Dataset Information

Improving Emotion Regulation Through Real-Time Neurofeedback Training on the Right Dorsolateral Prefrontal Cortex: Evidence From Behavioral and Brain Network Analyses.

ABSTRACT: We investigated if emotion regulation can be improved through self-regulation training on non-emotional brain regions, as well as how to change the brain networks implicated in this process. During the training period, the participants were instructed to up-regulate their right dorsolateral prefrontal cortex (rDLPFC) activity according to real-time functional near-infrared spectroscopy (fNIRS) neurofeedback signals, and there was no emotional element. The results showed that the training significantly increased emotion regulation, resting-state functional connectivity (rsFC) within the emotion regulation network (ERN) and frontoparietal network (FPN), and rsFC between the ERN and amygdala; however, training did not influence the rsFC between the FPN and the amygdala. However, self-regulation training on rDLPFC significantly improved emotion regulation and generally increased the rsFCs within the networks; the rsFC between the ERN and amygdala was also selectively increased. The present study also described a safe approach that may improve emotion regulation through self-regulation training on non-emotional brain regions.

SUBMITTER: Yu L

PROVIDER: S-EPMC8010650 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Depressive disorders contribute heavily to global disease burden; This is possibly because patients are often treated homogeneously, despite having heterogeneous symptoms with differing underlying neural mechanisms. A novel treatment that can directly influence the neural circuit relevant to an individual patient's subset of symptoms might more precisely and thus effectively aid in the alleviation of their specific symptoms. We tested this hypothesis in a proof-of-concept study using fMRI functional connectivity neurofeedback. We targeted connectivity between the left dorsolateral prefrontal cortex/middle frontal gyrus and the left precuneus/posterior cingulate cortex, because this connection has been well-established as relating to a specific subset of depressive symptoms. Specifically, this connectivity has been shown in a data-driven manner to be less anticorrelated in patients with melancholic depression than in healthy controls. Furthermore, a posterior cingulate dominant state-which results in a loss of this anticorrelation-is expected to specifically relate to an increase in rumination symptoms such as brooding. In line with predictions, we found that, with neurofeedback training, the more a participant normalized this connectivity (restored the anticorrelation), the more related (depressive and brooding symptoms), but not unrelated (trait anxiety), symptoms were reduced. Because these results look promising, this paradigm next needs to be examined with a greater sample size and with better controls. Nonetheless, here we provide preliminary evidence for a correlation between the normalization of a neural network and a reduction in related symptoms. Showing their reproducibility, these results were found in two experiments that took place several years apart by different experimenters. Indicative of its potential clinical utility, effects of this treatment remained one-two months later.Clinical trial registration: Both experiments reported here were registered clinical trials (UMIN000015249, jRCTs052180169).

Project description:Substance-dependent individuals often lack the ability to adjust decisions flexibly in response to the changes in reward contingencies. Prediction errors (PEs) are thought to mediate flexible decision-making by updating the reward values associated with available actions. In this study, we explored whether the neurobiological correlates of PEs are altered in alcohol dependence. Behavioral, and functional magnetic resonance imaging (fMRI) data were simultaneously acquired from 34 abstinent alcohol-dependent patients (ADP) and 26 healthy controls (HC) during a probabilistic reward-guided decision-making task with dynamically changing reinforcement contingencies. A hierarchical Bayesian inference method was used to fit and compare learning models with different assumptions about the amount of task-related information subjects may have inferred during the experiment. Here, we observed that the best-fitting model was a modified Rescorla-Wagner type model, the "double-update" model, which assumes that subjects infer the knowledge that reward contingencies are anti-correlated, and integrate both actual and hypothetical outcomes into their decisions. Moreover, comparison of the best-fitting model's parameters showed that ADP were less sensitive to punishments compared to HC. Hence, decisions of ADP after punishments were loosely coupled with the expected reward values assigned to them. A correlation analysis between the model-generated PEs and the fMRI data revealed a reduced association between these PEs and the BOLD activity in the dorsolateral prefrontal cortex (DLPFC) of ADP. A hemispheric asymmetry was observed in the DLPFC when positive and negative PE signals were analyzed separately. The right DLPFC activity in ADP showed a reduced correlation with positive PEs. On the other hand, ADP, particularly the patients with high dependence severity, recruited the left DLPFC to a lesser extent than HC for processing negative PE signals. These results suggest that the DLPFC, which has been linked to adaptive control of action selection, may play an important role in cognitive inflexibility observed in alcohol dependence when reinforcement contingencies change. Particularly, the left DLPFC may contribute to this impaired behavioral adaptation, possibly by impeding the extinction of the actions that no longer lead to a reward.

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Improving Emotion Regulation Through Real-Time Neurofeedback Training on the Right Dorsolateral Prefrontal Cortex: Evidence From Behavioral and Brain Network Analyses.

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