Chronic full-band recordings with graphene microtransistors as neural interfaces for discrimination of brain states
Author
Publication date
2024ISSN
2055-6764
Abstract
Brain states such as sleep, anesthesia, wakefulness, or coma are characterized by specific patterns of cortical activity dynamics, from local circuits to full-brain emergent properties. We previously demonstrated that full-spectrum signals, including the infraslow component (DC, direct current-coupled), can be recorded acutely in multiple sites using flexible arrays of graphene solution-gated field-effect transistors (gSGFETs). Here, we performed chronic implantation of 16-channel gSGFET arrays over the rat cerebral cortex and recorded full-band neuronal activity with two objectives: (1) to test the long-term stability of implanted devices; and (2) to investigate full-band activity during the transition across different levels of anesthesia. First, we demonstrate it is possible to record full-band signals with stability, fidelity, and spatiotemporal resolution for up to 5.5 months using chronic epicortical gSGFET implants. Second, brain states generated by progressive variation of levels of anesthesia could be identified as traditionally using the high-pass filtered (AC, alternating current-coupled) spectrogram: from synchronous slow oscillations in deep anesthesia through to asynchronous activity in the awake state. However, the DC signal introduced a highly significant improvement for brain-state discrimination: the DC band provided an almost linear information prediction of the depth of anesthesia, with about 85% precision, using a trained algorithm. This prediction rose to about 95% precision when the full-band (AC + DC) spectrogram was taken into account. We conclude that recording infraslow activity using gSGFET interfaces is superior for the identification of brain states, and further supports the preclinical and clinical use of graphene neural interfaces for long-term recordings of cortical activity.
Document Type
Article
Document version
Published version
Language
English
Subject (CDU)
57 - Biological sciences in general
Keywords
Pages
9
Publisher
Royal Society of Chemistry
Is part of
Nanoscale Horizons
Citation
Camassa, A.; Barbero-Castillo, A.; Bosch, M. [et al.]. Chronic full-band recordings with graphene microtransistors as neural interfaces for discrimination of brain states. Nanoscale Horizons, 2024, p. 1-9. Disponible en: <https://pubs.rsc.org/en/content/articlelanding/2024/nh/d3nh00440f>. Fecha de acceso: 16 feb. 2024. DOI: 10.1039/D3NH00440F
Grant agreement number
info:eu-repo/grantAgreement/ES/EU/H2020/881603
info:eu-repo/grantAgreement/ES/EU/H2020/785219
Note
This research has been funded by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 881603 (GrapheneCore3) and No. 785219 (GrapheneCore2) to all teams. The IDIBAPS team was also funded by CORTICOMOD PID2020-112947RB-I00 funded by MCIN/AEI/10.13039/501100011033 and by Departament de Recerca i Universitats de la Generalitat de Catalunya (AGAUR 2021-SGR01165). IDIBAPS is funded by the CERCA program (Generalitat de Catalunya). The fabrication of the graphene-based solutiongated field-effect transistors (gSGFETs) arrays has made use of the Spanish ICTS Network MICRONANOFABS, partially supported by MICINN and the ICTS NANBIOSIS, specifically by the Micro-NanoTechnology Unit U8 of the CIBER-BBN. C. N. M. also acknowledge funding from the Departament de Recerca i Universitats de la Generalitat de Catalunya (2021SGR00495), by the Spanish MICIN PID2021-126117NA-I00, and by CIBER-BBN (CB06/01/0049). The authors acknowledge the financial support provided by CIBER-BBN and the Instituto de Salud Carlos III with assistance from the European Regional Development. Thanks to Amin Samipour for comments and to Tony Donegan
for language editing.
This item appears in the following Collection(s)
- Ciències Bàsiques [94]
Rights
© CC BY-NC 3.0 DEED - Attribution-NonCommercial 3.0 Unported
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc/3.0/


