Biology subjects

Sabnis, G.

Publications and source records attributed to Sabnis, G..

7 recordsLinked to original sources

bioRxiv2026

Genotype-dependent behavioral signatures of opioid withdrawal revealed by automated behavioral quantification

Opioid withdrawal drives continued opioid use in opioid use disorder (OUD), which affected an estimated 4 million Americans in 2024. However, only one non-opioid medication is FDA-approved for withdrawal treatment, and there is great need for more diverse treatment options. Mice are powerful models to study opioid withdrawal because they allow for controlled drug and environmental exposure, express OUD-relevant phenotypes, and provide access to withdrawal-relevant tissue at exact time points. However, mouse models are hindered by their over reliance on a single genetic background (C57BL/6J). Using a single background risks limiting the translatability of preclinical studies as they are projected into genetically diverse human populations. Inferring withdrawal intensity in mice typically relies on single behaviors or indexes of behaviors, and applying these approaches across mouse genetic diversity faces a critical challenge: differing withdrawal responses could either encode differences in withdrawal severity, or differences in withdrawal-evoked behaviors independent of severity. To overcome this limitation, a more comprehensive assessment of opioid withdrawal behaviors across mouse genetic diversity is needed. Here we leverage computer vision and machine learning to automate the simultaneous quantification of many mouse behaviors during opioid withdrawal in eight diverse strains of mice. We reveal that all eight strains exhibit withdrawal-evoked behaviors, and these behaviors are sufficient to identify the withdrawal state of the mouse. However, the effect of withdrawal on individual behaviors or indexes of behaviors is not uniform across genetic diversity, and extending methods tuned in one strain risks misclassifying withdrawal in others. Finally our approach was able to distinguish graded withdrawal intensity, as defined by opioid exposurewithin3of4strainstested. We therefore conclude that opioid withdrawal behavior is profoundly shaped by genetic background, but withdrawal severity can nonetheless be established within a background. Combining automated computer-vision phenotyping with genotype-aware modeling provides a scalable path toward genetically informed withdrawal assessment in diverse mouse populations, with the ultimate goal of identifying the genetic and biological networks that modulate opioid withdrawal and may represent new targets for treating opioid use disorder.

animal behavior and cognition↗

bioRxiv2026

Integrating supervised and unsupervised machine learning for behavior segmentation reveals latent frailty signatures and improves aging clocks in isogenic and outbred mice

1Manual frailty index (FI) assessment in mice is a strong predictor of morbidity and mortality, and is frequently used in mechanistic and translational geroscience. However, it is labor-intensive, requires expert training, and is vulnerable to scorer variability. We previously developed a visual frailty index (vFI) that objectively predicts age and frailty using expert-defined, supervised behavioral features extracted from open-field videos. However, relying solely on human-defined features may miss subtle, latent behavioral signatures of aging. Here, we test whether unsupervised behavioral discovery using Keypoint-MoSeq (KPMS) could uncover these hidden signatures and improve the prediction of aging-related outcomes. Using a large dataset of isogenic C57BL/6J (B6J) and genetically diverse Diversity Outbred (DO) mice, we find that unsupervised features are highly predictive of chronological age, biological frailty, and the proportion of life lived. Notably, while supervised features overall outperformed unsupervised features in these tasks, combining both feature sets yielded the highest predictive accuracy across all outcomes. Despite these improvements, models trained on either feature set failed to generalize across strains, confirming that behavioral manifestations of aging are strongly population-specific. These findings demonstrate that supervised and unsupervised machine vision provide complementary information, establishing a highly sensitive, scalable, and non-invasive framework for objective and scalable geroscience in rodents.

animal behavior and cognition↗

bioRxiv2024

Visual detection of seizures in mice using supervised machine learning

1Seizures are caused by abnormally synchronous brain activity that can result in changes in muscle tone, such as twitching, stiffness, limpness, or rhythmic jerking. These behavioral manifestations are clear on visual inspection and the most widely used seizure scoring systems in preclinical models, such as the Racine scale in rodents, use these behavioral patterns in semiquantitative seizure intensity scores. However, visual inspection is time-consuming, low-throughput, and partially subjective, and there is a need for rigorously quantitative approaches that are scalable. In this study, we used supervised machine learning approaches to develop automated classifiers to predict seizure severity directly from noninvasive video data. Using the PTZ-induced seizure model in mice, we trained video-only classifiers to predict ictal events, combined these events to predict an univariate seizure intensity for a recording session, as well as time-varying seizure intensity scores. Our results show, for the first time, that seizure events and overall intensity can be rigorously quantified directly from overhead video of mice in a standard open field using supervised approaches. These results enable high-throughput, noninvasive, and standardized seizure scoring for downstream applications such as neurogenetics and therapeutic discovery.

neuroscience↗

bioRxiv2023

Highly Accurate and Precise Determination of Mouse Mass Using Computer Vision

1Changes in body mass are a key indicator of health and disease in humans and model organisms. Animal body mass is routinely monitored in husbandry and preclinical studies. In rodent studies, the current best method requires manually weighing the animal on a balance which has at least two consequences. First, direct handling of the animal induces stress and can have confounding effects on studies. Second, the acquired mass is static and not amenable to continuous assessment, and rapid mass changes can be missed. A noninvasive and continuous method of monitoring animal mass would have utility in multiple areas of biomedical research. Here, we test the feasibility of determining mouse body mass using video data. We combine computer vision methods with statistical modeling to demonstrate the feasibility of our approach. Our methods determine mouse mass with 4.8% error across highly genetically diverse mouse strains, with varied coat colors and mass. This error is low enough to replace manual weighing with image-based assessment in most mouse studies. We conclude that visual determination of rodent mass using video enables noninvasive and continuous monitoring and can improve animal welfare and preclinical studies.

genetics↗

bioRxiv2021

A machine vision based frailty index for mice

1Chronological aging is uniform, but biological aging is heterogeneous. Clinically, this heterogeneity manifests itself in health status and mortality, and it distinguishes healthy from unhealthy aging. Frailty indexes (FIs) serve as an important tool in gerontology to capture health status. FIs have been adapted for use in mice and are an effective predictor of mortality risk. To accelerate our understanding of biological aging, high-throughput approaches to pre-clinical studies are necessary. Currently, however, mouse frailty indexing is manual and relies on trained scorers, which imposes limits on scalability and reliability. Here, we introduce a machine learning based visual frailty index (vFI) for mice that operates on video data from an open field assay. We generate a large mouse FI dataset of both male and female mice. From video data on these same mice, we use neural networks to extract morphometric, gait, and other behavioral features that correlate with manual FI score and age. We use these features to train a regression model that accurately predicts the normalized FI score within 0.04 {+/-} 0.002 (mean absolute error). We show that features of biological aging are encoded in open-field video data and can be used to construct a vFI that can complement or replace current manual FI methods. We use the vFI data to examine sex-specific aspects of aging in mice. This vFI provides increased accuracy, reproducibility, and scalability, that will enable large scale mechanistic and interventional studies of aging in mice.

animal behavior and cognition↗

bioRxiv2020

Gait-level analysis of mouse open field behavior using deep learning-based pose estimation

1Gait and whole body posture are sensitive measures of the proper functioning of numerous neural circuits, and are often perturbed in many neurological, neuromuscular, and neuropsychiatric illnesses. Rodents provide a tractable model for elucidating disease mechanisms and interventions, however, studying gait and whole body posture in rodent models requires specialized methods and remains challenging. Here, we develop a simple assay that allows adoption of the commonly used open field apparatus for gait and whole body posture analysis. We leverage modern neural networks to abstract a mouse into keypoints and extract gait and whole body coordination metrics of the animal. Gait-level analysis allows us to detect every step of the animals movement and provides high resolution information about the animals behavior. We quantitate gait and whole body posture with high precision and accuracy across 62 highly visually diverse strains of mice. We apply our approach to characterize four genetic mutants with known gait deficits. In extended analysis, we demonstrate that multiple autism spectrum disorder (ASD) models show gait and posture deficits, implying this is a general feature of ASD. We conduct a large strain survey of 1898 mice, and find that gait and whole body posture measures are highly heritable in the laboratory mouse, and fall into three classes. Furthermore, the reference mouse strain, C57BL/6J, has a distinctly different gait and posture compared to other standard laboratory and wild-derived strains. We conduct a genome wide association study (GWAS) to define the genetic architecture of mouse movement in the open field. In sum, we describe a simple, sensitive, accurate, scalable, and ethologically relevant method of mouse gait and whole body posture analysis for behavioral neurogenetics. These results provide one of the largest laboratory mouse gait-level data resources for the research community and show the utility of automated machine learning approaches for deriving biological insights.

animal behavior and cognition↗

bioRxiv2020

Action detection using a neural network elucidates the genetics of mouse grooming behavior

Automated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate sequential motions. Grooming, a prototypical stereotyped behavior, is often used as an endophenotype in psychiatric genetics. Using mouse grooming behavior as an example, we develop a general purpose neural network architecture capable of dynamic action detection at human observer-level performance and operate across dozens of mouse strains with high visual diversity. We provide insights into the amount of human annotated training data that are needed to achieve such performance. We survey grooming behavior in the open field in 2500 mice across 62 strains, determine its heritable components, conduct GWAS to outline its genetic architecture, and perform PheWAS to link human psychiatric traits through shared underlying genetics. Our general machine learning solution that automatically classifies complex behaviors in large datasets will facilitate systematic studies of mechanisms underlying these behaviors.

bioinformatics↗