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      Default mode network connectivity distinguishes chemotherapy-treated breast cancer survivors from controls.

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          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.

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          Author and article information

          Journal
          Proc. Natl. Acad. Sci. U.S.A.
          Proceedings of the National Academy of Sciences of the United States of America
          1091-6490
          0027-8424
          Jul 9 2013
          : 110
          : 28
          Affiliations
          [1 ] Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA. skesler@stanford.edu
          Article
          1214551110
          10.1073/pnas.1214551110
          3710809
          23798392
          2c2f75f1-e428-4fdf-b24d-b07ec580aec5
          History

          fMRI,machine learning
          fMRI, machine learning

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