ANet: Autoencoder-Based Local Field Potential Feature Extractor for Evaluating An Antidepressant Effect in Mice after Administering Kratom Leaf Extracts

TL;DR

This study introduces ANet, an autoencoder-based method, to evaluate and compare the antidepressant-like effects of Kratom extracts and fluoxetine in mice via local field potential analysis, demonstrating high similarity and effective classification.

Contribution

The paper presents a novel autoencoder-based anomaly detection approach for analyzing LFP features to evaluate Kratom extracts' antidepressant effects, including multi-class discrimination and visualization.

Findings

Kratom syrup shows 85.62% similarity to fluoxetine in LFP features.

ANet achieves approximately 80% accuracy and F1-score in classifying responses.

The method enables visualization of latent features for better understanding of effects.

Abstract

Kratom (KT) typically exerts antidepressant (AD) effects. However, evaluating which form of KT extracts possesses AD properties similar to the standard AD fluoxetine (flu) remained challenging. Here, we adopted an autoencoder (AE)-based anomaly detector called ANet to measure the similarity of mice's local field potential (LFP) features that responded to KT leave extracts and AD flu. The features that responded to KT syrup had the highest similarity to those that responded to the AD flu at 85.62 $\pm$ 0.29%. This finding presents the higher feasibility of using KT syrup as an alternative substance for depressant therapy than KT alkaloids and KT aqueous, which are the other candidates in this study. Apart from the similarity measurement, we utilized ANet as a multi-task AE and evaluated the performance in discriminating multi-class LFP responses corresponding to the effect of different KT extracts and AD flu simultaneously. Furthermore, we visualized learned latent features among LFP responses qualitatively and quantitatively as t-SNE projection and maximum mean discrepancy distance, respectively. The classification results reported the accuracy and F1-score of 79.78 $\pm$ 0.39% and 79.53 $\pm$ 0.00%. In summary, the outcomes of this research might help therapeutic design devices for an alternative substance profile evaluation, such as Kratom-based form in real-world applications.