Title: Functional architecture of the aging brain

The intrinsic functional connectome can reveal how a lifetime of learning and lived experience is represented in the functional architecture of the aging brain. We investigated whether network dedifferentiation, a hallmark of brain aging, reflects a global shift in network dynamics, or comprises network-specific changes that reflect the changing landscape of aging cognition. We implemented a novel multi-faceted strategy involving multi-echo fMRI acquisition and de-noising, individualized cortical parcellation, and multivariate (gradient and edge-level) functional connectivity methods. Twenty minutes of resting-state fMRI data and cognitive assessments were collected in younger (n=181) and older (n=120) adults. Dimensionality in the BOLD signal was lower for older adults, consistent with global network dedifferentiation. Functional connectivity gradients were largely age-invariant. In contrast, edge-level connectivity showed widespread changes with age, revealing discrete, network-specific dedifferentiation patterns. Visual and somatosensory regions were more integrated within the functional connectome; default and frontoparietal regions showed greater coupling; and the dorsal attention network was less differentiated from transmodal regions. Associations with cognition suggest that the formation and preservation of integrated, large-scale brain networks supports complex cognitive abilities. However, into older adulthood, the connectome is dominated by large-scale network disintegration, global dedifferentiation and network-specific dedifferentiation associated with age-related cognitive change.

[1]  Charles J. Lynch,et al.  Improving precision functional mapping routines with multi-echo fMRI , 2021, Current Opinion in Behavioral Sciences.

[2]  Jonathan D. Power,et al.  Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI , 2020, Cell reports.

[3]  David J. Madden,et al.  Influence of structural and functional brain connectivity on age-related differences in fluid cognition , 2020, Neurobiology of Aging.

[4]  Boris C. Bernhardt,et al.  Dispersion of functional gradients across the adult lifespan , 2020, NeuroImage.

[5]  P. Thompson,et al.  Atlas55+: Brain Functional Atlas of Resting-State Networks for Late Adulthood , 2020, bioRxiv.

[6]  Reinder Vos de Wael,et al.  Macroscale connectome manifold expansion in adolescence , 2020 .

[7]  Danielle S. Bassett,et al.  Multimodal network dynamics underpinning working memory , 2020, Nature Communications.

[8]  Katrin Amunts,et al.  Functional network reorganization in older adults: Graph-theoretical analyses of age, cognition and sex , 2020, NeuroImage.

[9]  Lutz Jäncke,et al.  Functional dedifferentiation of associative resting state networks in older adults – A longitudinal study , 2020, NeuroImage.

[10]  R. Henson,et al.  The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors , 2019, bioRxiv.

[11]  Daniel S. Margulies,et al.  BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets , 2019, Communications Biology.

[12]  R Nathan Spreng,et al.  Take a deep breath: Multiecho fMRI denoising effectively removes head motion artifacts, obviating the need for global signal regression , 2019, Proceedings of the National Academy of Sciences.

[13]  Xi-Nian Zuo,et al.  Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion. , 2019, Cerebral cortex.

[14]  Wiro J. Niessen,et al.  Patterns of functional connectivity in an aging population: The Rotterdam Study , 2019, NeuroImage.

[15]  Bratislav Misic,et al.  Loneliness and meaning in life are reflected in the intrinsic network architecture of the brain , 2019, Social cognitive and affective neuroscience.

[16]  R. N. Spreng,et al.  The Shifting Architecture of Cognition and Brain Function in Older Adulthood , 2019, Perspectives on psychological science : a journal of the Association for Psychological Science.

[17]  Mert R. Sabuncu,et al.  Global signal regression strengthens association between resting-state functional connectivity and behavior , 2019, NeuroImage.

[18]  John A. E. Anderson,et al.  Intrinsic neurocognitive network connectivity differences between normal aging and mild cognitive impairment are associated with cognitive status and age , 2019, Neurobiology of Aging.

[19]  B. Yeo,et al.  Imaging-based parcellations of the human brain , 2018, Nature Reviews Neuroscience.

[20]  Haochang Shou,et al.  On testing for spatial correspondence between maps of human brain structure and function , 2018, NeuroImage.

[21]  Reinder Vos de Wael,et al.  Atypical functional connectome hierarchy in autism , 2018, Nature Communications.

[22]  E. Leibenluft,et al.  The Integration of Functional Brain Activity from Adolescence to Adulthood , 2018, The Journal of Neuroscience.

[23]  Jonathan D. Power,et al.  Ridding fMRI data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data , 2018, Proceedings of the National Academy of Sciences.

[24]  A. Dagher,et al.  Dopamine Signaling Modulates the Stability and Integration of Intrinsic Brain Networks , 2018, bioRxiv.

[25]  Stephen J. Gotts,et al.  Brain networks, dimensionality, and global signal averaging in resting-state fMRI: Hierarchical network structure results in low-dimensional spatiotemporal dynamics , 2017, NeuroImage.

[26]  Jessica S. Damoiseaux,et al.  Effects of aging on functional and structural brain connectivity , 2017, NeuroImage.

[27]  Michael W. Cole,et al.  Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks , 2017, Proceedings of the National Academy of Sciences.

[28]  S. Choi,et al.  Individual parcellation of resting fMRI with a group functional connectivity prior , 2017, NeuroImage.

[29]  Rodrigo M. Braga,et al.  Parallel Interdigitated Distributed Networks within the Individual Estimated by Intrinsic Functional Connectivity , 2017, Neuron.

[30]  Benedikt A. Poser,et al.  Multi-echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals , 2017, NeuroImage.

[31]  Ben D. Fulcher,et al.  An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI , 2017, NeuroImage.

[32]  Evan M. Gordon,et al.  Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI , 2017, bioRxiv.

[33]  Mert R. Sabuncu,et al.  Heritability analysis with repeat measurements and its application to resting-state functional connectivity , 2017, Proceedings of the National Academy of Sciences.

[34]  Evan M. Gordon,et al.  Individual-specific features of brain systems identified with resting state functional correlations , 2017, NeuroImage.

[35]  Elizabeth Jefferies,et al.  Situating the default-mode network along a principal gradient of macroscale cortical organization , 2016, Proceedings of the National Academy of Sciences.

[36]  R. N. Spreng,et al.  Attenuated anticorrelation between the default and dorsal attention networks with aging: evidence from task and rest , 2016, Neurobiology of Aging.

[37]  C. Grady,et al.  Age differences in the functional interactions among the default, frontoparietal control, and dorsal attention networks , 2016, Neurobiology of Aging.

[38]  Vladimir Batagelj,et al.  Analysis and visualization of large networks with program package Pajek , 2016, Complex Adapt. Syst. Model..

[39]  R. Buckner,et al.  Parcellating Cortical Functional Networks in Individuals , 2015, Nature Neuroscience.

[40]  Evan M. Gordon,et al.  Functional System and Areal Organization of a Highly Sampled Individual Human Brain , 2015, Neuron.

[41]  N. Maurits,et al.  A Brain-Wide Study of Age-Related Changes in Functional Connectivity. , 2015, Cerebral cortex.

[42]  Todd W. Thompson,et al.  Resting-state anticorrelations between medial and lateral prefrontal cortex: Association with working memory, aging, and individual differences , 2014, Cortex.

[43]  Anna Rieckmann,et al.  Neuromodulation and aging: implications of aging neuronal gain control on cognition , 2014, Current Opinion in Neurobiology.

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

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

[46]  Iroise Dumontheil,et al.  Development of abstract thinking during childhood and adolescence: The role of rostrolateral prefrontal cortex , 2014, Developmental Cognitive Neuroscience.

[47]  R. N. Spreng,et al.  The default network and self‐generated thought: component processes, dynamic control, and clinical relevance , 2014, Annals of the New York Academy of Sciences.

[48]  R Nathan Spreng,et al.  Resting-state functional connectivity MRI reveals active processes central to cognition. , 2014, Wiley interdisciplinary reviews. Cognitive science.

[49]  Kathleen Veizel,et al.  Wechsler Memory Scale–Fourth Edition , 2014 .

[50]  R. N. Spreng,et al.  Structural Covariance of the Default Network in Healthy and Pathological Aging , 2013, The Journal of Neuroscience.

[51]  Noah D. Brenowitz,et al.  Integrated strategy for improving functional connectivity mapping using multiecho fMRI , 2013, Proceedings of the National Academy of Sciences.

[52]  Hang Joon Jo,et al.  Correcting Brain-Wide Correlation Differences in Resting-State FMRI , 2013, Brain Connect..

[53]  N. Fox,et al.  NIH Toolbox for Assessment of Neurological and Behavioral Function , 2013, Neurology.

[54]  A. McIntosh,et al.  Multivariate statistical analyses for neuroimaging data. , 2013, Annual review of psychology.

[55]  Stephanie M. Carlson,et al.  Hot and Cool Executive Function in Childhood and Adolescence: Development and Plasticity , 2012 .

[56]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

[57]  Mark D'Esposito,et al.  Focal Brain Lesions to Critical Locations Cause Widespread Disruption of the Modular Organization of the Brain , 2012, Journal of Cognitive Neuroscience.

[58]  Hang Joon Jo,et al.  Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression , 2012, Brain Connect..

[59]  Wen-Ming Luh,et al.  Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI , 2012, NeuroImage.

[60]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[61]  Bryan R. Conroy,et al.  A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex , 2011, Neuron.

[62]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[63]  Anthony Randal McIntosh,et al.  Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review , 2011, NeuroImage.

[64]  Adam Gazzaley,et al.  In Brief , 2011, Nature Reviews Neuroscience.

[65]  Hang Joon Jo,et al.  Mapping sources of correlation in resting state FMRI, with artifact detection and removal , 2010, NeuroImage.

[66]  Bruce Fischl,et al.  Accurate and robust brain image alignment using boundary-based registration , 2009, NeuroImage.

[67]  KJ Worsley,et al.  SurfStat: A Matlab toolbox for the statistical analysis of univariate and multivariate surface and volumetric data using linear mixed effects models and random field theory , 2009, NeuroImage.

[68]  Emilio Ferrer,et al.  Fluid Reasoning and the Developing Brain , 2009, Frontiers in neuroscience.

[69]  Mark D'Esposito,et al.  Group comparisons: imaging the aging brain. , 2008, Social cognitive and affective neuroscience.

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

[71]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[72]  Robert C. Welsh,et al.  Decreased neural specialization in old adults on a working memory task , 2006, Neuroreport.

[73]  Benjamin J. Shannon,et al.  Molecular, Structural, and Functional Characterization of Alzheimer's Disease: Evidence for a Relationship between Default Activity, Amyloid, and Memory , 2005, The Journal of Neuroscience.

[74]  Ann B. Lee,et al.  Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[75]  Anthony Randal McIntosh,et al.  Partial least squares analysis of neuroimaging data: applications and advances , 2004, NeuroImage.

[76]  Abraham Z. Snyder,et al.  A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume , 2004, NeuroImage.

[77]  M. Greicius,et al.  Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI , 2004, Proc. Natl. Acad. Sci. USA.

[78]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[79]  Peter C Austin,et al.  A brief note on overlapping confidence intervals. , 2002, Journal of vascular surgery.

[80]  T. Ernst,et al.  Biexponential modeling of multigradient‐echo MRI data of the brain , 2001, Magnetic resonance in medicine.

[81]  G H Glover,et al.  Image‐based method for retrospective correction of physiological motion effects in fMRI: RETROICOR , 2000, Magnetic resonance in medicine.

[82]  Robert Tibshirani,et al.  Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy , 1986 .

[83]  P. Carpenter,et al.  Individual differences in working memory and reading , 1980 .

[84]  S. Folstein,et al.  “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician , 1975 .

[85]  R. Reitan.  Validity of the Trail Making Test as an Indicator of Organic Brain Damage , 1958 .

[86]  R. N. Spreng,et al.  Dual-retrieval models and neurocognitive impairment. , 2014, Journal of experimental psychology. Learning, memory, and cognition.

[87]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.

[88]  J. Kaas.  The organization of neocortex in mammals: implications for theories of brain function. , 1987, Annual review of psychology.

[89]  V. Leirer,et al.  Development and validation of a geriatric depression screening scale: a preliminary report. , 1982, Journal of psychiatric research.