Habelt, B., Wirth, C., Afanasenkau, D. et al. (4 more authors) (2021) A multimodal neuroprosthetic interface to record, modulate and classify electrophysiological biomarkers relevant to neuropsychiatric disorders. Frontiers in Bioengineering and Biotechnology, 9. 770274. ISSN 2296-4185
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Authors. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
Keywords: | event-related potentials/ERP; brain stimulation; neuroprosthetics; flexible electronics; biomedical interfaces; machine learning; neuropsychiatric disorders |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 03 Nov 2021 07:49 |
Last Modified: | 03 Nov 2021 07:49 |
Status: | Published |
Publisher: | Frontiers Media |
Refereed: | Yes |
Identification Number: | 10.3389/fbioe.2021.770274 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179936 |