Objective. The development of electrode arrays able to reliably record brain electrical activity is a critical issue in brain machine interface (BMI) technology. In the present study we undertook a comprehensive physico-chemical, physiological, histological and immunohistochemical characterization of new single-walled carbon nanotubes (SWCNT)-based electrode arrays grafted onto medium-density polyethylene (MD-PE) films. Approach. The long-term electrical stability, flexibility, and biocompatibility of the SWCNT arrays were investigated in vivo in laboratory rats by two-months recording and analysis of subdural electrocorticogram (ECoG). Ex-vivo characterization of a thin flexible and single probe SWCNT/polymer electrode is also provided. Main results. The SWCNT arrays were able to capture high quality and very stable ECoG signals across 8 weeks. The histological and immunohistochemical analyses demonstrated that SWCNT arrays show promising biocompatibility properties and may be used in chronic conditions. The SWCNT-based arrays are flexible and stretchable, providing low electrode-tissue impedance, and, therefore, high compliance with the irregular topography of the cortical surface. Finally, reliable evoked synaptic local field potentials in rat brain slices were recorded using a special SWCNT-polymer-based flexible electrode. Significance. The results demonstrate that the SWCNT arrays grafted in MD-PE are suitable for manufacturing flexible devices for subdural ECoG recording and might represent promising candidates for long-term neural implants for epilepsy monitoring or neuroprosthetic BMI.

Chronic neural interfacing with cerebral cortex using single-walled carbon nanotube-polymer grids / Pavone, L.; Moyanova, S.; Mastroiacovo, F.; Fazi, L.; Busceti, C.; Gaglione, A.; Martinello, K.; Fucile, S.; Bucci, D.; Prioriello, A.; Nicoletti, F.; Fornai, F.; Morales, P.; Senesi, R.. - In: JOURNAL OF NEURAL ENGINEERING. - ISSN 1741-2560. - 17:3(2020). [10.1088/1741-2552/ab98db]

Chronic neural interfacing with cerebral cortex using single-walled carbon nanotube-polymer grids

Fucile S.
Investigation
;
Nicoletti F.;
2020

Abstract

Objective. The development of electrode arrays able to reliably record brain electrical activity is a critical issue in brain machine interface (BMI) technology. In the present study we undertook a comprehensive physico-chemical, physiological, histological and immunohistochemical characterization of new single-walled carbon nanotubes (SWCNT)-based electrode arrays grafted onto medium-density polyethylene (MD-PE) films. Approach. The long-term electrical stability, flexibility, and biocompatibility of the SWCNT arrays were investigated in vivo in laboratory rats by two-months recording and analysis of subdural electrocorticogram (ECoG). Ex-vivo characterization of a thin flexible and single probe SWCNT/polymer electrode is also provided. Main results. The SWCNT arrays were able to capture high quality and very stable ECoG signals across 8 weeks. The histological and immunohistochemical analyses demonstrated that SWCNT arrays show promising biocompatibility properties and may be used in chronic conditions. The SWCNT-based arrays are flexible and stretchable, providing low electrode-tissue impedance, and, therefore, high compliance with the irregular topography of the cortical surface. Finally, reliable evoked synaptic local field potentials in rat brain slices were recorded using a special SWCNT-polymer-based flexible electrode. Significance. The results demonstrate that the SWCNT arrays grafted in MD-PE are suitable for manufacturing flexible devices for subdural ECoG recording and might represent promising candidates for long-term neural implants for epilepsy monitoring or neuroprosthetic BMI.
2020
biocompatibility; brain machine interface; ECoG; neural interfaces; polymer carbon nanotubes composite; single-walled carbon nanotube; subdural ECoG arrays
01 Pubblicazione su rivista::01a Articolo in rivista
Chronic neural interfacing with cerebral cortex using single-walled carbon nanotube-polymer grids / Pavone, L.; Moyanova, S.; Mastroiacovo, F.; Fazi, L.; Busceti, C.; Gaglione, A.; Martinello, K.; Fucile, S.; Bucci, D.; Prioriello, A.; Nicoletti, F.; Fornai, F.; Morales, P.; Senesi, R.. - In: JOURNAL OF NEURAL ENGINEERING. - ISSN 1741-2560. - 17:3(2020). [10.1088/1741-2552/ab98db]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1475925
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