Stimulus-Related Independent Component and Voxel-Wise Analysis of Human Brain Activity during Free Viewing of a Feature Film

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments

Work factors and psychological distress in nurses' aides: a prospective cohort study

<p>Abstract</p> <p>Background</p> <p>Nurses' aides (assistant nurses), the main providers of practical patient care in many countries, are doing both emotional and heavy physical work, and are exposed to frequent social encounters in their job. There is scarce knowledge, though, of how working conditions are related to psychological distress in this occupational group. The aim of this study was to identify work factors that predict the level of psychological distress in nurses' aides.</p> <p>Methods</p> <p>The sample of this prospective study comprised 5076 Norwegian nurses' aides, not on leave when they completed a mailed questionnaire in 1999. Of these, 4076 (80.3 %) completed a second questionnaire 15 months later. A wide spectrum of physical, psychological, social, and organisational work factors were measured at baseline. Psychological distress (anxiety and depression) was assessed at baseline and follow-up by the SCL-5, a short version of Hopkins Symptom Checklist-25.</p> <p>Results</p> <p>In a linear regression model of the level of psychological distress at follow-up, with baseline level of psychological distress, work factors, and background factors as independent variables, work factors explained 2 % and baseline psychological distress explained 34 % of the variance. Exposures to role conflicts, exposures to threats and violence, working in apartment units for the aged, and changes in the work situation between baseline and follow-up that were reported to result in less support and encouragement were positively associated with the level of psychological distress. Working in psychiatric departments, and changes in the work situation between baseline and follow-up that gave lower work pace were negatively associated with psychological distress.</p> <p>Conclusion</p> <p>The study suggests that work factors explain only a modest part of the psychological distress in nurses' aides. Exposures to role conflicts and threats and violence at work may contribute to psychological distress in nurses' aides. It is important that protective measures against violent patients are implemented, and that occupational health officers offer victims of violence appropriate support or therapy. It is also important that health service organisations focus on reducing role conflicts, and that leaders listen to and consider the views of the staff.</p

Focused Multi-task Learning Using Gaussian Processes

Abstract. Given a learning task for a data set, learning it together with related tasks (data sets) can improve performance. Gaussian process models have been applied to such multi-task learning scenarios, based on joint priors for functions underlying the tasks. In previous Gaussian process approaches, all tasks have been assumed to be of equal importance, whereas in transfer learning the goal is asymmetric: to enhance performance on a target task given all other tasks. In both settings, transfer learning and joint modelling, negative transfer is a key problem: performance may actually decrease if the tasks are not related closely enough. In this paper, we propose a Gaussian process model for the asymmetric setting, which learns to “explain away ” non-related variation in the additional tasks, in order to focus on improving performance on the target task. In experiments, our model improves performance compared to single-task learning, symmetric multi-task learning using hierarchical Dirichlet processes, and transfer learning based on predictive structure learning