Brain structural connectivity distinguishes patients at risk for cognitive decline after carotid interventions

While brain connectivity analyses have been demonstrated to identify ill patients for a number of diseases, their ability to predict cognitive impairment after brain injury is not well established. Traditional post brain injury models, such as stroke, are limited for this evaluation because pre‐injury brain connectivity patterns are infrequently available. Patients with severe carotid stenosis, in contrast, often undergo non‐emergent revascularization surgery, allowing the collection of pre and post‐operative imaging, may experience brain insult due to perioperative thrombotic/embolic infarcts or hypoperfusion, and can suffer post‐operative cognitive decline. We hypothesized that a distributed function such as memory would be more resilient in patients with brains demonstrating higher degrees of modularity. To test this hypothesis, we analyzed preoperative structural connectivity graphs (using T1 and DWI MRI) for 34 patients that underwent carotid intervention, and evaluated differences in graph metrics using the Brain Connectivity Toolbox. We found that patients with lower binary component number, binary community number and weighted community number prior to surgery were at greater risk for developing cognitive decline. These findings highlight the promise of brain connectivity analyses to predict cognitive decline following brain injury and serve as a clinical decision support tool. Hum Brain Mapp 37:2185–2194, 2016. © 2016 Wiley Periodicals, Inc.

[1]  L. Bozzao,et al.  Embolism to the Brain During Carotid Stenting and Surgery , 2007, Acta chirurgica Belgica.

[2]  J. Lani.  Rey Auditory Verbal Learning Test (RAVLT) , 2010 .

[3]  J. Rapoport,et al.  Abnormal Cortical Growth in Schizophrenia Targets Normative Modules of Synchronized Development , 2014, Biological Psychiatry.

[4]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[5]  A. McIntosh,et al.  Mapping cognition to the brain through neural interactions. , 1999, Memory.

[6]  S. Bressler,et al.  Large-scale brain networks in cognition: emerging methods and principles , 2010, Trends in Cognitive Sciences.

[7]  Olaf Sporns,et al.  The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..

[8]  Javier DeFelipe,et al.  From the Connectome to the Synaptome: An Epic Love Story , 2010, Science.

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

[10]  Owen Carmichael,et al.  Associations Among Vascular Risk Factors, Carotid Atherosclerosis, and Cortical Volume and Thickness in Older Adults , 2012, Stroke.

[11]  Dinggang Shen,et al.  Altered brain network modules induce helplessness in major depressive disorder. , 2014, Journal of affective disorders.

[12]  M M Mesulam,et al.  Large‐scale neurocognitive networks and distributed processing for attention, language, and memory , 1990, Annals of neurology.

[13]  Essa Yacoub,et al.  A Hough transform global probabilistic approach to multiple-subject diffusion MRI tractography , 2011, Medical Image Anal..

[14]  M. D’Esposito,et al.  Functional Reintegration of Prefrontal Neural Networks for Enhancing Recovery After Brain Injury , 2006, The Journal of head trauma rehabilitation.

[15]  P. Basser,et al.  Estimation of the effective self-diffusion tensor from the NMR spin echo. , 1994, Journal of magnetic resonance. Series B.

[16]  Desmond J. Higham,et al.  Network analysis detects changes in the contralesional hemisphere following stroke , 2011, NeuroImage.

[17]  S. Black,et al.  National Institute of Neurological Disorders and Stroke–Canadian Stroke Network Vascular Cognitive Impairment Harmonization Standards , 2006, Stroke.

[18]  C. Lazaridis,et al.  Comparison of cognitive function after carotid artery stenting versus carotid endarterectomy. , 2014, European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery.

[19]  A. Saykin,et al.  Differential Memory Test Sensitivity for Diagnosing Amnestic Mild Cognitive Impairment and Predicting Conversion to Alzheimer's Disease , 2009, Neuropsychology, development, and cognition. Section B, Aging, neuropsychology and cognition.

[20]  F. Hillary,et al.  Chronology and chronicity of altered resting-state functional connectivity after traumatic brain injury. , 2015, Journal of neurotrauma.

[21]  D. Sackett,et al.  Beneficial effect of carotid endarterectomy in symptomatic patients with high-grade carotid stenosis. , 1991, The New England journal of medicine.

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

[23]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[24]  Joaquín Goñi,et al.  Abnormal rich club organization and functional brain dynamics in schizophrenia. , 2013, JAMA psychiatry.

[25]  Paul M. Thompson,et al.  Tractography density and network measures in Alzheimer'S disease , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[26]  Yong He,et al.  BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics , 2013, PloS one.

[27]  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.

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

[29]  Kristen J. Kokotilo,et al.  Reorganization of Brain Function During Force Production After Stroke: A Systematic Review of the Literature , 2009, Journal of neurologic physical therapy : JNPT.

[30]  Shantanu H. Joshi,et al.  Brain connectivity and novel network measures for Alzheimer's disease classification , 2015, Neurobiology of Aging.

[31]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.

[32]  Liu Zuxiang,et al.  Altered Functional Connectivity within and between Brain Modules in Absence Epilepsy: A Resting-State Functional Magnetic Resonance Imaging Study , 2013, BioMed research international.

[33]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[34]  CinziaCalautti,et al.  Functional Neuroimaging Studies of Motor Recovery After Stroke in Adults , 2003 .

[35]  Xiaoqi Huang,et al.  Disrupted Brain Connectivity Networks in Drug-Naive, First-Episode Major Depressive Disorder , 2011, Biological Psychiatry.

[36]  Massimo Marchiori,et al.  Error and attacktolerance of complex network s , 2004 .

[37]  O. Spreen,et al.  A Compendium of Neuropsychological Tests: Administration, Norms, and Commentary , 1991 .

[38]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[39]  Wei Zhou,et al.  Prospective neurocognitive evaluation of patients undergoing carotid interventions. , 2012, Journal of vascular surgery.

[40]  C. Caltagirone,et al.  Cognitive Performance following Carotid Endarterectomy or Stenting in Asymptomatic Patients with Severe ICA Stenosis , 2013, Cardiovascular psychiatry and neurology.