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

Shelli R. Kesler, Jeffrey S. Wefel, S. M. Hadi Hosseini, Maria Cheung, Christa L. Watson and Fumiko Hoeft
PNAS July 9, 2013. 110 (28) 11600-11605; https://doi.org/10.1073/pnas.1214551110
Shelli R. Kesler
aDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305;bStanford Cancer Institute, Palo Alto, CA 94305;
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  • For correspondence: skesler@stanford.edu
Jeffrey S. Wefel
cDepartment of Neuro-Oncology, University of Texas M. D. Anderson Cancer Center, Houston, TX 77030;
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S. M. Hadi Hosseini
aDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305;
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Maria Cheung
aDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305;
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Christa L. Watson
dMemory and Aging Center, Department of Neurology, and
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Fumiko Hoeft
eDepartment of Psychiatry, University of California, San Francisco, CA 94143
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  1. Edited by Marcus E. Raichle, Washington University in St. Louis, St. Louis, MO, and approved May 28, 2013 (received for review August 24, 2012)

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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.

  • fMRI
  • machine learning

Footnotes

  • ↵1To whom correspondence should be addressed. E-mail: skesler{at}stanford.edu.
  • Author contributions: S.R.K. and J.S.W. designed research; S.R.K., S.M.H.H., and C.L.W. performed research; S.R.K., S.M.H.H., M.C., and F.H. contributed new reagents/analytic tools; S.R.K. supervised and directed data analyses; S.M.H.H., M.C., and F.H. analyzed data; and S.R.K., J.S.W., S.M.H.H., M.C., C.L.W., and F.H. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1214551110/-/DCSupplemental.

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Chemotherapy and default mode network
Shelli R. Kesler, Jeffrey S. Wefel, S. M. Hadi Hosseini, Maria Cheung, Christa L. Watson, Fumiko Hoeft
Proceedings of the National Academy of Sciences Jul 2013, 110 (28) 11600-11605; DOI: 10.1073/pnas.1214551110

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Chemotherapy and default mode network
Shelli R. Kesler, Jeffrey S. Wefel, S. M. Hadi Hosseini, Maria Cheung, Christa L. Watson, Fumiko Hoeft
Proceedings of the National Academy of Sciences Jul 2013, 110 (28) 11600-11605; DOI: 10.1073/pnas.1214551110
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