AI-driven decoding of naturalistic behaviors enables tailored detection of depressive-like behavior in mice.

ABSTRACT: Major depressive disorder (MDD) is an etiologically diverse psychiatric disease with heterogeneous manifestations, making it difficult to diagnose with conventional assessment standards. In addition, the obvious incompatibility of the standard survey-based tests for human MDD and the behavioral assays for depressive-like phenotypes in mice makes clear the requirement for a non-invasive method for quantifying the expression of depressive-like state in naturalistic contexts. Here, we introduce a self-supervised machine learning platform, CLOSER (Contrastive Learning-based Observer-free analysis of Spontaneous behavior for Ethogram Representation), to monitor the spontaneous behavior in a depressive disease model with enhanced precision, reliability, and efficiency. This framework incorporate

SUBMITTER: Oh H 

PROVIDER: S-EPMC12827409 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature