One session of fMRI-Neurofeedback training on motor imagery modulates 
whole-brain effective connectivity and dynamical complexity

De Fillippi, Eleonora; Marins, Theo; Escrichs, Anira; Gilson, Matthieu; Moll, Jorge; Tovar-Moll, Fernanda; Deco, Gustavo

doi:10.1093/texcom/tgac027

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One session of fMRI-Neurofeedback training on motor imagery modulates whole-brain effective connectivity and dynamical complexity

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Deco, Gustavo
Catalan Institution for Research and Advanced Studies (ICREA), University Pompeu Fabra, Barcelona, Spain;
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia;

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Citation

De Fillippi, E., Marins, T., Escrichs, A., Gilson, M., Moll, J., Tovar-Moll, F., et al. (2022). One session of fMRI-Neurofeedback training on motor imagery modulates whole-brain effective connectivity and dynamical complexity. Cerebral Cortex Communications, 3(3): tgac027. doi:10.1093/texcom/tgac027.

Cite as: https://hdl.handle.net/21.11116/0000-000B-15BC-A

Abstract

In the past decade, several studies have shown that Neurofeedback (NFB) by functional magnetic resonance imaging can alter the functional coupling of targeted and non-targeted areas. However, the causal mechanisms underlying these changes remain uncertain. Here, we applied a whole-brain dynamical model to estimate Effective Connectivity (EC) profiles of resting-state data acquired before and immediately after a single-session NFB training for 17 participants who underwent motor imagery NFB training and 16 healthy controls who received sham feedback. Within-group and between-group classification analyses revealed that only for the NFB group it was possible to accurately discriminate between the 2 resting-state sessions. NFB training-related signatures were reflected in a support network of direct connections between areas involved in reward processing and implicit learning, together with regions belonging to the somatomotor, control, attention, and default mode networks, identified through a recursive-feature elimination procedure. By applying a data-driven approach to explore NFB-induced changes in spatiotemporal dynamics, we demonstrated that these regions also showed decreased switching between different brain states (i.e. metastability) only following real NFB training. Overall, our findings contribute to the understanding of NFB impact on the whole brain's structure and function by shedding light on the direct connections between brain areas affected by NFB training.