Brain-Computer Interfaces for Treatment of Focal Dystonia

Abstract

Task-specificity in isolated focal dystonias is a powerful feature that may successfully be targeted with therapeutic brain-computer interfaces. While performing a symptomatic task, the patient actively modulates momentary brain activity (disorder signature) to match activity during an asymptomatic task (target signature), which is expected to translate into symptom reduction.

FIG. 1.

Simplified schematic of functional and structural neural network alterations in focal dystonia. Colored areas depict cortical targets of neurofeedback brain–computer interface (BCI) intervention; gray areas depict other critical regions within the dystonic neural network. Alterations in inferior parietal (IPC) and premotor (PreM) cortex are thought to precede and influence those in the primary sensorimotor cortex (M1/S1). It is expected that BCI-based modulation of IPC-PreM activity leads to attenuation of M1/S1 activity, which, in turn, results in normalized M1/S1 output, including to the basal ganglia (BG) and cerebellum (Cbl). Solid black arrows indicate cortical sensorimotor modulation directly targeted by the proposed neurofeedback BCI intervention; dashed arrows indicate downstream effects resulting from cortical modulation. IFG, inferior frontal gyrus; MFG, middle frontal gyrus; OC, occipital cortex; SMA, supplementary motor area; SPC, superior parietal cortex; Th, thalamus.

FIG. 2.

(A) Overview of the neurofeedback BCI paradigm for treatment of focal task-specific dystonia, including a high-density EEG recording system, an ML-BCI platform, and a visual neurofeedback monitoring system. The ML-BCI platform transforms the momentary EEG activity into continuous visual neurofeedback that is based on the difference between the transient disorder signature and the target signature. While performing a symptomatic task, the patient actively regulates their brain activity to match the disorder signature to the target signature of a related but asymptomatic motor task. (B) Throughout the BCI training, the disorder signature is expected to gradually correspond to the target signature, which, in turn, is expected to be associated with clinically relevant symptom reduction. BCI, brain–computer interface; EEG, electroencephalography; ML, machine learning.