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Ghosn, S.

Publications and source records attributed to Ghosn, S..

2 recordsLinked to original sources

Automated detection of blink reflexes evoked by optogenetic stimulation of TRPV1-expressing corneal nociceptors in transgenic mice

BackgroundConventional rodent models for the study of corneal pain commonly evoke eye blink reflex using methods that indiscriminately activate polymodal nociceptors, mechanoreceptors, and thermoreceptors at temporal resolutions that dont closely match the sub-second timescale of underlying neural dynamics. New methodWe introduce a novel automated behavioral paradigm for detecting blink reflexes in transgenic TRPV1-ChR2-EYFP mice, enabled by cell-type-specific, millisecond-precision optogenetic stimulation of corneal nociceptors (490 nm light). Using multi-feature quantification, we achieve robust automated detection using univariate and multivariate classifiers. ResultsTRPV1-ChR2-EYFP mice exhibited blink reflexes to high-intensity blue light (490 nm, 10 ms pulses) in a threshold-dependent manner (N=3). Blink probability was 77.1 {+/-} 17.1% at high intensity (2.77 mW/mm2) versus 4.2 {+/-} 4.2% at low intensity (0.46 mW/mm2). Red light (638 nm) produced no intensity-dependent change. Noxious air puff evoked blinks in >95% of trials under all conditions. DeepLabCut-based pose estimation extracted six features quantifying the blink reflex, enabling automated detection with [≥]98% accuracy using univariate and multivariate classifiers. Comparison with existing methodsUnlike conventional air puff paradigms, this optogenetic approach enables precise, cell-type-specific stimulation of corneal nociceptors, supporting automated analysis of blink responses at sub-second resolution. ConclusionsThis video tracking behavioral method using machine learning algorithms that accurately classify blink versus no-blink enables high-throughput and observer-independent empirical assessment of blink reflex, suggestive of corneal pain. Moreover, inducing blink reflex in TRPV1-ChR2 mice using high-intensity blue light also demonstrates nociceptive-specific behavioral responses analogous to somatosensory optogenetically-evoked hindpaw pain in the same animal genotype.

neuroscience↗

Machine Learning Electroencephalography Biomarkers Predictive of Epworth Sleepiness Scale

Excessive daytime sleepiness (EDS) causes difficulty in concentrating and continuous fatigue during the day. In a clinical setting, the assessment and diagnosis of EDS relies mostly on subjective questionnaires and verbal reports, which compromises the effectiveness of available therapies. In this study, we used a computational pipeline for the automated, rapid, high-throughput and objective analysis of retrospective encephalography (EEG) data to develop objective, surrogate biomarkers for EDS, thereby defining the quantitative EEG changes in individuals with High Epworth Sleepiness Scale (ESS) (n=31), compared to a group of individuals with Low ESS (n=41) at Cleveland Clinic. Signal processing of EEG showed significantly different EEG features in the Low ESS group compared to High ESS, including power enhancement in the alpha and beta bands, and attenuation in the delta and theta bands. Moreover, machine learning algorithms trained on the binary classification of High vs Low ESS reached >80% accuracy. These results demonstrate that EEG data contain information in the form of rhythmic activity that could be leveraged for the quantitative assessment of EDS using machine learning.

neuroscience↗