Towards Building Cognitive Brain Computer Interfaces for Controlling Neural Variability During Repetitive Transcranial Magnetic Stimulation Treatment in Depression

Depression is a widespread mental health disorder characterized by persistent low mood, anhedonia, and cognitive deficits. Many studies in the past have assessed the use of a repetitive form of Transcranial magnetic stimulation (rTMS) as an intervention in a form that is approved by regulatory bodies such as the FDA. Brain-computer interfaces (BCIs) are increasingly being used to study neural dynamics and their potential to enhance cognitive functioning in clinical populations. In this study, we investigate whether cognitive states matter for TMS treatment in Depression. We simultaneously administered rTMS as we acquired electroencephalography (EEG) to study the cognitive state effects in two different groups-Depressed (N=9) and Healthy controls (N=8), during resting state and for various cognitively engaging tasks. We then specifically computed the strength of activations in the fronto-parietal network (FPN) versus the default mode network (DMN) during stimulation for their significance in reflecting the trade-off in the cortico-limbic circuit activations. Our findings suggest that the cognitive state of an individual should be considered to control for effective and selective recruitment of FPN in contrast to DMN cognitive circuits with rTMS intervention. We finally present results upon implementing the paradigm in one longitudinal study participant, who undertook rTMS treatment for 6 weeks in a precisely cognitively calibrated design mentioned above. This study's findings offer a foundation for developing a closed loop brain-computer interfaces (cBCIs) for precise cognitive rehabilitation and performance optimization of rTMS intervention in depression.