For the first time, Rutgers scientists have used a diagnostic technique in the field of opioid addiction that they believe can determine which opioid-addicted patients are most likely to relapse.
Using an algorithm that looks for patterns of brain structure and functional connectivity, the researchers were able to distinguish the prescription opioids users of healthy participants. If the treatment is successful, their brains will resemble the brain of someone who is not addicted to opioids.
“People may say one thing, but brain patterns don’t lie,” said Suchismita Ray, principal investigator and associate professor in the Department of Health Informatics at the Rutgers School of Health Professions. “The file brain patterns that the algorithm identified from brain volume and the biomarkers of functional connectivity of prescription opioid users have great promise to improve the current diagnosis. “
In the study, published in NeuroImage: clinic, Ray and colleagues used MRIs to look at the file brain structure and function in people diagnosed with a prescription opioid use disorder who sought treatment compared to individuals with no history of opioid use.
The scans examined the brain network that was believed to be responsible for drug addiction and compulsive drug use. At the end of treatment, if this brain network has not changed, more treatment is needed.
“Our approach was able to separate prescription opioid users from healthy participants based on brain volume and functional connectivity data. We would not have been able to detect differences in functional connectivity between groups without the analysis of “Opioid use disorder has reached epidemic proportions in the United States, raising an urgent need for biological diagnostic tools that can improve predictions of disease characteristics,” Ray said in a statement. study.
People with opioid use disorder are currently being diagnosed based on the data they provide, which researchers say may be subject to bias. This machine learning algorithm, which quickly extracts massive amounts of data, offers the potential to detect whether the brain functional connectivity and the structure in a recovering opioid user has returned to normal or almost in practicenormal levels after treatment.
Given the high rate of overdose deaths and relapses in opioid users, it is crucial to accurately diagnose opioid-addicted patients to improve treatment outcomes and prevent overdose deaths, the researchers said.
Ravi D. Mill et al, Structural MRI and functional connectivity features predict current clinical status and persistence behavior in prescription opioid users, NeuroImage: clinic (2021). DOI: 10.1016 / j.nicl.2021.102663
Citation: A New Diagnostic Method May Predict the Risk of Relapse in the Recovery of Prescribed Opioid Addicts (2021, June 22) Retrieved June 22, 2021 at https://medicalxpress.com/news/2021-06 -diagnostic-method-relapse-recovering-prescription.html
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