A Multimodal Neuroprosthetic Interface to Record, Modulate and Classify Electrophysiological Biomarkers Relevant to Neuropsychiatric Disorders

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.

Prefrontal Electrophysiological Biomarkers and Mechanism-Based Drug Effects in a Rat Model of Alcohol Addiction

“…The procedures to fabricate, characterise and implant the used neuroprosthetics have been described in detail before [30,45,46]. Brie y, micro-EcoG interfaces were produced by an additive manufacturing approach using the 3D bioprinter 3DDiscovery ™ Evolution (regenHU Ltd.).…”

“…Data is given as event-related spectral perturbation (ERSP) in decibels (dB). e) Maximum ERO activity (as ERSP in dB) within delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (> 30 Hz) frequency bands and over the whole frequency range.f) Latencies of maximum ERO activity within delta, theta, alpha, beta and gamma bands and over the whole frequency range. g) Frequencies of maximum ERO activity within delta, theta, alpha, beta and gamma bands and over the whole frequency range.…”

“…We previously developed an electrocorticographic (ECoG) interface [30] tailored to capture prefrontal ERP and neural oscillations and we here applied this new technology to a well-established animal model for alcohol addiction and relapse behavior. In this model rats are subjected to long-term voluntary alcohol consumption in a four-bottle procedure, repeatedly interrupted with abstinence periods.…”