Impact of tractogram filtering and graph creation for structural connectomics in subjects with mild cognitive impairment
Abstract: One particular challenge of brain connectomics deals with inferring differences in the brain due to diseases such as Alzheimer's. More specifically, structural connectomics aims at investigating the connectivity between regions in the brain based on the distribution of neuronal fibers. The first step in generating structural connectomes is to perform tractography reconstruction on diffusion MRI (dMRI) data, to extract the most likely pathways of neural fibers. However, current tractography reconstruction algorithms suffer from having high sensitivity and low specificity. Thus, the following steps of creating, analyzing and deriving graphs metrics from connectivity maps based on tractography impair the reliable assessment of structural connectivity. A promising method to improve tractography and subsequent structural connectomes is to apply tractogram filtering methods. In this study, the impact of tractogram filtering on structural connectomics and derived graph measures of subjects with mild cognitive impairment (MCI), specifically using spherical-deconvolution informed filtering of tractograms (SIFT), is experimentally examined. Moreover, the study also aims at inferring the effects of tractogram filtering in machine-learning based classification of the aforementioned structural connectomes. The pipeline in this experimental setup uses registration tools from FSL, tractography tools from MRTrix3Tissue as well as Keras for classification. The results from the given experiments show, that graph measures such as nodestrength and betweenness centrality are altered for the individual nodes. This leads to new connectomes with nodes, which are more important after tractogram filtering. This effect was also seen in connectomes weighted by fractional anisotropy (FA), mean diffusivity (MD) and radial diffusivity (RD). Moreover, structural connectomes based on filtered tractograms yield a higher classification performance. The best classification performance was reached with 88.65% on raw connectomes. Limiting factors in this experimental setup are identified as the small number of subjects at hand and computation time and the errors introduced by image registration and tractography parameterization.
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