Abstract
Most mental disorders, such as addictive diseases or schizophrenia, are characterized by impaired cognitive function and behavior control originating from disturbances within prefrontal neural networks. 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. This study, therefore, aimed to develop a biocompatible and multimodal neuroprosthesis to measure and modulate prefrontal neurophysiological features of neuropsychiatric symptoms. We used a 3D-printing technology to rapidly prototype customized bioelectronic implants through robot-controlled deposition of soft silicones and a conductive platinum ink. We implanted the device epidurally above the medial prefrontal cortex of rats and obtained auditory event-related brain potentials in treatment-naïve animals, after alcohol administration and following neuromodulation through implant-driven electrical brain stimulation and cortical delivery of the anti-relapse medication naltrexone. Towards smart neuroprosthetic interfaces, we furthermore developed machine learning algorithms to autonomously classify treatment effects within the neural recordings. The neuroprosthesis successfully captured neural activity patterns reflecting intact stimulus processing and alcohol-induced neural depression. Moreover, implant-driven electrical and pharmacological stimulation enabled successful enhancement of neural activity. A machine learning approach based on stepwise linear discriminant analysis was able to deal with sparsity in the data and distinguished treatments with high accuracy. Our work demonstrates the feasibility of multimodal bioelectronic systems to monitor, modulate and identify healthy and affected brain states with potential use in a personalized and optimized therapy of neuropsychiatric disorders.
Highlights
The prefrontal cortex (PFC) represents a fundamental structure for top-down behavioral control and modulates attention, working memory, reward evaluation and the ability to self-control actions, emotions and stress
We initially developed a custom implantable device covering the surface of the frontal lobe of the rat cortex
According to their position above the mPFC, electrodes were labelled as frontocentral (FC), frontal left (FL), frontal right (FR), medial central (MC), medial left (ML), medial right (MR), posterior central (PC), posterior left (PL) and posterior right (PR)
Summary
The prefrontal cortex (PFC) represents a fundamental structure for top-down behavioral control and modulates attention, working memory, reward evaluation and the ability to self-control actions, emotions and stress. Disturbances of prefrontal neural network activity underlie many cognitive and behavioral impairments observed in neuropsychiatric diseases such as addictive disorders, schizophrenia or autism. The complex etiology of these diseases makes optimal treatment challenging (Gao et al, 2012). Current therapeutical interventions often present with side effects and lack long-term efficacy. Systemic pharmacotherapy shows poor topical specificity and adaptability to changes in patient-specific needs regarding treatment duration and intensity (Olofsson and Bouton, 2019). The chronic reoccurrence and high relapse rates following treatment (Agenagnew and Kassaw, 2020) warrant new approaches
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