Document Type

Conference Paper

Publication Date

2023

DOI

10.1117/12.2655568

Publication Title

Medical Imaging 2023: Computer-Aided Diagnosis, Proceedings of SPIE 12465

Volume

12465

Pages

1-5

Conference Name

SPIE Medical Imaging 2023: Computer Aided Diagnosis, February 19-24, 2023, San Diego, California

Abstract

According to the Centers for Disease Control and Prevention (CDC) more than 932,000 people in the US have died since 1999 from a drug overdose. Just about 75% of drug overdose deaths in 2020 involved Opioid, which suggests that the US is in an Opioid overdose epidemic. Identifying individuals likely to develop Opioid use disorder (OUD) can help public health in planning effective prevention, intervention, drug overdose and recovery policies. Further, a better understanding of prediction of overdose leading to the neurobiology of OUD may lead to new therapeutics. In recent years, very limited work has been done using statistical analysis of functional magnetic resonance imaging (fMRI) methods to analyze the neurobiology of Opioid addictions in humans. In this work, for the first time in the literature, we propose a machine learning (ML) framework to predict OUD users utilizing clinical fMRI-BOLD (Blood oxygen level dependent) signal from OUD users and healthy controls (HC). We first obtain the features and validate these with those extracted from selected brain subcortical areas identified in our previous statistical analysis of the fMRI-BOLD signal discriminating OUD subjects from that of the HC. The selected features from three representative brain areas such as default mode network (DMN), salience network (SN), and executive control network (ECN) for both OUD participants and HC subjects are then processed for OUD and HC subjects’ prediction. Our leave one out cross validated results with sixty-nine OUD and HC cases show 88.40% prediction accuracies. These results suggest that the proposed techniques may be utilized to gain a greater understanding of the neurobiology of OUD leading to novel therapeutic development.

Rights

Copyright 2023 Society of Photo‑Optical Instrumentation Engineers (SPIE).

One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.

Original Publication Citation

Temtam, A., Ma, L., Moeller, F. G., Sadique, M. S., & Iftekharuddin, K. M. (2023). Opioid use disorder prediction using machine learning of fMRI data. In K.M. Iftekharuddin & W. Chen (Eds.), Medical Imaging 2023: Computer-Aided Diagnosis, Proceedings of SPIE 12465 (1-5). SPIE. https://doi.org/10.1117/12.2655568

ORCID

0000-0002-6734-6802 (Sadique), 0000-0001-8316-4163 (Iftekharuddin)

Download

Share

COinS