Deciphering network dysregulations and temporo-spatial dynamics in disorders of consciousness: insights from minimum spanning tree analysis.

Objectives: The neural mechanism associated with impaired consciousness is not fully clear. We aim to explore the association between static and dynamic minimum spanning tree (MST) characteristics and neural mechanism underlying impaired consciousness.

Methods: MSTs were constructed based on full-length functional magnetic resonance imaging (fMRI) signals and fMRI signal segments within each time window.

Revolutionizing treatment for disorders of consciousness: a multidisciplinary review of advancements in deep brain stimulation.

Among the existing research on the treatment of disorders of consciousness (DOC), deep brain stimulation (DBS) offers a highly promising therapeutic approach. This comprehensive review documents the historical development of DBS and its role in the treatment of DOC, tracing its progression from an experimental therapy to a detailed modulation approach based on the mesocircuit model hypothesis. The mesocircuit model hypothesis suggests that DOC arises from disruptions in a critical network of brain regions, providing a framework for refining DBS targets.

Anatomical-related factors and outcome of percutaneous short-term spinal cord stimulation electrode shift in patients with disorders of consciousness: a retrospective study.

Background: Disorders of consciousness (DoC) represent a spectrum of neurological conditions that pose significant treatment challenges. Percutaneous short-term spinal cord stimulation (SCS) has emerged as a promising experimental diagnostic treatment to assess and potentially improve consciousness levels. However, the effectiveness of this intervention is frequently compromised by the shift of electrodes, particularly in the cervical region, which can negatively affect therapeutic outcomes.

Anti-phasic oscillatory development for speech and noise processing in cochlear implanted toddlers.

Human brain demonstrates amazing readiness for speech and language learning at birth, but the auditory development preceding such readiness remains unknown. Cochlear implanted (CI) children (n = 67; mean age 2.77 year ± 1.

The brain nebula: minimally invasive brain-computer interface by endovascular neural recording and stimulation.

A brain-computer interface (BCI) serves as a direct communication channel between brain activity and external devices, typically a computer or robotic limb. Advances in technology have led to the increasing use of intracranial electrical recording or stimulation in the treatment of conditions such as epilepsy, depression, and movement disorders. This indicates that BCIs can offer clinical neurological rehabilitation for patients with disabilities and functional impairments.

Brain-computer interface digital prescription for neurological disorders.

Neurological and psychiatric diseases can lead to motor, language, emotional disorder, and cognitive, hearing or visual impairment By decoding the intention of the brain in real time, the Brain-computer interface (BCI) can first assist in the diagnosis of diseases, and can also compensate for its damaged function by directly interacting with the environment; In addition, provide output signals in various forms, such as actual motion, tactile or visual feedback, to assist in rehabilitation training; Further intervention in brain disorders is achieved by close-looped neural modulation. In this article, we envision the future BCI digital prescription system for patients with different functional disorders and discuss the key contents in the prescription the brain signals, coding and decoding protocols and interaction paradigms, and assistive technology. Then, we discuss the details that need to be specially included in the digital prescription for different intervention technologies.

Clinical neuromodulatory effects of deep brain stimulation in disorder of consciousness: A literature review.

Background: The management of patients with disorders of consciousness (DOC) presents substantial challenges in clinical practice. Deep brain stimulation (DBS) has emerged as a potential therapeutic approach, but the lack of standardized regulatory parameters for DBS in DOC hinders definitive conclusions.

Objective: This comprehensive review aims to provide a detailed summary of the current issues concerning patient selection, target setting, and modulation parameters in clinical studies investigating the application of DBS for DOC patients.

Neurophysiological evidence for goal-oriented modulation of speech perception.

Speech perception depends on the dynamic interplay of bottom-up and top-down information along a hierarchically organized cortical network. Here, we test, for the first time in the human brain, whether neural processing of attended speech is dynamically modulated by task demand using a context-free discrimination paradigm. Electroencephalographic signals were recorded during 3 parallel experiments that differed only in the phonological feature of discrimination (word, vowel, and lexical tone, respectively).

EEG Based Dynamic Functional Connectivity Analysis in Mental Workload Tasks With Different Types of Information.

The accurate evaluation of operators' mental workload in human-machine systems plays an important role in ensuring the correct execution of tasks and the safety of operators. However, the performance of cross-task mental workload evaluation based on physiological metrics remains unsatisfactory. To explore the changes in dynamic functional connectivity properties with varying mental workload in different tasks, four mental workload tasks with different types of information were designed and a newly proposed dynamic brain network analysis method based on EEG microstate was applied in this paper.

Effects of Jaw Clench Actions on Steady-State Visual Evoked Potential Detection at Some Typical Frequencies.

More and more hybrid brain-computer interfaces (BCI) supplement traditional single-modality BCI in practical applications. Combinations based on steady-state visual evoked potential (SSVEP) and electromyography (EMG) are the widely used hybrid BCIs. The EMG of jaw clench is commonly used together with SSVEP.

Evaluation of Mental Workload in Working Memory Tasks with Different Information Types Based on EEG.

To explore the effectiveness of using Electro- encephalogram (EEG) spectral power and multiscale sample entropy for accessing mental workload in different tasks, working memory tasks with different information types (verbal, object and spatial) and various mental loads were designed based on the N-Back paradigm. Subjective scores, accuracy and response time were used to verify the rationality of the tasks. EEGs from 18 normal adults were acquired when tasks were being performed, an independent component analysis (ICA) based artifact removal method were applied to get clean data.

Disulfiram Chelated With Copper Inhibits the Growth of Gastric Cancer Cells by Modulating Stress Response and Wnt/β-catenin Signaling.

Disulfiram (DSF) is a well-known drug for alcohol abuse. In recent decades, DSF has been demonstrated to exhibit anti-tumor activity; DSF chelated with copper shows enhanced anti-tumor effect. Our goal was to explore the effect of DSF/Cu complex on the growth and metastasis of gastric cancer (GC) and .

Effects of fatigue on steady state motion visual evoked potentials: Optimised stimulus parameters for a zoom motion-based brain-computer interface.

Background And Objective: In flicker-based steady-state visual evoked potentials (SSVEP) brain-computer interface (BCI), the system performance decreases due to prolonged repeated visual stimulation. To reduce the performance decrease due to visual fatigue, the zoom motion based steady-state motion visual evoked potentials (SSMVEPs) paradigm had been proposed. In this study, the stimulation parameters of the paradigm are optimised to mitigate the decrease in detection accuracy for SSMVEP due to visual fatigue.

A Radial Zoom Motion-Based Paradigm for Steady State Motion Visual Evoked Potentials.

In steady state visual evoked potential (SSVEP)-based brain-computer interfaces, prolonged repeated flicker stimulation would reduce the system performance. To reduce the visual discomfort and fatigue, while ensuring recognition accuracy, and information transmission rate (ITR), a novel motion paradigm based on the steady-state motion visual evoked potentials (SSMVEPs) is proposed. The novel SSMVEP paradigm of the radial zoom motion was realized using the sinusoidal form to modulate the size of the stimuli.

[Study of Steady State Motion Visual Evoked Potential-based Visual Stimulation of BCI System].

Objective: To propose a square's ring motion stimulation based on steady-state motion visual evoked potential, and compare it with the commonly used visual stimulation modes (Newton's ring motion, square flicker and circular flicker).

Methods: EEG signals were collected while 9 experimental subjects gazing at four stimulation and pattern analyzed by Canonical Correlation Analysis (CCA). Stimulation were evaluated by recognition accuracy and subjective scores.

Effects of slow and regular breathing exercise on cardiopulmonary coupling and blood pressure.

Investigation of the interaction between cardiovascular variables and respiration provides a quantitative and noninvasive approach to assess the autonomic control of cardiovascular function. The aim of this paper is to investigate the changes of cardiopulmonary coupling (CPC), blood pressure (BP) and pulse transit time (PTT) during a stepwise-paced breathing (SPB) procedure (spontaneous breathing followed by paced breathing at 14, 12.5, 11, 9.

[Study on the Optimum Order of Autoregressive Models for Heart Rate Variability Analysis].

Heart rate variability (HRV) analysis technology based on an autoregressive (AR) model is widely used in the assessment of autonomic nervous system function. The order of AR models has important influence on the accuracy of HRV analysis. This article presents a method to determine the optimum order of AR models.

[The Study of the Measurement of Heart Rate Variability Using ECG and Photoplethysmographic Signal].

In comparison with the measurement of heart rate variability from ECG and from photoplethysmographic signal from 46 healthy adults in their spontaneous breathing state. The beat-to-beat intervals in ECG and pulse-to-pulse intervals in photoplethysmographic signal are extracted, and then the parameters of heart rate variability are calculated. Three kinds of algorithms are chosen to get the pulse-to-pulse intervals, which are the intervals of maximum of second derivative, the maximum of PPG signal and the tangent intersection.

[Clinical data mining by exploring public MIMIC-II intensive care database].

This paper introduces a free and publicly open ICU database: multi-parameter intelligent monitoring in intensive care II: MIMIC-II, which has been built up and maintained by the laboratory of computational physiology at the Massachusetts Institute Technology, Beth Israel Deaconess Medical Center and Philips Healthcare over the past decade. This paper briefly introduces its infrastructure, implementation and applications in clinical studies. Clinical study pertaining to circadian variation in heart rate and blood pressure during sepsis is shown as a typical example of research performed with MIMIC-II.

[Change of pulse transit time during stepwise paced breathing].

To investigate the effect of stepwise paced breathing (PB) on pulse transit time (PTT), we collected physiological signals of electrocardiogram (ECG), respiration and arterial pulse wave during a procedure of stepwise PB, which consists of 6 different breathing rates changing in a protocol of 14.0-12.5-11.