Application of Machine Learning Models to Identify Differences in Neural Electrophysiological Properties Across Estrous Cycle Phases.

Machine learning models (MLMs) have been used to classify neuron subtypes based upon electrophysiological attributes. What has been less explored is using MLMs to assess differences within a neuron type, especially in the context of subtle changes induced by neuromodulation such as the rodent estrous cycle. Previous research found that the estrous cycle shifts rat nucleus accumbens (NAc) medium spiny neuron (MSN) electrophysiology, including action potential (AP), passive, and miniature excitatory post-synaptic current (mEPSC) properties. This plasticity provides a model system to investigate this question. We hypothesized that MLM classification accuracy would differ when trained across these data types, reflecting the information each encodes regarding estrous phase identity. To test this hypothesis, we extracted electrophysiological features across four estrous cycle phases from a publicly available dataset, and employed this data to train MLMs to classify estrous cycle phase origin. We found: MLMs identified estrous phase origin with up to 94% accuracy (Random Forest); MLM accuracy differed by model and feature set; passive, AP and mEPSC features yielded sequential performances with feature importance analysis revealing input resistance, AP burst length, and mEPSC baseline current as most discriminative for their respective feature sets; the late proestrus phase demonstrated the most distinct electrophysiological profile. These findings demonstrate that MLMs can detect changes associated with the estrous cycle and are useful for assessing electrophysiological differences within a neuron type. They further represent a broader application of MLMs as computational tools useful for understanding the sensitivity of neural properties to the influences of neuromodulatory cycles.