| Title: |
Differentiating Opioid Use Disorder from Healthy Controls via ML Analysis of RS-fMRI Networks |
| Authors: |
Temtam, Ahmed; Witherow, Megan A.; Ma, Liangsuo; Sadique, M. Shibly; Moeller, F. Gerard; Kenneth, C.; Wright, Dianne; Iftekharuddin, Khan M. |
| Source: |
Electrical & Computer Engineering Faculty Publications |
| Publisher Information: |
ODU Digital Commons |
| Publication Year: |
2025 |
| Collection: |
Old Dominion University: ODU Digital Commons |
| Subject Terms: |
Algorithms; Blood levels; Brain mapping; Classification; Cortex (cingulate); Demography; Drug abuse; Drug addiction; Executive function; Functional magnetic resonance imaging; Narcotics; Neurosciences; Population studies; Prefrontal cortex; Opioids; Statistical analysis; Sciences: Comprehensive works; Substance abuse disorder; Analytical; Diagnostic and Therapeutic Techniques and Equipment; Artificial Intelligence and Robotics; Substance Abuse and Addiction; Theory and Algorithms |
| Description: |
Objectives/Goals: This work aims to identify functional brain networks that differentiate opioid use disorder (OUD) subjects from healthy controls (HC) using machine learning (ML) analysis of resting-state fMRI (rs-fMRI). We investigate the default mode network (DMN), salience network (SN), and executive control network (ECN), as well as demographic features. Methods/Study Population: This work uses high-resolution rs-fMRI data from a National Institute on Drug Abuse study (IRB #HM20023630) with 31 OUD and 45 HC subjects. We extract rs-fMRI blood oxygenation level-dependent (BOLD) features from the DMN, SN, and ECN. The Boruta ML algorithm identifies statistically significant features and brain activity mapping visualizes regions of heightened neural activity for OUD. We conduct fivefold cross-validation classification experiments (OUD vs. HC) to assess the discriminative power of functional network features with and without incorporating demographic features. Demographic features are ranked based on ML classification importance. Follow-up Boruta analysis is performed to study the medial prefrontal cortex (mPFC), posterior cingulate cortex, and temporoparietal junctions in the DMN. Results/Anticipated Results: Boruta ML analysis identifies the DMN as the most salient functional network for differentiating OUD from HC, with 33% of DMN features found significant (p < 0.05), compared to 10% and 0% for the SN and ECN, respectively. The Boruta ML algorithm identifies age and education as the most significant demographic features. Brain activity mapping shows heightened neural activity in the DMN for OUD. The DMN exhibits the greatest discriminative power, with a mean AUC of 69.74%, compared to 47.14% and 54.15% for the SN and ECN, respectively. Fusing DMN BOLD features with the most important demographic features improves the mean AUC to 80.91% and the F1 score to 73.97%. Follow-up Boruta analysis highlights the mPFC as the most important functional hub within the DMN, with 65% significant features. ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
unknown |
| Relation: |
https://digitalcommons.odu.edu/ece_fac_pubs/519; https://digitalcommons.odu.edu/context/ece_fac_pubs/article/1529/viewcontent/Temtam_2025_Differentiating_Opioid_Use_DisorderOCR.pdf |
| DOI: |
10.1017/cts.2024.985 |
| Availability: |
https://digitalcommons.odu.edu/ece_fac_pubs/519; https://doi.org/10.1017/cts.2024.985; https://digitalcommons.odu.edu/context/ece_fac_pubs/article/1529/viewcontent/Temtam_2025_Differentiating_Opioid_Use_DisorderOCR.pdf |
| Rights: |
© The Authors, 2025. This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND 4.0) License , which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work. |
| Accession Number: |
edsbas.8BA454BC |
| Database: |
BASE |