title: Brain structural connectivity distinguishes patients at risk for cognitive decline after carotid interventions.
abstract: 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.
authors.affiliation: Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, Massachusetts.
authors.forename: Salil
authors.initials: S
authors.lastname: Soman
authors.affiliation: Laboratory of Neuro Imaging (LONI), Imaging Genetics Center (IGC), Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, California.
authors.forename: Gautam
authors.initials: G
authors.lastname: Prasad
authors.affiliation: Department of Vascular Surgery, Stanford University/Veterans Affairs Palo Alto Health Care System, Palo Alto, California.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Hitchner
authors.affiliation: Department of Radiology, Stanford University/Veterans Affairs Palo Alto Health Care System, Palo Alto, California.
authors.forename: Payam
authors.initials: P
authors.lastname: Massaband
authors.affiliation: Department of Radiology, Stanford University/Veterans Affairs Palo Alto Health Care System, Palo Alto, California.
authors.forename: Michael E
authors.initials: ME
authors.lastname: Moseley
authors.affiliation: Department of Vascular Surgery, Stanford University/Veterans Affairs Palo Alto Health Care System, Palo Alto, California.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhou
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Stanford University/Veterans Affairs Palo Alto Health Care System, Palo Alto, California.
authors.forename: Allyson C
authors.initials: AC
authors.lastname: Rosen
mesh_terms: D000368:Aged; D001921:Brain / Q000000981:diagnostic imaging*; D016893:Carotid Stenosis / Q000000981:diagnostic imaging* / Q000601:surgery*; D060825:Cognitive Dysfunction / Q000000981:diagnostic imaging*; D015331:Cohort Studies; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D000073216:Mental Status and Dementia Tests; D009434:Neural Pathways / Q000000981:diagnostic imaging; D009483:Neuropsychological Tests; D011183:Postoperative Complications / Q000000981:diagnostic imaging*; D012306:Risk
keywords: biological markers; brain; brain injuries; carotid stenosis; cognition disorders; connectome; decision making; magnetic resonance imaging; neuroimaging; risk factors