Biology subjects

Thomopoulos, S. I.

Publications and source records attributed to Thomopoulos, S. I..

3 recordsLinked to original sources

bioRxiv2017

White matter differences in Parkinson’s disease mapped using tractometry

Neurodegenerative disorders are characterized by a progressive loss of brain function. Improved precision in mapping the altered brain pathways can provide a deep understanding of the trajectory of decline. We propose a tractometry workflow for conducting group statistical analyses of point-wise microstructural measures along white matter fasciculi to identify patterns of abnormalities associated with disease. We combined state-of-the-art tools including fiber registration, tract simplification and fiber matching for accurate point-wise statistical analyses across populations. We test the utility of this method by identifying group differences between Parkinsons disease (PD) patients and healthy controls. We find statistically significant group differences in diffusion MRI derived measures along the anterior thalamic radiations (ATR), corticospinal tract (CST) and regions of the corpus callosum (CC). These pathways are essential for motor control systems within cortico-cortical and cortico-subcortical brain networks. Moreover, the reported pathological changes were not widespread but rather localized along several tracts. Point-wise tract analyses may therefore offer an advantage in anatomical specificity over traditional methods that assess mean microstructural measures across large regions of interest.

neuroscience↗

bioRxiv2017

FiberNet 2.0: An Automatic Neural Network Based Tool for Clustering White Matter Fibers in the Brain

The brains white matter fiber tracts are impaired in a range of common and devastating conditions, from Alzheimers disease to brain trauma, and in developmental disorders such as autism and neurogenetic syndromes. Many studies now examine the connectivity and microstructure of the brains neural pathways, spurring the development of algorithms to extract and measure tracts and fiber bundles. Clustering white matter (WM) fibers, from whole-brain tractography, into anatomically meaningful bundles is still a challenging problem. Existing tract segmentation methods use atlases or regions of interest (ROI) or unsupervised spectral clustering. Even so, atlas-based segmentation does not always partition the brain into a set of recognizable fiber bundles. Deep learning techniques can be applied to automatically segment and cluster white matter fibers. Here we propose a robust approach using convolutional neural networks (CNNs) to learn shape features of the fiber bundles, which we then exploit to cluster WM fibers into bundles. In a range of tests across diverse fiber bundles, we illustrate the accuracy of our method, and its ability to suppress false positive fibers.

neuroscience↗

bioRxiv2017

FiberNET: An ensemble deep learning framework for clustering white matter fibers

White matter tracts are commonly analyzed in studies of micro-structural integrity and anatomical connectivity in the brain. Over the last decade, it has been an open problem as to how best to cluster white matter fibers, extracted from whole-brain tractography, into anatomically meaningful groups. Some existing techniques use region of interest (ROI) based clustering, atlas-based labeling, or unsupervised spectral clustering. ROI-based clustering is popular for analyzing anatomical connectivity among a set of ROIs, but it does not always partition the brain into recognizable fiber bundles. Here we propose an approach using convolutional neural networks (CNNs) to learn shape features of the fiber bundles, which are then exploited to cluster white matter fibers. To achieve such clustering, we first need to re-parameterize the fibers in an intrinsic space. The clustering is performed in induced parameterized coordinates. To our knowledge, this is one of the first approaches for fiber clustering using deep learning techniques. The results show strong accuracy - on a par with or better than other state-of-the-art methods.

bioinformatics↗