Pupil dynamics alterations have been found in patients affected by a variety of neuropsychiatric conditions, including autism. Studies in mouse models have used pupillometry for phenotypic assessment and as a proxy for arousal. Both in mice and humans, pupillometry is noninvasive and allows for longitudinal experiments supporting temporal specificity; however, its measure requires dedicated setups. Here, we introduce a convolutional neural network that performs online pupillometry in both mice and humans in a web app format. This solution dramatically simplifies the usage of the tool for the nonspecialist and nontechnical operators. Because a modern web browser is the only software requirement, this choice is of great interest given its easy deployment and setup time reduction. The tested model performances indicate that the tool is sensitive enough to detect both locomotor-induced and stimulus-evoked pupillary changes, and its output is comparable to state-of-the-art commercial devices.

{MEYE}: Web-app for translational and real-time pupillometry

Aurelia Viglione
Data Curation
;
Leonardo Lupori
Methodology
;
Tommaso Pizzorusso
Writing – Review & Editing
2021

Abstract

Pupil dynamics alterations have been found in patients affected by a variety of neuropsychiatric conditions, including autism. Studies in mouse models have used pupillometry for phenotypic assessment and as a proxy for arousal. Both in mice and humans, pupillometry is noninvasive and allows for longitudinal experiments supporting temporal specificity; however, its measure requires dedicated setups. Here, we introduce a convolutional neural network that performs online pupillometry in both mice and humans in a web app format. This solution dramatically simplifies the usage of the tool for the nonspecialist and nontechnical operators. Because a modern web browser is the only software requirement, this choice is of great interest given its easy deployment and setup time reduction. The tested model performances indicate that the tool is sensitive enough to detect both locomotor-induced and stimulus-evoked pupillary changes, and its output is comparable to state-of-the-art commercial devices.
2021
Settore BIO/09 - Fisiologia
arousal; neural network; oddball; pupillometry; virtual reality; web app
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/110106
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