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For the current study the Lazarian stress-coping theory and the appendant model of psychosocial adjustment to chronic illness and disabilities (Pakenham, 1999) has shaped the foundation for identifying determinants of adjustment to ALS. We aimed to investigate the evolution of psychosocial adjustment to ALS and to determine its long-term predictors. A longitudinal study design with four measurement time points was therefore, used to assess patients' quality of life, depression, and stress-coping model related aspects, such as illness characteristics, social support, cognitive appraisals, and coping strategies during a period of 2 years. Regression analyses revealed that 55% of the variance of severity of depressive symptoms and 47% of the variance in quality of life at T2 was accounted for by all the T1 predictor variables taken together. On the level of individual contributions, protective buffering, and appraisal of own coping potential accounted for a significant percentage in the variance in severity of depressive symptoms, whereas problem management coping strategies explained variance in quality of life scores. Illness characteristics at T2 did not explain any variance of both adjustment outcomes. Overall, the pattern of the longitudinal results indicated stable depressive symptoms and quality of life indices reflecting a successful adjustment to the disease across four measurement time points during a period of about two years. Empirical evidence is provided for the predictive value of social support, cognitive appraisals, and coping strategies, but not illness parameters such as severity and duration for adaptation to ALS. The current study contributes to a better conceptualization of adjustment, allowing us to provide evidence-based support beyond medical and physical intervention for people with ALS.
Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair- wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e. g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within- day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage.
While decades of research have investigated and technically improved brain–computer interface (BCI)-controlled applications, relatively little is known about the psychological aspects of brain–computer interfacing. In 35 healthy students, we investigated whether extrinsic motivation manipulated via monetary reward and emotional state manipulated via video and music would influence behavioral and psychophysiological measures of performance with a sensorimotor rhythm (SMR)-based BCI. We found increased task-related brain activity in extrinsically motivated (rewarded) as compared with nonmotivated participants but no clear effect of emotional state manipulation. Our experiment investigated the short-term effect of motivation and emotion manipulation in a group of young healthy subjects, and thus, the significance for patients in the locked-in state, who may be in need of a BCI, remains to be investigated.
Despite high levels of distress, family caregivers of patients with cancer rarely seek psychosocial support and Internet-based interventions (IBIs) are a promising approach to reduce some access barriers. Therefore, we developed a self-guided IBI for family caregivers of patients with cancer (OAse), which, in addition to patients' spouses, also addresses other family members (e.g., adult children, parents). This study aimed to determine the feasibility of OAse (recruitment, dropout, adherence, participant satisfaction). Secondary outcomes were caregivers’ self-efficacy, emotional state, and supportive care needs. N = 41 family caregivers participated in the study (female: 65%), mostly spouses (71%), followed by children (20%), parents (7%), and friends (2%). Recruitment (47%), retention (68%), and adherence rates (76% completed at least 4 of 6 lessons) support the feasibility of OAse. Overall, the results showed a high degree of overall participant satisfaction (96%). There were no significant pre-post differences in secondary outcome criteria, but a trend toward improvement in managing difficult interactions/emotions (p = .06) and depression/anxiety (p = .06). Although the efficacy of the intervention remains to be investigated, our results suggest that OAse can be well implemented in caregivers’ daily lives and has the potential to improve family caregivers’ coping strategies.
Chronic alcohol use leads to specific neurobiological alterations in the dopaminergic brain reward system, which probably are leading to a reward deficiency syndrome in alcohol dependence. The purpose of our study was to examine the effects of such hypothesized neurobiological alterations on the behavioral level, and more precisely on the implicit and explicit reward learning. Alcohol users were classified as dependent drinkers (using the DSM-IV criteria), binge drinkers (using criteria of the USA National Institute on Alcohol Abuse and Alcoholism) or low-risk drinkers (following recommendations of the Scientific board of trustees of the German Health Ministry). The final sample (n = 94) consisted of 36 low-risk alcohol users, 37 binge drinkers and 21 abstinent alcohol dependent patients. Participants were administered a probabilistic implicit reward learning task and an explicit reward- and punishment-based trial-and-error-learning task. Alcohol dependent patients showed a lower performance in implicit and explicit reward learning than low risk drinkers. Binge drinkers learned less than low-risk drinkers in the implicit learning task. The results support the assumption that binge drinking and alcohol dependence are related to a chronic reward deficit. Binge drinking accompanied by implicit reward learning deficits could increase the risk for the development of an alcohol dependence.
Tactile stimulation is less frequently used than visual for brain-computer interface (BCI) control, partly because of limitations in speed and accuracy. Non-visual BCI paradigms, however, may be required for patients who struggle with vision dependent BCIs because of a loss of gaze control. With the present study, we attempted to replicate earlier results by Herweg et al. (2016), with several minor adjustments and a focus on training effects and usability. We invited 16 healthy participants and trained them with a 4-class tactile P300-based BCI in five sessions. Their main task was to navigate a virtual wheelchair through a 3D apartment using the BCI. We found significant training effects on information transfer rate (ITR), which increased from a mean of 3.10–9.50 bits/min. Further, both online and offline accuracies significantly increased with training from 65% to 86% and 70% to 95%, respectively. We found only a descriptive increase of P300 amplitudes at Fz and Cz with training. Furthermore, we report subjective data from questionnaires, which indicated a relatively high workload and moderate to high satisfaction. Although our participants have not achieved the same high performance as in the Herweg et al. (2016) study, we provide evidence for training effects on performance with a tactile BCI and confirm the feasibility of the paradigm.
Brain-Computer Interfaces (BCIs) provide communication channels independent from muscular control. In the current study we used two versions of the P300-BCI: one based on visual the other on auditory stimulation. Up to now, data on the impact of psychological variables on P300-BCI control are scarce. Hence, our goal was to identify new predictors with a comprehensive psychological test-battery. A total of N = 40 healthy BCI novices took part in a visual and an auditory BCI session. Psychological variables were measured with an electronic test-battery including clinical, personality, and performance tests. The personality factor “emotional stability” was negatively correlated (Spearman's rho = −0.416; p < 0.01) and an output variable of the non-verbal learning test (NVLT), which can be interpreted as ability to learn, correlated positively (Spearman's rho = 0.412; p < 0.01) with visual P300-BCI performance. In a linear regression analysis both independent variables explained 24% of the variance. “Emotional stability” was also negatively related to auditory P300-BCI performance (Spearman's rho = −0.377; p < 0.05), but failed significance in the regression analysis. Psychological parameters seem to play a moderate role in visual P300-BCI performance. “Emotional stability” was identified as a new predictor, indicating that BCI users who characterize themselves as calm and rational showed worse BCI performance. The positive relation of the ability to learn and BCI performance corroborates the notion that also for P300 based BCIs learning may constitute an important factor. Further studies are needed to consolidate or reject the presented predictors.