Eye tracking – The overlooked method to measure cognition in neurodegeneration?

Eye tracking (ET) studies are becoming increasingly popular due to rapid methodological and technological advances as well as the development of cost efficient and portable eye trackers. Although historically ET has been mostly employed in psychophysics or developmental cognition studies, there is also promising scope to use ET for movement disorders and measuring cognitive processes in neurodegeneration. Particularly, ET can be a powerful tool for cognitive and neuropsychological assessments of patients with pathologies affecting motor and verbal abilities, as tasks can be adapted without requiring motor (except eye movements) or verbal responses. In this review, we will examine the existing evidence of ET methods in neurodegenerative conditions and its potential clinical impact for cognitive assessment. We highlight that current evidence for ET is mostly focused on diagnostics of cognitive impairments in neurodegenerative disorders, where it is debatable whether it has any more sensitivity or specificity than existing cognitive assessments. By contrast, there is currently a lack of ET studies in more advanced disease stages, when patients’ motor and verbal functions can be significantly affected, and standard cognitive assessments are challenging or often not possible. We conclude that ET is a promising method not only for cognitive diagnostics but more importantly, for potential cognitive disease tracking in progressive neurodegenerative conditions

Cognitive workload monitoring in virtual reality based rescue missions with drones

The use of drones in search and rescue (SAR) missions can be very cognitively demanding. Since high levels of cognitive workload can negatively affect human performance, there is a risk of compromising the mission and leading to failure with catastrophic outcomes. Therefore, cognitive workload monitoring is the key to prevent the rescuers from taking dangerous decisions. Due to the difficulties of gathering data during real SAR missions, we rely on virtual reality. In this work, we use a simulator to induce three levels of cognitive workload related to SAR missions with drones. To detect cognitive workload, we extract features from different physiological signals, such as electrocardiogram, respiration, skin temperature, and photoplethysmography. We propose a recursive feature elimination method that combines the use of both an eXtreme Gradient Boosting (XGBoost) algorithm and the SHapley Additive exPlanations (SHAP) score to select the more representative features. Moreover, we address both a binary and a three-class detection approaches. To this aim, we investigate the use of different machine-learning algorithms, such as XGBoost, random forest, decision tree, k-nearest neighbors, logistic regression, linear discriminant analysis, gaussian naïve bayes, and support vector machine. Our results show that on an unseen test set extracted from 24 volunteers, an XGBoost with 24 features for discrimination reaches an accuracy of 80.2% and 62.9% in order to detect two and three levels of cognitive workload, respectively. Finally, our results are open the doors to a fine grained cognitive workload detection in the field of SAR missions

An intensive exercise-based training program reduces prefrontal activity during usual walking in patients with Parkinson’s disease

International audienceParkinson's disease (PD) leads to a progressive loss of locomotor automaticity. Consequently, PD patients rely more on executive resources for the control of gait, resulting in increased prefrontal activity while walking. Exercise-based training programs may improve automaticity of walking and reduce prefrontal activity in this population. This study aimed to assess the effect of an intensive multidisciplinary exercise-based training program on prefrontal activity and gait performance during usual walking in PD patients. Method: Fourteen patients (mean age: 67 ± 9; disease duration: 6 ± 5 years; Hoehn and Yahr score: 1.9 ± 0.6) were included in this study. They were assessed in ON stage at three different times at 5-week intervals: two times before the training program (T0 and T1) and once after the training program (T2). Gait performance (stride time, speed, stride length, cadence, and their respective coefficient of variation) and cortical activity in the dorsolateral prefrontal cortex (DLPFC) using functional near infrared spectroscopy (fNIRS) were measured during usual walking. Results: Patients had reduced cortical activity of the DLPFC at T2 compared to T1 (p = 0.003). Patients had shorter stride time at T2 compared to T1 (p = 0.025) and tended to have longer stride length at T2 than at T1 (p = 0.056). Conclusion: The training program led to positive effects on prefrontal activity and gait performance. Reduced prefrontal activity during usual walking after training program suggests that patients may have a greater reserve capacity to face more challenging walking conditions. Further studies will investigate the effect of this training on cortical activity during dual-task walking.

CUMCS: FINDINGS FROM THE ICARUS DATA BASE

Patients and Methods The ICARUS project is an epidemiological study conducted since 1995 in 79 menopause clinics across Italy. Women who attended the participating centers for the first time during the study period were eligible for the study. The protocol did not foresee any exclusion criteria. A total of 8,498 postmenopausal women (mean age 55 j; 5 years), who never received hormone replacement therapy (HRT) were enrolled in the study between January 1995 and December 1997

Frequency of Cardiovascular Risks Factor in Women Attending Menopause Clinics: Findings from the ICARUS Data Base

Coronary heart disease is generally considered a “man's disease,” but it also the greatest killer of women older than 50 [1]. Cardiovascular diseases remain the main cause of morbidity and mortality in postmenopausal women [1]. Coronary heart disease is more dependent on age in women than in men; women are usually 10 years older than men when any coronary manifestations first appear [2]. Hypertension, smoking, and hyper- cholesterolemia are frequently observed in women [3]

Effectiveness of two cognitive training programs on the performance of older drivers with a cognitive self-assessment bias

Purpose: Depending on the calibration of their cognitive abilities, some older drivers (ODs) might stop driving prematurely (under-estimators, UEs) and others could expose themselves to risky situations (over-estimators, OEs). The aim of the study was to compare the effectiveness of two cognitive training (CT) programs intended for ODs presenting a cognitive calibration bias. We hypothesized that CT with feedback on performance can help ODs to correctly calibrate their abilities and consequently adapt their driving behavior.Method: One hundred and six ODs (≥70 years) were assigned to two CT groups (with or without a driving simulator experience, DS). These interventions lasted about 36 h and were distributed over a 3-month period. ODs completed objective and subjective cognitive evaluations and an on-road driving evaluation before and after training.Results: The first results on 67 participants (40 from the CT group, and 27 from the CT + DS group) showed an improvement of their visual processing speed, their divided attention and their selective attention after training. Participants from both groups also had an improved TRIP tactical sub-score (Test Ride for Investigating Practical fitness to drive), indicating a better driving behavioral adaptation. Finally, although both training programs seemed to be equally effective in correcting cognitive calibration bias, the results indicated that 21 UEs and 10 OEs were well calibrated and thus correctly self-assessed their cognitive abilities after training.Conclusion: Both CT programs (with or without DS experience) seem to improve the visual attention of ODs. UEs appeared to be more susceptible than OEs to this training and were better calibrated after it