Published June 9, 2019
| Version v1
Conference paper
Effects of tractography filtering on the topology and interpretability of 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)
- COMUE Université Côte d'Azur (2015-2019) (COMUE 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)
- ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019)
- European Project: 694665,H2020 ERC,ERC-2015-AdG,CoBCoM(2016)
Description
The analysis of connectomes and their associated network metrics forms an important part of clinical studies. These connectomes are based on tractography algorithms to estimate the structural connectivity between brain regions. However, tractography algorithms, are prone to false positive connections and this affects the quality of the connectomes. Several tractography filtering techniques (TFTs) have been proposed to alleviate this issue in studies, but their effect on connectomic analyses of pathology has not been investigated. The aim of our work is to investigate how TFTs affect network metrics and their interpretation in the context of clinical studies.
Abstract
International audience
Additional details
- URL
- https://hal.archives-ouvertes.fr/hal-02056641
- URN
- urn:oai:HAL:hal-02056641v1
- Origin repository
- UNICA