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

Abstract

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.

Task-specific topology of brain networks supporting working memory and inhibition

“…We use MNE-python (Gramfort et al, 2014) for EEG preprocessing, the Frites software package (Combrisson et al, 2022) for estimation of functional connectivity, networkx (Hagberg et al, 2008) for graph measures calculation and lme4 (Bates et al, 2015) for modelling. The accuracy and response time for five segments of the test were calculated separately.…”

“…Information transfer in the brain involves different frequency bands, each with unique characteristics. Accordingly, our investigation was performed in four distinct frequency ranges: theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13), low beta (13)(14)(15)(16)(17)(18)(19)(20), and high beta (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). The exploration of inter-frequency interactions was beyond the scope of this study.…”

Neural interactions in the human frontal cortex dissociate reward and punishment learning

“…The rest of the significance testing procedure at the network level is similar to the local level, except that it is not applied within brain regions but within pairs of brain regions. Finally, for information-theoretic metrics and group-level statistics, we used a homemade Python software called Frites (Combrisson et al, 2022b).…”

Neural interactions in the human frontal cortex dissociate reward and punishment learning

“…Information-theoretic metrics and group-level statistics, are implemented in a homemade Python software called Frites ( Combrisson et al, 2022b ). The interaction information can be computed using the frites.conn.conn_ii function and the transfer entropy using the frites.conn.conn_te function.…”