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Monti, R. P.

Publications and source records attributed to Monti, R. P..

2 recordsLinked to original sources

Assessing tACS-induced phosphene perception using closed-loop Bayesian optimization

Transcranial alternating current stimulation (tACS) can evoke illusory flash-like visual percepts known as phosphenes. The perception of phosphenes represents a major experimental challenge when studying tACS-induced effects on cognitive performance. Besides growing concerns that retinal phosphenes themselves could potentially have neuromodulatory effects, the perception of phosphenes may also modify the alertness of participants. Past research has shown that stimulation intensity, frequency and electrode montage affect phosphene perception. However, to date, the effect of an additional tACS parameter on phosphene perception has been completely overlooked: the relative phase difference between stimulation electrodes. This is a crucial and timely topic given the confounding nature of phosphene perception and the increasing number of studies reporting changes in cognitive function following tACS phase manipulations. However, studying phosphene perception for different frequencies and phases simultaneously is not tractable using standard approaches, as the physiologically plausible range of parameters results in a combinatorial explosion of experimental conditions, yielding impracticable experiment durations. To overcome this limitation, here we applied a Bayesian optimization approach to efficiently sample an exhaustive tACS parameter space. Moreover, unlike conventional methodology, which involves subjects judging the perceived phosphene intensity on a rating scale, our study leveraged the strength of human perception by having the optimization driven based on a subjects relative judgement. Applying Bayesian optimization for two different montages, we found that phosphene perception was affected by differences in the relative phase between cortical electrodes. The results were replicated in a second study involving new participants and validated using computational modelling. In summary, our results have important implications for the experimental design and conclusions drawn from future tACS studies investigating the effects of phase on cognition.

neuroscience

Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization

Understanding the unique contributions of frontoparietal networks (FPN) in cognition is challenging because different FPNs spatially overlap and are co-activated for diverse tasks. In order to characterize these networks involves studying how they activate across many different cognitive tasks, which previously has only been possible with meta-analyses. Here, building upon meta-analyses as a starting point, we use neuroadaptive Bayesian optimization, an approach combining real-time analysis of functional neuroimaging data with machine-learning, to discover cognitive tasks that dissociate ventral and dorsal FPN activity from a large pool of tasks. We identify and subsequently refine two cognitive tasks (Deductive Reasoning and Tower of London) that are optimal for dissociating the FPNs. The identified cognitive tasks are not those predicted by meta-analysis, highlighting a different mapping between cognitive tasks and FPNs than expected. The optimization approach converged on a similar neural dissociation independently for the two different tasks, suggesting a possible common underlying functional mechanism and the need for neurally-derived cognitive taxonomies.

neuroscience