AIMC Topic: Brain Concussion

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Neural oscillation mechanisms of repetitive subconcussive impacts: a network study of microstate-specific cross-frequency coupling.

The journal of headache and pain
BACKGROUND: Repetitive subconcussive impacts are linked to headache pathophysiology, yet the role of electroencephalography (EEG) microstates and cross-frequency coupling in repetitive subconcussive (SC) neural alterations remains unclear. This study...

Ultra-early detection of S100B biomarkers using a nanophotonic biosensor with deep learning quantification: A clinical model based on EDAS patients.

Biosensors & bioelectronics
BACKGROUND: Ultra-early detection of brain injury biomarkers within the critical first hour post-injury remains a major clinical challenge in mild traumatic brain injury (mTBI) management. Conventional platforms (e.g., ELISA) suffer from limited sens...

A Machine Learning Approach to Concussion Risk Estimation Among Players Exhibiting Visible Signs in Professional Hockey.

Sports medicine (Auckland, N.Z.)
BACKGROUND: The identification of concussion risk factors, such as visible signs and mechanisms of injury, improves concussion identification. Exploring individual risk factors, such as concussion history, may help to improve existing concussion risk...

Development of predictive model for the neurological deterioration among mild traumatic brain injury patients using machine learning algorithms.

Neurosurgical review
BACKGROUND: Mild traumatic brain injury (mTBI) comprises a majority of traumatic brain injury (TBI) cases. While some mTBI would suffer neurological deterioration (ND) and therefore have poorer prognosis. This study was designed to develop the predic...

Assessment of Sports Concussion in Female Athletes: A Role for Neuroinformatics?

Neuroinformatics
Over the past decade, the intricacies of sports-related concussions among female athletes have become readily apparent. Traditional clinical methods for diagnosing concussions suffer limitations when applied to female athletes, often failing to captu...

Brain age prediction using interpretable multi-feature-based convolutional neural network in mild traumatic brain injury.

NeuroImage
BACKGROUND: Convolutional neural network (CNN) can capture the structural features changes of brain aging based on MRI, thus predict brain age in healthy individuals accurately. However, most studies use single feature to predict brain age in healthy...

Applications of Machine Learning in Prognostication of Mild Traumatic Brain Injury: A Systematic Review.

American journal of physical medicine & rehabilitation
OBJECTIVE: The aim of the study is to review the literature regarding the current state and clinical applicability of machine learning models in prognosticating the outcomes of patients with mild traumatic brain injury in the early clinical presentat...

Classification of short and long term mild traumatic brain injury using computerized eye tracking.

Scientific reports
Accurate, and objective diagnosis of brain injury remains challenging. This study evaluated useability and reliability of computerized eye-tracker assessments (CEAs) designed to assess oculomotor function, visual attention/processing, and selective a...