Technologies for detecting and monitoring drivers' states: A systematic review.

Driver fatigue or drowsiness detection techniques can significantly enhance road safety measures and reduce traffic accidents. These approaches used different sensor technologies to acquire the human physiological and behavioral characteristics to investigate the driver's vigilance state. Although the driver's vigilance detection technique has attracted significant interest recently, few studies have been conducted to review it systematically.

Detection of Low Resilience Using Data-Driven Effective Connectivity Measures.

Conventional thresholding techniques for graph theory analysis, such as absolute, proportional and mean degree, have often been used in characterizing human brain networks under different mental disorders, such as mental stress. However, these approaches may not always be reliable as conventional thresholding approaches are subjected to human biases. Using a mental resilience study, we investigate if data-driven thresholding techniques such as Global Cost Efficiency (GCE-abs) and Orthogonal Minimum Spanning Trees (OMSTs) could provide equivalent results, whilst eliminating human biases.

Altered functional connectivity subserving expressed emotion environments in schizophrenia: An fNIRS study.

Living in high-expressed emotion (EE) environments, characterized by critical, hostile, or over-involved family attitudes, has been linked to increased relapse rates among individuals with schizophrenia (SZ). In our previous work (Wang et al., 2023), we conducted the first feasibility study of using functional near-infrared spectroscopy (fNIRS) with our developed EE stimuli to examine cortical hemodynamics in SZ.

MicroRNA classification and discovery for major depressive disorder diagnosis: Towards a robust and interpretable machine learning approach.

Background: Major depressive disorder (MDD) is notably underdiagnosed and undertreated due to its complex nature and subjective diagnostic methods. Biomarker identification would help provide a clearer understanding of MDD aetiology. Although machine learning (ML) has been implemented in previous studies to study the alteration of microRNA (miRNA) levels in MDD cases, clinical translation has not been feasible due to the lack of interpretability (i.

Using an Interpretable Amino Acid-Based Machine Learning Method to Enhance the Diagnosis of Major Depressive Disorder.

Major depressive disorder (MDD) is a leading cause of disability worldwide. At present, however, there are no established biomarkers that have been validated for diagnosing and treating MDD. This study sought to assess the diagnostic and predictive potential of the differences in serum amino acid concentration levels between MDD patients and healthy controls (HCs), integrating them into interpretable machine learning models.

Numerical Solution of Inverse Problem in Functional Near Infrared Spectroscopy using L1-Norm Method.

It has been more than three decades since researchers began investigating functional near-infrared spectroscopy (fNIRs) and its applications with near-infrared light for use in both clinical and pre-clinical settings. In order to increase the accuracy of fNIRs of complex tissue structures, it is necessary to create more advanced image reconstruction methods. Real fNIRs data have been used to develop an implementation of the L1-Norm approach for tackling the inverse problem in this work.

An fNIRS investigation of novel expressed emotion stimulations in schizophrenia.

Living in high expressed emotion (EE) environments tends to increase the relapse rate in schizophrenia (SZ). At present, the neural substrates responsible for high EE in SZ remain poorly understood. Functional near-infrared spectroscopy (fNIRS) may be of great use to quantitatively assess cortical hemodynamics and elucidate the pathophysiology of psychiatric disorders.

Clinical outcomes and thermodynamics aspect of direct brain cooling in severe head injury.

Background: Brain cooling therapy is one of the subjects of interest, and currently, data on direct brain cooling are lacking. Hence, the objective is to investigate the clinical outcomes and discuss the thermodynamics aspect of direct brain cooling on severely injured brain patients.

Methods: This pilot study recruited the severely injured brain patients who were then randomized to either a direct brain cooling therapy group using a constant cooling temperature system or a control group.

EDR-Net: Lightweight Deep Neural Network Architecture for Detecting Referable Diabetic Retinopathy.

Present architecture of convolution neural network for diabetic retinopathy (DR-Net) is based on normal convolution (NC). It incurs high computational cost as NC uses a multiplicative weight that measures a combined correlation in both cross-channel and spatial dimension of layer's inputs. This might cause the overall DR-Net architecture to be over-parameterised and computationally inefficient.

Wavelet-Based Fractal Analysis of rs-fMRI for Classification of Alzheimer's Disease.

The resting-state functional magnetic resonance imaging (rs-fMRI) modality has gained widespread acceptance as a promising method for analyzing a variety of neurological and psychiatric diseases. It is established that resting-state neuroimaging data exhibit fractal behavior, manifested in the form of slow-decaying auto-correlation and power-law scaling of the power spectrum across low-frequency components. With this property, the rs-fMRI signal can be broken down into fractal and nonfractal components.

Resilience-Building for Mental Health among Early Childhood Educators: A Systematic Review and Pilot-Study towards an EEG-VR Resilience Building Intervention.

Resilience is a key factor that reflects a teacher's ability to utilize their emotional resources and working skills to provide high-quality teaching to children. Resilience-building interventions aim to promote positive psychological functioning and well-being. However, there is lack of evidence on whether these interventions improve the well-being or mental health of teachers in early childhood education (ECE) settings.

Detection of Collaterals from Cone-Beam CT Images in Stroke.

Collateral vessels play an important role in the restoration of blood flow to the ischemic tissues of stroke patients, and the quality of collateral flow has major impact on reducing treatment delay and increasing the success rate of reperfusion. Due to high spatial resolution and rapid scan time, advance imaging using the cone-beam computed tomography (CBCT) is gaining more attention over the conventional angiography in acute stroke diagnosis. Detecting collateral vessels from CBCT images is a challenging task due to the presence of noises and artifacts, small-size and non-uniform structure of vessels.

Integration of the Cortical Haemodynamic Response Measured by Functional Near-Infrared Spectroscopy and Amino Acid Analysis to Aid in the Diagnosis of Major Depressive Disorder.

Background: Major depressive disorder (MDD) is a debilitating condition with a high disease burden and medical comorbidities. There are currently few to no validated biomarkers to guide the diagnosis and treatment of MDD. In the present study, we evaluated the differences between MDD patients and healthy controls (HCs) in terms of cortical haemodynamic responses during a verbal fluency test (VFT) using functional near-infrared spectroscopy (fNIRS) and serum amino acid profiles, and ascertained if these parameters were correlated with clinical characteristics.

Water and oxygen co-induced microstructure relaxation and evolution in CHNHPbI.

Owing to perovskite possessing the outstanding optoelectronic properties, perovskite-based solar cells show prominent performance. The stability of perovskite-based solar cells hampers the progress of commercialization, so it is important to understand the microstructure mechanism of perovskite degradation under the humidity and oxygen environmental conditions. In this study, a meaningful Debye-type dielectric relaxation was observed under water vapor and oxygen co-treatment conditions.

Bimodal Data Fusion of Simultaneous Measurements of EEG and fNIRS during Lower Limb Movements.

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have temporal and spatial characteristics that may complement each other and, therefore, pose an intriguing approach for brain-computer interaction (BCI). In this work, the relationship between the hemodynamic response and brain oscillation activity was investigated using the concurrent recording of fNIRS and EEG during ankle joint movements. Twenty subjects participated in this experiment.

Automatic colonic polyp detection using integration of modified deep residual convolutional neural network and ensemble learning approaches.

Background And Objective: The increased incidence of colorectal cancer (CRC) and its mortality rate have attracted interest in the use of artificial intelligence (AI) based computer-aided diagnosis (CAD) tools to detect polyps at an early stage. Although these CAD tools have thus far achieved a good accuracy level to detect polyps, they still have room to improve further (e.g.

A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility.

There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility.

fNIRS-based functional connectivity estimation using semi-metric analysis to study decision making by nursing students and registered nurses.

This study aims to investigate the generalizability of the semi-metric analysis of the functional connectivity (FC) for functional near-infrared spectroscopy (fNIRS) by applying it to detect the dichotomy in differential FC under affective and neutral emotional states in nursing students and registered nurses during decision making. The proposed method employs wavelet transform coherence to construct FC networks and explores semi-metric analysis to extract network redundancy features, which has not been considered in conventional fNIRS-based FC analyses. The trials of the proposed method were performed on 19 nursing students and 19 registered nurses via a decision-making task under different emotional states induced by affective and neutral emotional stimuli.