Brain–computer interfaces (BCIs) can be used to monitor and provide real-time feedback on brain signals, directly inuencing external systems, such as virtual environments (VE), to support self-regulation. We piloted a novel immersive, rst-person shooting BCI-VE during which the avatars’ movement speed was directly inuenced by neural activity in the supplementary motor area (SMA). Previous analyses revealed behavioral and localized neural eects for active versus reduced contingency neurofeedback in a randomized controlled trial design. However, the modeling of neural dynamics during such complex tasks challenges traditional event-related approaches. To overcome this limitation, we employed a data-driven framework utilizing group-level independent networks derived from BOLD-specic components of the multi-echo fMRI data obtained during the BCI regulation. Individual responses were estimated through dual regression. The spatial independent components corresponded to established cognitive networks and task-specic networks related to gaming actions. Compared to reduced contingency neurofeedback, active regulation induced signicantly elevated fractional amplitude of low-frequency uctuations (fALFF) in a frontoparietal control network, and spatial reweighting of a salience/ventral aention network, with stronger expression in SMA, prefrontal cortex, inferior parietal lobule, and occipital regions. These ndings underscore the distributed network engagement of BCI regulation during a behavioral task in an immersive virtual environment.
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