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+, 27C-, 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 dif fi culties following BC chemotherapy.
Original language | English (US) |
---|---|
Pages (from-to) | 11600-11605 |
Number of pages | 6 |
Journal | Proceedings of the National Academy of Sciences of the United States of America |
Volume | 110 |
Issue number | 28 |
DOIs | |
State | Published - Jul 9 2013 |
Keywords
- Machine learning
- fMRI
ASJC Scopus subject areas
- General
MD Anderson CCSG core facilities
- Clinical Trials Office