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

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Title: Sensitive and robust chemical detection using an olfactory brain-computer interface.
Authors: Shor, Erez1 (AUTHOR), Herrero-Vidal, Pedro1,2 (AUTHOR), Dewan, Adam3,4 (AUTHOR), Uguz, Ilke5 (AUTHOR), Curto, Vincenzo F.6 (AUTHOR), Malliaras, George G.6 (AUTHOR), Savin, Cristina1,2,7 (AUTHOR), Bozza, Thomas3 (AUTHOR), Rinberg, Dmitry1,2,8 (AUTHOR) rinberg@nyu.edu
Source: Biosensors & Bioelectronics. Jan2022, Vol. 195, pN.PAG-N.PAG. 1p.
Subjects: Brain-computer interfaces, Nose, Olfactory perception, Olfactory receptors, Chemical detectors, Olfactory bulb, Biological systems
Abstract: 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. • Developed a novel chemical detection system exploiting the mouse's sense of smell. • Neural-based chemical detection matches detection thresholds of well-trained mice. • Genetic engineering improves the sensitivity of the bioelectronic-nose. • Robust and stable detection of multiple chemicals from single animals is demonstrated. • Detection accuracy persists in the presence of background odors. [ABSTRACT FROM AUTHOR]
Copyright of Biosensors & Bioelectronics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Sensitive and robust chemical detection using an olfactory brain-computer interface.
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  Data: <searchLink fieldCode="AR" term="%22Shor%2C+Erez%22">Shor, Erez</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Herrero-Vidal%2C+Pedro%22">Herrero-Vidal, Pedro</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dewan%2C+Adam%22">Dewan, Adam</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Uguz%2C+Ilke%22">Uguz, Ilke</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Curto%2C+Vincenzo+F%2E%22">Curto, Vincenzo F.</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Malliaras%2C+George+G%2E%22">Malliaras, George G.</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Savin%2C+Cristina%22">Savin, Cristina</searchLink><relatesTo>1,2,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bozza%2C+Thomas%22">Bozza, Thomas</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rinberg%2C+Dmitry%22">Rinberg, Dmitry</searchLink><relatesTo>1,2,8</relatesTo> (AUTHOR)<i> rinberg@nyu.edu</i>
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  Data: 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. • Developed a novel chemical detection system exploiting the mouse's sense of smell. • Neural-based chemical detection matches detection thresholds of well-trained mice. • Genetic engineering improves the sensitivity of the bioelectronic-nose. • Robust and stable detection of multiple chemicals from single animals is demonstrated. • Detection accuracy persists in the presence of background odors. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Biosensors & Bioelectronics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.bios.2021.113664
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      – Code: eng
        Text: English
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    Subjects:
      – SubjectFull: Brain-computer interfaces
        Type: general
      – SubjectFull: Nose
        Type: general
      – SubjectFull: Olfactory perception
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      – SubjectFull: Olfactory receptors
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      – SubjectFull: Chemical detectors
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      – SubjectFull: Olfactory bulb
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      – SubjectFull: Biological systems
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      – TitleFull: Sensitive and robust chemical detection using an olfactory brain-computer interface.
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              Text: Jan2022
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