MEG-based brain–machine interface for neurofeedback modulation of insula

• Insula activity can be modulated via real-time MEG decoding in a brain–machine interface for neurofeedback.¹
• A double-blind randomized controlled crossover trial raises the evidence level for non-invasive BMI neurofeedback.^{1 1}

Weekly enrichment (2026-07-20)

• The study was published in Communications Biology in 2025 (volume 8, article 770) as a double-blind, randomized, controlled crossover trial testing whether people can volitionally modulate insula activity and whether that modulation causally affects pain.^{2 3}
• Nineteen healthy participants completed neurofeedback training aimed at both upmodulation and downmodulation of right insula activity using the team’s magnetoencephalography (MEG)-based brain–machine interface.^{2 3}
• The feedback signal driving the interface was the root-mean-square of estimated insular cortical currents reconstructed from MEG source localization, letting participants see and steer their own insula activity in near real time.^{2 4}
• Insula activity differed significantly between the upmodulation and downmodulation training sessions, indicating participants exerted cognitive control over the region.^{2 3}
• Resting-state insula activity measured after downmodulation training was significantly lower than after upmodulation training, suggesting the training produced carry-over effects beyond the feedback blocks.^{2 3}
• Downmodulation training was associated with increased pain thresholds relative to upmodulation, but the interaction effect was not statistically significant, so the pain-threshold change was not conclusively specific to condition.^{2 4}
• The authors conclude that humans can cognitively modulate insula activity as a route toward therapeutic MEG neurofeedback systems, while explicitly cautioning that the data do not provide direct evidence of a causal link between insula modulation and changes in pain perception.^{2 3}

Footnotes

1. https://news.google.com/rss/articles/CBMiX0FVX3lxTFBlVC16ZTI5T0otS25jX2VDRVpVaTNLNGxpSVY4ZDFMTXdpekVGVGp3MlZaV0o2NjN1WS1mbVVMcFBneTM4YzRLTkk5a0hqaTJRbjlOQVNXay1Ea1ZaeG84?oc=5 ↩ ↩² ↩³

2. https://www.nature.com/articles/s42003-025-08176-8 ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷

3. https://ora.ox.ac.uk/objects/uuid:0c67ce5f-77a0-4a2e-aa69-c11b8798ddfd ↩ ↩² ↩³ ↩⁴ ↩⁵

4. https://doi.org/10.21203/rs.3.rs-4299129/v1 ↩ ↩²