Applications of wavelet transform in classification of local field potential recorded from the rat brain in conditioned place preference paradigm

TL;DR

This paper explores wavelet-based feature extraction and machine learning classification of rat brain LFP data to distinguish reward responses in a conditioned place preference paradigm, revealing neural activity patterns associated with different rewards.

Contribution

It introduces a novel wavelet scattering approach combined with machine learning for multi-label classification of LFP data in CPP, highlighting the importance of NAc and HIP connectivity.

Findings

Wavelet scattering achieved 80% classification accuracy across groups.

High accuracy (over 99%) for specific conditions like Food-post-test-HIP.

NAc activity is key for morphine-induced CPP, HIP for food-induced CPP.

Abstract

This study investigates the multi-label classification of Local Field Potential (LFP) data from the hippocampus (HIP) and nucleus accumbens (NAc) in the rat brain, focusing on reward responses using the Conditioned Place Preference (CPP) paradigm. Rats were conditioned with saline, morphine, and food rewards, and LFP recordings were conducted from both HIP and NAc during pre- and post-tests. The LFP data were classified into four categories: treatment types, test phases, recording channels, and chamber positions within the CPP setup. Features were extracted using Continuous Wavelet Transform (CWT), Wavelet Coherence, and Wavelet Scattering. Classification was performed via Decision Trees, Multilayer Perceptrons, and Support Vector Machines. Notably, in the Food group, HIP and combined HIP-NAc features yielded the highest classification accuracy for CPP chambers, whereas NAc features excelled in the Morphine group. Employing wavelet scattering, an 80% classification accuracy was achieved across treatment groups, test phases, and channels. Exceptionally high classification accuracies were observed for Food-post-test-HIP (99.75%) and Morphine-post-test-NAc (99.58%). The study reveals that NAc activity is pivotal for morphine-induced CPP, whereas HIP and HIP-NAc connectivity are crucial for food-induced CPP. The proposed methodology provides a novel avenue for precisely classifying LFP data, shedding light on neural circuit activities underlying behavioral responses.