Frontiers in Human Neuroscience最新文献

Purpose: This study examined story retelling in individuals with aphasia who scored at or above the 93.8 cutoff on the Aphasia Quotient (AQ) of the Western Aphasia Battery-Revised (WAB-R). The performance of these participants deemed "not aphasic by WAB" (NABW) was compared with the performance of non-aphasic participants and individuals with anomic aphasia.

Method: Most participants were from a test development dataset for the Brief Assessment of Transactional Success in communication in aphasia (BATS), including four groups of 16 individuals: (1) a group who tested NABW; (2) a group with anomic aphasia matched on gender, age, education, and time post-onset; (3) a group with mild anomic aphasia who scored just below the NABW cutoff; and (4) a group of non-aphasic individuals matched on gender, age, and education with the NABW group. Groups were compared on main concepts of the BATS story retelling. Groups with aphasia were also compared on the main concepts of stories retold by non-aphasic conversation partners following co-construction of stories and on self-reported scores of the impact of aphasia on everyday communication.

Results: The results showed significant differences in the retelling of the story's main concepts between the non-aphasic control and conversation partner groups, with non-monotonic decreases in performance in comparisons of groups with and without aphasia: from non-aphasic to NABW to mildly anomic to anomic. Individuals deemed NABW (and their conversation partners) did not perform significantly better than individuals with mild anomic aphasia (and their conversation partners) on story retell main concepts. There were significant differences in the production of AphasiaBank discourse main concepts between the group with anomia and both the non-aphasic and NABW groups, but not between the non-aphasic and NABW or those with mild aphasia.

Conclusion: Individuals with aphasia who scored "non-aphasic" on the WAB demonstrated impairments in story retelling that align with their self-report of diminished everyday communicative functioning. This finding adds to growing support for the addition of a new measure of functional communication to the core outcome set of measures utilized in aphasia research. We propose the BATS, a measure that is sensitive across the spectrum of aphasia severity, including cases of mild and subclinical aphasia.

This study investigates the integration of augmented reality (AR) teaching environments with multimodal physiological monitoring for children with neurodevelopmental disorders. We collected EEG, ECG, and eye-tracking data from 115 children (ASD n = 45, ADHD n = 38, SLD n = 32) during AR-enhanced learning tasks. The multimodal fusion approach achieved 89.3% classification accuracy in identifying disorder-specific patterns. Key biomarkers included frontal theta power variations (p < 0.001), heart rate variability indices (LF/HF ratio), and fixation duration patterns. AR environments reduced cognitive load by 27% compared to traditional settings while maintaining engagement levels. Personalized intervention based on real-time physiological feedback improved attention performance by 31.2% and social interaction scores by 24.8% over 12 months. These findings demonstrate the efficacy of combining AR technology with physiological monitoring for adaptive special education.

Introduction: The ability to perform visually-guided motor tasks requires the transformation of visual information into programmed motor outputs. When the guiding visual information does not align spatially with the motor output, the brain processes rules to integrate somatosensory information into an appropriate motor response. Performance on such rule-based, "cognitive-motor integration" (CMI) tasks has been shown to be affected by sex, age, and in several neurologic conditions. The present study sought to (1) expand on these findings by examining whether such performance differences are related to levels of sex steroid hormones, and (2) characterize the relationship between hormone levels and any structural differences in brain regions responsible for complex motor control.

Methods: Thirty-six healthy individuals (18 females) underwent MRI scanning to acquire anatomical brain images. They performed two touchscreen-based eye-hand coordination tasks, including a standard direct interaction task and one which involved CMI; target location and motor action were dissociated in the CMI task. Saliva samples collected on the day of testing were used to determine estrogen, progesterone, and testosterone levels.

Results: Multiple regression analyses revealed age to be a small but significant predictors of performance in a CMI condition with visual feedback reversal. We found that after accounting for this age effect, testosterone was a significant predictor of CMI performance in this group. We also observed that the relationship between testosterone levels and complex performance was related to grey matter thickness and volume in visuomotor control regions.

Conclusion: These data suggest that underlying brain networks controlling simultaneous thought and action may differ as a function of sex steroid hormone concentrations, and that small performance declines emerge in the working-age years.

Introduction and aim: Technological advancements in neuroscience have transformed our understanding of the processes underlying behavior and cognition. The study aims to investigate the changes in functional brain cognitive networks activated by 3DVR haptic-based simulation training in endodontics.

Materials and methods: Fifteen dental students (7 females and 8 males), mean age 21.5 ± 0.5 years, trained for the first time with a 3DVR-haptic simulator in April 2025, participated in the study. The one-group pretest-postest interventional design was approved by the Ethics Committee of MU-Plovdiv. Before and after a one-hour VR-endodontic access practice, we measured connectivity changes in the 49 Brodmann fields using swLORETA qEEG. Data were standardized using an age-stratified normative base through Z-score calculations. The qEEG recordings were processed by Neuroguide 3.3.7 and statistically analyzed with Navistat, p < 0.05. To control the cognitive effects the participants completed a classic neuropsychological test for assessing executive functions before and after VR training: the Trail Making Test (TMT) Part A and Part B.

Results: The most significant changes in brain activity were observed in the beta frequency range (18-24 Hz), primarily in the left frontal dorsolateral area, and the left insula and hippocampus. A significant change in activity was found in the mediobasal temporal cortex on both the left and right sides. According to connectomics, the most substantial changes were observed in the structures of 6, 7 and 8 ICN. The TMT data showed significant difference in records of the average time, difference and ratio scores before and after training in VR (p < 0.05).

Conclusion: 3DVR-haptic simulation training in endodontic actively engages cognitive modalities and brain structers related to executive functions, visuospatial sense, attention, and working memory, resilience to stress, and the need for reward. Objective electrophysiological changes correlate with improvements in neuropsychological performance TMT. Despite the limitations of this study, we argue that swLORETA qEEG method is a novel objective approach with a promising potential for validation the effects of 3DVR-haptic simulation training on cognitive functions, offering better temporal resolution compared to fMRI, along with lower costs and safety.

Introduction: To compare the immediate effects of a single bout of virtual reality (VR) aerobic exercise versus traditional aerobic exercise on depressive mood in middle-school students and to explore the underlying Electroencephalogram (EEG) microstate and power-spectrum mechanisms.

Methods: Forty middle-school students were classified into depressed and healthy groups based on the PHQ-9 and completed 15 min of moderate-intensity conventional cycling and VR cycling in a crossover design. Mood was assessed with the Brief Mood Scale (BFS) before and after each intervention, and resting-state EEG was recorded. EEG signals were processed using power spectrum analysis and microstate analysis, and correlation analysis was conducted between BFS questionnaire scores and microstate parameters.

Results: Both interventions significantly increased vigor and pleasure while reducing depression and lethargy (p < 0.001); VR was superior to traditional cycling in improving vigor, depression, and lethargy (p < 0.01). At baseline, the depressed group showed elevated occurrence and contribution of microstate C with abnormal centroids. After exercise, microstate distributions normalized in both groups; VR specifically reduced microstate C occurrence (p < 0.05), prolonged microstate D duration (p < 0.05), and enhanced B → C and D → B transitions (p < 0.05). Post-VR B → C transition rate correlated negatively with depression (r = -0.462), whereas microstate D occurrence correlated positively with pleasure (r = 0.450). Relative theta, alpha, and beta power decreased after exercise (p < 0.01), without additional VR-mediated power-spectrum gains.

Conclusion: A single bout of VR-assisted aerobic exercise more effectively enhances immediate mood in middle-school students with depressive symptoms than traditional cycling, likely owing to stronger modulation of microstate dynamics linked to emotion and attention networks and of oscillatory activity in specific frequency bands.

Background: Dopamine agonist withdrawal syndrome (DAWS) is a severe condition reported primarily in Parkinson's disease (PD) but increasingly recognized in restless legs syndrome (RLS). While DAWS is classically associated with high-dose dopamine agonists (DAs) in Parkinson's disease, it has also been reported in RLS patients treated with low-dose therapy (≤ 0.75 mg pramipexole equivalent), although such cases remain rare. While direct evidence is lacking, psychological and relational stressors, in conjunction with prior medication adjustments, could plausibly modulate DAWS severity through a mechanism akin to kindling.

Case presentation: We describe the case of a 51-year-old male who developed severe DAWS after withdrawing from low-dose pramipexole (0.26 mg) prescribed for RLS. A 6-month venlafaxine taper, completed 2 weeks before DA tapering, may have increased neurochemical vulnerability. Initial dose reduction caused akathisia, tremors, panic attacks, RLS worsening, and depressive symptoms. After brief reinstatement, abrupt cessation triggered painful electric-like sensations in the lower back and emotional collapse. The patient was transitioned to rotigotine (2 mg/day), together with other psychotropic medications, which provided partial and temporary relief. Symptoms relapsed during tapering, with marked worsening occurring in parallel with episodes of severe relational stress within a close personal connection. Clinical assessments explored these interactions as potential psychological stressors, as reported by the patient. Given the temporal association between these stressors and symptom relapses, relational factors may have contributed to the severity and recurrence of DAWS episodes. At 13 months after complete DA discontinuation, the patient has regained nearly full functionality, although episodes of marked fatigue and significant bedtime RLS persists.

Discussion and conclusion: This case illustrates that DAWS can occur in RLS patients even at low DA doses, with atypical symptoms possibly involving autonomic and central sensitization. Relational stress may significantly exacerbate symptom severity, potentially leading to profound neurological destabilization through mechanisms such as cross-system hypersensitivity or a kindling-like process, as suggested by existing literature. This factor may need to be systematically assessed in DAWS management. As a rare patient-authored account, this report contributes to the understanding of DAWS in non-PD populations and highlights the need for longitudinal research to guide safer withdrawal protocols and integrated care.

Introduction: Post-stroke cognitive impairment (PSCI) is common and often under-recognized, particularly in the acute phase. Most cognitive screening tools provide only a global score, overlooking domain-specific deficits that influence functional recovery. The Addenbrooke's Cognitive Examination-III (ACE-III) is a comprehensive cognitive test whose utility for acute stroke patients remains under-studied. This study evaluated the diagnostic performance of the ACE-III against the stroke-specific Oxford Cognitive Screen (OCS) in detecting PSCI following first-ever stroke in the acute period.

Methods: Patients with first-ever stroke and no history of cognitive impairment were prospectively assessed within seven days of onset using both the OCS and ACE-III. PSCI was defined by impairment in one or more cognitive domains on the OCS. The discriminatory capacity of the ACE-III for detecting PSCI was examined, and associations between specific cognitive deficits and functional dependence were analyzed.

Results: The OCS detected PSCI in 70% of the 30 patients that were recruited. The ACE-III demonstrated good discriminatory capacity (AUC = 0.897); however, it failed to detect PSCI in five patients identified by the OCS, and misclassified two aphasic but cognitively intact patients as impaired. Two patients classified as impaired on ACE-III were deemed cognitively intact by OCS, underscoring its limitations in stroke populations. Standard and stroke-specific ACE-III cut-offs demonstrated suboptimal accuracy for acute screening.

Conclusion: While ACE-III performs well at the group level, it may miss relevant cognitive impairment in the acute stroke setting. Domain-based, stroke-specific tools such as the OCS more reliably detect deficits and may offer greater clinical utility for early cognitive profiling and rehabilitation.

The healthcare field increasingly relies on autonomous systems for the detection and analysis of Multiple Sclerosis (MS) to minimize diagnostic delays, resource burdens, reduce the progression of disability, and enhance clinical decision-making efficiency. Such systems ensure accurate and timely treatment, ultimately improved patient outcomes. In this study, a hybrid framework combining deep learning-based feature extraction, metaheuristic feature selection, and machine learning (ML) classifiers is proposed for accurate MS classification. All MRI images were preprocessed using Contrast Limited Adaptive Histogram Equalization (CLAHE), resizing, and normalization to enhance contrast and standardize the input dimensions. Deep features were extracted using the pretrained VGG16 convolutional neural network (CNN), in which the fully connected layers were removed, and the convolutional base was used to obtain high-dimensional features per image. To reduce dimensionality and improve classification performance, the Whale Optimization Algorithm (WOA) was employed to select the most discriminative subset of features using a Support Vector Machine (SVM)-based fitness function. Multiple classifiers were then trained and evaluated using the optimized feature set. Among them, the Artificial Neural Network integrated with WOA (ANN+WOA) achieved the highest classification accuracy of 98%, demonstrating the potential of the proposed model for reliable, efficient, and automated MS diagnosis.

Electroencephalogram (EEG)-based emotion recognition has emerged as a compelling direction in affective computing, driven by its ability to provide objective, neural-level insights into emotional states. However, the high-dimensional and complex spatial and functional characteristics of EEG data present substantial challenges for accurate modeling. To address this, we propose Multilayer-GTCN (Multilayer Graph Transformer Convolutional Network), which combines the strengths of Graph Convolutional Networks (GCNs) and Graph Transformer layers to effectively capture both local and global dependencies in EEG signals. The framework employs a dual-graph design over feature nodes: a physical proximity graph instantiated as a complete topology to stabilize information flow, and a functional connectivity graph whose edges are correlations derived from inter-feature relationships. Within this representation, GCN layers consolidate stable relational patterns, while transformer-based graph convolutions capture long-range dependencies and transient interactions across the feature space. Combining the two encoded views results in representations that jointly capture localized structure and global context, providing a robust basis for affective decoding. Extensive experiments on benchmark datasets confirm the effectiveness of our approach, achieving 98.24 ± 1.74% on SEED, 95.82 ± 1.89% on SEED-IV, and 93.35 ± 4.08% (valence) / 94.11 ± 2.98% (arousal) on DEAP. These results highlight the efficiency and flexibility of Multilayer-GTCN across varied datasets. By merging a physical proximity graph with correlation-based functional connectivity in a multilayer architecture, this study lays a foundation for scalable affective-computing systems and delivers a framework to guide upcoming advances in neural signal study.

Introduction: Understanding how emotions are encoded at the neural level remains a central challenge in human neuroscience. Facial expressions are among the most powerful and frequently used stimuli to study emotion processing. Face perception itself is a complex function supported by a core network-including bilateral occipito-fusiform and superior temporal regions-and an extended network involving anterior structures such as the bilateral amygdalae. However, previous findings on how emotional content modulates these networks have been inconsistent.

Methods: To disentangle perceptual and affective components of face emotion processing, we combined high-frequency pupillometry with functional magnetic resonance imaging (fMRI). Pupillary dilation serves as a sensitive index of two distinct processes: perceptual load, reflecting the informational complexity of a face, and arousal, indicating its immediate sensory impact. In our study, 25 participants (13 female) viewed faces expressing anger, fear, happiness, or neutrality as well as luminance-matched houses serving as control stimuli. A one-back task unrelated to emotion masked the true experimental purpose.

Results: Relative to houses, faces elicited stronger pupillary dilations as well as enhanced blood-oxygen-level-dependent (BOLD) activity in bilateral occipital and fusiform cortices as well as in both amygdalae. Among facial expressions, angry faces evoked the largest pupillary dilations, while fearful faces elicited the strongest neural responses within a right-lateralized network centered on the superior temporal sulcus (rSTS). Across all faces>houses (conjunction minimum-statistic inference), pupil size correlated positively with BOLD activity in the right fusiform gyrus (rFFG), left inferior occipital gyrus (lIOG), bilateral calcarine cortex, and bilateral lingual gyrus.

Discussion: These findings indicate that emotional faces impose a higher perceptual load than matched control stimuli, engaging a distributed network spanning early visual and attention-related areas. In conclusion, our results suggest that emotional quality is specified early in the perceptual process, with divergent pupillary and neural signatures separating arousal-driven threat responses (anger) from socially complex alarm cues (fear).