Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-07T21:10:28.909Z Has data issue: false hasContentIssue false
  • English
  • Français

Multivariate Pattern Analysis of fMRI in Breast Cancer Survivors and Healthy Women

Published online by Cambridge University Press:  18 October 2013

S.M. Hadi Hosseini  and
Shelli R. Kesler
S.M. Hadi Hosseini
Affiliation:
Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
Shelli R. Kesler*
Affiliation:
Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California Stanford Cancer Institute, Palo Alto, California
*
Correspondence and reprint requests to: Shelli Kesler,401 Quarry Road, MC5795,Stanford, CA 94305-5795. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Advances in breast cancer (BC) treatments have resulted in significantly improved survival rates. However, BC chemotherapy is often associated with several side effects including cognitive dysfunction. We applied multivariate pattern analysis (MVPA) to functional magnetic resonance imaging (fMRI) to find a brain connectivity pattern that accurately and automatically distinguishes chemotherapy-treated (C+) from non-chemotherapy treated (C−) BC females and healthy female controls (HC). Twenty-seven C+, 29 C−, and 30 HC underwent fMRI during an executive-prefrontal task (Go/Nogo). The pattern of functional connectivity associated with this task discriminated with significant accuracy between C+ and HC groups (72%, p = .006) and between C+ and C− groups (71%, p = .012). However, the accuracy of discrimination between C− and HC was not significant (51%, p = .46). Compared with HC, behavioral performance of the C+ and C− groups during the task was intact. However, the C+ group demonstrated altered functional connectivity in the right frontoparietal and left supplementary motor area networks compared to HC, and in the right middle frontal and left superior frontal gyri networks, compared to C−. Our results provide further evidence that executive function performance may be preserved in some chemotherapy-treated BC survivors through recruitment of additional neural connections. (JINS, 2013, 19, 1–11)

Keywords

Machine learningResponse inhibitionfMRIFunctional connectivityBreast cancerExecutive function
Type
Symposia
Information
Journal of the International Neuropsychological Society , Volume 20 , Issue 4 , April 2014 , pp. 391 - 401
Copyright
Copyright © The International Neuropsychological Society 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ahles, T.A. (2012). Brain vulnerability to chemotherapy toxicities. Psychooncology, doi:10.1002/pon.3196 Google Scholar
Aksu, Y., Miller, D.J., Kesidis, G., Bigler, D.C., Yang, Q.X. (2011). An MRI-derived definition of MCI-to-AD conversion for long-term, automatic prognosis of MCI patients. PLoS One, 6(10), e25074. doi:10.1371/journal.pone.0025074 Google Scholar
Aron, A.R., Behrens, T.E., Smith, S., Frank, M.J., Poldrack, R.A. (2007). Triangulating a cognitive control network using diffusion-weighted magnetic resonance imaging (MRI) and functional MRI. Journal of Neuroscience, 27(14), 3743–3752. doi:27/14/3743 [pii]10.1523/JNEUROSCI.0519-07.2007 Google Scholar
Bai, F., Watson, D.R., Shi, Y., Wang, Y., Yue, C., Yuhuan, Teng., Zhang, Z. (2011). Specifically progressive deficits of brain functional marker in amnestic type mild cognitive impairment. PLoS One, 6(9), e24271. doi:10.1371/journal.pone.0024271 Google Scholar
Behzadi, Y., Restom, K., Liau, J., Liu, T.T. (2007). A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage, 37(1), 90–101.CrossRefGoogle ScholarPubMed
Bruno, J., Hosseini, S.M., Kesler, S. (2012). Altered resting state functional brain network topology in chemotherapy-treated breast cancer survivors. Neurobiolology of Disease, 48(3), 329–338. doi:10.1016/j.nbd.2012.07.009 Google Scholar
Cimprich, B., Reuter-Lorenz, P., Nelson, J., Clark, P.M., Therrien, B., Normolle, D., Welsh, R.C. (2010). Prechemotherapy alterations in brain function in women with breast cancer. Journal of Clinical and Experimental Neuropsychology, 32(3), 324–331. doi:913518343 [pii]10.1080/13803390903032537Google Scholar
Craddock, R.C., Holtzheimer, P.E. III, Hu, X.P., Mayberg, H.S. (2009). Disease state prediction from resting state functional connectivity. Magnetic Resonance in Medicine, 62(6), 1619–1628. doi:10.1002/mrm.22159 Google Scholar
de Ruiter, M.B., Reneman, L., Boogerd, W., Veltman, D.J., Caan, M., Douaud, G., Schagen, S.B. (2012). Late effects of high-dose adjuvant chemotherapy on white and gray matter in breast cancer survivors: Converging results from multimodal magnetic resonance imaging. Human Brain Mapping, 33(12), 2971–2983. doi:10.1002/hbm.21422 Google Scholar
de Ruiter, M.B., Reneman, L., Boogerd, W., Veltman, D.J., van Dam, F.S., Nederveen, A.J., Schagen, S.B. (2011). Cerebral hyporesponsiveness and cognitive impairment 10 years after chemotherapy for breast cancer. Human Brain Mapping, 32(8), 1206–1219. doi:10.1002/hbm.21102 Google Scholar
Deprez, S., Amant, F., Smeets, A., Peeters, R., Leemans, A., Van Hecke, W., Sunaert, S. (2012). Longitudinal assessment of chemotherapy-induced structural changes in cerebral white matter and its correlation with impaired cognitive functioning. Journal of Clinical Oncology, 30(3), 274–281. doi:10.1200/JCO.2011.36.8571 Google Scholar
Deprez, S., Amant, F., Yigit, R., Porke, K., Verhoeven, J., Van den Stock, J., Sunaert, S. (2011). Chemotherapy-induced structural changes in cerebral white matter and its correlation with impaired cognitive functioning in breast cancer patients. Human Brain Mapping, 32(3), 480–493. doi:10.1002/hbm.21033 Google Scholar
Deprez, S., Billiet, T., Sunaert, S., Leemans, A. (2013). Diffusion tensor MRI of chemotherapy-induced cognitive impairment in non-CNS cancer patients: A review. Brain Imaging and Behavior. doi:10.1007/s11682-012-9220-1 CrossRefGoogle ScholarPubMed
Du, X., Goodwin, J.S. (2001). Patterns of use of chemotherapy for breast cancer in older women: Findings from Medicare claims data. Journal of Clinical Oncology, 19(5), 1455–1461.Google Scholar
Eberling, J.L., Wu, C., Tong-Turnbeaugh, R., Jagust, W.J. (2004). Estrogen- and tamoxifen-associated effects on brain structure and function. Neuroimage, 21(1), 364–371. doi:S1053811903005457 [pii]Google Scholar
Eichenbaum, H. (2000). A cortical-hippocampal system for declarative memory. Nature Reviews Neuroscience, 1(1), 41–50. doi:10.1038/35036213 CrossRefGoogle ScholarPubMed
Ferguson, R.J., McDonald, B.C., Saykin, A.J., Ahles, T.A. (2007). Brain structure and function differences in monozygotic twins: Possible effects of breast cancer chemotherapy. Journal of Clinical Oncology, 25(25), 3866–3870. doi:25/25/3866 [pii]10.1200/JCO.2007.10.8639Google Scholar
Glover, G.H., Lai, S. (1998). Self-navigated spiral fMRI: Interleaved versus single-shot. Magnetic Resonance Medicine, 39(3), 361–368.Google Scholar
Greicius, M.D., Flores, B.H., Menon, V., Glover, G.H., Solvason, H.B., Reiss, A.L., Schatzberg, A.F. (2007). Resting-state functional connectivity in major depression: Abnormally increased contributions from subgenual cingulate cortex and thalamus. Biological Psychiatry, 62(5), 429–437. doi:10.1016/j.biopsych.2006.09.020 Google Scholar
Haynes, J.-D., Rees, G. (2006). Decoding mental states from brain activity in humans. Nature Reviews Neuroscience, 7(7), 523–534. doi:10.1038/nrn1931 CrossRefGoogle ScholarPubMed
Hoeft, F., McCandliss, B.D., Black, J.M., Gantman, A., Zakerani, N., Hulme, C., Gabrieli, J.D. (2011). Neural systems predicting long-term outcome in dyslexia. Proceedings of the National Academy of Sciences of the United States of America, 108(1), 361–366. doi:10.1073/pnas.1008950108 Google Scholar
Hosseini, S.M., Koovakkattu, D., Kesler, S.R. (2012). Altered small-world properties of gray matter networks in breast cancer. BMC Neurology, 12(1), 28. doi:10.1186/1471-2377-12-28 CrossRefGoogle ScholarPubMed
Janelsins, M.C., Kohli, S., Mohile, S.G., Usuki, K., Ahles, T.A., Morrow, G.R. (2011). An update on cancer- and chemotherapy-related cognitive dysfunction: Current status. Seminars in Oncology, 38(3), 431–438. doi:10.1053/j.seminoncol.2011.03.014 Google Scholar
Jones, D.T., Machulda, M.M., Vemuri, P., McDade, E.M., Zeng, G., Senjem, M.L., Jack, C.R. Jr (2011). Age-related changes in the default mode network are more advanced in Alzheimer disease. Neurology, 77(16), 1524–1531. doi:10.1212/WNL.0b013e318233b33d Google Scholar
Kamitani, Y., Tong, F. (2005). Decoding the visual and subjective contents of the human brain. Nature Neuroscience, 8(5), 679–685. doi:10.1038/nn1444 Google Scholar
Kesler, S., Janelsins, M., Koovakkattu, D., Palesh, O., Mustian, K., Morrow, G., Dhabhar, F.S. (2013). Reduced hippocampal volume and verbal memory performance associated with interleukin-6 and tumor necrosis factor-alpha levels in chemotherapy-treated breast cancer survivors. Brain Behavior and Immunity, 30(Suppl), S109–S116. doi:10.1016/j.bbi.2012.05.017 Google Scholar
Kesler, S.R., Bennett, F.C., Mahaffey, M.L., Spiegel, D. (2009). Regional brain activation during verbal declarative memory in metastatic breast cancer. Clinical Cancer Research, 15(21), 6665–6673. doi:10.1158/1078-0432.CCR-09-1227 Google Scholar
Kesler, S.R., Kent, J.S., O'Hara, R. (2011). Prefrontal cortex and executive function impairments in primary breast cancer. Journal of the American Medical Association - Neurology, 68(11), 1447–1453. doi:10.1001/archneurol.2011.245 Google Scholar
Kesler, S.R., Wefel, J.S., Hosseini, S.M., Cheung, M., Watson, C.L., Hoeft, F. (2013). Default mode network connectivity distinguishes chemotherapy-treated breast cancer survivors from controls. Proceedings of the National Academy of Sciences of the United States of America, 110(28), 11600–11605. doi:10.1073/pnas.1214551110 Google Scholar
Leh, S.E., Petrides, M., Strafella, A.P. (2010). The neural circuitry of executive functions in healthy subjects and Parkinson's disease. Neuropsychopharmacology, 35(1), 70–85.Google Scholar
Mar Fan, H., Houédé-Tchen, N., Chemerynsky, I., Yi, Q.-L., Xu, W., Harvey, B. (2010). Menopausal symptoms in women undergoing chemotherapy-induced and natural menopause: A prospective controlled study. Annals of Oncology, 21(5), 983–987.CrossRefGoogle ScholarPubMed
Marzelli, M.J., Hoeft, F., Hong, D.S., Reiss, A.L. (2011). Neuroanatomical spatial patterns in Turner syndrome. Neuroimage, 55(2), 439–447. doi:10.1016/j.neuroimage.2010.12.054 Google Scholar
McDonald, B.C., Conroy, S.K., Ahles, T.A., West, J.D., Saykin, A.J. (2010). Gray matter reduction associated with systemic chemotherapy for breast cancer: A prospective MRI study. Breast Cancer Research and Treatment, 123(3), 819–828. doi:10.1007/s10549-010-1088-4 Google Scholar
McDonald, B.C., Conroy, S.K., Ahles, T.A., West, J.D., Saykin, A.J. (2012). Alterations in brain activation during working memory processing associated with breast cancer and treatment: A prospective functional magnetic resonance imaging study. Journal of Clinical Oncology, 30(20), 2500–2508. doi:10.1200/JCO.2011.38.5674 Google Scholar
Menon, V., Adleman, N.E., White, C.D., Glover, G.H., Reiss, A.L. (2001). Error-related brain activation during a Go/NoGo response inhibition task. Human Brain Mapping, 12(3), 131–143. doi:10.1002/1097-0193(200103)12:3<131::AID-HBM1010>3.0.CO;2-C [pii]Google Scholar
Mostofsky, S.H., Schafer, J.G., Abrams, M.T., Goldberg, M.C., Flower, A.A., Boyce, A., Pekar, J.J. (2003). fMRI evidence that the neural basis of response inhibition is task-dependent. Brain Research, 17(2), 419–430.Google Scholar
Noble, W.S. (2006). What is a support vector machine? Nature Biotechnology, 24(12), 1565–1567. doi:10.1038/nbt1206-1565 Google Scholar
O'Dwyer, L., Lamberton, F., Bokde, A.L., Ewers, M., Faluyi, Y.O., Tanner, C., Hampel, H. (2012). Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment. PLoS One, 7(2), e32441. doi:10.1371/journal.pone.0032441 CrossRefGoogle ScholarPubMed
O'Dwyer, L., Lamberton, F., Matura, S., Scheibe, M., Miller, J., Rujescu, D., Hampel, H. (2012). White matter differences between healthy young ApoE4 carriers and non-carriers identified with tractography and support vector machines. PLoS One, 7(4), e36024. doi:10.1371/journal.pone.0036024 Google Scholar
O'Toole, A.J., Jiang, F., Abdi, H., Penard, N., Dunlop, J.P., Parent, M.A. (2007). Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data. Journal of Cognitive Neuroscience, 19(11), 1735–1752. doi:10.1162/jocn.2007.19.11.1735 CrossRefGoogle Scholar
Orru, G., Pettersson-Yeo, W., Marquand, A.F., Sartori, G., Mechelli, A. (2012). Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review. Neuroscience Biobehavioral Reviews, 36(4), 1140–1152. doi:10.1016/j.neubiorev.2012.01.004 Google Scholar
Pereira, F., Mitchell, T., Botvinick, M. (2009). Machine learning classifiers and fMRI: A tutorial overview. Neuroimage, 45(1 Suppl), S199–S209. doi:10.1016/j.neuroimage.2008.11.007 Google Scholar
Phillips, K.M., Jim, H.S., Small, B.J., Laronga, C., Andrykowski, M.A., Jacobsen, P.B. (2012). Cognitive functioning after cancer treatment: A 3-year longitudinal comparison of breast cancer survivors treated with chemotherapy or radiation and noncancer controls. Cancer, 118(7), 1925–1932. doi:10.1002/cncr.26432 Google Scholar
Picton, T.W., Stuss, D.T., Alexander, M.P., Shallice, T., Binns, M.A., Gillingham, S. (2007). Effects of focal frontal lesions on response inhibition. Cerebral Cortex, 17(4), 826–838. doi:bhk031 [pii]10.1093/cercor/bhk031Google Scholar
Reuter-Lorenz, P.A., Cimprich, B. (2013). Cognitive function and breast cancer: Promise and potential insights from functional brain imaging. Breast Cancer Research and Treatment, 137(1), 33–43. doi:10.1007/s10549-012-2266-3 Google Scholar
Rice, M.E., Harris, G.T. (2005). Comparing effect sizes in follow-up studies: ROC Area, Cohen's d, and r. Law and Human Behavior, 29(5), 615–620. doi:10.1007/s10979-005-6832-7 Google Scholar
Rodin, G., Ahles, T.A. (2012). Accumulating evidence for the effect of chemotherapy on cognition. Journal of Clinical Oncology, 30(29), 3568–3569. doi:10.1200/JCO.2012.43.5776 Google Scholar
Scherling, C., Collins, B., Mackenzie, J., Bielajew, C., Smith, A. (2011). Pre-chemotherapy differences in visuospatial working memory in breast cancer patients compared to controls: An FMRI study. Frontiers in Human Neuroscience, 5, 122. doi:10.3389/fnhum.2011.00122 Google Scholar
Scherling, C., Collins, B., Mackenzie, J., Bielajew, C., Smith, A. (2012). Prechemotherapy differences in response inhibition in breast cancer patients compared to controls: A functional magnetic resonance imaging study. Journal of Clinical and Experimental Neuropsychology, 34(5), 543–560. doi:10.1080/13803395.2012.666227 Google Scholar
Sheline, Y.I., Raichle, M.E., Snyder, A.Z., Morris, J.C., Head, D., Wang, S., Mintun, M.A. (2010). Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biological Psychiatry, 67(6), 584–587. doi:10.1016/j.biopsych.2009.08.024 Google Scholar
Silverman, D.H., Dy, C.J., Castellon, S.A., Lai, J., Pio, B.S., Abraham, L., Ganz, P.A. (2007). Altered frontocortical, cerebellar, and basal ganglia activity in adjuvant-treated breast cancer survivors 5-10 years after chemotherapy. Breast Cancer Research and Treatment, 103(3), 303–311. doi:10.1007/s10549-006-9380-z Google Scholar
Simmonds, D.J., Pekar, J.J., Mostofsky, S.H. (2008). Meta-analysis of Go/No-go tasks demonstrating that fMRI activation associated with response inhibition is task-dependent. Neuropsychologia, 46(1), 224–232. doi:S0028-3932(07)00268-0 [pii]10.1016/j.neuropsychologia.2007.07.015Google Scholar
Stilley, C.S., Bender, C.M., Dunbar-Jacob, J., Sereika, S., Ryan, C.M. (2010). The impact of cognitive function on medication management: Three studies. Health Psychology, 29(1), 50–55. doi:10.1037/a0016940 Google Scholar
Swick, D., Ashley, V., Turken, A.U. (2008). Left inferior frontal gyrus is critical for response inhibition. BMC Neuroscience, 9, 102. doi:10.1186/1471-2202-9-102 Google Scholar
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1), 273–289. doi:10.1006/nimg.2001.0978S1053811901909784 [pii]Google Scholar
Vardy, J. (2009). Cognitive function in breast cancer survivors. Cancer Treatment Research, 151, 387–419. doi:10.1007/978-0-387-75115-3_24 Google Scholar
Wefel, J.S., Saleeba, A.K., Buzdar, A.U., Meyers, C.A. (2010). Acute and late onset cognitive dysfunction associated with chemotherapy in women with breast cancer. Cancer, 116(14), 3348–3356. doi:10.1002/cncr.25098 Google Scholar
Wefel, J.S., Schagen, S.B. (2012). Chemotherapy-related cognitive dysfunction. Current Neurological and Neuroscience Reports, 12(3), 267–275. doi:10.1007/s11910-012-0264-9 Google Scholar
Whitfield-Gabrieli, S., Nieto-Castanon, A. (2012). Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connectivity, 2(3), 125–141. doi:10.1089/brain.2012.0073 Google Scholar
Zeng, L.L., Shen, H., Liu, L., Wang, L., Li, B., Fang, P., Hu, D. (2012). Identifying major depression using whole-brain functional connectivity: A multivariate pattern analysis. Brain, 135(Pt 5), 1498–1507. doi:10.1093/brain/aws059 Google Scholar
Zhang, D., Shen, D. (2012). Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease. Neuroimage, 59(2), 895–907. doi:10.1016/j.neuroimage.2011.09.069 Google Scholar
Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D. (2011). Multimodal classification of Alzheimer's disease and mild cognitive impairment. Neuroimage, 55(3), 856–867. doi:10.1016/j.neuroimage.2011.01.008 Google Scholar