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Signal Detection Theory May Be Useful for Characterizing Cognitive Fatigue in Multiple Sclerosis – Neuroscience News

Summary: Study reveals an association between signal detection theory, brain activation patterns, and subjective state fatigue. In those with multiple sclerosis, greater effects of fatigue were seen.

Source: Kessler Foundation

Using signal detection theory, Kessler Foundation researchers furthered 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 detection 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 findings 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 Kessler Foundation.

Because subjective feelings of cognitive fatigue fail to correlate with objective measures of performance, researchers have sought to identify an objective behavioral measure that covaries with the subjective experience of fatigue. 

Prior research at Kessler Foundation showed that signal detection 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 Kessler researchers have previously identified as 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 Rocco Ortenzio Neuroimaging Center at Kessler Foundation, which is dedicated solely to rehabilitation research.

Researchers used a demanding 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 block of the tasks.

Because subjective feelings of cognitive fatigue fail to correlate with objective measures of performance, researchers have sought to identify an objective behavioral measure that covaries with the subjective experience of fatigue. Image is in the public domain

“We demonstrated that response bias was related to subjective state fatigue in MS,” said lead author Dr. Román, National MS Society postdoctoral fellow at Kessler Foundation.

“This reinforces our previous finding of the same relationship in controls and provides additional support for this signal detection theory metric as an objective measure of cognitive fatigue.”  

Cognitive fatigue is a feature of many neurodegenerative conditions, including MS, according to Dr. Wylie, director of the Ortenzio Center.

“By building on this promising avenue of research, we are establishing the basis for a new set of tools,” he explained, “which will help us develop effective interventions for treating this disabling condition in a wide range of individuals and ameliorate its impact on their daily functioning, employment, and quality of life.”  

Funding: New Jersey Commission for Brain Injury Research (10.005.BIR1) and the National Multiple Sclerosis Society (RG 4232A1/1)

About this multiple sclerosis research news

Author: Carolann Murphy
Source: Kessler Foundation
Contact: Carolann Murphy – Kessler Foundation
Image: The image is in the public domain

Original Research: Open access.
“Signal Detection Theory as a Novel Tool to Understand Cognitive Fatigue in Individuals with Multiple Sclerosis” by Glenn Wylie et al. Frontiers in Behavioral Neuroscience

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Abstract

Signal Detection Theory as a Novel Tool to Understand Cognitive Fatigue in Individuals with Multiple Sclerosis

Multiple Sclerosis (MS) affects 2.8 million persons worldwide. One of the most persistent, pervasive, and debilitating symptoms of MS is cognitive fatigue.

While this has been known for over a century, cognitive fatigue has been difficult to study because patients’ subjective (self-reported) cognitive fatigue has consistently failed to correlate with more objective measures, such as reaction time (RT) and accuracy.

Here, we investigated whether more nuanced metrics of performance, specifically the metrics of Signal Detection Theory (SDT), would show a relationship to cognitive fatigue even if RT and accuracy did not. We also measured brain activation to see whether SDT metrics were 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 demanding working memory paradigm. Participants reported their fatigue before and after each block, and their performance was used to calculate SDT metrics (Perceptual Certainty and Criterion) and RT and accuracy.

The results showed that the SDT metric of Criterion (i.e., response bias) was positively correlated with subjective cognitive fatigue. Moreover, the activation in brain areas previously shown to be related to cognitive fatigue, such as the striatum, was also related to Criterion.

These results suggest that the metrics of SDT may represent a novel tool with which to study cognitive fatigue in MS and other neurological populations.

These results hold promise for characterizing cognitive fatigue in MS and developing effective interventions in the future.

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