Graph Theory Analysis Reveals Resting-State Compensatory Mechanisms in Healthy Aging and Prodromal Alzheimer’s Disease

Several theories of cognitive compensation have been suggested to explain sustained cognitive abilities in healthy brain aging and early neurodegenerative processes. The growing number of studies investigating various aspects of task-based compensation in these conditions is contrasted by the shortage of data about resting-state compensatory mechanisms. Using our proposed criterion-based framework for compensation, we investigated 45 participants in three groups: (i) patients with mild cognitive impairment (MCI) and positive biomarkers indicative of Alzheimer’s disease (AD); (ii) cognitively normal young adults; (iii) cognitively normal older adults. To increase reliability, three sessions of resting-state functional magnetic resonance imaging for each participant were performed on different days (135 scans in total). To elucidate the dimensions and dynamics of resting-state compensatory mechanisms, we used graph theory analysis along with volumetric analysis. Graph theory analysis was applied based on the Brainnetome atlas, which provides a connectivity-based parcellation framework. Comprehensive neuropsychological examinations including the Rey Auditory Verbal Learning Test (RAVLT) and the Trail Making Test (TMT) were performed, to relate graph measures of compensatory nodes to cognition. To avoid false-positive findings, results were corrected for multiple comparisons. First, we observed an increase of degree centrality in cognition related brain regions of the middle frontal gyrus, precentral gyrus and superior parietal lobe despite local atrophy in MCI and healthy aging, indicating a resting-state connectivity increase with positive biomarkers. When relating the degree centrality measures to cognitive performance, we observed that greater connectivity led to better RAVLT and TMT scores in MCI and, hence, might constitute a compensatory mechanism. The detection and improved understanding of the compensatory dynamics in healthy aging and prodromal AD is mandatory for implementing and tailoring preventive interventions aiming at preserved overall cognitive functioning and delayed clinical onset of dementia.

[1]  M. N. Rajah,et al.  Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing , 2018, Nature Reviews Neuroscience.

[2]  Özgür A. Onur,et al.  Test-retest variability of resting-state networks in healthy aging and prodromal Alzheimer's disease , 2018, NeuroImage: Clinical.

[3]  C. Jack,et al.  NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease , 2018, Alzheimer's & Dementia.

[4]  T. Yarkoni,et al.  Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning , 2017, Perspectives on psychological science : a journal of the Association for Psychological Science.

[5]  E. Asano,et al.  Spatio-temporal dynamics of working memory maintenance and scanning of verbal information , 2017, Clinical Neurophysiology.

[6]  Hojjat Adeli,et al.  Graph Theory and Brain Connectivity in Alzheimer’s Disease , 2017, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[7]  G. Rees,et al.  Operationalizing compensation over time in neurodegenerative disease , 2017, Brain : a journal of neurology.

[8]  Keith A. Johnson,et al.  A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers , 2016, Neurology.

[9]  Yu Zhang,et al.  The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture , 2016, Cerebral cortex.

[10]  R. Adolphs,et al.  Building a Science of Individual Differences from fMRI , 2016, Trends in Cognitive Sciences.

[11]  C. Jack,et al.  Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria , 2016, Alzheimer's & Dementia.

[12]  A. Babajani-Feremi,et al.  Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer’s disease , 2015, Brain Imaging and Behavior.

[13]  Rafael Malach,et al.  Intracranial recordings reveal transient response dynamics during information maintenance in human cerebral cortex , 2015, Human brain mapping.

[14]  Qihao Guo,et al.  Auditory Verbal Learning Test is Superior to Rey-Osterrieth Complex Figure Memory for Predicting Mild Cognitive Impairment to Alzheimer's Disease. , 2015, Current Alzheimer research.

[15]  P. Scheltens,et al.  Diagnostic impact of CSF biomarkers for Alzheimer's disease in a tertiary memory clinic , 2015, Alzheimer's & Dementia.

[16]  Satrajit S. Ghosh,et al.  Prediction as a Humanitarian and Pragmatic Contribution from Human Cognitive Neuroscience , 2015, Neuron.

[17]  Lubica Benuskova,et al.  The age-related posterior-anterior shift as revealed by voxelwise analysis of functional brain networks , 2014, Front. Aging Neurosci..

[18]  Simon B Eickhoff,et al.  Meta-analysis in human neuroimaging: computational modeling of large-scale databases. , 2014, Annual review of neuroscience.

[19]  P. Fox,et al.  Bridging the gap between functional and anatomical features of cortico‐cerebellar circuits using meta‐analytic connectivity modeling , 2014, Human brain mapping.

[20]  Nick C Fox,et al.  Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria , 2014, The Lancet Neurology.

[21]  Eberhard Fuchs,et al.  Adult Neuroplasticity: More Than 40 Years of Research , 2014, Neural plasticity.

[22]  J. Fletcher,et al.  Age, plasticity, and homeostasis in childhood brain disorders , 2013, Neuroscience & Biobehavioral Reviews.

[23]  Angela R. Laird,et al.  Tackling the multifunctional nature of Broca's region meta-analytically: Co-activation-based parcellation of area 44 , 2013, NeuroImage.

[24]  Larry J. Seidman,et al.  Distinct cortical networks activated by auditory attention and working memory load , 2013, NeuroImage.

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

[26]  Angela R. Laird,et al.  Is There “One” DLPFC in Cognitive Action Control? Evidence for Heterogeneity From Co-Activation-Based Parcellation , 2012, Cerebral cortex.

[27]  Susan L. Whitfield-Gabrieli,et al.  Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks , 2012, Brain Connect..

[28]  Özgür A. Onur,et al.  Aging-related changes of neural mechanisms underlying visual-spatial working memory , 2012, Neurobiology of Aging.

[29]  Amy L. Shelton,et al.  Reduction of Hippocampal Hyperactivity Improves Cognition in Amnestic Mild Cognitive Impairment , 2012, Neuron.

[30]  S. Aschenbrenner,et al.  Eine Normierungsstudie eines modifizierten Trail Making Tests im deutschsprachigen Raum , 2012 .

[31]  Denise C. Park,et al.  Both left and right posterior parietal activations contribute to compensatory processes in normal aging , 2012, Neuropsychologia.

[32]  D. Linden The Challenges and Promise of Neuroimaging in Psychiatry , 2012, Neuron.

[33]  Angela R. Laird,et al.  Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation , 2011, NeuroImage.

[34]  D. Muresanu,et al.  Neuroregeneration in neurodegenerative disorders , 2011, BMC neurology.

[35]  Yuan Zhou,et al.  Abnormal Cortical Networks in Mild Cognitive Impairment and Alzheimer's Disease , 2010, PLoS Comput. Biol..

[36]  Michael Vourkas,et al.  Tracking brain dynamics via time-dependent network analysis , 2010, Journal of Neuroscience Methods.

[37]  Jeremy D. Schmahmann,et al.  The Role of the Cerebellum in Cognition and Emotion: Personal Reflections Since 1982 on the Dysmetria of Thought Hypothesis, and Its Historical Evolution from Theory to Therapy , 2010, Neuropsychology Review.

[38]  S. H. A. Chen,et al.  Modality Specific Cerebro-Cerebellar Activations in Verbal Working Memory: An fMRI Study , 2010, Behavioural neurology.

[39]  U. Lindenberger,et al.  A theoretical framework for the study of adult cognitive plasticity. , 2010, Psychological bulletin.

[40]  Yong He,et al.  Graph-based network analysis of resting-state functional MRI. , 2010 .

[41]  John Ashburner,et al.  Computational anatomy with the SPM software. , 2009, Magnetic resonance imaging.

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

[43]  Jessica A. Turner,et al.  Neuroinformatics Original Research Article , 2022 .

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

[45]  C. Miniussi,et al.  Transcranial magnetic stimulation improves naming in Alzheimer disease patients at different stages of cognitive decline , 2008, European journal of neurology.

[46]  P. Reuter-Lorenz,et al.  Neurocognitive Aging and the Compensation Hypothesis , 2008 .

[47]  R. Cabeza,et al.  Que PASA? The posterior-anterior shift in aging. , 2008, Cerebral cortex.

[48]  Jed A. Meltzer,et al.  Effects of Working Memory Load on Oscillatory Power in Human Intracranial EEG , 2007, Cerebral cortex.

[49]  G. Kinsella,et al.  Executive function and its assessment , 2007 .

[50]  Edgar Erdfelder,et al.  G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences , 2007, Behavior research methods.

[51]  Carles Falcón,et al.  Repetitive transcranial magnetic stimulation effects on brain function and cognition among elders with memory dysfunction. A randomized sham-controlled study. , 2006, Cerebral cortex.

[52]  Karl J. Friston,et al.  Voxel-based morphometry of the human brain: Methods and applications , 2005 .

[53]  John D E Gabrieli,et al.  The role of the prefrontal cortex in the maintenance of verbal working memory: an event-related FMRI analysis. , 2005, Neuropsychology.

[54]  Marc W Howard,et al.  Gamma oscillations correlate with working memory load in humans. , 2003, Cerebral cortex.

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

[56]  D. Stuss,et al.  Principles of frontal lobe function , 2002 .

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

[58]  J. Logan,et al.  Under-Recruitment and Nonselective Recruitment Dissociable Neural Mechanisms Associated with Aging , 2002, Neuron.

[59]  J. Morris,et al.  Current concepts in mild cognitive impairment. , 2001, Archives of neurology.

[60]  S. Lux,et al.  Normierungsstudie zum Verbalen Lern- und Merkfähigkeitstest (VLMT) , 1999 .

[61]  R. C. Oldfield The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.