Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data

Combrisson, Etienne; Allegra, Michele; Basanisi, Ruggero; Ince, Robin A. A.; Giordano, Bruno L.; Bastin, Julien; Brovelli, Andrea

doi:10.1016/j.neuroimage.2022.119347

Cited by 19 publications

(14 citation statements)

References 122 publications

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“…The toolbox includes different methods that cope with multiplecomparison correction problems, such as test-and cluster-wise p-value corrections. The implemented framework also supports both fixed-and random-effect models to adapt to inter-individuals and inter-sessions variability (Combrisson et al, 2022).…”

Section: Measures Of Information and Group-level Statisticsmentioning

confidence: 99%

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

Combriss¹,

Basanisi²,

Cordeiro³

et al. 2022

JOSS

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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.

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Section: Measures Of Information and Group-level Statisticsmentioning

confidence: 99%

“…Combrisson et al (2022). Frites: A Python package for functional connectivity analysis and group-level statistics of neurophysiological data.…”

mentioning

confidence: 99%

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

Combriss¹,

Basanisi²,

Cordeiro³

et al. 2022

JOSS

Self Cite

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“…Further, NIT includes standard and widely used non-parametric hypothesis testing methods, but does not yet include group statistics, which to the best of our knowledge among information theoretic toolboxes has been only implemented in FRITES [32]. However, the output of NIT analyses can be easily used as input to group statistics toolboxes [32]. Further, the study of PID is a burgeoning field with many measures and advances being elaborated [49,80,154,155].…”

Section: Comparisons With Existing Information Theoretic Toolboxes Fo...mentioning

confidence: 99%

“…ME models are present in some specialized toolboxes [29]. Further, NIT includes standard and widely used non-parametric hypothesis testing methods, but does not yet include group statistics, which to the best of our knowledge among information theoretic toolboxes has been only implemented in FRITES [32]. However, the output of NIT analyses can be easily used as input to group statistics toolboxes [32].…”

Section: Comparisons With Existing Information Theoretic Toolboxes Fo...mentioning

confidence: 99%

“…Several algorithms have been proposed for the application of IT to these low-dimensional neural data [6,23,24]. Their ability to provide accurate and data-robust information estimates has been extensively validated and demonstrated on electrophysiological recordings, including on spike trains of small populations and on LFPs and EEGs [24][25][26][27], and their use and dissemination has been aided by software toolboxes [25,[28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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NIT: an open-source tool for information theoretic analysis of neural population data

Maffulli

Casal

Celotto

et al. 2022

Preprint

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Information theory provides a popular and principled framework for the analysis of neural data. It allows to uncover in an assumption-free way how neurons encode and transmit information, capturing both linear and non-linear coding mechanisms and including the information carried by interactions of any order. To facilitate its application, here we present Neuroscience Information Toolbox (NIT), a new toolbox for the accurate information theoretical analysis of neural data. NIT contains widely used tools such as limited sampling bias corrections and discretization of neural probabilities for the calculation of stimulus coding in low-dimensional representation of neural activity (e.g. Local Field Potentials or the activity of small neural population).Importantly, it adds a range of recent tools for quantifying information encoding by large populations of neurons or brain areas, for the directed transmission of information between neurons or areas, and for the calculation of Partial Information Decompositions to quantify the behavioral relevance of neural information and the synergy and redundancy among neurons and brain areas. Further, because information theoretic algorithms have been previously validated mainly with electrophysiological recordings, here we used realistic simulations and analysis of real data to study how to optimally apply information theory to the analysis of two-photon calcium imaging data, which are particularly challenging due to their lower signal-to-noise and temporal resolution. We also included algorithms (based on parametric and non-parametric copulas) to compute robustly information specifically with analog signals such as calcium traces. We provide indications on how to best process calcium imaging traces and to apply NIT depending on the type of calcium indicator, imaging frame rate and firing rate levels. In sum, NIT provides a toolbox for the comprehensive and effective information theoretic analysis of all kinds of neural data, including calcium imaging.

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Information flow between motor cortex and striatum reverses during skill learning

Lemke,

Celotto,

Maffulli

et al. 2024

Current Biology

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Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data

Cited by 19 publications

References 122 publications

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

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

NIT: an open-source tool for information theoretic analysis of neural population data

Information flow between motor cortex and striatum reverses during skill learning

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