The functional connectome of cognitive reserve

Cognitive Reserve (CR) designates the brain's capacity to actively cope with insults through a more efficient use of its resources/networks. It was proposed in order to explain the discrepancies between the observed cognitive ability and the expected capacity for an individual. Typical proxies of CR include education and Intelligence Quotient but none totally account for the variability of CR and no study has shown if the brain's greater efficiency associated with CR can be measured. We used a validated model to estimate CR from the residual variance in memory and general executive functioning, accounting for both brain anatomical (i.e., gray matter and white matter signal abnormalities volume) and demographic variables (i.e., years of formal education and sex). Functional connectivity (FC) networks and topological properties were explored for associations with CR. Demographic characteristics, mainly accounted by years of formal education, were associated with higher FC, clustering, local efficiency and strength in parietal and occipital regions and greater network transitivity. Higher CR was associated with a greater FC, local efficiency and clustering of occipital regions, strength and centrality of the inferior temporal gyrus and higher global efficiency. Altogether, these findings suggest that education may facilitate the brain's ability to form segregated functional groups, reinforcing the view that higher education level triggers more specialized use of neural processing. Additionally, this study demonstrated for the first time that CR is associated with more efficient processing of information in the human brain and reinforces the existence of a fine balance between segregation and integration. Hum Brain Mapp 37:3310–3322, 2016.. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

[1]  Edward T. Bullmore,et al.  Network-based statistic: Identifying differences in brain networks , 2010, NeuroImage.

[2]  Yaakov Stern,et al.  Cognitive Reserve: Implications for Assessment and Intervention , 2013, Folia Phoniatrica et Logopaedica.

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

[4]  Anders M. Dale,et al.  Sequence-independent segmentation of magnetic resonance images , 2004, NeuroImage.

[5]  Marcus Richards,et al.  Lifetime Antecedents of Cognitive Reserve , 2003, Journal of clinical and experimental neuropsychology.

[6]  橋本 衛,et al.  非 Alzheimer 型認知症 , 2011 .

[7]  Yuri Rassovsky,et al.  Brain and cognitive reserve: Mediator(s) and construct validity, a critique , 2011, Journal of clinical and experimental neuropsychology.

[8]  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..

[9]  The inferior temporal and orbitofrontal cortex in analysing emotional pictures , 2001, NeuroImage.

[10]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[11]  K. Mardia Measures of multivariate skewness and kurtosis with applications , 1970 .

[12]  Brian T. Gold,et al.  Combined ERP/fMRI evidence for early word recognition effects in the posterior inferior temporal gyrus , 2013, Cortex.

[13]  D. Harvey,et al.  Measuring cognitive reserve based on the decomposition of episodic memory variance. , 2010, Brain : a journal of neurology.

[14]  Á. Silva,et al.  Adaptação à população portuguesa da tradução do Mini Mental State Examination (MMSE) , 1994 .

[15]  Cornelis J. Stam,et al.  Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain , 2008, NeuroImage.

[16]  Y. Stern What is cognitive reserve? Theory and research application of the reserve concept , 2002, Journal of the International Neuropsychological Society.

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

[18]  G. Alexander,et al.  Inverse relationship between education and parietotemporal perfusion deficit in Alzheimer's disease , 1992, Annals of neurology.

[19]  Mareike Grotheer,et al.  Neuroimaging Evidence of a Bilateral Representation for Visually Presented Numbers , 2016, The Journal of Neuroscience.

[20]  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.

[21]  R. Katzman.,et al.  Clinical, pathological, and neurochemical changes in dementia: A subgroup with preserved mental status and numerous neocortical plaques , 1988, Annals of neurology.

[22]  Mario A. Parra,et al.  Visual short-term memory binding in Alzheimer’s disease and depression , 2010, Journal of Neurology.

[23]  S. Henderson Epidemiology of dementia. , 1998, Annales de medecine interne.

[24]  Barbara Spanò,et al.  The impact of cognitive reserve on brain functional connectivity in Alzheimer's disease. , 2015, Journal of Alzheimer's disease : JAD.

[25]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

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

[27]  O. Sporns,et al.  The economy of brain network organization , 2012, Nature Reviews Neuroscience.

[28]  Macro- and micro-structural white matter differences correlate with cognitive performance in healthy aging , 2016, Brain Imaging and Behavior.

[29]  M. Ziegler,et al.  Cognitive Reserve in Young and Old Healthy Subjects: Differences and Similarities in a Testing-the-Limits Paradigm with DSST , 2014, PloS one.

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

[31]  Pedro Silva Moreira,et al.  Exploring the Factor Structure of Neurocognitive Measures in Older Individuals , 2015, PloS one.

[32]  A. Neubauer,et al.  Intelligence and neural efficiency , 2009, Neuroscience & Biobehavioral Reviews.

[33]  Jun Li,et al.  Brain Anatomical Network and Intelligence , 2009, NeuroImage.

[34]  R. Busch,et al.  Review of Normative Data For Common Screening Measures Used to Evaluate Cognitive Functioning in Elderly Individuals , 2008, The Clinical neuropsychologist.

[35]  Yaakov Stern,et al.  A common neural network for cognitive reserve in verbal and object working memory in young but not old. , 2008, Cerebral cortex.

[36]  Anders M. Dale,et al.  MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths , 2009, NeuroImage.

[37]  Y. Stern,et al.  Efficiency, capacity, compensation, maintenance, plasticity: emerging concepts in cognitive reserve , 2013, Trends in Cognitive Sciences.

[38]  Y. Stern Cognitive Reserve and Alzheimer Disease , 2006, Alzheimer disease and associated disorders.

[39]  R. Katzman.,et al.  Education and the prevalence of dementia and Alzheimer's disease , 1993, Neurology.

[40]  W. Markesbery,et al.  Head Circumference, Education and Risk of Dementia: Findings from the Nun Study , 2003, Journal of clinical and experimental neuropsychology.

[41]  R. Lomax,et al.  The Effect of Varying Degrees of Nonnormality in Structural Equation Modeling , 2005 .

[42]  B. Lebowitz,et al.  Norms for the Mini-Mental State Examination in a healthy population , 1999, Neurology.

[43]  Jonathan Winawer,et al.  A Brain Area for Visual Numerals , 2013, The Journal of Neuroscience.

[44]  N. Sousa,et al.  The Bounds Of Education In The Human Brain Connectome , 2015, Scientific Reports.

[45]  Yong He,et al.  Hemisphere- and gender-related differences in small-world brain networks: A resting-state functional MRI study , 2011, NeuroImage.

[46]  Kathy E. Johnson,et al.  The cognitive reserve hypothesis: a longitudinal examination of age-associated declines in reasoning and processing speed. , 2009, Developmental Psychology.

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

[48]  Anders M. Dale,et al.  Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature , 2010, NeuroImage.

[49]  R. Kane,et al.  T1 cortical hypointensities and their association with cognitive disability in multiple sclerosis , 2010, Multiple sclerosis.

[50]  K. Grill-Spector,et al.  Neural representations of faces and limbs neighbor in human high-level visual cortex: evidence for a new organization principle , 2011, Psychological Research.

[51]  R. V. Van Heertum,et al.  Brain networks associated with cognitive reserve in healthy young and old adults. , 2005, Cerebral cortex.

[52]  Karen L. Siedlecki,et al.  Quantifying Cognitive Reserve in Older Adults by Decomposing Episodic Memory Variance: Replication and Extension , 2013, Journal of the International Neuropsychological Society.

[53]  David H. Salat,et al.  Inter-individual variation in blood pressure is associated with regional white matter integrity in generally healthy older adults , 2012, NeuroImage.

[54]  P Pietrini,et al.  Association of premorbid intellectual function with cerebral metabolism in Alzheimer's disease: implications for the cognitive reserve hypothesis. , 1997, The American journal of psychiatry.

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

[56]  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.