Predictive neuromodulation of cingulo-frontal neural dynamics in major depressive disorder using a brain-computer interface system: A simulation study

Abstract

IntroductionDeep brain stimulation (DBS) is a promising therapy for treatment-resistant major depressive disorder (MDD). MDD involves the dysfunction of a brain network that can exhibit complex nonlinear neural dynamics in multiple frequency bands. However, current open-loop and responsive DBS methods cannot track the complex multiband neural dynamics in MDD, leading to imprecise regulation of symptoms, variable treatment effects among patients, and high battery power consumption.MethodsHere, we develop a closed-loop brain-computer interface (BCI) system of predictive neuromodulation for treating MDD. We first use a biophysically plausible ventral anterior cingulate cortex (vACC)-dorsolateral prefrontal cortex (dlPFC) neural mass model of MDD to simulate nonlinear and multiband neural dynamics in response to DBS. We then use offline system identification to build a dynamic model that predicts the DBS effect on neural activity. We next use the offline identified model to design an online BCI system of predictive neuromodulation. The online BCI system consists of a dynamic brain state estimator and a model predictive controller. The brain state estimator estimates the MDD brain state from the history of neural activity and previously delivered DBS patterns. The predictive controller takes the estimated MDD brain state as the feedback signal and optimally adjusts DBS to regulate the MDD neural dynamics to therapeutic targets. We use the vACC-dlPFC neural mass model as a simulation testbed to test the BCI system and compare it with state-of-the-art open-loop and responsive DBS treatments of MDD.ResultsWe demonstrate that our dynamic model accurately predicts nonlinear and multiband neural activity. Consequently, the predictive neuromodulation system accurately regulates the neural dynamics in MDD, resulting in significantly smaller control errors and lower DBS battery power consumption than open-loop and responsive DBS.DiscussionOur results have implications for developing future precisely-tailored clinical closed-loop DBS treatments for MDD.

Robust adaptive deep brain stimulation control of in-silico non-stationary Parkinsonian neural oscillatory dynamics

“…Extending our adaptive estimator to simultaneously estimate the linear matrix parameters online and completely remove the offline model fitting experiment is another direction that requires further investigation [71][72][73]. Third, there are rich explorations of effective feedback signals and closed-loop neuromodulation treatments for other neurological and neuropsychiatric disorders such as epilepsy [74][75][76] and major depression [39,77,78]. Our robust adaptive DBS control framework is flexible to model different types of input DBS parameters and output neural activity.…”

“…where C † = (CC T ) −1 C is the pseudo-inverse, or a more sophisticated dynamic Kalman estimator [38][39][40].…”

Deep brain stimulation, lesioning, focused ultrasound: update on utility

“…Closed-loop BCI with predictive neuromodulation has also been used to treat MDD with improved efficacy compared to standard non-BCI neuromodulation methods. The system predicts the non-linear and multiband neurodynamics in MDD and can manage and control the diseased neural dynamics to effectively produce a therapeutic output signal [133] . Additionally, BCI has been shown to improve ET outcomes, as the use of dBCI in ET has diminished the total stimulation applied since it can disable neural stimulation when the patient is not actively using their limbs.…”

Robust Adaptive Deep Brain Stimulation Control of Non-Stationary Cortex-Basal Ganglia-Thalamus Network Models in Parkinson’s Disease

“…where C † = (CC T ) −1 C is the pseudo-inverse, or a more sophisticated dynamic Kalman estimator [46,47,48]. Simulation studies have shown that linear DBS improves over the on-off DBS for PD [31,29,27].…”