Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors.

Artificial neural networks (ANNs) are showing increasing promise as decision support tools in medicine and particularly in neuroscience and neuroimaging. Recently, there has been increasing work on using neural networks to classify individuals with concussion using electroencephalography (EEG) data. However, to date the need for research grade equipment has limited the applications to clinical environments.

Machine Learning for Subtyping Concussion Using a Clustering Approach.

Concussion subtypes are typically organized into commonly affected symptom areas or a combination of affected systems, an approach that may be flawed by bias in conceptualization or the inherent limitations of interdisciplinary expertise. The purpose of this study was to determine whether a bottom-up, unsupervised, machine learning approach, could more accurately support concussion subtyping. Initial patient intake data as well as objective outcome measures including, the Patient-Reported Outcomes Measurement Information System (PROMIS), Dizziness Handicap Inventory (DHI), Pain Catastrophizing Scale (PCS), and Immediate Post-Concussion Assessment and Cognitive Testing Tool (ImPACT) were retrospectively extracted from the Advance Concussion Clinic's database.

The significance of blood and salivary oxidative stress markers and chemerin in gestational diabetes mellitus.

Objective: Gestational diabetes mellitus (GDM) is a medical complication of pregnancy. The aim of this study was to evaluate the correlations between the salivary and blood levels of oxidative stress markers and an adipokine chemerin, which play a role in the pathogenesis of GDM.

Materials And Methods: Study groups (Control (n = 29), GDM (n = 22)) had been assessed clinically healthy oral hygiene, according to the age range between 25 and 40 years, BMI<30 kg/m2, who were non-smokers and who were not having systemic diseases.

Recurrent neural network-based acute concussion classifier using raw resting state EEG data.

Concussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and that individuals suffering from a concussion are more prone to experience additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications.

Disrupted Information Flow in Resting-State in Adolescents With Sports Related Concussion.

Children and youths are at a greater risk of concussions than adults, and once injured, take longer to recover. A key feature of concussion is an increase in functional connectivity, yet it remains unclear how changes in functional connectivity relate to the patterns of information flow within resting state networks following concussion and how these relate to brain function. We applied a data-driven measure of directed effective brain connectivity to compare the patterns of information flow in healthy adolescents and adolescents with subacute concussion during the resting state condition.