Distributed Patterns of Brain Activity Underlying Real-Time fMRI Neurofeedback Training

Rotem Kopel, Kirsten Emmert, Frank Scharnowski, Sven Haller, Dimitri Van De Ville
2017 IEEE Transactions on Biomedical Engineering  
Neurofeedback (NF) based on real-time functional magnetic resonance imaging (rt-fMRI) is an exciting neuroimaging application. In most rt-fMRI NF studies, the activity level of a single region of interest (ROI) is provided as a feedback signal and the participants are trained to up or down regulate the feedback signal. NF training effects are typically analyzed using a confirmatory univariate approach, i.e., changes in the target ROI are explained by a univariate linear modulation. However,
more » ... ning to selfregulate the ROI activity through NF is mediated by distributed changes across the brain. Here, we deploy a multivariate decoding model for assessing NF training effects across the whole brain. Specifically, we first explain the NF training effect by a posthoc multivariate model that leads to a pattern of coactivation based on 90 functional atlas regions. We then use cross validation to reveal the set of brain regions with the best fit. This novel approach was applied to the data from a rt-fMRI NF study where the participants learned to down regulate the auditory cortex. We found that the optimal model consisted of 16 brain regions whose coactivation patterns best described the training effect over the NF training days. Cross validation of the multivariate model showed that it generalized across the participants. Interestingly, the participants could be clustered into two groups with distinct patterns of coactivation, potentially reflecting different NF learning strategies. Overall, our findings revealed that multiple brain regions are involved in learning to regulate an activity in a single ROI, and thus leading to a better understanding of the mechanisms underlying NF training. Abstract-Neurofeedback (NF) based on real-time functional magnetic resonance imaging (rt-fMRI) is an exciting neuroimaging application. In most rt-fMRI NF studies, the activity level of a single region of interest (ROI) is provided as a feedback signal, which the participants are trained to up or down-regulate. Neurofeedback training effects are typically analyzed using a confirmatory univariate approach, i.e., changes in the target ROI are explained by a univariate linear modulation. However, learning to self-regulate ROI activity through NF is mediated by distributed changes across the brain. Here we deploy a multivariate decoding model for assessing NF training effects across the whole brain. Specifically, we first explain the NF training effect by a post-hoc multivariate model that leads to a pattern of co-activation based on 90 functional atlas regions. We then use cross-validation to reveal the set of brain regions with the best fit. This novel approach was applied to data from a rt-fMRI NF study where participants learned to down-regulate the auditory cortex. We found that the optimal model consisted of 16 brain regions whose co-activation patterns best described the training effect over the NF training days. Cross-validation of the multivariate model showed that it generalized across participants. Interestingly, the participants could be clustered into two groups with distinct patterns of co-activation, potentially reflecting different NF learning strategies. Overall, our findings revealed that multiple brain regions are involved in learning to regulate activity in a single ROI, and thus lead to a better understanding of the mechanisms underlying NF training.
doi:10.1109/tbme.2016.2598818 pmid:28541186 fatcat:5y5quiuum5esldvbptvgoozjhe