Summary: The study shows an association between signal recognition theory, brain activation patterns and subjective state fatigue. Greater effects of fatigue have been observed in patients with multiple sclerosis.
Source: Kessler Foundation
Using signal detection theory, Kessler Foundation researchers deepened their understanding of the mechanisms of cognitive fatigue in a recent neuroimaging study comparing participants with multiple sclerosis (MS) and controls.
Researchers found an association between signal recognition theory metrics, subjective “state” fatigue, and brain activation patterns in both groups.
The MS group showed greater effects of fatigue, as evidenced by their response bias patterns.
These results were reported in Frontiers in behavioral neuroscience. The authors are Cristina Almeida Flores Román, PhD, John DeLuca, PhD, Bing Yao, PhD, Helen M. Genova, PhD, and Glenn Wylie, DPhil, of the Kessler Foundation.
Because subjective feelings of cognitive fatigue do not correlate with objective measures of performance, researchers have attempted to identify an objective measure of behavior that covaries with the subjective experience of fatigue.
Previous research from the Kessler Foundation showed that signal recognition metrics (perceptual certainty and response bias) correlated with changes in cognitive fatigue, as well as with activation in the striatum of the basal ganglia — an area of the brain that Kessler researchers had previously identified as being sensitive to changes in cognitive fatigue.
They continued their investigation in this study of MS, which is often complicated by symptoms of fatigue, including cognitive fatigue.
The study was conducted at the Kessler Foundation’s Rocco Ortenzio Neuroimaging Center, which is dedicated solely to rehabilitation research.
Researchers used a sophisticated working memory paradigm to induce cognitive fatigue in 50 participants, 30 with MS and 20 controls.
All participants underwent structural and functional magnetic resonance imaging (fMRI) and were assessed using the Visual Analogue Scale of Fatigue (VAS-F) at baseline and after each task block.
“We have shown that response bias is associated with subjective state fatigue in MS,” said lead author Dr. Román, National MS Society Postdoctoral Fellow at the Kessler Foundation.
“This reinforces our previous finding of the same relationship in controls and provides additional support for this signal recognition theory metric as an objective measure of cognitive fatigue.”
according to dr Wylie, director of the Ortenzio Center, cognitive fatigue is a feature of many neurodegenerative diseases, including MS.
“By building on this promising research avenue, we are laying the foundation for a new set of tools,” he explained, “that will help us design effective interventions to treat this disabling condition in a broad range of people and to understand its impact on people improve their daily functioning, employment and quality of life.”
Financing: New Jersey Commission for Brain Injury Research (10.005.BIR1) and National Multiple Sclerosis Society (RG 4232A1/1)
About this news from multiple sclerosis research
Author: Carolann Murphy
Source: Kessler Foundation
Contact: Carolann Murphy-Kessler Foundation
Picture: The image is in the public domain
Original research: Open access.
“Signal Detection Theory as a Novel Tool for Understanding Cognitive Fatigue in Individuals with Multiple Sclerosis” by Glenn Wylie et al. Frontiers in behavioral neuroscience
Signal detection theory as a novel tool for understanding cognitive fatigue in individuals with multiple sclerosis
Multiple sclerosis (MS) affects 2.8 million people worldwide. One of the most persistent, pervasive, and debilitating symptoms of MS is cognitive fatigue.
Although known for over a century, cognitive fatigue has been difficult to study because patients’ subjective (self-reported) cognitive fatigue consistently did not correlate with more objective measures such as reaction time (RT) and accuracy.
Here we examined whether more sophisticated performance metrics, specifically the signal recognition theory (SDT) metrics, would show a relationship to cognitive fatigue even when RT and accuracy did not. We also measured brain activation to see if SDT metrics are related to activation in brain areas that have been shown to be sensitive to cognitive fatigue.
Fifty participants (30 MS, 20 controls) took part in this study and cognitive fatigue was induced using four blocks of a challenging working memory paradigm. Participants reported their fatigue before and after each block, and their performance was used to calculate Perceptual Certainty and Criterion (SDT) metrics, as well as RT and accuracy.
Results showed that the SDT metric of the criterion (i.e. response bias) was positively correlated with subjective cognitive fatigue. Additionally, activation in brain areas previously shown to be related to cognitive fatigue, such as the striatum, was also associated with Criterion.
These results suggest that the SDT metrics could provide a novel tool to study cognitive fatigue in MS and other neurological populations.
These results hold promise for characterizing cognitive fatigue in MS and for developing effective interventions in the future.