期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 110, 期 28, 页码 11600-11605出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1214551110
关键词
fMRI; machine learning
资金
- National Institutes of Health Grant [1DP2 OD004445-01]
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 difficulties following BC chemotherapy.
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