Brain structural connectivity distinguishes patients at risk for cognitive decline after carotid interventions |
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Authors: | Salil Soman Gautam Prasad Elizabeth Hitchner Payam Massaband Michael E. Moseley Wei Zhou Allyson C. Rosen |
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Affiliation: | 1. Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, Massachusetts;2. Laboratory of Neuro Imaging (LONI), Imaging Genetics Center (IGC), Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, California;3. Department of Vascular Surgery, Stanford University/Veterans Affairs Palo Alto Health Care System, Palo Alto, California;4. Department of Radiology, Stanford University/Veterans Affairs Palo Alto Health Care System, Palo Alto, California;5. Department of Psychiatry and Behavioral Sciences, Stanford University/Veterans Affairs Palo Alto Health Care System, Palo Alto, California |
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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. |
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Keywords: | biological markers brain brain injuries cognition disorders decision making magnetic resonance imaging connectome neuroimaging carotid stenosis risk factors |
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