Enhancing Major Depressive Disorder Diagnosis With Dynamic-Static Fusion Graph Neural Networks.

Major Depressive Disorder (MDD) is a debilitating, complex mental condition with unclear mechanisms hindering diagnostic progress. Research links MDD to abnormal brain connectivity using functional magnetic resonance imaging (fMRI). Yet, existing fMRI-based MDD models suffer from limitations, including neglecting dynamic network traits, lacking interpretability, and struggling with small datasets.

Semantic processing during continuous speech production: an analysis from eye movements and EEG.

Introduction: Speech production involves neurological planning and articulatory execution. How speakers prepare for articulation is a significant aspect of speech production research. Previous studies have focused on isolated words or short phrases to explore speech planning mechanisms linked to articulatory behaviors, including investigating the eye-voice span (EVS) during text reading.

Study on neural entrainment to continuous speech using dynamic source connectivity analysis.

Many recent studies investigating the processing of continuous natural speech have employed electroencephalography (EEG) due to its high temporal resolution. However, most of these studies explored the response mechanism limited to the electrode space. In this study, we intend to explore the underlying neural processing in the source space, particularly the dynamic functional interactions among different regions during neural entrainment to speech.

Multimodal evidence for predictive coding in sentence oral reading.

Sentence oral reading requires not only a coordinated effort in the visual, articulatory, and cognitive processes but also supposes a top-down influence from linguistic knowledge onto the visual-motor behavior. Despite a gradual recognition of a predictive coding effect in this process, there is currently a lack of a comprehensive demonstration regarding the time-varying brain dynamics that underlines the oral reading strategy. To address this, our study used a multimodal approach, combining real-time recording of electroencephalography, eye movements, and speech, with a comprehensive examination of regional, inter-regional, sub-network, and whole-brain responses.

Emotion recognition using spatial-temporal EEG features through convolutional graph attention network.

Constructing an efficient human emotion recognition model based on electroencephalogram (EEG) signals is significant for realizing emotional brain-computer interaction and improving machine intelligence.In this paper, we present a spatial-temporal feature fused convolutional graph attention network (STFCGAT) model based on multi-channel EEG signals for human emotion recognition. First, we combined the single-channel differential entropy (DE) feature with the cross-channel functional connectivity (FC) feature to extract both the temporal variation and spatial topological information of EEG.

Detection of Brain Network Communities During Natural Speech Comprehension From Functionally Aligned EEG Sources.

In recent years, electroencephalograph (EEG) studies on speech comprehension have been extended from a controlled paradigm to a natural paradigm. Under the hypothesis that the brain can be approximated as a linear time-invariant system, the neural response to natural speech has been investigated extensively using temporal response functions (TRFs). However, most studies have modeled TRFs in the electrode space, which is a mixture of brain sources and thus cannot fully reveal the functional mechanism underlying speech comprehension.

MXene/Polylactic Acid Fabric-Based Resonant Cavity for Realizing Simultaneous High-Performance Electromagnetic Interference (EMI) Shielding and Efficient Energy Harvesting.

Proliferation in telecommunications and integrated/intelligent devices entails an intense concern for electromagnetic interference (EMI) shielding and versatility. It remains an activated passion to launch infusive EMI shielding materials integrated with self-powered peculiarities. Herein, a double-layered MXene/polylactic acid (PLA) fabric resonance cavity (D-MPF-RC) comprised of two MXene/PLA fabrics (MPFs) with alternating MXene and PLA structures that are separated by a poly(tetrafluoroethylene) (PTFE) frame is developed.

Inferring Brain State Dynamics Underlying Naturalistic Stimuli Evoked Emotion Changes With dHA-HMM.

The brain functional mechanisms underlying emotional changes have been primarily studied based on the traditional task design with discrete and simple stimuli. However, the brain state transitions when exposed to continuous and naturalistic stimuli with rich affection variations remain poorly understood. This study proposes a dynamic hyperalignment algorithm (dHA) to functionally align the inter-subject neural activity.

Renormalization of Thalamic Sub-Regional Functional Connectivity Contributes to Improvement of Cognitive Function after Liver Transplantation in Cirrhotic Patients with Overt Hepatic Encephalopathy.

Objective: The role of preoperative overt hepatic encephalopathy (OHE) in the neurophysiological mechanism of cognitive improvement after liver transplantation (LT) remains elusive. This study aimed to explore changes in sub-regional thalamic functional connectivity (FC) after LT and their relationship with neuropsychological improvement using resting-state functional MRI (rs-fMRI) data in cirrhotic patients with and without a history of OHE.

Materials And Methods: A total of 51 cirrhotic patients, divided into the OHE group (n = 21) and no-OHE group (n = 30), and 30 healthy controls were enrolled in this prospective study.

Investigation of functional brain network reconfiguration during exposure to naturalistic stimuli using graph-theoretical analysis.

One of the most significant features of the human brain is that it can dynamically reconfigure itself to adapt to a changing environment. However, dynamic interaction characteristics of the brain networks in naturalistic scenes remain unclear.We used open-source functional magnetic resonance imaging (fMRI) data from 15 participants who underwent fMRI scans while watching an audio-visual movie 'Forrest Gump'.

Identification of minimal hepatic encephalopathy based on dynamic functional connectivity.

To investigate whether dynamic functional connectivity (DFC) metrics can better identify minimal hepatic encephalopathy (MHE) patients from cirrhotic patients without any hepatic encephalopathy (noHE) and healthy controls (HCs). Resting-state functional MRI data were acquired from 62 patients with cirrhosis (MHE, n = 30; noHE, n = 32) and 41 HCs. We used the sliding time window approach and functional connectivity analysis to extract the time-varying properties of brain connectivity.

Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning.

Hepatic encephalopathy (HE) is a neurocognitive dysfunction based on metabolic disorders caused by severe liver disease, which has a high one-year mortality. Mild hepatic encephalopathy (MHE) has a high risk of converting to overt HE, and thus the accurate identification of MHE from cirrhosis with no HE (noHE) is of great significance in reducing mortality. Previously, most studies focused on studying abnormality in the static brain networks of MHE to find biomarkers.

Constructing Accurate and Efficient Deep Spiking Neural Networks With Double-Threshold and Augmented Schemes.

Spiking neural networks (SNNs) are considered as a potential candidate to overcome current challenges, such as the high-power consumption encountered by artificial neural networks (ANNs); however, there is still a gap between them with respect to the recognition accuracy on various tasks. A conversion strategy was, thus, introduced recently to bridge this gap by mapping a trained ANN to an SNN. However, it is still unclear that to what extent this obtained SNN can benefit both the accuracy advantage from ANN and high efficiency from the spike-based paradigm of computation.

EEG Assessment in a 2-Year-Old Child with Prolonged Disorders of Consciousness: 3 Years' Follow-up.

A 2-year-old girl, diagnosed with traumatic brain injury and epilepsy following car trauma, was followed up for 3 years (a total of 15 recordings taken at 0, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 14, 19, 26, and 35 months). There is still no clear guidance on the diagnosis, treatment, and prognosis of children with disorders of consciousness. At each appointment, recordings included the child's height, weight, pediatric Glasgow Coma Scale (pGCS), Coma Recovery Scale-Revised (CRS-R), Gesell Developmental Schedule, computed tomography or magnetic resonance imaging, electroencephalogram, frequency of seizures, oral antiepileptic drugs, stimulation with subject's own name (SON), and median nerve electrical stimulation (MNS).

Tracking the Dynamic Functional Network Interactions During Goal-Directed Auditory Tasks by Brain State Clustering.

Both perceiving and processing external sound stimuli as well as actively maintaining and updating relevant information (i.e., working memory) are critical for communication and problem solving in everyday acoustic environments.

Revealing the Dynamic Brain Connectivity from Perception of Speech Sound to Semantic Processing by EEG.

Understanding brain processing mechanisms from the perception of speech sounds to high-level semantic processing is vital for effective human-robot communication. In this study, 128-channel electroencephalograph (EEG) signals were recorded when subjects were listening to real and pseudowords in Mandarin. By using an EEG source reconstruction method and a sliding-window Granger causality analysis, we analyzed the dynamic brain connectivity patterns.

Decoding sound categories based on whole-brain functional connectivity patterns.

2Sound decoding is important for patients with sensory loss, such as the blind. Previous studies on sound categorization were conducted by estimating brain activity using univariate analysis or voxel-wise multivariate decoding methods and suggested some regions were sensitive to auditory categories. It is proposed that feedback connections between brain areas may facilitate auditory object selection.

Investigating the Brain Neural Mechanism when Signature Objects were Masked during a Scene Categorization Task using Functional MRI.

Objects play vital roles in scene categorization. Although a number of studies have researched on the neural responses during object and object-based scene recognition, few studies have investigated the neural mechanism underlying object-masked scene categorization. Here, we used functional magnetic resonance imaging (fMRI) to measure the changes in brain activations and functional connectivity (FC) while subjects performed a visual scene-categorization task with different numbers of 'signature objects' masked.

Brain Regional Homogeneity Changes in Cirrhotic Patients with or without Hepatic Encephalopathy Revealed by Multi-Frequency Bands Analysis Based on Resting-State Functional MRI.

Objective: To investigate brain regional homogeneity (ReHo) changes of multiple sub-frequency bands in cirrhotic patients with or without hepatic encephalopathy using resting-state functional MRI.

Materials And Methods: This study recruited 46 cirrhotic patients without clinical hepatic encephalopathy (noHE), 38 cirrhotic patients with clinical hepatic encephalopathy (HE), and 37 healthy volunteers. ReHo differences were analyzed in slow-5 (0.

Semantically Congruent Sounds Facilitate the Decoding of Degraded Images.

Semantically congruent sounds can facilitate perception of visual objects in the human brain. However, the manner in which semantically congruent sounds affect cognitive processing for degraded visual stimuli remains unclear. We presented participants with naturalistic degraded images and semantically congruent sounds from different conceptual categories in three modalities: degraded visual only, auditory only, and auditory and degraded visual.

Revealing the Semantic Association between Perception of Scenes and Significant Objects by Representational Similarity Analysis.

'Significant' objects contribute greatly to scene recognition. The lateral occipital complex (LOC), parahippocampal place area (PPA), and retrosplenial cortex (RSC) play a crucial role in the cognitive processing of objects and scenes. However, the associated mechanism between objects and scenes remains unclear.

Impact of previous episodes of hepatic encephalopathy on short-term brain function recovery after liver transplantation: a functional connectivity strength study.

Neuropsychological studies have documented an incomplete reversal of pre-existing cognitive dysfunction in cirrhotic patients after liver transplantation (LT) and have found this is more severe in patients with hepatic encephalopathy (HE). In this study, we aimed to investigate the impact of prior HE episodes on post-transplantation brain function recovery. Resting-state functional magnetic resonance imaging data was collected from 30 healthy controls and 33 cirrhotic patients (HE, n = 15 and noHE, n = 18) before and one month after LT.

Cross-modal integration of polyphonic characters in Chinese audio-visual sentences: a MVPA study based on functional connectivity.

This study aimed to investigate the functional connectivity in the brain during the cross-modal integration of polyphonic characters in Chinese audio-visual sentences. The visual sentences were all semantically reasonable and the audible pronunciations of the polyphonic characters in corresponding sentences contexts varied in four conditions. To measure the functional connectivity, correlation, coherence and phase synchronization index (PSI) were used, and then multivariate pattern analysis was performed to detect the consensus functional connectivity patterns.

EEG source reconstruction evidence for the noun-verb neural dissociation along semantic dimensions.

One of the long-standing issues in neurolinguistic research is about the neural basis of word representation, concerning whether grammatical classification or semantic difference causes the neural dissociation of brain activity patterns when processing different word categories, especially nouns and verbs. To disentangle this puzzle, four orthogonalized word categories in Chinese: unambiguous nouns (UN), unambiguous verbs (UV), ambiguous words with noun-biased semantics (AN), and ambiguous words with verb-biased semantics (AV) were adopted in an auditory task for recording electroencephalographic (EEG) signals from 128 electrodes on the scalps of twenty-two subjects. With the advanced current density reconstruction (CDR) algorithm and the constraint of standardized low-resolution electromagnetic tomography, the spatiotemporal brain dynamics of word processing were explored with the results that in multiple time periods including P1 (60-90ms), N1 (100-140ms), P200 (150-250ms) and N400 (350-450ms), noun-verb dissociation over the parietal-occipital and frontal-central cortices appeared not only between the UN-UV grammatical classes but also between the grammatically identical but semantically different AN-AV pairs.

Investigation of the influence of emotions on working memory capacity using ERP and ERSP.

Recent studies have reported that there are individual differences in working memory (WM), and that WM may be affected by emotions. To date, it remains controversial whether emotions impair or facilitate WM and whether there are individual differences in their effect on WM. In this study, three emotions (negative, neutral, and positive) were induced by a video database that was established according to the emotional stimuli habit of Chinese people.