Cortical stimulation has been used for brain mapping for over a century, and a standard assumption is that stimulation interferes with task execution due to local effects at the stimulation site. Stimulation can however produce afterdischarges which interfere with functional localization and can lead to unwanted seizures.  https://www.selleckchem.com/products/rbn013209.html  We previously showed that (a) cognitive effort can terminate these afterdischarges, (b) when termination thus occurs, there are electrocorticography changes throughout the cortex, not just at sites with afterdischarges or sites thought functionally important for the cognitive task used, and (c) thresholds for afterdischarges and functional responses can change among stimulation trials. We here show that afterdischarge termination can occur prior to overt performance of the cognitive tasks used to terminate them. These findings, taken together, demonstrate that task-related brain changes are not limited to one or a group of functional regions or a specific network, and not limited to the time directly surrounding overt task execution. Discrete locations, networks and times importantly underpin clinical behaviors. However, brain activity that is diffuse in location and extended in time also affect task execution and can affect brain mapping. This may in part reflect fluctuating levels of attention, engagement, or motivation during testing.Emotion recognition plays an important part in human-computer interaction (HCI). Currently, the main challenge in electroencephalogram (EEG)-based emotion recognition is the non-stationarity of EEG signals, which causes performance of the trained model decreasing over time. In this paper, we propose a two-level domain adaptation neural network (TDANN) to construct a transfer model for EEG-based emotion recognition. Specifically, deep features from the topological graph, which preserve topological information from EEG signals, are extracted using a deep neural network. These features are then passed through TDANN for two-level domain confusion. The first level uses the maximum mean discrepancy (MMD) to reduce the distribution discrepancy of deep features between source domain and target domain, and the second uses the domain adversarial neural network (DANN) to force the deep features closer to their corresponding class centers. We evaluated the domain-transfer performance of the model on both our self-built data set and the public data set SEED. In the cross-day transfer experiment, the ability to accurately discriminate joy from other emotions was high sadness (84%), anger (87.04%), and fear (85.32%) on the self-built data set. The accuracy reached 74.93% on the SEED data set. In the cross-subject transfer experiment, the ability to accurately discriminate joy from other emotions was equally high sadness (83.79%), anger (84.13%), and fear (81.72%) on the self-built data set. The average accuracy reached 87.9% on the SEED data set, which was higher than WGAN-DA. The experimental results demonstrate that the proposed TDANN can effectively handle the domain transfer problem in EEG-based emotion recognition.Anorexia nervosa is an eating disorder characterized by intense fear of gaining weight and a distorted body image which usually leads to low caloric intake and hyperactivity. The underlying mechanism and pathogenesis of anorexia nervosa is still poorly understood. In order to learn more about the underlying pathophysiology of anorexia nervosa and to find further possible treatment options, several animal models mimicking anorexia nervosa have been developed. The aim of this review is to systematically search different databases and provide an overview of existing animal models and to discuss the current knowledge gained from animal models of anorexia nervosa. For the systematic data search, the Pubmed-Medline database, Embase database, and Web of Science database were searched. After removal of duplicates and the systematic process of selection, 108 original research papers were included in this systematic review. One hundred and six studies were performed with rodents and 2 on monkeys. Eighteen different animal models for anorexia nervosa were used in these studies. Parameters assessed in many studies were body weight, food intake, physical activity, cessation of the estrous cycle in female animals, behavioral changes, metabolic and hormonal alterations. The most commonly used animal model (75 of the studies) is the activity-based anorexia model in which typically young rodents are exposed to time-reduced access to food (a certain number of hours a day) with unrestricted access to a running wheel. Of the genetic animal models, one that is of particular interest is the anx/anx mice model. Animal models have so far contributed many findings to the understanding of mechanisms of hunger and satiety, physical activity and cognition in an underweight state and other mechanisms relevant for anorexia nervosa in humans.We introduce a new and time-efficient memory-encoding paradigm for functional magnetic resonance imaging (fMRI). This paradigm is optimized for mapping multiple contrasts using a mixed design, using auditory (environmental/vocal) and visual (scene/face) stimuli. We demonstrate that the paradigm evokes robust neuronal activity in typical sensory and memory networks. We were able to detect auditory and visual sensory-specific encoding activities in auditory and visual cortices. Also, we detected stimulus-selective activation in environmental-, voice-, scene-, and face-selective brain regions (parahippocampal place and fusiform face area). A subsequent recognition task allowed the detection of sensory-specific encoding success activity (ESA) in both auditory and visual cortices, as well as sensory-unspecific positive ESA in the hippocampus. Further, sensory-unspecific negative ESA was observed in the precuneus. Among others, the parallel mixed design enabled sustained and transient activity comparison in contrast to rest blocks. Sustained and transient activations showed great overlap in most sensory brain regions, whereas several regions, typically associated with the default-mode network, showed transient rather than sustained deactivation. We also show that the use of a parallel mixed model had relatively little influence on positive or negative ESA. Together, these results demonstrate a feasible, versatile, and brief memory-encoding task, which includes multiple sensory stimuli to guarantee a comprehensive measurement. This task is especially suitable for large-scale clinical or population studies, which aim to test task-evoked sensory-specific and sensory-unspecific memory-encoding performance as well as broad sensory activity across the life span within a very limited time frame.