results highlight the key role played by the FST circuit in the process of healthy aging. Notably, the same methodology can be generally applied to identify the structural–functional connectivity patterns correlating to other biomarkers than ChA.

[1]  P. Uhlhaas,et al.  Preferential Detachment During Human Brain Development: Age- and Sex-Specific Structural Connectivity in Diffusion Tensor Imaging (DTI) Data , 2013, Cerebral cortex.

[2]  G. Lautenschlager,et al.  Mediators of long-term memory performance across the life span. , 1996, Psychology and aging.

[3]  Christos Davatzikos,et al.  Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders. , 2014, Schizophrenia bulletin.

[4]  C. Jack,et al.  Alzheimer's Disease Neuroimaging Initiative , 2008 .

[5]  Joanna M. Wardlaw,et al.  Brain volumetric changes and cognitive ageing during the eighth decade of life , 2015, Human brain mapping.

[6]  Sebastiano Stramaglia,et al.  Extreme brain events: Higher-order statistics of brain resting activity and its relation with structural connectivity , 2015 .

[7]  Stuart J. Ritchie,et al.  Brain age predicts mortality , 2017, Molecular Psychiatry.

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

[9]  Mark J. West,et al.  Regionally specific loss of neurons in the aging human hippocampus , 1993, Neurobiology of Aging.

[10]  R. Cameron Craddock,et al.  A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics , 2013, NeuroImage.

[11]  E. Bullmore,et al.  The hubs of the human connectome are generally implicated in the anatomy of brain disorders , 2014, Brain : a journal of neurology.

[12]  D. Head,et al.  Selective aging of the human cerebral cortex observed in vivo: differential vulnerability of the prefrontal gray matter. , 1997, Cerebral cortex.

[13]  S. Resnick,et al.  Longitudinal Magnetic Resonance Imaging Studies of Older Adults: A Shrinking Brain , 2003, The Journal of Neuroscience.

[14]  T. Bartsch,et al.  The hippocampus in aging and disease: From plasticity to vulnerability , 2015, Neuroscience.

[15]  Mary E. Meyerand,et al.  Age-Related Reorganizational Changes in Modularity and Functional Connectivity of Human Brain Networks , 2014, Brain Connect..

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

[17]  R. Kondratov,et al.  The circadian clock and pathology of the ageing brain , 2012, Nature Reviews Neuroscience.

[18]  Daniele Marinazzo,et al.  Synergy, redundancy and unnormalized Granger causality , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[19]  Marcus Kaiser,et al.  Predicting age across human lifespan based on structural connectivity from diffusion tensor imaging , 2014, 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings.

[20]  Reisa A. Sperling,et al.  The association between tau PET and retrospective cortical thinning in clinically normal elderly , 2017, NeuroImage.

[21]  Luca Faes,et al.  Synergetic and redundant information flow detected by unnormalized Granger causality: application to resting state fMRI. , 2015, IEEE transactions on bio-medical engineering.

[22]  Joaquín Goñi,et al.  Changes in structural and functional connectivity among resting-state networks across the human lifespan , 2014, NeuroImage.

[23]  P. V. van Zijl,et al.  Three‐dimensional tracking of axonal projections in the brain by magnetic resonance imaging , 1999, Annals of neurology.

[24]  M. A. Muñoz,et al.  A novel brain partition highlights the modular skeleton shared by structure and function , 2014, Scientific Reports.

[25]  Y. Stern,et al.  Differences between chronological and brain age are related to education and self-reported physical activity , 2016, Neurobiology of Aging.

[26]  Danielle van Westen,et al.  Diffusion tensor imaging and tractography of the white matter in normal aging: The rate-of-change differs between segments within tracts. , 2018, Magnetic resonance imaging.

[27]  O. Sporns,et al.  White matter maturation reshapes structural connectivity in the late developing human brain , 2010, Proceedings of the National Academy of Sciences.

[28]  Daniele Marinazzo,et al.  Enhanced prefrontal functional–structural networks to support postural control deficits after traumatic brain injury in a pediatric population , 2017, Network Neuroscience.

[29]  Joseph Y. Nashed,et al.  Increased functional connectivity after stroke correlates with behavioral scores in non-human primate model , 2017, Scientific Reports.

[30]  S. Swinnen,et al.  Subcortical volume analysis in traumatic brain injury: The importance of the fronto-striato-thalamic circuit in task switching , 2014, Cortex.

[31]  Stefan Klöppel,et al.  BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer’s Disease , 2013, PloS one.

[32]  C. Fennema-Notestine,et al.  Effects of age on tissues and regions of the cerebrum and cerebellum , 2001, Neurobiology of Aging.

[33]  Stephen H. D. Jackson,et al.  Ageing, genes, environment and epigenetics: what twin studies tell us now, and in the future. , 2012, Age and ageing.

[34]  Wiepke Cahn,et al.  Accelerated Brain Aging in Schizophrenia: A Longitudinal Pattern Recognition Study. , 2016, The American journal of psychiatry.

[35]  D. Long Networks of the Brain , 2011 .

[36]  S. Swinnen,et al.  Task switching in traumatic brain injury relates to cortico‐subcortical integrity , 2014, Human brain mapping.

[37]  Roberto Cabeza,et al.  Assessing the effects of age on long white matter tracts using diffusion tensor tractography , 2009, NeuroImage.

[38]  K. Hasan,et al.  Predictive classification of pediatric bipolar disorder using atlas-based diffusion weighted imaging and support vector machines , 2015, Psychiatry Research: Neuroimaging.

[39]  Sebastiano Stramaglia,et al.  Lagged and instantaneous dynamical influences related to brain structural connectivity , 2015, Front. Psychol..

[40]  S. Swinnen,et al.  Functional Brain Activation Associated with Inhibitory Control Deficits in Older Adults. , 2016, Cerebral cortex.

[41]  Andrea Klug,et al.  The Hippocampus Book , 2016 .

[42]  M. O’Sullivan,et al.  Activate your online subscription , 2001, Neurology.

[43]  M. Pellicoro,et al.  Conserved Ising Model on the Human Connectome , 2015, 1509.02697.

[44]  T. S. Monteiro,et al.  Age-Related Declines in Motor Performance are Associated With Decreased Segregation of Large-Scale Resting State Brain Networks , 2018, Cerebral cortex.

[45]  A Pfefferbaum,et al.  Neuroradiological characterization of normal adult ageing. , 2007, The British journal of radiology.

[46]  Douglas E. Vaughan,et al.  Molecular and physiological manifestations and measurement of aging in humans , 2017, Aging cell.

[47]  Daniel S. Margulies,et al.  Predicting brain-age from multimodal imaging data captures cognitive impairment , 2016, NeuroImage.

[48]  Robert Leech,et al.  Prediction of brain age suggests accelerated atrophy after traumatic brain injury , 2015, Annals of neurology.

[49]  Jonathan D. Power,et al.  Prediction of Individual Brain Maturity Using fMRI , 2010, Science.

[50]  S. Swinnen,et al.  Disturbed cortico‐subcortical interactions during motor task switching in traumatic brain injury , 2013, Human brain mapping.

[51]  S. Rombouts,et al.  Reduced resting-state brain activity in the "default network" in normal aging. , 2008, Cerebral cortex.

[52]  S. Swinnen,et al.  Aging and Inhibitory Control of Action: Cortico-Subthalamic Connection Strength Predicts Stopping Performance , 2012, The Journal of Neuroscience.

[53]  David J. Sharp,et al.  Increased brain-predicted aging in treated HIV disease , 2017, Neurology.

[54]  S. Swinnen,et al.  Reduced basal ganglia function when elderly switch between coordinated movement patterns. , 2010, Cerebral cortex.

[55]  Stefan Klöppel,et al.  Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters , 2010, NeuroImage.

[56]  T. S. Monteiro,et al.  Anatomy of Subcortical Structures Predicts Age-Related Differences in Skill Acquisition , 2018, Cerebral cortex.

[57]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[58]  Kelly A. Durbin,et al.  Aging and the effects of emotion on cognition: Implications for psychological interventions for depression and anxiety. , 2015, PsyCh journal.

[59]  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.

[60]  Denise C. Park,et al.  Decreased segregation of brain systems across the healthy adult lifespan , 2014, Proceedings of the National Academy of Sciences.

[61]  R Cameron Craddock,et al.  A whole brain fMRI atlas generated via spatially constrained spectral clustering , 2012, Human brain mapping.

[62]  Daniele Marinazzo,et al.  Information Transfer and Criticality in the Ising Model on the Human Connectome , 2014, PloS one.

[63]  G. Edelman,et al.  A measure for brain complexity: relating functional segregation and integration in the nervous system. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[64]  M. Loeb,et al.  Aging, frailty and age-related diseases , 2010, Biogerontology.

[65]  J. Morris,et al.  Tangles and plaques in nondemented aging and “preclinical” Alzheimer's disease , 1999, Annals of neurology.

[66]  Weiguang Zhang,et al.  Common methods of biological age estimation , 2017, Clinical interventions in aging.

[67]  Daniela M. Witten,et al.  An Introduction to Statistical Learning: with Applications in R , 2013 .

[68]  Christian Gaser,et al.  Gender-specific impact of personal health parameters on individual brain aging in cognitively unimpaired elderly subjects , 2014, Front. Aging Neurosci..

[69]  R. Cabeza Hemispheric asymmetry reduction in older adults: the HAROLD model. , 2002, Psychology and aging.

[70]  P. Bonifazi,et al.  Group-Level Progressive Alterations in Brain Connectivity Patterns Revealed by Diffusion-Tensor Brain Networks across Severity Stages in Alzheimer’s Disease , 2017, bioRxiv.

[71]  Alan Peters,et al.  Structural changes that occur during normal aging of primate cerebral hemispheres , 2002, Neuroscience & Biobehavioral Reviews.

[72]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[73]  S. Swinnen,et al.  A proactive task set influences how response inhibition is implemented in the basal ganglia , 2016, Human brain mapping.

[74]  Daniele Marinazzo,et al.  Consensus clustering approach to group brain connectivity matrices , 2016, Network Neuroscience.

[75]  Rosa Cossart,et al.  Dissecting functional connectivity of neuronal microcircuits: experimental and theoretical insights , 2011, Trends in Neurosciences.

[76]  Eileen Luders,et al.  Estimating brain age using high-resolution pattern recognition: Younger brains in long-term meditation practitioners , 2016, NeuroImage.

[77]  A. Dale,et al.  Thinning of the cerebral cortex in aging. , 2004, Cerebral cortex.

[78]  J. Townsend,et al.  Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. , 2000, Radiology.

[79]  S. Stramaglia,et al.  Patient‐specific computational modeling of cortical spreading depression via diffusion tensor imaging , 2017, International journal for numerical methods in biomedical engineering.

[80]  J. Morrison,et al.  Life and death of neurons in the aging brain. , 1997, Science.

[81]  Joelle Zimmermann,et al.  Structural architecture supports functional organization in the human aging brain at a regionwise and network level , 2016, Human brain mapping.

[82]  Jan Sijbers,et al.  Subcortical volumetric changes across the adult lifespan: Subregional thalamic atrophy accounts for age-related sensorimotor performance declines , 2015, Cortex.

[83]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[84]  DiezIbai,et al.  Information Flow Between Resting-State Networks , 2015 .

[85]  R. Sutherland,et al.  The aging hippocampus: cognitive, biochemical and structural findings. , 2003, Cerebral cortex.

[86]  V. Haughton,et al.  Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data. , 2001, AJNR. American journal of neuroradiology.

[87]  Giovanni Montana,et al.  Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker , 2016, NeuroImage.

[88]  Markus H. Sneve,et al.  Relationship between structural and functional connectivity change across the adult lifespan: A longitudinal investigation , 2017 .

[89]  Wiro J Niessen,et al.  White Matter Degeneration with Aging: Longitudinal Diffusion MR Imaging Analysis. , 2016, Radiology.

[90]  Gagan S. Wig,et al.  Resting-State Network Topology Differentiates Task Signals across the Adult Life Span , 2017, The Journal of Neuroscience.

[91]  Erlend Hodneland,et al.  Cortico-striatal connectivity and cognition in normal aging: A combined DTI and resting state fMRI study , 2011, NeuroImage.

[92]  J. M. Cortes,et al.  Variational Bayesian localization of EEG sources with generalized Gaussian priors , 2012 .