Stress-associated alterations in amygdala-striatal activity: a multi-level analysis of distributional, dynamical, and computational signatures.

Chronic stress is associated with persistent alterations in neural circuit function, yet how these changes are expressed across multiple descriptive levels remains unclear. Here, we re-analyzed in vivo GCaMP8s recordings from BLA-DMS and CeA-DMS projection pathways using complementary distributional, dynamical, and computational approaches. Distributional analyses based on Kullback-Leibler (KL) divergence revealed stress-associated changes in the temporal organization of neural activity patterns, particularly following acute aversive perturbations where stressed animals exhibited prolonged deviations from baseline distributions. Dynamical analyses using phenomenological second-order regression models revealed corresponding alterations in recovery-related properties. Following footshock, stress was associated with shifts in principal eigenvalue distributions and reconstructed quasi-potential profiles, whereas learned lever press-reward behaviors exhibited broadly similar local stability despite differences in coefficient structure. Notably, stress-related differences in reconstructed dynamics were detectable during task acquisition even when overt behavioral performance remained comparable between groups. To examine whether these qualitative signatures could arise from simple computational principles, we implemented a minimal artificial neural network (ANN) framework as a hypothesis-generating sufficiency test. Models incorporating asymmetric optimization objectives reproduced selected experimental signatures, whereas symmetric objectives did not. Together, these findings suggest that chronic stress is associated with a reorganization of neural activity patterns across multiple descriptive levels, altering responses to perturbations and producing detectable changes in neural activity organization before overt behavioral divergence emerges.