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

Abstract

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.

Associations of White Matter and Basal Ganglia Microstructure to Cognitive Fatigue Rate in Multiple Sclerosis

“…Lastly, cognitive fatigue rate was found to be associated with trait cognitive fatigue, but not depression, anxiety, whole brain lesion volume, SDMT performance, or performance during a fatigue inducing task (i.e., RT, accuracy). The latter is consistent with previous examinations showing that performance measures, such as reaction time and accuracy, are weakly correlated with fatigue ratings ( 66 – 68 ).…”

Avoidance-motivational intensity modulated the effect of negative emotion on working memory

“…Within-subject 2 × 3 repeated-measures ANOVAs were conducted for the dependent variables: reaction times, accuracy and indexes established by signal detection theory (sensitivity and response bias) . Signal detection analysis was conducted to separate sensitivity from response bias, which could independently affect the accuracy [ 40 ]. This analysis was necessary when exploring which process was influenced by the effective states.…”

“…In the present study, apart from overall accuracy, signal detection metrics were reported: sensitivity and response bias, which could independently influence accuracy [ 40 ]. It is interesting to note in both tasks, the impairment effects of high avoidance-motivational intensity on accuracy, sensitivity and response bias were not observed.…”

Determinants of cognitive fatigue: Evaluating the effects of brain injury, disease and tasks

“…In previous work, we have argued that CF is related to a change in the balance between effort and reward 54,57,58 . That is, CF is related to maintaining an aspect of homeostasis whereby the effort expended merits the reward received.…”

“…An LME was used (3dLMEr from the AFNI suite of processing tools) with Task (2-back vs. mSDMT), Sex (men vs. women) and Run (runs 1-4) as factors; the VAS-F scores, and age were included as quantitative variables; participant was included as a random factor. As in previous work 15,21,54,55 only runs resulting in CF were included in the analysis (i.e., runs in which the VAS-F score was greater than zero). This resulted in the exclusion of ~ 20% of runs (see supplemental data section details).…”