Mishra, V. R. | Search bioRxiv

bioRxiv2021

Towards Understanding Comprehensive Morphometric Changes and Its Correlation with Cognition and Exposure to Fighting in Active Professional Boxers

Professional athletes exposed to repetitive head impacts are at increased risk for developing a progressive neurological syndrome known as traumatic encephalopathy syndrome and neuropathology seen on autopsy called chronic traumatic encephalopathy (CTE). The early identification of individuals at increased risk for CTE is important and the search for biomarkers is underway. In this study, we utilized data from a large cohort study to compare differences in regional brain volumes, cortical thickness, voxel-based morphometric (VBM)-derived measures, and graph-theoretical measures derived from large-scale topographical maps in active professional boxers. We compared the above morphometric measures between active professional boxers with low cognitive scores (impaired boxers) and active professional boxers with intact cognitive scores (nonimpaired boxers). The cognitive scores were evaluated through neuropsychological evaluation. As an exploratory analysis, we also examined the power of various machine-learning algorithms to identify impaired and nonimpaired boxers using both group-level regression-driven analysis and previously identified hypothesis-driven cortical thickness and volumetric measures. We found significant group-level differences between impaired and nonimpaired boxers in cortical thickness in a single brain region (right precuneus), differences in VBM-derived gray matter density encompassing the caudate, putamen, and thalamus; and white matter density encompassing the right paracentral lobule, but no differences in any graph-theoretical network properties. Additionally, we found that a priori hypothesis-driven T1-derived cortical thickness and volumetric analysis performed better than traditional regression-based analysis. Overall, this study suggests that neuroanatomical differences exist between impaired and nonimpaired active professional boxers, and that hypothesis-driven techniques are likely necessary to become reliable biomarkers.

neuroscience↗

bioRxiv2019

A robust deep neural network for denoising task-based fMRI data: An application to working memory and episodic memory

In this study, a deep neural network (DNN) is proposed to reduce the noise in task-based fMRI data without explicitly modeling noise. The DNN artificial neural network consists of one temporal convolutional layer, one long short-term memory (LSTM) layer, one time-distributed fully-connected layer, and one unconventional selection layer in sequential order. The LSTM layer takes not only the current time point but also what was perceived in a previous time point as its input to characterize the temporal autocorrelation of fMRI data. The fully-connected layer weights the output of the LSTM layer, and the output denoised fMRI time series is selected by the selection layer. Assuming that task-related neural response is limited to gray matter, the model parameters in the DNN network are optimized by maximizing the correlation difference between gray matter voxels and white matter or ventricular cerebrospinal fluid voxels. Instead of targeting a particular noise source, the proposed neural network takes advantage of the task design matrix to better extract task-related signal in fMRI data. The DNN network, along with other traditional denoising techniques, has been applied on simulated data, working memory task fMRI data acquired from a cohort of healthy subjects and episodic memory task fMRI data acquired from a small set of healthy elderly subjects. Qualitative and quantitative measurements were used to evaluate the performance of different denoising techniques. In the simulation, DNN improves fMRI activation detection and also adapts to varying hemodynamic response functions across different brain regions. DNN efficiently reduces physiological noise and generates more homogeneous task-response correlation maps in real data.

neuroscience↗