Assessing Cognitive Function in Multiple Sclerosis With Digital Tools: Observational Study

Background Cognitive impairment (CI) is one of the most prevalent symptoms of multiple sclerosis (MS). However, it is difficult to include cognitive assessment as part of MS standard care since the comprehensive neuropsychological examinations are usually time-consuming and extensive. Objective To improve access to CI assessment, we evaluated the feasibility and potential assessment sensitivity of a tablet-based cognitive battery in patients with MS. Methods In total, 53 participants with MS (24 [45%] with CI and 29 [55%] without CI) and 24 non-MS participants were assessed with a tablet-based cognitive battery (Adaptive Cognitive Evaluation [ACE]) and standard cognitive measures, including the Symbol Digit Modalities Test (SDMT) and the Paced Auditory Serial Addition Test (PASAT). Associations between performance in ACE and the SDMT/PASAT were explored, with group comparisons to evaluate whether ACE modules can capture group-level differences. Results Correlations between performance in ACE and the SDMT (R=–0.57, P<.001), as well as PASAT (R=–0.39, P=.01), were observed. Compared to non-MS and non-CI MS groups, the CI MS group showed a slower reaction time (CI MS vs non-MS: P<.001; CI MS vs non-CI MS: P=.004) and a higher attention cost (CI MS vs non-MS: P=.02; CI MS vs non-CI MS: P<.001). Conclusions These results provide preliminary evidence that ACE, a tablet-based cognitive assessment battery, provides modules that could potentially serve as a digital cognitive assessment for people with MS. Trial Registration ClinicalTrials.gov NCT03569618; https://clinicaltrials.gov/ct2/show/NCT03569618

Impact of inspiratory threshold loading on brain activity and cognitive performances in healthy humans

In healthy humans, inspiratory threshold loading deteriorates cognitive performances. This can result from motor-cognitive interference (activation of motor respiratory-related cortical networks vs. executive resources allocation), sensory-cognitive interference (dyspnea vs. shift in attentional focus), or both. We hypothesized that inspiratory loading would concomitantly induce dyspnea, activate motor respiratory-related cortical networks, and deteriorate cognitive performance. We reasoned that a concomitant activation of cortical networks and cognitive deterioration would be compatible with motor-cognitive interference, particularly in case of a predominant alteration of executive cognitive performances. Symmetrically, we reasoned that a predominant alteration of attention-depending performances would suggest sensory-cognitive interference. Twenty-five volunteers (12 men; 19.5-51.5 years) performed the Paced Auditory Serial Addition test (PASAT-A and B; calculation capacity, working memory, attention), the Trail Making Test (TMT-A, visuospatial exploration capacity; TMT-B, visuospatial exploration capacity and attention), and the Corsi block-tapping test (visuospatial memory, short-term and working memory) during unloaded breathing and inspiratory threshold loading in random order. Loading consistently induced dyspnea and respiratory-related brain activation. It was associated with deteriorations inPASAT A (52 [45.5;55.5] (median [interquartile range]) to 48 [41;54.5], p=0.01), PASAT B (55 [47.5;58] to 51 [44.5;57.5], p=0.01), and TMT B (44s [36;54.5] to 53s [42;64], p=0.01), but did not affect TMT-A and Corsi. The concomitance of cortical activation and cognitive performance deterioration is compatible with competition for cortical resources (motor-cognitive interference), while the profile of cognitive impairment (PASAT and TMT-B but not TMT-A and Corsi) is compatible with a contribution of attentional distraction (sensory-cognitive interference). Both mechanisms are therefore likely at play.

Investigating mechanisms of cognitive control training: neural signatures of PASAT performance in depressed patients

Major depression disorder (MDD) is characterized by cognitive control (CC) dysfunctions associated with increased attention toward negative information. The paced auditory serial addition task (PASAT) has been used as a targeted training of CC and studies show promising effects on depressive symptoms. However, neural mechanisms underlying its efficacy are still unclear. Based on previous findings of feedback-locked event-related potentials in healthy subjects, we investigated neural signatures during PASAT performance in 46 depressed patients. We found significantly larger amplitudes after negative than positive feedback for the P300 and late positive potential (LPP). However, this difference was not significant for the feedback-related negativity (FRN). Moreover, no associations of valence-specific ERPs and PASAT performance nor depressive symptoms were found. This indicates that depressed patients seem unable to use neural activation in late feedback processing stages (P300, LPP) to adapt accordingly. Moreover, lack of valence-specific neural reaction in early feedback processing stages (FRN) might point toward emotional indifference in depressed patients.Trial registration number: NCT03518749 Date of registration: May 8, 2018.

Validation of Discrete and Regression-Based Performance and Cognitive Fatigability Normative Data for the Paced Auditory Serial Addition Test in Multiple Sclerosis

Cognitive fatigability is an objective performance decrement that occurs over time during a task requiring sustained cognitive effort. Although cognitive fatigability is a common and debilitating symptom in multiple sclerosis (MS), there is currently no standard for its quantification. The objective of this study was to validate the Paced Auditory Serial Addition Test (PASAT) discrete and regression-based normative data for quantifying performance and cognitive fatigability in an Ontario-based sample of individuals with MS. Healthy controls and individuals with MS completed the 3″ and 2″ versions of the PASAT. PASAT performance was measured with total correct, dyad, and percent dyad scores. Cognitive fatigability scores were calculated by comparing performance on the first half (or third) of the task to the last half (or third). The results revealed that the 3″ PASAT was sufficient to detect impaired performance and cognitive fatigability in individuals with MS given the increased difficulty of the 2″ version. In addition, using halves or thirds for calculating cognitive fatigability scores were equally effective methods for detecting impairment. Finally, both the discrete and regression-based norms classified a similar proportion of individuals with MS as having impaired performance and cognitive fatigability. These newly validated discrete and regression-based PASAT norms provide a new tool for clinicians to document statistically significant cognitive fatigability in their patients.

The Changes of Cognition in Teenagers after Playing Video Games

Recent studies have shown that video games alter cognition in teenagers, but it is unknown how teen cognition changes in the short-term directly after playing video games. This study measured the differences in selective attention, processing speed, sustained attention, and cognitive flexibility of video-game playing (VGP) adolescents at different time intervals after playing video games. Three different reasoning tests were used: the Paced Auditory Serial Addition Test (PASAT), the Sustained Attention to Response Task (SART), and the Stroop Task. A custom 250 question arithmetic test was also used, but was unable to uncover processing speed differences between VGPs after playing video games. All VGP participants in the study underwent reasoning tests before playing video games, played League of Legends (LoL)—known for its intensive use of spatial awareness, cognitive decision making, and working memory—for an hour, and then took reasoning tests once again at different times after having played LoL. This experiment affirms that video games create cognitive enhancement, though the experiment uncovered that VGP teens have worsened selective and sustained attention as time elapses after playing video games. Future research may want to extend the variable of time to test how video games affect attention or cognition by time in a longer term with a larger sample size.

Predicting Fatigue in Long Duration Mountain Events with a Single Sensor and Deep Learning Model

Aim: To determine whether an AI model and single sensor measuring acceleration and ECG could model cognitive and physical fatigue for a self-paced trail run. Methods: A field-based protocol of continuous fatigue repeated hourly induced physical (~45 min) and cognitive (~10 min) fatigue on one healthy participant. The physical load was a 3.8 km, 200 m vertical gain, trail run, with acceleration and electrocardiogram (ECG) data collected using a single sensor. Cognitive load was a Multi Attribute Test Battery (MATB) and separate assessment battery included the Finger Tap Test (FTT), Stroop, Trail Making A and B, Spatial Memory, Paced Visual Serial Addition Test (PVSAT), and a vertical jump. A fatigue prediction model was implemented using a Convolutional Neural Network (CNN). Results: When the fatigue test battery results were compared for sensitivity to the protocol load, FTT right hand (R2 0.71) and Jump Height (R2 0.78) were the most sensitive while the other tests were less sensitive (R2 values Stroop 0.49, Trail Making A 0.29, Trail Making B 0.05, PVSAT 0.03, spatial memory 0.003). The best prediction results were achieved with a rolling average of 200 predictions (102.4 s), during set activity types, mean absolute error for ‘walk up’ (MAE200 12.5%), and range of absolute error for ‘run down’ (RAE200 16.7%). Conclusion: We were able to measure cognitive and physical fatigue using a single wearable sensor during a practical field protocol, including contextual factors in conjunction with a neural network model. This research has practical application to fatigue research in the field.

Predicting Fatigue in Long Duration Mountain Events with a Single Sensor and Deep Learning Model

Aim: To determine whether an AI model and single sensor measuring acceleration and ECG could model cognitive and physical fatigue for a self-paced trail run. Methods: A field-based protocol of continuous fatigue repeated hourly induced physical (~45 minutes) and cognitive (~10 minutes) fatigue on one healthy participant. Physical load was a 3.8 km, 200 m vertical gain, trail run with acceleration and electrocardiogram (ECG) data collected using a single sensor. Cognitive load was a Multi Attribute Test Battery (MATB) and separate assessment battery including the Finger Tap Test (FTT), Stroop, Trail Making A and B, Spatial Memory, Paced Visual Serial Addition Test (PVSAT), and a vertical jump. A fatigue prediction model was implemented using a Convolutional Neural Network (CNN). Results: When the fatigue test battery results were compared for sensitivity to the protocol load, FTT right hand (R2 0.71) and Jump Height (R2 0.78) were the most sensitive while the other tests were less sensitive (R2 values Stroop 0.49, Trail Making A 0.29, Trail Making B 0.05, PVSAT 0.03, spatial memory 0.003). Best prediction results were achieved with a rolling average of 200 predictions (102.4 s), during set activity types, mean absolute error for &lsquo;walk up&rsquo; (MAE200 12.5%) and range of absolute error for &lsquo;run down&lsquo; (RAE200 16.7%). Conclusion: We were able to measure cognitive and physical fatigue using a single wearable sensor during a practical field protocol including contextual factors in conjunction with a neural network model. This research has practical application to fatigue research in the field.

Cortisol Response to Psychological Stress in Patients with Schizophrenia in The Chinese Population

Background: Accumulating evidence suggests that hypothalamic-pituitary-adrenal (HPA) axis dysfunction might play an important role in the pathophysiology of schizophrenia. Cortisol secretion levels can reflect the function of the HPA axis.This study aimed to explore the cortisol response to psychological stress in patients with schizophrenia. Methods: In this cross-sectional study study, patients with schizophrenia (n = 104) and healthy volunteers (n = 59) were asked to complete psychological stress challenge tasks, which included the Paced Auditory Serial Addition Task and Mirror-Tracing Persistence Task, and then, saliva was collected to measure cortisol levels. Emotions and psychopathology were assessed by the Positive and Negative Affect Schedule (PANAS) and Positive and Negative Syndrome Scale (PANSS). Results: (1) that the cortisol stress response and negative emotions in patients with schizophrenia differed significantly from those in healthy controls; (2) there were significant interactions between sampling time and diagnosis for saliva cortisol levels; (3) there were significant interactions between scoring time and diagnosis for the negative affect score of the PANAS; and (4) the changes in salivary cortisol levels and negative affect scores before and after the psychological stress challenge tasks were not correlated with clinical symptoms in patients with schizophrenia. Conclusions: These findings indicated that an abnormal cortisol stress response might be a stable biological characteristic of schizophrenia.