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Structural and functional interplay in anxiety related classification: a graph signal processing approach

Giovanna Orrù 1 Pierre Maurel 1 Julie Coloigner 1
1 Empenn
INSERM - Institut National de la Santé et de la Recherche Médicale, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Anxiety disorders are one of the most common mental health conditions with a high rate of everyday life disability. Connectivity is steadily gaining relevance to increase our knowledge of psychiatric diseases. Graph signal processing (GSP) is a new framework to integrate structural connectivity and brain function. We propose here a graph-based analysis using GSP metrics and classification procedure, to identify anxiety biomarkers. Results suggest that the joint consideration of structure-function features improves their discriminatory accuracy, and our understanding of the pathophysiology of anxiety.
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https://hal.inria.fr/hal-03450470
Contributor : Julie Coloigner Connect in order to contact the contributor
Submitted on : Friday, November 26, 2021 - 9:18:03 AM
Last modification on : Friday, January 21, 2022 - 3:11:59 AM

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ISBI_2021_Orru.pdf
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  • HAL Id : hal-03450470, version 1

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Giovanna Orrù, Pierre Maurel, Julie Coloigner. Structural and functional interplay in anxiety related classification: a graph signal processing approach. ISBI 2021 - IEEE International Symposium on Biomedical Imaging, Apr 2021, Nice, France. pp.1-4. ⟨hal-03450470⟩

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