Self-regulation of the Anterior Insula: Reinforcement Learning using Real-time fMRI Neurofeedback

Emma J Lawrence, Li Su, Gareth J Barker, Nick Medford, Jeffery Dalton, Steve C R Williams, Niels Birbaumer, Ralf Veit, Sitaram Ranganatha, Jerzy Bodurka, Michael Brammer, Vincent Giampietro, Anthony S David

Research output: Contribution to journalArticlepeer-review

67 Citations (Scopus)


The anterior insula (AI) plays a key role in affective processing, and insular dysfunction has been noted in several clinical conditions. Real-time functional MRI neurofeedback (rtfMRI-NF) provides a means of helping people learn to self-regulate activation in this brain region. Using the Blood Oxygenated Level Dependant (BOLD) signal from the right AI (RAI) as neurofeedback, we trained participants to increase RAI activation. In contrast, another group of participants were shown 'control' feedback from another brain area. Pre- and post- training affective probes were shown, with subjective ratings and skin conductance response (SCR) measured. We also investigated a reward-related reinforcement learning model of rtfMRI-NF In contrast to controls, we hypothesised a positive linear increase in RAI activation in participants shown feedback from this region, alongside increases in valence ratings and skin conductance response (SCR) to affective probes. Hypothesis-driven analyses showed a significant interaction between the RAI / control neurofeedback groups and the effect of self-regulation. Whole-brain analyses revealed a significant linear increase in RAI activation across four training runs in the group who received feedback from RAI. Increased activation was also observed in the caudate body and thalamus, likely representing feedback-related learning. No positive linear trend was observed in the RAI in the group receiving control feedback, suggesting that these data are not a general effect of cognitive strategy or control feedback. The control group did, however, show diffuse activation across the putamen, caudate and posterior insula which may indicate the representation of false feedback. No significant training-related behavioural differences were observed for valence ratings, or SCR. In addition, correlational analyses based on a reinforcement learning model showed the dorsal anterior cingulate cortex underpinned learning in both groups. In summary, these data demonstrate that it is possible to regulate the RAI using rtfMRI-NF within one scanning session, and that such reward-related learning is mediated by the dorsal anterior cingulate.
Original languageEnglish
Article numberN/A
Pages (from-to)113-124
Number of pages12
Issue numberN/A
Publication statusPublished - Mar 2013


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