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Proc Natl Acad Sci U S A. 2013 Jul 9;110(28):11600-5. doi: 10.1073/pnas.1214551110. Epub 2013 Jun 24.

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

Author information

1
Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA. skesler@stanford.edu

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.

KEYWORDS:

fMRI; machine learning

PMID:
23798392
PMCID:
PMC3710809
DOI:
10.1073/pnas.1214551110
[Indexed for MEDLINE]
Free PMC Article

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