We all make mistakes, and some cost more than others. Now, researchers have peered inside the brain to see what happens at the moment we realize our error -- and its price. Their findings may have implications for understanding obsessive-compulsive disorder.
We all make mistakes, and some cost more than others. Now, researchers have peered inside the brain to see what happens at the moment we realize our error - and its price. Their findings may have implications for understanding obsessive-compulsive disorder.
Dr. Stephan Taylor, an associate professor of psychiatry at the University of Michigan Medical School, and colleagues used fMRI to image 12 healthy adults who were asked to respond to a series of 360 visual-based tests. Results were published April 12 in the Journal of Neuroscience.
Some tests carried a monetary reward between a quarter and $2, while some carried penalties of the same size. Others carried no reward or penalty. The participants were told they had a $10 "credit" to begin and that they would receive real cash depending on their balance at the end.
The participants had to correctly, and within a deadline of a few hundred milliseconds, press a button corresponding to one of two alphabetic letter pairs. They were instructed to determine which letter was the odd one out in a series of other letters. Some of the letter sequences were more confusing than others. Subjects received immediate feedback telling them if they were wrong or too late in responding.
Researchers found that a particular part of the brain called the rostral anterior cingulate cortex, or rACC, becomes much more active when a person realizes he or she has made an error that carries consequences, such as losing money.
By contrast, the same area of the brain does not show the same level of activity when the mistake doesn't carry a penalty or when a correct action carries a reward. The rACC is thought to be involved with emotional responses, and scientists had suspected it might also be involved in response to costly errors. But this is the first brain imaging study to test that idea, according to Taylor.
These researchers had previously shown that the rACC area became much more active in response to a no-penalty error in the brains of a small group of obsessive-compulsive disorder patients, compared with people without the condition. OCD is often characterized by an untoward anxiety or fear about errors or failures in certain aspects of everyday life. Repetitive patterns of behavior are a mechanism to ward off or prevent such events.
"It's very interesting to us that the same part of the brain that responded in our OCD study on regular, no-cost errors also responded in healthy individuals when we made the error count more," Taylor said.
Investigators want to study patients with OCD using the same experiment to help pinpoint which of their brain circuits have gone awry.
For more information from the Diagnostic Imaging archives: '
Supertensor imaging locates complex fibers
Imaging genomics unveils roots of aggression
Strategic goals take shape in functional brain MR imaging
Emerging AI Algorithm Shows Promise for Abbreviated Breast MRI in Multicenter Study
April 25th 2025An artificial intelligence algorithm for dynamic contrast-enhanced breast MRI offered a 93.9 percent AUC for breast cancer detection, and a 92.3 percent sensitivity in BI-RADS 3 cases, according to new research presented at the Society for Breast Imaging (SBI) conference.
Could AI-Powered Abbreviated MRI Reinvent Detection for Structural Abnormalities of the Knee?
April 24th 2025Employing deep learning image reconstruction, parallel imaging and multi-slice acceleration in a sub-five-minute 3T knee MRI, researchers noted 100 percent sensitivity and 99 percent specificity for anterior cruciate ligament (ACL) tears.
New bpMRI Study Suggests AI Offers Comparable Results to Radiologists for PCa Detection
April 15th 2025Demonstrating no significant difference with radiologist detection of clinically significant prostate cancer (csPCa), a biparametric MRI-based AI model provided an 88.4 percent sensitivity rate in a recent study.
Emerging AI Algorithm Shows Promise for Abbreviated Breast MRI in Multicenter Study
April 25th 2025An artificial intelligence algorithm for dynamic contrast-enhanced breast MRI offered a 93.9 percent AUC for breast cancer detection, and a 92.3 percent sensitivity in BI-RADS 3 cases, according to new research presented at the Society for Breast Imaging (SBI) conference.
Could AI-Powered Abbreviated MRI Reinvent Detection for Structural Abnormalities of the Knee?
April 24th 2025Employing deep learning image reconstruction, parallel imaging and multi-slice acceleration in a sub-five-minute 3T knee MRI, researchers noted 100 percent sensitivity and 99 percent specificity for anterior cruciate ligament (ACL) tears.
New bpMRI Study Suggests AI Offers Comparable Results to Radiologists for PCa Detection
April 15th 2025Demonstrating no significant difference with radiologist detection of clinically significant prostate cancer (csPCa), a biparametric MRI-based AI model provided an 88.4 percent sensitivity rate in a recent study.
2 Commerce Drive
Cranbury, NJ 08512