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

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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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Dates and versions

hal-03450470 , version 1 (26-11-2021)

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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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