Effects of tractography filtering on the topology and interpretability of connectomes

Matteo Frigo (1, 2) , Samuel Deslauriers-Gauthier (1, 2) , Drew Parker (3) , Abdol Aziz Ould Ismail (3) , Junghoon John Kim (4) , Ragini Verma (3) , Rachid Deriche (1, 2)

1 ATHENA - Computational Imaging of the Central Nervous System
2 COMUE UCA - COMUE Université Côte d'Azur (2015-2019)
3 Penn Applied Connectomics and Imaging Group [Philadelphia]
4 Department of Molecular, Cellular & Biomedical Sciences [New York]

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.

Domaines: Imagerie médicale; Traitement du signal et de l'image [eess.SP]

Matteo Frigo, Samuel Deslauriers-Gauthier, Drew Parker, Abdol Aziz Ould Ismail, Junghoon John Kim, et al.. Effects of tractography filtering on the topology and interpretability of connectomes. OHBM 2019 - Organization for Human Brain Mapping, Jun 2019, Roma, Italy. ⟨hal-02056641⟩