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MRI correlates of chronic symptoms in mild traumatic brain injury

Posted by on Friday, December 6, 2019 in Machine Learning.

Kerley, C. I., Schilling, K. G., Blaber, J., Miller, B., Newton, A., Anderson, A. W., Landman, B. A., Rex, T. S. “MRI correlates of chronic symptoms in mild traumatic brain injury.” In SPIE Medical Imaging, International Society for Optics and Photonics, 2020.  (Accepted)

Full text: NIHMSID, arXiv

Abstract

Some veterans with a history of mild traumatic brain injury (mTBI) have reported experiencing auditory and visual dysfunction that persist beyond the acute phase of the incident. The etiology behind these symptoms is difficult to characterize, since mTBI is defined by negative imaging findings on current clinical imaging. There are several competing hypotheses that could explain functional deficits; one example is shear injury, which may manifest in diffusion-weighted magnetic resonance (MR) imaging (DWI). Herein, we explore this alternative hypothesis in a pilot study of multi-parametric MR imaging. Briefly, we consider a cohort of 8 mTBI patients relative to 22 control subjects using structural T1-weighted imaging (T1w) and connectivity with DWI. 1,344 metrics were extracted per subject from whole brain regions and connectivity patterns in sensory networks. For each set of imaging-derived metrics, the control subject metrics were embedded in a low-dimensional manifold with principal component analysis, after which mTBI subject metrics were projected into the same space. These manifolds were employed to train support vector machines (SVM) to classify subjects as controls or mTBI. Two of the SVMs trained achieved near-perfect accuracy averaged across four-fold cross-validation. Additionally, we present correlations between manifold dimensions and 22 self-reported mTBI symptoms and find that five principal components from the manifolds (one component from the T1w manifold and four components from the DWI manifold) are significantly correlated with symptoms (p<0.05, uncorrected). The novelty of this work is that the DWI and T1w imaging metrics seem to contain information critical for distinguishing between mTBI and control subjects. This work presents an analysis of the pilot phase of data collection of the Quantitative Evaluation of Visual and Auditory Dysfunction and Multi-Sensory Integration in Complex TBI Patients study and defines specific hypotheses to be tested in the full sample.

Keywords: mild traumatic brain injury, support vector machine, principal component analysis, MRI

Imaging Metric Analysis
Figure 3. A schematic overview of the imaging metric analysis. First, the imaging metrics are normalized by converting the raw imaging metrics to z-scores using the mean 𝜇controls and standard deviation 𝜎controls of the control subjects. Principal Component Analysis (PCA) is performed using the z-scores of the control subjects, resulting in three lower-dimensional PCA spaces (one for each metric set), which the mTBI subjects’ z-scores are projected into. Next, to analyze the metric sets individually, the PCA components of a single set and the subjects’ ages are used to train a four-fold cross-validated Support Vector Machine (SVM) to classify subjects as controls or mTBI. Starting with the first principal component, the entire PCA space of each metric set is swept, adding a single component to the SVM at each iteration. After all components have been swept, CO, the number of principal components that produces the most optimal classifier, can be determined for each metric set based on the validation set performance (averaged across the four cross-validation folds). Finally, to analyze the metric sets together, the iterative SVM training process is repeated on the combined set of CO components from each metric set. In this step, the process starts with the first principal component from each metric set then adds an additional component from each metric set to the classifier at each iteration.

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