Published 2020 | Version v1
Journal article

Diffusion MRI Tractography Filtering Techniques Change the Topology of Structural Connectomes

Others:
Computational Imaging of the Central Nervous System (ATHENA) ; Inria Sophia Antipolis - Méditerranée (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Université Côte d'Azur (UCA)
Penn Applied Connectomics and Imaging Group [Philadelphia] ; University of Pennsylvania [Philadelphia]-Center for Biomedical Image Computing & Analytics [Philadelphia] (CBICA) ; Department of Radiology [Philadelphia] ; University of Pennsylvania [Philadelphia]-University of Pennsylvania [Philadelphia]-Department of Radiology [Philadelphia] ; University of Pennsylvania [Philadelphia]
Department of Molecular, Cellular & Biomedical Sciences [New York] ; CUNY School of Medicine ; City University of New York [New York] (CUNY)-City University of New York [New York] (CUNY)
This work received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (ERC Advanced Grant agreement No 694665: CoBCoM - Computational Brain Connectivity Mapping) and NIH R01NS065980, PA-Department of Health PACT award and NIH R01NS096606.Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.
ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019)
European Project: 694665,H2020 ERC,ERC-2015-AdG,CoBCoM(2016)

Citation

An error occurred while generating the citation.

Description

Objective. The use of non-invasive techniques for the estimation of structural brain networks (i.e. connectomes) opened the door to large-scale investigations on the functioning and the architecture of the brain, unveiling the link between neurological disorders and topological changes of the brain network. This study aims at assessing if and how the topology of structural connectomes estimated non-invasively with diffusion MRI is affected by the employment of tractography filtering techniques in structural connectomic pipelines. Additionally, this work investigates the robustness of topological descriptors of filtered connectomes to the common practice of density-based thresholding. Approach. We investigate the changes in global efficiency, characteristic path length, modularity and clustering coefficient on filtered connectomes obtained with the spherical deconvolution informed filtering of tractograms and using the convex optimization modelling for microstructure informed tractography. The analysis is performed on both healthy subjects and patients affected by traumatic brain injury and with an assessment of the robustness of the computed graph-theoretical measures with respect to density-based thresholding of the connectome. Main Result. Our results demonstrate that tractography filtering techniques change the topology of brain networks, and thus alter network metrics both in the pathological and the healthy cases. Moreover, the measures are shown to be robust to density-based thresholding. Significance. The present work highlights how the inclusion of tractography filtering techniques in connectomic pipelines requires extra caution as they systematically change the network topology both in healthy subjects and patients affected by traumatic brain injury. Finally, the practice of low-to-moderate density-based thresholding of the connectomes is confirmed to have negligible effects on the topological analysis.

Abstract

International audience

Additional details

Created:
December 4, 2022
Modified:
November 28, 2023