"Fully automated segmentation of the corticospinal tract using the TractSeg algorithm in patients with brain tumors".

OBJECTIVE: Tractography has been used to define the presurgical location of white matter tracts, but this is subjective and time-intensive, making incorporation to imaging workflow at scale problematic. The objective is to validate a fully automated pipeline using the TractSeg algorithm (Wasserthal et al. NeuroImage 2018;183:239-253) to segment the corticospinal tract in patients with brain tumors adjacent to the corticospinal tract. METHODS: The process of importing a structural MPRAGE sequence and raw diffusion weighted images from PACS, executing the TractSeg algorithm, overlaying the resulting bilateral corticospinal tracts on the MPRAGE image, and exporting this composite image to PACS was automated. This procedure was used to segment the corticospinal tract in 28 patients with brain masses adjacent to or displacing the corticospinal tract. These segmentations were compared with both manual deterministic tractography performed with DSI Studio using seeds placed in the pons and an automated tractography method in DSI Studio. RESULTS: The automated algorithm was able to segment the bilateral corticospinal tracts in all 28 patients whereas the manual reference method and DSI Studio based automated tractography were unsuccessful in 2 and 1 patients, respectively. In all cases, the TractSeg segmentations very closely matched the manual segmentations. Also, TractSeg appeared to include larger portions of the lateral corticospinal tract fibers than the other 2 methods. CONCLUSION: The TractSeg algorithm demonstrated robust performance in segmenting the corticospinal tract in patients with brain tumors adjacent to this tract. The algorithm is fast to perform and has great potential for optimizing and streamlining neurosurgical planning.

Topological Data Analysis of Functional MRI Connectivity in Time and Space Domains.

The functional architecture of the brain can be described as a dynamical system where components interact in flexible ways, constrained by physical connections between regions. Using correlation, either in time or in space, as an abstraction of functional connectivity, we analyzed resting state fMRI data from 1003 subjects. We compared several data preprocessing strategies and found that independent component-based nuisance regression outperformed other strategies, with the poorest reproducibility in strategies that include global signal regression. We also found that temporal vs. spatial functional connectivity can encode different aspects of cognition and personality. Topological analyses using persistent homology show that persistence barcodes are significantly correlated to individual differences in cognition and personality, with high reproducibility. Topological data analyses, including approaches to model connectivity in the time domain, are promising tools for representing high-level aspects of cognition, development, and neuropathology.