Abstract
The objective of this study is to determine if patients with traumatic brain injury (TBI) have similar pathological changes in brain network organization as patients with Alzheimer’s disease (AD) using functional connectome data reconstructed from resting-state fMRI (rsfMRI). To achieve our objective a novel machine learning technique is proposed that uses a top-down reverse engineering approach to identify abnormal network alterations in functional connectome data that are common to patients with AD and TBI. In general, if the proposed machine learning approach classifies a TBI connectome as AD, then this suggests a common network pathology exists in the connectomes of AD and TBI. The advantage of proposed machine learning technique is two-fold: 1) existing longitudinal TBI imaging data is not required, and 2) the potential risk of a TBI patient converting to AD later in life does not require a lengthy and potentially expensive longitudinal imaging study. Experiments are provided that show the AD pathology learned by our connectome-based machine learning technique is able to correctly identify TBI patients with 80% accuracy. In summary, this research may lead to early interventions that can dramatically increase the quality of life for TBI patients who may convert to AD.
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Han, K., Mac Donald, C.L., Johnson, A.M., Barnes, Y., Wierzechowski, L., Zonies, D., Oh, J., Flaherty, S., Fang, R., Raichle, M.E., et al.: Disrupted modular organization of resting-state cortical functional connectivity in us military personnel following concussive mildblast-related traumatic brain injury. Neuroimage 84, 76–96 (2014)
Messé, A., Caplain, S., Pélégrini-Issac, M., Blancho, S., Lévy, R., Aghakhani, N., Montreuil, M., Benali, H., Lehéricy, S.: Specific and evolving resting-state network alterations in post-concussion syndrome following mild traumatic brain injury. PloS one 8(6), e65470 (2013)
Mormino, E.C., Smiljic, A., Hayenga, A.O., H. Onami, S., Greicius, M.D., Rabinovici, G.D., Janabi, M., Baker, S.L., Yen, I.V., Madison, C.M., Miller, B.L., Jagust, W.J.: Relationships between beta-amyloid and functional connectivity in different components of the default mode network in aging. Cerebral Cortex (2011)
Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E.: Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59(3), 2142–2154 (2012)
Power, J.D., Mitra, A., Laumann, T.O., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E.: Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84, 320–341 (2014)
Qureshi, S.U., Kimbrell, T., Pyne, J.M., Magruder, K.M., Hudson, T.J., Petersen, N.J., Yu, H.J., Schulz, P.E., Kunik, M.E.: Greater prevalence and incidence of dementia in older veterans with posttraumatic stress disorder. J. Am. Geriatr. Soc. 58(9), 1627–1633 (2010)
Rubinov, M., Sporns, O.: Weight-conserving characterization of complex functional brain networks. Neuroimage 56(4), 2068–2079 (2011)
Schäfer, J., Strimmer, K.: A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Statistical applications in genetics and molecular biology 4(1) (2005)
Sporns, O.: The human connectome: origins and challenges. Neuroimage 80, 53–61 (2013)
Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B 58, 267–288 (1994)
Wang, J., Zuo, X., Dai, Z., Xia, M., Zhao, Z., Zhao, X., Jia, J., Han, Y., He, Y.: Disrupted functional brain connectome in individuals at risk for alzheimer’s disease. Biological Psychiatry 73(5), 472–481 (2013)
Wee, C.Y., Yap, P.T., Zhang, D., Denny, K., Browndyke, J.N., Potter, G.G., Welsh-Bohmer, K.A., Wang, L., Shen, D.: Identification of MCI individuals using structural and functional connectivity networks. NeuroImage 59(3), 2045–2056 (2012)
Xia, M., Wang, J., He, Y.: BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS ONE 8(7), e68910 (2013)
Yaffe, K., Vittinghoff, E., Lindquist, K., Barnes, D., Covinsky, K.E., Neylan, T., Kluse, M., Marmar, C.: Posttraumatic stress disorder and risk of dementia among US veterans. Arch. Gen. Psychiatry 67(6), 608–613 (2010)
Zhao, X., Liu, Y., Wang, X., Liu, B., Xi, Q., Guo, Q., Jiang, H., Jiang, T., Wang, P.: Disrupted small-world brain networks in moderate alzheimer’s disease: a resting-state fmri study. PloS one 7(3), e33540 (2012)
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© 2015 Springer International Publishing Switzerland
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Vanderweyen, D. et al. (2015). Identifying Abnormal Network Alterations Common to Traumatic Brain Injury and Alzheimer’s Disease Patients Using Functional Connectome Data. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds) Machine Learning in Medical Imaging. MLMI 2015. Lecture Notes in Computer Science(), vol 9352. Springer, Cham. https://doi.org/10.1007/978-3-319-24888-2_28
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DOI: https://doi.org/10.1007/978-3-319-24888-2_28
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