Abstract #0594
Using Structural Connectivity Graph Analysis to Predict Cognitive Decline in Patients After Carotid Endarterectomy
Salil Soman 1,2 , Gautam Prasad 3,4 , Elizabeth Hitchner 5 , Wei Zhou 5,6 , Michael Moseley 7 , and Allyson Rosen 8,9
1
Radiology, Stanford University, Menlo Park,
CA, United States,
2
California
War Related Illness and Injury Study Center, Palo Alto
Veteras Affairs Hospital, Palo Alto, CA, United States,
3
LONI,
University of Southern California, Los Angeles, CA,
United States,
4
Psychology,
Stanford University, CA, United States,
5
Vascular
Surgery, Stanford University, CA, United States,
6
Vascular
Surgery, Veterans Affairs Palo Alto Health Care System,
CA, United States,
7
Radiology,
Stanford University, CA, United States,
8
Pschology,
Stanford University, CA, United States,
9
Pscychology,
Veterans Affairs Palo Alto Health Care System, CA,
United States
Some patients with carotid stenosis that undergo carotid
surgery afterwards experience cognitive decline.
Identifying these patients before surgery would allow
targeting of therapies to minimize disability. We
hypothesized that structural connectivity graphs could
identify these patients. We performed T1, DTI, and
neuropsychological testing prior to surgery. Repeat
neuropsychological testing was then performed 1 month
later. FreeSurfer 5.3 whole brain segmentation, whole
brain HARDI tractography, and connectivity analysis were
then performed. The graph analysis methods weighted
optimal community structure & binary connected
component sizes metrics both predicted patients that
would experience cognitive decline with 81% sensitivity
83% and specificity (FDR .05).
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