SNet: Self-adaptive weighted fusion and self-adaptive aligned network for multi-modal MRI segmentation.

Background: Accurate segmentation of lesions is beneficial for quantitative analysis and precision medicine in multimodal magnetic resonance imaging (MRI).

Purpose: Currently, multimodal MRI fusion segmentation networks still face two main issues. On one hand, simple feature concatenation fails to fully capture the complex relationships between different modalities, as it overlooks the importance of dynamically changing feature weights across modalities.

EEG Pattern Comparison and Classification Performance of Motor Imagery Between Supernumerary and Inherent Limbs.

Adding supernumerary robotic limbs (SRLs) to humans and controlling them directly through the brain are main goals for movement augmentation. However, whether neural patterns that are distinct from the traditional inherent limbs motor imagery (MI) paradigm can be extracted, which is essential for the high-dimensional control of external equipment. In this study, a novel type of MI paradigm based on SRLs was proposed, consisting of "the sixth-finger", "the third-arm" and "the third-leg", and validated the distinctness of EEG response patterns between the novel and the traditional (hand, arm and leg) MI paradigm.

Dosimetric effects of bladder volume changes in MR-guided radiotherapy for cervical cancer.

Purpose: Bladder volume variations during radiotherapy can significantly influence dose distribution to both target volumes and surrounding organs-at-risk (OARs). This study aims to assess the dosimetric impact of variable bladder volume on the clinical target volume (CTV) and OARs in cervical cancer patients undergoing MR-guided radiotherapy.

Method: A total of 27 cervical cancer patients were included in this study: 12 received radical radiotherapy, and 15 underwent postoperative radiotherapy.

St-RegSeg: an unsupervised registration-based framework for multimodal magnetic resonance imaging stroke lesion segmentation.

Background: Stroke is one of the leading causes of disability and death worldwide. Ischemic stroke accounts for 75-90% of all stroke incidents. Assessing the size and location of the stroke lesion is crucial for treatment decisions, especially those related to urgent vascular reconstruction surgery.

Attenuated task-responsive representations of hippocampal place cells induced by amyloid-beta accumulation.

Alzheimer's disease (AD) is a typical neurodegenerative disease featuring deficits in spatial memory, which relies on spatial representations by hippocampal place cells. Place cells exhibit task-responsive representation to support memory encoding and retrieval processes. Yet, it remains unclear how this task-responsive spatial representation was interrupted under AD pathologies.

Impact of different auditory environments on task performance and EEG activity.

Mental workload could affect human performance. An inappropriate workload level, whether too high or too low, leads to discomfort and decreased task performance. Auditory stimuli have been shown to act as an emotional medium to influence the workload.

Enhancing ERD Activation and Functional Connectivity via the Sixth-Finger Motor Imagery in Stroke Patients.

Motor imagery (MI) is widely employed in stroke rehabilitation due to the event-related desynchronization (ERD) phenomenon in sensorimotor cortex induced by MI is similar to actual movement. However, the traditional BCI paradigm, in which the patient imagines the movement of affected hand (AH-MI) with a weak ERD caused by the damaged brain regions, retards motor relearning process. In this work, we applied a novel MI paradigm based on the "sixth-finger" (SF-MI) in stroke patients and systematically uncovered the ERD pattern enhancement of novel MI paradigm compared to traditional MI paradigm.

EEG Characteristic Comparison of Motor Imagery Between Supernumerary and Inherent Limb: Sixth-Finger MI Enhances the ERD Pattern and Classification Performance.

Adding supernumerary robotic limbs (SRLs) to humans and controlling them directly through the brain are main goals for movement augmentation. However, it remains uncertain whether neural patterns different from the traditional inherent limbs motor imagery (MI) can be extracted, which is essential for high-dimensional control of external devices. In this work, we established a MI neo-framework consisting of novel supernumerary robotic sixth-finger MI (SRF-MI) and traditional right-hand MI (RH-MI) paradigms and validated the distinctness of EEG response patterns between two MI tasks for the first time.

Detection of early neurological deterioration using a quantitative electroencephalography system in patients with large vessel occlusion stroke after endovascular treatment.

Background: Early neurological deterioration (END) is a serious complication in patients with large vessel occlusion (LVO) stroke. However, modalities to monitor neurological function after endovascular treatment (EVT) are lacking. This study aimed to evaluate the diagnostic accuracy of a quantitative electroencephalography (qEEG) system for detecting END.

Nomogram-based geometric and hemodynamic parameters for predicting the growth of small untreated intracranial aneurysms.

Previous studies have shown that the growth status of intracranial aneurysms (IAs) predisposes to rupture. This study aimed to construct a nomogram for predicting the growth of small IAs based on geometric and hemodynamic parameters. We retrospectively collected the baseline and follow-up angiographic images (CTA/ MRA) of 96 small untreated saccular IAs, created patient-specific vascular models and performed computational fluid dynamics (CFD) simulations.

Prevalence and prognosis of acute ischemic stroke coexisting with unruptured intracranial aneurysms.

Objectives: The prevalence of unruptured intracranial aneurysms (UIAs) in the acute ischemic stroke (AIS) cohort is probably higher than in the general population. This study investigated the prevalence of UIAs in AIS patients and the management risk and prognosis when treating AIS.

Methods: From January 2020 to January 2023, we conducted a single-center retrospective study at Tianjin Huanhu Hospital.

Spontaneous alpha-band oscillations reflect individual differences in audiovisual temporal perception.

Light, and sound are persistently out of sync for subjective temporal perception called point of subjective simultaneity (PSS). It is stable within individuals but variable among individuals. Previous studies found that spontaneous alpha power, functioning in attention-related brain states, predicts individual PSS in the temporal order judgment (TOJ) task.

Neural oscillations reflect the individual differences in the temporal perception of audiovisual speech.

Multisensory integration occurs within a limited time interval between multimodal stimuli. Multisensory temporal perception varies widely among individuals and involves perceptual synchrony and temporal sensitivity processes. Previous studies explored the neural mechanisms of individual differences for beep-flash stimuli, whereas there was no study for speech.

Beyond alpha band: prestimulus local oscillation and interregional synchrony of the beta band shape the temporal perception of the audiovisual beep-flash stimulus.

Multisensory integration is more likely to occur if the multimodal inputs are within a narrow temporal window called temporal binding window (TBW). Prestimulus local neural oscillations and interregional synchrony within sensory areas can modulate cross-modal integration. Previous work has examined the role of ongoing neural oscillations in audiovisual temporal integration, but there is no unified conclusion.

An Efficient Multi-Task Synergetic Network for Polyp Segmentation and Classification.

Colonoscopy is considered the best diagnostic tool for early detection and resection of polyps, which can effectively prevent consequential colorectal cancer. In clinical practice, segmenting and classifying polyps from colonoscopic images have a great significance since they provide precious information for diagnosis and treatment. In this study, we propose an efficient multi-task synergetic network (EMTS-Net) for concurrent polyp segmentation and classification, and we introduce a polyp classification benchmark for exploring the potential correlations of the above-mentioned two tasks.

The effect of prestimulus low-frequency neural oscillations on the temporal perception of audiovisual speech.

Objective: Perceptual integration and segregation are modulated by the phase of ongoing neural oscillation whose frequency period is broader than the size of the temporal binding window (TBW). Studies have shown that the abstract beep-flash stimuli with about 100 ms TBW were modulated by the alpha band phase. Therefore, we hypothesize that the temporal perception of speech with about hundreds of milliseconds of TBW might be affected by the delta-theta phase.

A novel approach to automatic sleep stage classification using forehead electrophysiological signals.

Background: Sleep stage scoring is very important for the effective diagnosis and intervention of sleep disorders. However, the current automatic sleep staging methods generally have the problems of poor model generalization ability and non-portable acquisition equipment.

Method: In this paper, we propose a novel automatic sleep scoring system based on forehead electrophysiological signals that is more effective and convenient than other systems.

Lack of Habituation in Migraine Patients Based on High-Density EEG Analysis Using the Steady State of Visual Evoked Potential.

Migraine is a periodic disorder in which a patient experiences changes in the morphological and functional brain, leading to the abnormal processing of repeated external stimuli in the inter-ictal phase, known as the habituation deficit. This is a significant feature clinically of migraine in both two types with aura or without aura and plays an essential role in studying pathophysiological differences between these two groups. Several studies indicated that the reason for migraine aura is cortical spreading depression (CSD) but did not clarify its impact on migraine without aura and lack of habituation.

A novel silent speech recognition approach based on parallel inception convolutional neural network and Mel frequency spectral coefficient.

Silent speech recognition breaks the limitations of automatic speech recognition when acoustic signals cannot be produced or captured clearly, but still has a long way to go before being ready for any real-life applications. To address this issue, we propose a novel silent speech recognition framework based on surface electromyography (sEMG) signals. In our approach, a new deep learning architecture Parallel Inception Convolutional Neural Network (PICNN) is proposed and implemented in our silent speech recognition system, with six inception modules processing six channels of sEMG data, separately and simultaneously.

Auditory event-related potentials based on name stimuli: A pilot study.

In recent years, diagnostic studies of brain disorders based on auditory event-related potentials (AERP) have become a hot topic. Research showed that AERP might help to detect patient consciousness, especially using the subjects' own name (SON). In this study, we conducted a preliminary analysis of the brain response to Chinese name stimuli.

Segmentation Method of Cerebral Aneurysms Based on Entropy Selection Strategy.

The segmentation of cerebral aneurysms is a challenging task because of their similar imaging features to blood vessels and the great imbalance between the foreground and background. However, the existing 2D segmentation methods do not make full use of 3D information and ignore the influence of global features. In this study, we propose an automatic solution for the segmentation of cerebral aneurysms.

The application and perspective of hyperbaric oxygen therapy in acute ischemic stroke: From the bench to a starter?

Stroke has become a significant cause of death and disability globally. Along with the transition of the world's aging population, the incidence of acute ischemic stroke is increasing year by year. Even with effective treatment modalities, patients are not guaranteed to have a good prognosis.