A multimodal neuroprosthetic interface to record, modulate and classify electrophysiological correlates of cognitive function

Abstract

Most mental disorders are characterised by impaired cognitive function and behaviour control. Their often chronic reoccurring nature and the lack of efficient therapies necessitate the development of new treatment strategies. Brain-computer interfaces, equipped with multiple sensing and stimulation abilities, offer a new toolbox, whose suitability for diagnosis and therapy of mental disorders has not yet been explored. Here, we developed a soft and multimodal neuroprosthesis to measure and modulate prefrontal neurophysiological features of neuropsychiatric symptoms. We implanted the device epidurally above the medial prefrontal cortex of rats and obtained auditory event-related brain potentials reflecting intact neural stimulus processing and alcohol-induced neural impairments. Moreover, implant-driven electrical and pharmacological stimulation enabled successful modulation of neural activity. Finally, we developed machine learning algorithms which can deal with sparsity in the data and distinguish effects with high accuracy. Our work underlines the potential of multimodal bioelectronic systems to enable a personalised and optimised therapy.