Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest

Growing evidence has shown that brain activity at rest slowly wanders through a repertoire of different states, where whole-brain functional connectivity (FC) temporarily settles into distinct FC patterns. Nevertheless, the functional role of resting-state activity remains unclear. Here, we investigate how the switching behavior of resting-state FC relates with cognitive performance in healthy older adults. We analyse resting-state fMRI data from 98 healthy adults previously categorized as being among the best or among the worst performers in a cohort study of >1000 subjects aged 50+ who underwent neuropsychological assessment. We use a novel approach focusing on the dominant FC pattern captured by the leading eigenvector of dynamic FC matrices. Recurrent FC patterns – or states – are detected and characterized in terms of lifetime, probability of occurrence and switching profiles. We find that poorer cognitive performance is associated with weaker FC temporal similarity together with altered switching between FC states. These results provide new evidence linking the switching dynamics of FC during rest with cognitive performance in later life, reinforcing the functional role of resting-state activity for effective cognitive processing.

[1]  A. McIntosh,et al.  Aging Effects on Whole-Brain Functional Connectivity in Adults Free of Cognitive and Psychiatric Disorders. , 2016, Cerebral cortex.

[2]  Morten L. Kringelbach,et al.  Single or multiple frequency generators in on-going brain activity: A mechanistic whole-brain model of empirical MEG data , 2017, NeuroImage.

[3]  E A Leicht,et al.  Community structure in directed networks. , 2007, Physical review letters.

[4]  G. Tononi.  Information integration: its relevance to brain function and consciousness. , 2010, Archives italiennes de biologie.

[5]  G Tononi,et al.  Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices. , 2000, Cerebral cortex.

[6]  G. Busatto,et al.  Resting-state functional connectivity in normal brain aging , 2013, Neuroscience & Biobehavioral Reviews.

[7]  F. Islami,et al.  Multimorbidity: Epidemiology and Risk Factors in the Golestan Cohort Study, Iran , 2016, Medicine.

[8]  Lorraine K. Tyler,et al.  Age-related functional reorganization, structural changes, and preserved cognition , 2014, Neurobiology of Aging.

[9]  Jason B. Mattingley,et al.  Functional brain networks related to individual differences in human intelligence at rest , 2016, Scientific Reports.

[10]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[11]  Murray Shanahan,et al.  Effects of lesions on synchrony and metastability in cortical networks , 2015, NeuroImage.

[12]  Morten L. Kringelbach,et al.  Modeling the outcome of structural disconnection on resting-state functional connectivity , 2012, NeuroImage.

[13]  HighWire Press.  The journal of neuroscience : the official journal of the Society for Neuroscience. , 1981 .

[14]  David M. Groppe,et al.  Neurophysiological Investigation of Spontaneous Correlated and Anticorrelated Fluctuations of the BOLD Signal , 2013, The Journal of Neuroscience.

[15]  M. Breakspear.  Dynamic models of large-scale brain activity , 2017, Nature Neuroscience.

[16]  W. Gibson,et al.  On the origins of the ‘global signal’ determined by functional magnetic resonance imaging in the resting state , 2016, Journal of neural engineering.

[17]  Olaf Sporns,et al.  Connectivity and complexity: the relationship between neuroanatomy and brain dynamics , 2000, Neural Networks.

[18]  Maurizio Corbetta,et al.  Resting-State Temporal Synchronization Networks Emerge from Connectivity Topology and Heterogeneity , 2015, PLoS Comput. Biol..

[19]  Morten L. Kringelbach,et al.  Exploring the network dynamics underlying brain activity during rest , 2014, Progress in Neurobiology.

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

[21]  G Deco,et al.  Functional Graph Alterations in Schizophrenia: A Result from a Global Anatomic Decoupling? , 2012, Pharmacopsychiatry.

[22]  M. Jenkinson.  Non-linear registration aka Spatial normalisation , 2007 .

[23]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[24]  M. Kringelbach,et al.  Metastability and Coherence: Extending the Communication through Coherence Hypothesis Using A Whole-Brain Computational Perspective , 2016, Trends in Neurosciences.

[25]  Mark W. Woolrich,et al.  Resting-state fMRI in the Human Connectome Project , 2013, NeuroImage.

[26]  R. Kahn,et al.  Efficiency of Functional Brain Networks and Intellectual Performance , 2009, The Journal of Neuroscience.

[27]  Stephen M. Smith,et al.  Temporally-independent functional modes of spontaneous brain activity , 2012, Proceedings of the National Academy of Sciences.

[28]  P. Bak,et al.  Complexity, contingency, and criticality. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Margaret King,et al.  State of the art and perspectives , 2004, Machine Translation.

[30]  M. Schölvinck,et al.  Neural basis of global resting-state fMRI activity , 2010, Proceedings of the National Academy of Sciences.

[31]  Stamatios N. Sotiropoulos,et al.  An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging , 2016, NeuroImage.

[32]  Mark W. Woolrich,et al.  Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , 2007, NeuroImage.

[33]  Raja Parasuraman,et al.  Neurocognitive enhancement in older adults: Comparison of three cognitive training tasks to test a hypothesis of training transfer in brain connectivity , 2014, NeuroImage.

[34]  Mark W. Woolrich,et al.  Bayesian analysis of neuroimaging data in FSL , 2009, NeuroImage.

[35]  Y. Stern,et al.  Cognitive reserve in aging. , 2011, Current Alzheimer research.

[36]  Michael W. Cole,et al.  Global Connectivity of Prefrontal Cortex Predicts Cognitive Control and Intelligence , 2012, The Journal of Neuroscience.

[37]  Gustavo Deco,et al.  Functional connectivity dynamics: Modeling the switching behavior of the resting state , 2015, NeuroImage.

[38]  Nuno Sousa,et al.  The association between stress and mood across the adult lifespan on default mode network , 2016, Brain Structure and Function.

[39]  Eswar Damaraju,et al.  Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.

[40]  Stephen M. Smith,et al.  Investigations into resting-state connectivity using independent component analysis , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[41]  Mark W. Woolrich,et al.  Spectrally resolved fast transient brain states in electrophysiological data , 2016, NeuroImage.

[42]  J. Zihl,et al.  Mood is a key determinant of cognitive performance in community-dwelling older adults: a cross-sectional analysis , 2013, AGE.

[43]  Dimitri Van De Ville,et al.  The dynamic functional connectome: State-of-the-art and perspectives , 2017, NeuroImage.

[44]  I. Deary,et al.  The neuroscience of human intelligence differences , 2010, Nature Reviews Neuroscience.

[45]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[46]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[47]  Abdennaceur Kachouri,et al.  Mapping the dynamic repertoire of the resting brain , 2013, NeuroImage.

[48]  Mikko Sams,et al.  Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity , 2012, Brain Connect..

[49]  Morten L. Kringelbach,et al.  Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms , 2017, NeuroImage.

[50]  Mark W. Woolrich,et al.  FSL , 2012, NeuroImage.

[51]  Timothy Edward John Behrens,et al.  Characterization and propagation of uncertainty in diffusion‐weighted MR imaging , 2003, Magnetic resonance in medicine.

[52]  Russell A. Poldrack,et al.  Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives , 2015, NeuroImage.

[53]  O. Sporns,et al.  Key role of coupling, delay, and noise in resting brain fluctuations , 2009, Proceedings of the National Academy of Sciences.

[54]  Gustavo Deco,et al.  Metastability and Coherence: Extending the Communication through Coherence Hypothesis Using a Whole-Brain Computational Perspective , 2016, Trends in Neurosciences.

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

[56]  Jun Li,et al.  Brain spontaneous functional connectivity and intelligence , 2008, NeuroImage.

[57]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[58]  Ravi S. Menon,et al.  Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques , 2013, Human brain mapping.

[59]  Gustavo Deco,et al.  Bottom up modeling of the connectome: Linking structure and function in the resting brain and their changes in aging , 2013, NeuroImage.

[60]  G. Deco,et al.  Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors , 2012, The Journal of Neuroscience.

[61]  Edward T. Bullmore,et al.  Deep sleep divides the cortex into opposite modes of anatomical–functional coupling , 2016, Brain Structure and Function.

[62]  Patrício Costa,et al.  The functional connectome of cognitive reserve , 2016, Human brain mapping.

[63]  M. Sigman,et al.  Signature of consciousness in the dynamics of resting-state brain activity , 2015, Proceedings of the National Academy of Sciences.

[64]  Kevin Murphy,et al.  The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.

[65]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[66]  Barry Horwitz,et al.  The elusive concept of brain connectivity , 2003, NeuroImage.

[67]  M. Ikram,et al.  Exploring strategies to operationalize cognitive reserve: A systematic review of reviews , 2015, Journal of clinical and experimental neuropsychology.

[68]  Benjamin J. Shannon,et al.  Coherent spontaneous activity identifies a hippocampal-parietal memory network. , 2006, Journal of neurophysiology.

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

[70]  Joana Almeida Palha,et al.  The Use of Bayesian Latent Class Cluster Models to Classify Patterns of Cognitive Performance in Healthy Ageing , 2013, PloS one.

[71]  G. Deco,et al.  Emerging concepts for the dynamical organization of resting-state activity in the brain , 2010, Nature Reviews Neuroscience.

[72]  W. Uttal.  On models and mechanisms , 1992, Behavioral and Brain Sciences.

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

[74]  Thomas E. Nichols,et al.  Functional connectomics from resting-state fMRI , 2013, Trends in Cognitive Sciences.

[75]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[76]  N. Sousa,et al.  Clinical, physical and lifestyle variables and relationship with cognition and mood in aging: a cross-sectional analysis of distinct educational groups , 2013, Front. Aging Neurosci..

[77]  Olaf Sporns,et al.  Network structure of cerebral cortex shapes functional connectivity on multiple time scales , 2007, Proceedings of the National Academy of Sciences.

[78]  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.