Sensitive and robust chemical detection using an olfactory brain-computer interface.

Brain-computer interface (BMIs) Chemical sensing Mouse olfaction Neural engineering Neural signals Pattern recognition

Journal

Biosensors & bioelectronics
ISSN: 1873-4235
Titre abrégé: Biosens Bioelectron
Pays: England
ID NLM: 9001289

Informations de publication

Date de publication:
01 Jan 2022
Historique:
received: 13 04 2021
revised: 09 08 2021
accepted: 20 09 2021
pubmed: 9 10 2021
medline: 5 11 2021
entrez: 8 10 2021
Statut: ppublish

Résumé

When it comes to detecting volatile chemicals, biological olfactory systems far outperform all artificial chemical detection devices in their versatility, speed, and specificity. Consequently, the use of trained animals for chemical detection in security, defense, healthcare, agriculture, and other applications has grown astronomically. However, the use of animals in this capacity requires extensive training and behavior-based communication. Here we propose an alternative strategy, a bio-electronic nose, that capitalizes on the superior capability of the mammalian olfactory system, but bypasses behavioral output by reading olfactory information directly from the brain. We engineered a brain-computer interface that captures neuronal signals from an early stage of olfactory processing in awake mice combined with machine learning techniques to form a sensitive and selective chemical detector. We chronically implanted a grid electrode array on the surface of the mouse olfactory bulb and systematically recorded responses to a large battery of odorants and odorant mixtures across a wide range of concentrations. The bio-electronic nose has a comparable sensitivity to the trained animal and can detect odors on a variable background. We also introduce a novel genetic engineering approach that modifies the relative abundance of particular olfactory receptors in order to improve the sensitivity of our bio-electronic nose for specific chemical targets. Our recordings were stable over months, providing evidence for robust and stable decoding over time. The system also works in freely moving animals, allowing chemical detection to occur in real-world environments. Our bio-electronic nose outperforms current methods in terms of its stability, specificity, and versatility, setting a new standard for chemical detection.

Identifiants

pubmed: 34624799
pii: S0956-5663(21)00701-6
doi: 10.1016/j.bios.2021.113664
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

113664

Subventions

Organisme : NIDA NIH HHS
ID : R90 DA043849
Pays : United States

Informations de copyright

Copyright © 2021 Elsevier B.V. All rights reserved.

Auteurs

Erez Shor (E)

Neuroscience Institute, New York University Langone Health, New York, NY, 10016, USA.

Pedro Herrero-Vidal (P)

Neuroscience Institute, New York University Langone Health, New York, NY, 10016, USA; Center for Neural Science, New York University, New York, NY, 10003, USA.

Adam Dewan (A)

Department of Neurobiology, Northwestern University, Evanston, IL, 60208, USA; Department of Psychology, Florida State University, Tallahassee, FL, 32306, USA.

Ilke Uguz (I)

Electrical Engineering, Columbia University, 5798 New York, NY, 10027, USA.

Vincenzo F Curto (VF)

Division of Electrical Engineering, Department of Engineering, Cambridge University, Cambridge, UK.

George G Malliaras (GG)

Division of Electrical Engineering, Department of Engineering, Cambridge University, Cambridge, UK.

Cristina Savin (C)

Neuroscience Institute, New York University Langone Health, New York, NY, 10016, USA; Center for Neural Science, New York University, New York, NY, 10003, USA; Center for Data Science, New York University, New York, NY, 10003, USA.

Thomas Bozza (T)

Department of Neurobiology, Northwestern University, Evanston, IL, 60208, USA.

Dmitry Rinberg (D)

Neuroscience Institute, New York University Langone Health, New York, NY, 10016, USA; Center for Neural Science, New York University, New York, NY, 10003, USA; Department of Physics, New York University, New York, NY 10003, USA. Electronic address: rinberg@nyu.edu.

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Classifications MeSH