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This basic mechanism of attentional limitations may lead to different compensation profiles in patients, with or without cognitive dysfunction, depending on the compensation stage. We suggest several objective and subjective measures to evaluate this cognitive-vestibular compensation hypothesis.Businesses and scholars have been trying to improve marketing effect by optimizing mobile marketing interfaces aesthetically as users browse freely and aimlessly through mobile marketing interfaces. Although the layout is an important design factor that affects interface aesthetics, whether it can trigger customer's aesthetic preferences in mobile marketing remains unexplored. To address this issue, we employ an empirical methodology of event-related potentials (EPR) in this study from the perspective of cognitive neuroscience and psychology. Subjects are presented with a series of mobile marketing interface images of different layouts with identical marketing content. Their EEG waves were recorded as they were required to distinguish a target stimulus from the others. After the experiment, each of the subjects chose five stimuli interfaces they like and five they dislike. By analyzing the ERP data derived from the EEG data and the behavioral data, we find significant differences between the disliked interfaces and the other interfaces in the ERP component of P2 from the frontal-central area in the 200-400 ms post-stimulus onset time window and LPP from both the frontal-central and parietal-occipital area in the 400-600 ms time window. The results support the hypothesis that humans do make rapid implicit aesthetic preferences for interface layouts and suggest that even under a free browsing context like the mobile marketing context, interface layouts that raise high emotional arousal can still attract more user attention and induce users' implicit aesthetic preference.Introduction Dual tasking is common in activities of daily living (ADLs) and the ability to perform them usually declines with age. While cognitive aspects influence dual task (DT) performance, most DT-cost (DT-C) related metrics include only time- or speed- delta without weighting the accuracy of cognitive replies involved in the task. Objectives The primary study goal was to weight the accuracy of cognitive replies as a contributing factor when estimating DT-C using a new index of DT-C that considers the accuracy of cognitive replies (P-index) in the instrumented timed up and go test (iTUG). Secondarily, to correlate the novel P-index with domains of the Mini-Mental State Examination (MMSE). Methods Sixty-three participants (≥85 years old) took part in this study. The single task (ST) and DT iTUG tests were performed in a semi-random order. Both the time taken to complete the task measured utilizing an inertial measurement unit (IMU), and the accuracy of the cognitive replies were used to create the novel P DT-C.Background Concussion subtypes are typically organized into commonly affected symptom areas or a combination of affected systems, an approach that may be flawed by bias in conceptualization or the inherent limitations of interdisciplinary expertise. Objective The purpose of this study was to determine whether a bottom-up, unsupervised, machine learning approach, could more accurately support concussion subtyping. Methods Initial patient intake data as well as objective outcome measures including, the Patient-Reported Outcomes Measurement Information System (PROMIS), Dizziness Handicap Inventory (DHI), Pain Catastrophizing Scale (PCS), and Immediate Post-Concussion Assessment and Cognitive Testing Tool (ImPACT) were retrospectively extracted from the Advance Concussion Clinic's database. A correlation matrix and principal component analysis (PCA) were used to reduce the dimensionality of the dataset. Sklearn's agglomerative clustering algorithm was then applied, and the optimal number of clusters within the paild traumatic brain injury.Depression has become one of the main afflictions that threaten people's mental health. However, the current traditional diagnosis methods have certain limitations, so it is necessary to find a method of objective evaluation of depression based on intelligent technology to assist in the early diagnosis and treatment of patients. Because the abnormal speech features of patients with depression are related to their mental state to some extent, it is valuable to use speech acoustic features as objective indicators for the diagnosis of depression. In order to solve the problem of the complexity of speech in depression and the limited performance of traditional feature extraction methods for speech signals, this article suggests a Three-Dimensional Convolutional filter bank with Highway Networks and Bidirectional GRU (Gated Recurrent Unit) with an Attention mechanism (in short 3D-CBHGA), which includes two key strategies. (1) The three-dimensional feature extraction of the speech signal can timely realize the expression ability of those depression signals. (2) Based on the attention mechanism in the GRU network, the frame-level vector is weighted to get the hidden emotion vector by self-learning. Experiments show that the proposed 3D-CBHGA can well establish mapping from speech signals to depression-related features and improve the accuracy of depression detection in speech signals.Accurate detection of driving fatigue is helpful in significantly reducing the rate of road traffic accidents. Electroencephalogram (EEG) based methods are proven to be efficient to evaluate mental fatigue. Due to its high non-linearity, as well as significant individual differences, how to perform EEG fatigue mental state evaluation across different subjects still keeps challenging. In this study, we propose a Label-based Alignment Multi-Source Domain Adaptation (LA-MSDA) for cross-subject EEG fatigue mental state evaluation. Specifically, LA-MSDA considers the local feature distributions of relevant labels between different domains, which efficiently eliminates the negative impact of significant individual differences by aligning label-based feature distributions. In addition, the strategy of global optimization is introduced to address the classifier confusion decision boundary issues and improve the generalization ability of LA-MSDA. Experimental results show LA-MSDA can achieve remarkable results on EEG-based fatigue mental state evaluation across subjects, which is expected to have wide application prospects in practical brain-computer interaction (BCI), such as online monitoring of driver fatigue, or assisting in the development of on-board safety systems.Intersectionality contends that sex/gender is constituted of and with other social categories, and that the social structures giving rise to inequality should be addressed in research. This is a powerful and important perspective from which to investigate the processes and consequences of social group memberships, one which has been overlooked by most neuroscientific research. In particular, neurofeminism, a field of critical neuroscience that challenges neuroscientific assumptions, methods and interpretations of data that reinforce sexism, has ignored intersectionality to date. In contrast, research in the field of psychology has been engaging with intersectionality for more than a decade. In reflecting on how intersectionality has advanced feminist research in psychology, this paper provides a critical analysis of potential novel research avenues for neurofeminism. Sodium palmitate purchase We identify three main research themes guided by intersectionality. The first theme involves research centered on understanding the socio-structural causes of health inequalities experienced by individuals with intersecting marginalized social identities; the second concerns research addressing the psychological processing of social group memberships that underlies the enactment of systemic discriminatory practices; and the third theme comprises intersectionality research that aims to challenge psychological epistemology. Drawing parallels between the fields of psychology and neuroscience, we explore the potential benefits and risks of advancing an intersectionality-informed neurofeminism.Spatial memory is an important cognitive function for human daily life and may present dysfunction or decline due to aging or clinical diseases. Functional near-infrared spectroscopy neurofeedback (fNIRS-NFB) is a promising neuromodulation technique with several special advantages that can be used to improve human cognitive functions by manipulating the neural activity of targeted brain regions or networks. In this pilot study, we intended to test the feasibility of fNIRS-NFB to enhance human spatial memory ability. The lateral parietal cortex, an accessible cortical region in the posterior medial hippocampal-cortical network that plays a crucial role in human spatial memory processing, was selected as the potential feedback target. A placebo-controlled fNIRS-NFB experiment was conducted to instruct individuals to regulate the neural activity in this region or an irrelevant control region. Experimental results showed that individuals learned to up-regulate the neural activity in the region of interest successfully. A significant increase in spatial memory performance was found after 8-session neurofeedback training in the experimental group but not in the control group. Furthermore, neurofeedback-induced neural activation increase correlated with spatial memory improvement. In summary, this study preliminarily demonstrated the feasibility of fNIRS-NFB to improve human spatial memory and has important implications for further applications.Patients with posttraumatic stress disorder (PTSD) might have white matter abnormalities. However, less is known about white matter changes after exposing a specific traumatic event. The purpose of this study was to explore the abnormalities of diffusion in cerebral white matter and its relationship with the clinical symptoms in patients with PTSD by using diffusion tensor imaging (DTI). Diffusion-weighted imaging of the cerebrum was performed in typhoon survivors with (n = 27) and without PTSD (n = 33) and healthy controls (HCs) (n = 30). Differences in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated among groups using voxel-based analysis of the DTI data. Correlations between diffusion indices and clinical symptoms in patients with PTSD were also assessed. Both patients with PTSD and trauma-exposed control (TEC) group showed increased FA in the anterior limb of the internal capsule, forceps of the corpus callosum, and corona radiata relative to the HC group. Additionally, there was a negative correlation between FA values in the white matter and the clinical symptoms. Trauma exposure may result in disruption of cerebral white matter in individuals with or without PTSD, particularly in the frontal fibers. Aberrant white matter alterations may be associated with the severity of PTSD symptoms.Background The beneficial effects of both single-session bouts of aerobic exercise and therapeutic exercise interventions on the cortical regions associated with top-down attentional control [i.e., prefrontal cortex (PFC)] have been well documented. However, it remains unclear whether aerobic exercise can be used to buffer against suppressive influences on the dorsolateral PFC (dlPFC). Objective The current study sought to determine whether a single session of moderate intensity aerobic exercise can offset the expected suppressive effects of continuous theta burst stimulation (cTBS) targeting the dorsolateral prefrontal cortex (dlPFC). Methods Twenty-two right-handed participants (aged 19-30) completed a 20-minute movement-only control session [10% heart rate reserve (HRR)] and moderate intensity (50% HRR) exercise in a counterbalanced order. Following each exercise session, participants received active cTBS to the left dlPFC. Changes in executive functions were quantified using a Flanker paradigm employed at baseline, post-exercise and post-cTBS time points.