Identifying Abnormal Network Alterations Common to Traumatic Brain Injury and Alzheimer’s Disease Patients Using Functional Connectome Data
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.
Unable to display preview. Download preview PDF.
- 1.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)CrossRefGoogle Scholar
- 3.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)Google Scholar
- 8.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)Google Scholar
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.