Communication Dans Un Congrès Année : 2025

Benchmarking Brain Connectivity Graph Inference: A Novel Validation Approach

Résumé

Inferring a binary connectivity graph from resting-state fMRI data for a single subject requires making several methodological choices and assumptions that can significantly affect the results. In this study, we investigate the robustness of existing edge detection methods when relaxing a common assumption: the sparsity of the graph. We propose a new pipeline to generate synthetic data and to benchmark the state of the art in graph inference. Simulated correlation matrices are designed to have a set of given zeros and a constraint on the signal-to-noise ratio. We compare approaches based on covariance or precision matrices, emphasizing their implications for connectivity inference. This framework allows us to assess the sensitivity of connectivity estimations and edge detection methods to different parameters.

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Dates et versions

hal-04995510 , version 1 (18-03-2025)

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  • HAL Id : hal-04995510 , version 1

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Alice Chevaux, Ali Fahkar, Kévin Polisano, Irène Gannaz, Sophie Achard. Benchmarking Brain Connectivity Graph Inference: A Novel Validation Approach. 33rd European Signal Processing Conference (EUSIPCO 2025), Sep 2025, Palerme, Italy. ⟨hal-04995510⟩
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