Frites: A Python package for functional connectivity analysis and group-level statistics of neurophysiological data

• Published in: Journal of open source software Vol. 7; no. 79; p. 3842
• Main Authors: Combrisson, Etienne, Basanisi, Ruggero, Cordeiro, Vinicius Lima, Ince, Robin A. A, Brovelli, Andrea
• Format: Journal Article
• Language: English
• Published: Open Journals 11.11.2022
• Subjects: Cognitive science; Neuroscience
• ISSN: 2475-9066; 2475-9066
• DOI: 10.21105/joss.03842

The field of cognitive computational neuroscience addresses open questions regarding the complex relation between cognitive functions and the dynamic coordination of neural activity over large-scale and hierarchical brain networks. State-of-the-art approaches involve the characterization of brain regions and inter-areal interactions that participate in cognitive processes (Battaglia & Brovelli, 2020). More precisely, the study of cognitive brain networks underlies linking local neural activity or interactions between brain regions to experimental variables, such as sensory stimuli or behavioral responses. The relation between the brain data and external variables might take complex forms (e.g. non-linear relationships) with strong variations across brain regions and participants. Therefore, powerful measures of information are required to detect complex relations and the statistical relevance at the population level should be able to adapt to the inter subject variability.