Advancements and Future Directions in Real-Time fMRI Neurofeedback Research

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurotechnology and Neuroimaging".

Deadline for manuscript submissions: closed (31 October 2025) | Viewed by 2092

Special Issue Editors


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Guest Editor
Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
Interests: brain connectivity; substance use; neurocognitive maturation; adolescence

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Guest Editor
Functional MRI Laboratory, University of Michigan, Ann Arbor, MI 48109, USA
Interests: neuroimaging methods; neurofeedback; computational modeling

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Guest Editor
Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
Interests: neurofeedback; internalizing disorders; novel treatments

Special Issue Information

Dear Colleagues,

Real-time fMRI neurofeedback (rtfMRI-nf) has emerged as a powerful tool to train individuals to regulate specific brain regions or networks. Basic science applications of rtfMRI-nf have demonstrated success in neurofeedback-related learning, and clinical applications of rtfMRI-nf have shown improved cognitive, emotional, and/or behavioral functioning in response to neurofeedback training. Although many advancements have been made in the rigor of rtfMRI-nf research, the following gaps remain: (1) Mechanisms of Action: The neural mechanisms underlying volitional control of brain activity, and subsequent brain–behavior linkages, remain unclear; (2) Long-Term Effects: There is limited evidence on the time course of neurofeedback learning; (3) Generalization of Results: There is insufficient evidence on whether neurofeedback leads to broad cognitive, emotional, or behavioral improvements across different contexts or individuals; (4) Sample Size and Replication: Larger, well-powered studies are needed to confirm reliability and generalizability; and (5) Individual Differences: There is limited research on predictors of neurofeedback success (e.g., personality traits, baseline brain activity patterns, cognitive factors). Original manuscripts and review papers addressing these knowledge gaps will likely advance basic science and clinical applications of rtfMRI-nf.

Dr. Meghan Martz
Dr. Scott Peltier
Dr. Stefanie Russman Block
Guest Editors

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Keywords

  • neurofeedback
  • real-time fMRI
  • neuromodulation
  • imaging methods
  • self-regulation

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Published Papers (1 paper)

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Research

21 pages, 2047 KB  
Article
A Feasibility Study of Real-Time FMRI with Neurofeedback of Motor Performance in Cerebellar Ataxia
by Joshua G. Berenbaum, Cherie L. Marvel, Jonathan M. Lisinski, Jeffrey S. Soldate, Owen P. Morgan, Ashley N. Kucharski, Luca P. Lutzel, Jonathan A. Ecker, Laura C. Rice, Amy Mistri, Prianca A. Nadkarni, Liana S. Rosenthal and Stephen M. LaConte
Brain Sci. 2026, 16(2), 120; https://doi.org/10.3390/brainsci16020120 - 23 Jan 2026
Viewed by 1402
Abstract
Background/Objectives: Neurodegenerative cerebellar ataxia (CA) is a movement disorder caused by progressive cell death in the cerebellum. Motor imagery represents a potential therapeutic tool to improve motor function by “exercising” brain regions associated with movement, without the need for overt activity. This study [...] Read more.
Background/Objectives: Neurodegenerative cerebellar ataxia (CA) is a movement disorder caused by progressive cell death in the cerebellum. Motor imagery represents a potential therapeutic tool to improve motor function by “exercising” brain regions associated with movement, without the need for overt activity. This study assessed the feasibility of combining motor imagery with real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) to improve motor function in CA. Methods: During finger tapping conditions, 16 participants with CA pushed a button at the same frequency in time with cross flashing at 1 Hz or 4 Hz, and this information was used to train the model. During motor imagery, participants imagined finger tapping while undergoing rt-fMRI-NF with visual feedback, steering them toward activating their motor circuit. Afterwards, they completed finger tapping again. FMRI analysis compared successful motor imagery trials versus all other imagery events. Brain activity on successful trials was covaried with pre–post rt-fMRI-NF tapping improvement scores. Results: Tapping was more accurate at 1 Hz than 4 Hz, and larger tapping error rates correlated with greater movement impairments. While not significant at the group level, 9 of the 16 participants improved tapping accuracy following rt-fMRI-NF. The size of motor improvements correlated with successful motor imagery activity at 1 Hz in the frontal lobe, insula, parietal lobe, basal ganglia, and cerebellum. Motor improvements were not associated with neurological impairment severity, mood, cognition, or imagery vividness. Conclusions: Feasibility was demonstrated for motor imagery therapy with neurofeedback to potentially improve fine motor precision in people with CA. Brain regions relevant to this process may be considered for targets of non-invasive therapeutic interventions. Full article
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