In a recent interview, Joshua Gowin, M.D., discussed emerging brain MRI research that showed a significant association between heavy lifetime use of cannabis and reduced brain activity for working memory tasks in young adults.
In a brain magnetic resonance imaging (MRI) study of over 1,000 young adults, researchers found that 63 percent of those who have used cannabis 1,000 or more times have reduced brain activation for working memory tasks.
For the cross-sectional study, recently published in JAMA Network Open, researchers reviewed functional MRI findings, urine toxicology reports and cannabis use data for 1,003 young adults (mean age of 28.7). According to the study, 88 participants were heavy cannabis users (1,000 or more uses of the drug) and 179 participants were moderate users (10 to 999 uses) with the remaining 736 participants described as non-users (less than 10 uses of cannabis).
In a recent interview, lead study author Joshua Gowin, M.D., said heavy lifetime use of cannabis was associated with lower brain activation during the working memory task with a pronounced impact on the anterior insula, medial prefrontal cortex, and dorsolateral prefrontal cortex.
(Editor’s note: For additional content on MRI, click here.)
“The prefrontal cortex is certainly associated with executive function and executive control. … One of the reasons why the prefrontal cortex is activated during the working memory task is because you have to keep track of a lot of things, and it's cognitively demanding. Since we saw lower activation in the cannabis users in (the prefrontal cortex), we suspect that … cannabis users had perhaps less ability to ramp up that brain region during the cognitively demanding working memory task,” noted Dr. Gowin, an assistant professor of radiology at the University of Colorado School of Medicine in Aurora, Colo.
(Editor’s note: For related content, see “Chest CT Study Shows Higher Emphysema Risk from Combination of Marijuana and Cigarette Smoking,” “Hybrid PET/MRI Assessment with Hippocampal Radiomics May Facilitate Early Alzheimer’s Diagnosis” and “A Closer Look at the New Appropriate Use Criteria for Brain PET: An Interview with Phillip Kuo, MD, Part 2.”
For more insights from Dr. Gowin, watch the video below.
Can Deep Learning Radiomics with bpMRI Bolster Accuracy for Prostate Cancer Prognosis?
January 22nd 2025An emerging deep learning radiomics model based on biparametric MRI (bpMRI) offered a 14 to 17 percent higher AUC range than PI-RADS scoring for predicting the aggressiveness of prostate cancer, according to new research findings.
Seven Takeaways from New CT and MRI Guidelines for Ovarian Cancer Staging
January 20th 2025In an update of previous guidelines from the European Society of Urogenital Radiology published in 2010, a 21-expert panel offered consensus recommendations on the utility of CT, MRI and PET-CT in the staging and follow-up imaging for patients with ovarian cancer.
Can Generative AI Facilitate Simulated Contrast Enhancement for Prostate MRI?
January 14th 2025Deep learning synthesis of contrast-enhanced MRI from non-contrast prostate MRI sequences provided an average multiscale structural similarity index of 70 percent with actual contrast-enhanced prostate MRI in external validation testing from newly published research.
Can MRI-Based AI Enhance Risk Stratification in Prostate Cancer?
January 13th 2025Employing baseline MRI and clinical data, an emerging deep learning model was 32 percent more likely to predict the progression of low-risk prostate cancer (PCa) to clinically significant prostate cancer (csPCa), according to new research.