Predictive Modeling of Rat Brain Local Field Potentials using Single-Variable and Multivariable Approaches

TL;DR

This study compares linear and deep learning models for predicting rat brain local field potentials, demonstrating that advanced nonlinear models like WCLSA outperform traditional methods and reveal neural connectivity patterns in reward processing.

Contribution

It introduces a wavelet coherence-enhanced deep learning model (WCOH CLSA) and benchmarks various models for neural prediction, highlighting the importance of nonlinear approaches.

Findings

WCLSA achieves up to 0.97 accuracy in predicting LFPs.

Wavelet coherence analysis shows strong connectivity in natural rewards.

Pharmacological effects disrupt or alter neural relationships.

Abstract

Accurate prediction of neural dynamics in the brain's reward circuitry is crucial for elucidating how natural and pharmacological rewards influence neural activity and connectivity. Traditional linear models, such as autoregressive (AR) and vector autoregressive (VAR), often inadequately capture the inherent nonlinear interactions in neural data. This study develops and benchmarks both linear and advanced deep learning models for predicting local field potentials (LFPs) in the rat hippocampus (HIP) and nucleus accumbens (NAc) across morphine, food, and saline conditions. We compared AR, VAR, long short-term memory (LSTM), and wavelet-based deep learning model (WCLSA). Additionally, a novel wavelet coherence-enhanced model (WCOH CLSA) was introduced to capture cross-region connectivity. Results indicate that WCLSA achieves superior predictive accuracy (up to 0.97 for HIP in food, 0.96 for NAc in morphine), while VAR performs competitively in the food group due to significant HIP-NAc correlation. Wavelet coherence analysis reveals robust connectivity in natural reward contexts and disrupted or nonlinear relationships under pharmacological influence. These findings highlight the differential engagement of HIP and NAc in reward processing and underscore the importance of advanced nonlinear models for capturing complex neural dynamics. The study provides a robust framework for predictive neuroscience and elucidates functional interactions within the reward circuitry.