Sleep deprivation selectively enhances interpersonal emotion recognition from dynamic facial expressions at long viewing times: An observational study.

Observational and experimental studies have shown that sleep deprivation disinhibits emotional responses to disturbing and rewarding external events. On the other hand, most studies on sleep deprivation and interpersonal emotion recognition report that sensitivity to others' emotions is dampened during sleep deprivation. This is at odds with current neuroscientific theories of social cognition that assume that affective experiences and emotion recognition in others are closely tied at the neural and physiological level. In this observational study we show that sleep deprivation can actually increase emotion recognition accuracy from dynamically unfolding facial expressions if they are viewed sufficiently long. Participants viewed 2-4 s or 8-10s video clips of female senders who facially communicated anger, disgust, fear or sadness to their romantic partner and evaluated the sender's affective state in a forced-choice paradigm, either during sleep deprivation after a night shift (N = 40) or after normal night sleep (N = 50). All participants showed a significant increase in emotion recognition accuracy from 2-4 s to 8-10 s stimulus presentation times. Emotion recognition accuracy did not differ between sleep-deprived and control participants for 2-4 s videos, but sleep-deprived participants showed significantly higher emotion recognition accuracy than control participants for 8-10 s videos. We surmise that this effect might be due to the break-down of prefrontal activity associated with sleep deprivation, which might not only disinhibit affective responses to external events but might also release simulation-based neural processes that contribute to interpersonal emotion recognition from dynamic facial expressions at longer time scales than usually investigated in emotion recognition studies.

Enhanced Optical Head Tracking for Cranial Radiation Therapy: Supporting Surface Registration by Cutaneous Structures.

PURPOSE: To support surface registration in cranial radiation therapy by structural information. The risk for spatial ambiguities is minimized by using tissue thickness variations predicted from backscattered near-infrared (NIR) light from the forehead. METHODS AND MATERIALS: In a pilot study we recorded NIR surface scans by laser triangulation from 30 volunteers of different skin type. A ground truth for the soft-tissue thickness was segmented from MR scans. After initially matching the NIR scans to the MR reference, Gaussian processes were trained to predict tissue thicknesses from NIR backscatter. Moreover, motion starting from this initial registration was simulated by 5000 random transformations of the NIR scan away from the MR reference. Re-registration to the MR scan was compared with and without tissue thickness support. RESULTS: By adding prior knowledge to the backscatter features, such as incident angle and neighborhood information in the scanning grid, we showed that tissue thickness can be predicted with mean errors of <0.2 mm, irrespective of the skin type. With this additional information, the average registration error improved from 3.4 mm to 0.48 mm by a factor of 7. Misalignments of more than 1 mm were almost thoroughly (98.9%) pushed below 1 mm. CONCLUSIONS: For almost all cases tissue-enhanced matching achieved better results than purely spatial registration. Ambiguities can be minimized if the cutaneous structures do not agree. This valuable support for surface registration increases tracking robustness and avoids misalignment of tumor targets far from the registration site.

Unsupervised learning of facial emotion decoding skills.

Research on the mechanisms underlying human facial emotion recognition has long focussed on genetically determined neural algorithms and often neglected the question of how these algorithms might be tuned by social learning. Here we show that facial emotion decoding skills can be significantly and sustainably improved by practice without an external teaching signal. Participants saw video clips of dynamic facial expressions of five different women and were asked to decide which of four possible emotions (anger, disgust, fear, and sadness) was shown in each clip. Although no external information about the correctness of the participant's response or the sender's true affective state was provided, participants showed a significant increase of facial emotion recognition accuracy both within and across two training sessions two days to several weeks apart. We discuss several similarities and differences between the unsupervised improvement of facial decoding skills observed in the current study, unsupervised perceptual learning of simple stimuli described in previous studies and practice effects often observed in cognitive tasks.

Compensatory premotor activity during affective face processing in subclinical carriers of a single mutant Parkin allele.

Patients with Parkinson's disease suffer from significant motor impairments and accompanying cognitive and affective dysfunction due to progressive disturbances of basal ganglia-cortical gating loops. Parkinson's disease has a long presymptomatic stage, which indicates a substantial capacity of the human brain to compensate for dopaminergic nerve degeneration before clinical manifestation of the disease. Neuroimaging studies provide evidence that increased motor-related cortical activity can compensate for progressive dopaminergic nerve degeneration in carriers of a single mutant Parkin or PINK1 gene, who show a mild but significant reduction of dopamine metabolism in the basal ganglia in the complete absence of clinical motor signs. However, it is currently unknown whether similar compensatory mechanisms are effective in non-motor basal ganglia-cortical gating loops. Here, we ask whether asymptomatic Parkin mutation carriers show altered patterns of brain activity during processing of facial gestures, and whether this might compensate for latent facial emotion recognition deficits. Current theories in social neuroscience assume that execution and perception of facial gestures are linked by a special class of visuomotor neurons ('mirror neurons') in the ventrolateral premotor cortex/pars opercularis of the inferior frontal gyrus (Brodmann area 44/6). We hypothesized that asymptomatic Parkin mutation carriers would show increased activity in this area during processing of affective facial gestures, replicating the compensatory motor effects that have previously been observed in these individuals. Additionally, Parkin mutation carriers might show altered activity in other basal ganglia-cortical gating loops. Eight asymptomatic heterozygous Parkin mutation carriers and eight matched controls underwent functional magnetic resonance imaging and a subsequent facial emotion recognition task. As predicted, Parkin mutation carriers showed significantly stronger activity in the right ventrolateral premotor cortex during execution and perception of affective facial gestures than healthy controls. Furthermore, Parkin mutation carriers showed a slightly reduced ability to recognize facial emotions that was least severe in individuals who showed the strongest increase of ventrolateral premotor activity. In addition, Parkin mutation carriers showed a significantly weaker than normal increase of activity in the left lateral orbitofrontal cortex (inferior frontal gyrus pars orbitalis, Brodmann area 47), which was unrelated to facial emotion recognition ability. These findings are consistent with the hypothesis that compensatory activity in the ventrolateral premotor cortex during processing of affective facial gestures can reduce impairments in facial emotion recognition in subclinical Parkin mutation carriers. A breakdown of this compensatory mechanism might lead to the impairment of facial expressivity and facial emotion recognition observed in manifest Parkinson's disease.