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

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&...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on biomedical circuits and systems 2023-02, Vol.PP (1), p.1-9
Main Authors: Nukitram, Jakkrit, Saengmolee, Wanumaidah, Chaisaen, Rattanaphon, Autthasan, Phairot, Sengnon, Narumon, Wungsintaweekul, Juraithip, Cheaha, Dania, Kumarnsit, Ekkasit, Sudhawiyangkul, Thapanun, Wilaiprasitporn, Theerawit
Format: Article
Language:English
Subjects:
Animals
Anomaly Detection
Antidepressant
Antidepressants
Drugs
Electrophysiological recording
Feature extraction
Fluoxetine
Influenza
Kratom
Local Field Potential
Mice
Multi-Task Autoencoder
Multitasking
Performance evaluation
Plant extracts
Similarity
Syrup
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 87.11 \pm 0.25%. 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 90.11 \pm 0.11% and 90.08 \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.
ISSN:1932-4545
1940-9990
DOI:10.1109/TBCAS.2023.3234280