ArticleCerebral cortex communications2022
One session of fMRI-Neurofeedback training on motor imagery modulates whole-brain effective connectivity and dynamical complexity.
Article in Cerebral cortex communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery.Brain informatics · 2025Article
- Structurally informed models of directed brain connectivity.Nature reviews. Neuroscience · 2025Review
- Modulating the sense of agency in functional neurological disorder using real-time fMRI neurofeedback: a proof-of-concept study.NeuroImage. Clinical · 2025Article
- Training individuals with schizophrenia to gain volitional control of the theory of mind network with real-time fMRI: A pilot study.Schizophrenia research. Cognition · 2024Article
- Neural oscillations during acupuncture imagery partially parallel that of real needling.Frontiers in neuroscience · 2023Article
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Authors and funding
7 authors.
Funding
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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.
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