| Literature DB >> 23798392 |
Shelli R Kesler1, Jeffrey S Wefel, S M Hadi Hosseini, Maria Cheung, Christa L Watson, Fumiko Hoeft.
Abstract
Breast cancer (BC) chemotherapy is associated with cognitive changes including persistent deficits in some individuals. We tested the accuracy of default mode network (DMN) resting state functional connectivity patterns in discriminating chemotherapy treated (C+) from non-chemotherapy (C-) treated BC survivors and healthy controls (HC). We also examined the relationship between DMN connectivity patterns and cognitive function. Multivariate pattern analysis was used to classify 30 C+, 27 C-, and 24 HC, which showed significant accuracy for discriminating C+ from C- (91.23%, P < 0.0001) and C+ from HC (90.74%, P < 0.0001). The C- group did not differ significantly from HC (47.06%, P = 0.60). Lower subjective memory function was correlated (P < 0.002) with greater hyperplane distance (distance from the linear decision function that optimally separates the groups). Disrupted DMN connectivity may help explain long-term cognitive difficulties following BC chemotherapy.Entities:
Keywords: fMRI; machine learning
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Year: 2013 PMID: 23798392 PMCID: PMC3710809 DOI: 10.1073/pnas.1214551110
Source DB: PubMed Journal: Proc Natl Acad Sci U S A ISSN: 0027-8424 Impact factor: 11.205