Semi-supervised active learning using convolutional auto- encoder and contrastive learning.

Active learning is a field of machine learning that seeks to find the most efficient labels to annotate with a given budget, particularly in cases where obtaining labeled data is expensive or infeasible. This is becoming increasingly important with the growing success of learning-based methods, which often require large amounts of labeled data. Computer vision is one area where active learning has shown promise in tasks such as image classification, semantic segmentation, and object detection.

Using Brainwave Patterns Recorded from Plant Pathology Experts to Increase the Reliability of AI-Based Plant Disease Recognition System.

One of the most challenging problems associated with the development of accurate and reliable application of computer vision and artificial intelligence in agriculture is that, not only are massive amounts of training data usually required, but also, in most cases, the images have to be properly labeled before models can be trained. Such a labeling process tends to be time consuming, tiresome, and expensive, often making the creation of large labeled datasets impractical. This problem is largely associated with the many steps involved in the labeling process, requiring the human expert rater to perform different cognitive and motor tasks in order to correctly label each image, thus diverting brain resources that should be focused on pattern recognition itself.

Identification of an early-stage Parkinson's disease neuromarker using event-related potentials, brain network analytics and machine-learning.

Objective: The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson's disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms.

Background: Currently, diagnosis of PD depends mainly on motor signs and symptoms. However, there is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies.

Deep Brain Stimulation Can Differentiate Subregions of the Human Subthalamic Nucleus Area by EEG Biomarkers.

Precise lead localization is crucial for an optimal clinical outcome of subthalamic nucleus (STN) deep brain stimulation (DBS) treatment in patients with Parkinson's disease (PD). Currently, anatomical measures, as well as invasive intraoperative electrophysiological recordings, are used to locate DBS electrodes. The objective of this study was to find an alternative electrophysiology tool for STN DBS lead localization.

Brain Network Analysis of EEG Recordings Can Be Used to Assess Cognitive Function in Teenagers With 15q13.3 Microdeletion Syndrome.

15q13.3 microdeletion syndrome causes a spectrum of cognitive disorders, including intellectual disability and autism. We assessed the ability of the EEG analysis algorithm Brain Network Analysis (BNA) to measure cognitive function in 15q13.

Improved Back-Projection Cortical Potential Imaging by Multi-resolution Optimization Technique.

Electroencephalogram (EEG) has evolved to be a well-established tool for imaging brain activity. This progress is mainly due to the development of high-resolution (HR) EEG methods. One class of HR-EEG is the cortical potential imaging (CPI), which aims to estimate the potential distribution on the cortical surface, which is much more informative than EEG.

Biological and Behavioral Patterns of Post-Stroke Depression in Rats.

Background: Post-stroke depression (PSD) is the most frequent psychiatric complication following ischemic stroke. It affects up to 60% of all patients and is associated with increased morbidity and mortality following ischemic stroke. The pathophysiology of PSD remains elusive and appears to be multifactorial, rather than "purely" biological or psychosocial in origin.

Noninvasive blood potassium measurement using signal-processed, single-lead ecg acquired from a handheld smartphone.

Objective: We have previously used a 12-lead, signal-processed ECG to calculate blood potassium levels. We now assess the feasibility of doing so with a smartphone-enabled single lead, to permit remote monitoring.

Patients And Methods: Twenty-one hemodialysis patients held a smartphone equipped with inexpensive FDA-approved electrodes for three 2min intervals during hemodialysis.

Back-Projection Cortical Potential Imaging: Theory and Results.

Electroencephalography (EEG) is the single brain monitoring technique that is non-invasive, portable, passive, exhibits high-temporal resolution, and gives a directmeasurement of the scalp electrical potential. Amajor disadvantage of the EEG is its low-spatial resolution, which is the result of the low-conductive skull that "smears" the currents coming from within the brain. Recording brain activity with both high temporal and spatial resolution is crucial for the localization of confined brain activations and the study of brainmechanismfunctionality, whichis then followed by diagnosis of brain-related diseases.

Brain Network Activation Analysis Utilizing Spatiotemporal Features for Event Related Potentials Classification.

The purpose of this study was to introduce an improved tool for automated classification of event-related potentials (ERPs) using spatiotemporally parcellated events incorporated into a functional brain network activation (BNA) analysis. The auditory oddball ERP paradigm was selected to demonstrate and evaluate the improved tool. The ERPs of each subject were decomposed into major dynamic spatiotemporal events.

Multimodal Neural Network for Rapid Serial Visual Presentation Brain Computer Interface.

Brain computer interfaces allow users to preform various tasks using only the electrical activity of the brain. BCI applications often present the user a set of stimuli and record the corresponding electrical response. The BCI algorithm will then have to decode the acquired brain response and perform the desired task.

Distinctive cardiac autonomic dysfunction following stress exposure in both sexes in an animal model of PTSD.

It is unclear whether the poor autonomic flexibility or dysregulation observed in patients with posttraumatic stress disorder (PTSD) represents a pre-trauma vulnerability factor or results from exposure to trauma. We used an animal model of PTSD to assess the association between the behavioral response to predator scent stress (PSS) and the cardiac autonomic modulation in male and female rats. The rats were surgically implanted with radiotelemetry devices to measure their electrocardiograms and locomotor activity (LMA).

Novel Bloodless Potassium Determination Using a Signal-Processed Single-Lead ECG.

Background: Hyper- and hypokalemia are clinically silent, common in patients with renal or cardiac disease, and are life threatening. A noninvasive, unobtrusive, blood-free method for tracking potassium would be an important clinical advance.

Methods And Results: Two groups of hemodialysis patients (development group, n=26; validation group, n=19) underwent high-resolution digital ECG recordings and had 2 to 3 blood tests during dialysis.

Convolutional Neural Network for Multi-Category Rapid Serial Visual Presentation BCI.

Brain computer interfaces rely on machine learning (ML) algorithms to decode the brain's electrical activity into decisions. For example, in rapid serial visual presentation (RSVP) tasks, the subject is presented with a continuous stream of images containing rare target images among standard images, while the algorithm has to detect brain activity associated with target images. Here, we continue our previous work, presenting a deep neural network model for the use of single trial EEG classification in RSVP tasks.

Preliminary evidence of reduced brain network activation in patients with post-traumatic migraine following concussion.

Post-traumatic migraine (PTM) (i.e., headache, nausea, light and/or noise sensitivity) is an emerging risk factor for prolonged recovery following concussion.

"Clinical brain profiling": a neuroscientific diagnostic approach for mental disorders.

Clinical brain profiling is an attempt to map a descriptive nosology in psychiatry to underlying constructs in neurobiology and brain dynamics. This paper briefly reviews the motivation behind clinical brain profiling (CBP) and presents some provisional validation using clinical assessments and meta-analyses of neuroscientific publications. The paper has four sections.

Brain Network Activation (BNA) reveals scopolamine-induced impairment of visual working memory.

The overarching goal of this event-related potential (ERP) study was to examine the effects of scopolamine on the dynamics of brain network activation using a novel ERP network analysis method known as Brain Network Activation (BNA). BNA was used for extracting group-common stimulus-activated network patterns elicited to matching probe stimuli in the context of a delayed matching-to-sample task following placebo and scopolamine treatments administered to healthy participants. The BNA extracted networks revealed the existence of two pathophysiological mechanisms following scopolamine, disconnection, and compensation.

Spatiotemporal representations of rapid visual target detection: a single-trial EEG classification algorithm.

Brain computer interface applications, developed for both healthy and clinical populations, critically depend on decoding brain activity in single trials. The goal of the present study was to detect distinctive spatiotemporal brain patterns within a set of event related responses. We introduce a novel classification algorithm, the spatially weighted FLD-PCA (SWFP), which is based on a two-step linear classification of event-related responses, using fisher linear discriminant (FLD) classifier and principal component analysis (PCA) for dimensionality reduction.

Passive fetal monitoring by adaptive wavelet denoising method.

Fetal Heart Rate (FHR) monitoring is one of the most important fetal well being tests. Existing FHR monitoring methods are based on Doppler ultrasound technique, which has several disadvantages. Passive fetal monitoring by phonocardiography is an appropriate alternative; however, its implementation is a challenging task due to low energy of fetal heart sounds and multiple interference signals presence.

Early post-stressor intervention with propranolol is ineffective in preventing posttraumatic stress responses in an animal model for PTSD.

The therapeutic value of β-adrenoceptor blockage, using propranolol, in the aftermath of traumatic experience is uncertain. A prospective, controlled animal model of posttraumatic stress disorder (PTSD) was employed to assess the effects of propranolol on long-term behavioral responses to stress. Animals exposed to predator scent stress received a single bolus of propranolol (10 or 15mg/kg) or vehicle 1h post-exposure.

Post-traumatic stress behavioural responses in inbred mouse strains: can genetic predisposition explain phenotypic vulnerability?

Clinical studies of twin pairs and families of post-traumatic stress disorder (PTSD) patients raise questions as to possible genetic predisposition to PTSD. Studies using isogenic animal populations exposed to a stress paradigm could elucidate the relative contributions of genotype and environment to endophenotypic expression. The prevalence of individuals displaying severely compromised behavioural responses to predator scent stress (PSS) was assessed in six inbred strains of mice in an animal model of PTSD that classifies individuals into groups according to the degree of their behavioural response.

Unsupervised fuzzy clustering analysis supports behavioral cutoff criteria in an animal model of posttraumatic stress disorder.

Background: Unsupervised fuzzy clustering (UFC) analysis is a mathematical technique that groups together objects in the multidimensional feature space according to a specified similarity measurement, thereby yielding clusters of similar data points that can be represented by a set of prototypes or centroids.

Methods: Since clinical studies of mental disorders distinguish between affected and unaffected individuals, we designed an inclusion/exclusion criteria (cutoff behavioral criteria [CBC]) approach for animal behavioral studies. The effect of classifying the study population into clearly affected versus clearly unaffected individuals according to behaviors on two behavioral paradigms was statistically significant.

Assessment of automated scoring of polysomnographic recordings in a population with suspected sleep-disordered breathing.

Study Objectives: To assess the accuracy of an automated system (Morpheus I Sleep Scoring System) for analyzing and quantifying polysomnographic data from a population with sleep-disordered breathing.

Setting: Sleep laboratory affiliated with a tertiary care academic medical center.

Measurements And Results: 31 diagnostic polysomnograms were blindly analyzed prospectively with the investigational automated system and manually by 2 registered polysomnography technologists (M1 & M2) from the same laboratory.

Somatosensory evoked potentials during a rubber-hand illusion in schizophrenia.

The rubber-hand illusion (RHI), an illusion in which tactile sensations are referred to a synthetic alien limb, is enhanced in schizophrenia patients. Somatosensory evoked responses of the illusion were compared between schizophrenia patients and normal control subjects. Schizophrenia patients had significant alterations in long latency evoked responses during the illusion.

The effect of contrast and luminance on mfERG responses in a monkey model of glaucoma.

Purpose: To evaluate the effect of contrast and luminance attenuation on the multifocal electroretinogram (mfERG) responses of normal and glaucomatous eyes of cynomolgus monkeys.

Methods: Nine adult male cynomolgus monkeys with unilateral experimentally induced glaucoma were used. Hypertension-induced damage was confirmed by tomography of the optic disc.