Poster De Conférence Année : 2026

Uncertain-graph framework for restingstate connectivity analysis in coma

Résumé

Coma leads to persistent cognitive deficits, but underlying network disruptions remain unclear. Resting-state functional connectivity (FC) often probes these changes with graph-based metrics. Traditional graph approaches have key limitations:
1. FC edges are based on correlations between regional mean signals, discarding voxel-level variability.
2. Graph construction relies on arbitrary thresholds, which may exclude patients with severely disrupted connectivity.
To address these challenges, we developed a voxel-informed, probabilistic framework for subject-level networks with finer granularity than traditional thresholded graphs.

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Dates et versions

hal-05554407 , version 1 (16-03-2026)

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  • HAL Id : hal-05554407 , version 1

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Arturo Cabrera Vazquez, Sophie Achard, Michel Dojat, Stein Silva. Uncertain-graph framework for restingstate connectivity analysis in coma. IABM 2026 - Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale, Mar 2026, Lyon, France. 2026. ⟨hal-05554407⟩
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