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

Combrisson, E., Allegra, M., Basanisi, R., Ince, R. A.A. , Giordano, B. L., Bastin, J. and Brovelli, A. (2022) Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data. NeuroImage, 258, 119347. (doi: 10.1016/j.neuroimage.2022.119347) (PMID:35660460)

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The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines and principles for neuroscientists known as Good Scientific Practice for conducting more reliable research. Still, every study remains almost unique in its combination of analytical and statistical approaches. While it is understandable considering the diversity of designs and brain data recording, it also represents a striking point against reproducibility. Here, we propose a non-parametric permutation-based statistical framework, primarily designed for neurophysiological data, in order to perform group-level inferences on non-negative measures of information encompassing metrics from information-theory, machine-learning or measures of distances. The framework supports both fixed- and random-effect models to adapt to inter-individuals and inter-sessions variability. Using numerical simulations, we compared the accuracy in ground-truth retrieving of both group models, such as test- and cluster-wise corrections for multiple comparisons. We then reproduced and extended existing results using both spatially uniform MEG and non-uniform intracranial neurophysiological data. We showed how the framework can be used to extract stereotypical task- and behavior-related effects across the population covering scales from the local level of brain regions, inter-areal functional connectivity to measures summarizing network properties. We also present an open-source Python toolbox called Frites1 that includes the proposed statistical pipeline using information-theoretic metrics such as single-trial functional connectivity estimations for the extraction of cognitive brain networks. Taken together, we believe that this framework deserves careful attention as its robustness and flexibility could be the starting point toward the uniformization of statistical approaches.

Item Type:Articles
Additional Information:EC and AB were supported by the PRC project “CausaL” (ANR-18-CE28-0016). This project/research has received funding from the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3). MA, AB were supported by FLAG ERA II “Joint Transnational Call 2017" - HBP - Basic and Applied Research 2, Brainsynch-Hit (ANR-17-HBPR-0001). RB acknowledges support through a PhD Scholarship awarded by the Neuroschool. This work has received support from the French government under the Programme Investissements d'Avenir, Initiative d'Excellence d'Aix-Marseille Université via A*Midex (AMX-19-IET-004) and ANR (ANR-17-EURE-0029) funding. RAAI was supported by the Wellcome Trust [214120/Z/18/Z]. JB was supported by ANR-17-CE37-0018 and ANR-18-CE28-0016.
Glasgow Author(s) Enlighten ID:Giordano, Dr Bruno and Ince, Dr Robin
Creator Roles:
Ince, R.Conceptualization, Methodology, Software, Supervision, Writing – review and editing
Giordano, B.Conceptualization, Methodology, Supervision, Writing – original draft
Authors: Combrisson, E., Allegra, M., Basanisi, R., Ince, R. A.A., Giordano, B. L., Bastin, J., and Brovelli, A.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Journal Name:NeuroImage
ISSN (Online):1095-9572
Published Online:31 May 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in NeuroImage 258:119347
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
307180Human Brain Project SGA_3Lars MuckliEuropean Commission (EC)945539Centre for Cognitive Neuroimaging
304240Beyond Pairwise Connectivity: developing an information theoretic hypergraph methodology for multi-modal resting state neuroimaging analysisRobin InceWellcome Trust (WELLCOTR)214120/Z/18/ZCentre for Cognitive Neuroimaging