Individualized rTMS Intervention Targeting Sleep Deprivation-Induced Vigilance Decline: Task fMRI-Guided Approach.

Study Objectives: Sleep deprivation (SD) is prevalent in our increasingly round-the-clock society. Optimal countermeasures such as ample recovery sleep are often unfeasible, and brief naps, while helpful, do not fully restore cognitive performance following SD. Thus, we propose that targeted interventions, such as repetitive transcranial magnetic stimulation (rTMS), may enhance cognitive performance recovery post-SD.

Thalamic-limbic circuit dysfunction and white matter topological alteration in Parkinson's disease are correlated with gait disturbance.

Background: Limbic structures have recently garnered increased attention in Parkinson's disease (PD) research. This study aims to explore changes at the whole-brain level in the structural network, specifically the white matter fibres connecting the thalamus and limbic system, and their correlation with the clinical characteristics of patients with PD.

Methods: Between December 2020 and November 2021, we prospectively enrolled 42 patients with PD and healthy controls at the movement disorder centre.

Hybrid ant colony-based inter-cluster routing protocol for FANET.

This study addresses the challenges in large-scale unmanned aerial vehicle (UAV) clusters, specifically the scalability issues and limitations of using reactive routing protocols for inter-cluster routing. These traditional methods place an excessive burden on cluster heads and struggle to adapt to frequently changing topologies, leading to decreased network performance. To solve these problems, we propose an innovative inter-cluster routing protocol (ICRP), which is based on a hybrid ant colony algorithm.

Association of Objective Sleep Characteristics and Incident Angina Pectoris: A Longitudinal Analysis from the Sleep Heart Health Study.

Purpose: While prior research has highlighted a significant association between sleep characteristics and angina pectoris (AP) incidence, the link between sleep efficiency (SE) and angina remains unexplored. This study seeks to elucidate the relationship between AP and objectively quantified SE.

Patients And Methods: We examined a cohort of 2990 participants (1320 males and 1670 females; mean age 63.

Cognitive impairment after sleep deprivation: The role of precuneus related connectivity on the intra-individual variability changes.

Objective: Intra-individual variability (IIV) in cognitive performance is thought to reflect the efficiency with which attentional resources are allocated in different circumstances requiring cognitive control. IIV in cognitive performance is associated with the strength of the negative correlation between task-positive network and default mode network (DMN) activity. In this study, we investigated the impact of sleep deprivation (SD) on functional connectivity (FC) between the DMN and psychomotor vigilance task-related network (PVT-RN), and its relationship with IIV in cognitive performance.

The restoration ability of a short nap after sleep deprivation on the brain cognitive function: A dynamic functional connectivity analysis.

Aims: The brain function impairment induced by sleep deprivation (SD) is temporary and can be fully reversed with sufficient sleep. However, in many cases, long-duration recovery sleep is not feasible. Thus, this study aimed to investigate whether a short nap after SD is sufficient to restore brain function.

White matter structural topologic efficiency predicts individual resistance to sleep deprivation.

Background: Sleep deprivation (SD) is commonplace in modern society and there are large individual differences in the vulnerability to SD. We aim to identify the structural network differences based on diffusion tensor imaging (DTI) that contribute to the individual different vulnerability to SD.

Methods: The number of psychomotor vigilance task (PVT) lapses was used to classify 49 healthy subjects on the basis of whether they were vulnerable or resistant to SD.

Ultra-high b-Value DWI in predicting progression risk of locally advanced rectal cancer: a comparative study with routine DWI.

Background: The prognosis prediction of locally advanced rectal cancer (LARC) was important to individualized treatment, we aimed to investigate the performance of ultra-high b-value DWI (UHBV-DWI) in progression risk prediction of LARC and compare with routine DWI.

Methods: This retrospective study collected patients with rectal cancer from 2016 to 2019. Routine DWI (b = 0, 1000 s/mm) and UHBV-DWI (b = 0, 1700 ~ 3500 s/mm) were processed with mono-exponential model to generate ADC and ADCuh, respectively.

Corrigendum: Classifying Vulnerability to Sleep Deprivation Using Resting-State Functional MRI Graph Theory Metrics.

[This corrects the article DOI: 10.3389/fnins.2021.

Improved interobserver agreement on nodule type and Lung-RADS classification of subsolid nodules using computer-aided solid component measurement.

Purpose: The Lung CT Screening Reporting and Data System (Lung-RADS) classification of subsolid nodules (SSNs) can be challenging due to limited interobserver agreement in determining the type and size of the nodule. Our study aimed to assess the effect of a computer-aided method on the interobserver agreement of Lung-RADS classification for SSNs.

Materials And Methods: This study consisted of 156 SSNs in 121 patients who underwent initial CT screening for lung cancer.

Predicting Attentional Vulnerability to Sleep Deprivation: A Multivariate Pattern Analysis of DTI Data.

Background: Large individual differences exist in sleep deprivation (SD) induced sustained attention deterioration. Several brain imaging studies have suggested that the activities within frontal-parietal network, cortico-thalamic connections, and inter-hemispheric connectivity might underlie the neural correlates of vulnerability/resistance to SD. However, those traditional approaches are based on average estimates of differences at the group level.

The Construction of a Risk Prediction Model Based on Neural Network for Pre-operative Acute Ischemic Stroke in Acute Type A Aortic Dissection Patients.

To establish a pre-operative acute ischemic stroke risk (AIS) prediction model using the deep neural network in patients with acute type A aortic dissection (ATAAD). Between January 2015 and February 2019, 300 ATAAD patients diagnosed by aorta CTA were analyzed retrospectively. Patients were divided into two groups according to the presence or absence of pre-operative AIS.

Classifying Vulnerability to Sleep Deprivation Using Resting-State Functional MRI Graph Theory Metrics.

Sleep deprivation (SD) has become very common in contemporary society, where people work around the clock. SD-induced cognitive deficits show large inter-individual differences and are trait-like with known neural correlates. However, few studies have used neuroimaging to predict vulnerability to SD.

Baseline Brain Gray Matter Volume as a Predictor of Acupuncture Outcome in Treating Migraine.

The present study aimed to investigate the use of imaging biomarkers to predict the outcome of acupuncture in patients with migraine without aura (MwoA). Forty-one patients with MwoA received 4 weeks of acupuncture treatment and two brain imaging sessions at the Beijing Traditional Chinese Medicine Hospital affiliated with Capital Medical University. Patients kept a headache diary for 4 weeks before treatment and during acupuncture treatment.

Predicting responses to electroconvulsive therapy in schizophrenia patients undergoing antipsychotic treatment: Baseline functional connectivity among regions with strong electric field distributions.

This study explored imaging predictors of electroconvulsive therapy (ECT) outcome in schizophrenia patients based on pre-treatment functional connectivity (FC) within regions with strong ECT electric fields distribution. Forty-seven patients received standard antipsychotic drugs combined with ECT as well as two brain imaging sessions. Regions of interest (ROI) with strong electric field distribution were determined by ECT simulation.

Sleep Stage Classification Using Time-Frequency Spectra From Consecutive Multi-Time Points.

Sleep stage classification is an open challenge in the field of sleep research. Considering the relatively small size of datasets used by previous studies, in this paper we used the Sleep Heart Health Study dataset from the National Sleep Research Resource database. A long short-term memory (LSTM) network using a time-frequency spectra of several consecutive 30 s time points as an input was used to perform the sleep stage classification.

Predicting response to electroconvulsive therapy combined with antipsychotics in schizophrenia using multi-parametric magnetic resonance imaging.

Electroconvulsive therapy (ECT) has been shown to be effective in schizophrenia, particularly when rapid symptom reduction is needed or in cases of resistance to drug treatment. However, there are no markers available to predict response to ECT. Here, we examine whether multi-parametric magnetic resonance imaging (MRI)-based radiomic features can predict response to ECT for individual patients.

Abnormal Resting-State Functional Connectivity in the Whole Brain in Lifelong Premature Ejaculation Patients Based on Machine Learning Approach.

Recent neuroimaging studies have indicated that abnormalities in brain structure and function may play an important role in the etiology of lifelong premature ejaculation (LPE). LPE patients have exhibited aberrant cortical structure, altered brain network function and abnormal brain activation in response to erotic pictures. However, it remains unclear whether resting-state whole brain functional connectivity (FC) is altered in LPE patients.

Association Between Connectivity of Hippocampal Sub-Regions and Auditory Verbal Hallucinations in Schizophrenia.

Hippocampal dysconnectivity has been detected in schizophrenia patients with auditory verbal hallucinations (AVHs). Neuroanatomical evidence has indicated distinct sub-regions in the hippocampus, but which sub-regions within the hippocampus may emerge dysfunction in the brain network, and the relationship between connection strength and the severity of this debilitating disorder have yet to be revealed. Masked independent component analysis (mICA), i.

Intersubject synchronisation analysis of brain activity associated with the instant effects of acupuncture: an fMRI study.

Objective: To use a promising analytical method, namely intersubject synchronisation (ISS), to evaluate the brain activity associated with the instant effects of acupuncture and compare the findings with traditional general linear model (GLM) methods.

Methods: 30 healthy volunteers were recruited for this study. Block-designed manual acupuncture stimuli were delivered at SP6, and sensations were measured after acupuncture stimulation.

Aberrant perfusion and its connectivity within default mode network of first-episode drug-naïve schizophrenia patients and their unaffected first-degree relatives.

Neural substrates behind schizophrenia (SZ) and its heritability mediated by brain function are largely unknown. Cerebral blood flow (CBF), as a biomarker of activation in the brain, reflects the neuronal metabolism, and is promisingly used to detect cerebral alteration thereby shedding light on the features of individuals at high genetic risk. We performed a cross-sectional functional magnetic resonance imaging (MRI) study enrolling 45 first-episode drug-naïve patients with SZ, 32 unaffected first-degree relatives of these patients, and 51 healthy controls (HCs).

White Matter Microstructural Properties are Related to Inter-Individual Differences in Cognitive Instability after Sleep Deprivation.

Several diseases are characterized by cognitive instability, which is amplified in the conditions of sleep deprivation (SD). Cognitive instability in SD can be examined by the number of lapses on the psychomotor vigilance test (PVT), which is considered to be a gold standard in the field. However, the number of PVT lapses widely range according to inter-individual differences, from apparent cognitive resistance to severe cognitive impairment.