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Traumatic Brain Injury l: Introduction

Traumatic Brain Injury l: Introduction

DefinitionTraumatic brain injury, or TBI, is a disturbance of normal brain function induced by an external mechanical force, such as a direct blow to the head or a penetrating injury. It can affect both brain structure and function, producing a wide range of clinical outcomes. TBI is a heterogeneous condition, meaning its effects may differ based on the type, location, and severity of the injury.Basis of ClassificationTBI is classified based on severity, injury mechanism, or pathophysiology. In...

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Updated: May 5, 2026

An Investigation of the Effects of Sports-related Concussion in Youth Using Functional Magnetic Resonance Imaging and the Head Impact Telemetry System

An Investigation of the Effects of Sports-related Concussion in Youth Using Functional Magnetic Resonance Imaging and the Head Impact Telemetry System

Published on: January 12, 2011

Identification and Connectomic Profiling of Concussion Using Bayesian Machine Learning.

Benjamin J Hacker^1,2, Phoebe E Imms¹, Ammar M Dharani¹

¹Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, Dana and David Dornsife College of Arts and Sciences, University of Southern California, Los Angeles, California, USA.

Journal of Neurotrauma

|March 14, 2024

Summary

This summary is machine-generated.

A new machine learning classifier accurately identifies concussion by analyzing brain connectivity patterns from MRI scans. This tool aids in early diagnosis, especially when other tests are inconclusive, improving patient outcomes.

Keywords:

concussion diagnosis machine learning mild traumatic brain injury

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Last Updated: May 5, 2026

An Investigation of the Effects of Sports-related Concussion in Youth Using Functional Magnetic Resonance Imaging and the Head Impact Telemetry System

An Investigation of the Effects of Sports-related Concussion in Youth Using Functional Magnetic Resonance Imaging and the Head Impact Telemetry System

Published on: January 12, 2011

A Neuroscientific Approach to the Examination of Concussions in Student-Athletes

A Neuroscientific Approach to the Examination of Concussions in Student-Athletes

Published on: December 8, 2014

Objectively Assessing Sports Concussion Utilizing Visual Evoked Potentials

Objectively Assessing Sports Concussion Utilizing Visual Evoked Potentials

Published on: April 27, 2021

Area of Science:

Neuroscience
Machine Learning
Medical Diagnostics

Background:

Early concussion diagnosis is crucial for preventing long-term issues and improving neurocognitive outcomes.
Diagnosis can be challenging when neurological, imaging, or cognitive tests yield equivocal results.
Novel diagnostic tools are needed to complement existing clinical assessments for concussion.

Purpose of the Study:

To develop and validate a Bayesian machine learning classifier for concussion detection.
To utilize cortico-cortical connectome mapping from MRI data for diagnosis.
To identify specific brain connection patterns indicative of concussion in individuals with near-normal cognition.

Main Methods:

A Bayesian machine learning classifier was developed using features from white matter connectivity matrices derived from MRI scans.
Classifier performance was evaluated on discovery and independent validation datasets comprising healthy controls and mild traumatic brain injury (mTBI) patients.
Feature saliency was assessed by training individual classifier models on single connection types.

Main Results:

The classifier achieved over 99% accuracy in both discovery and validation samples, demonstrating robust generalizability.
Thirteen specific bilateral cortico-cortical connection pairs were identified as highly predictive of concussion status.
These key connections involve prefrontal, fronto-limbic, fronto-subcortical, and occipito-temporal pathways, particularly within the ventral visual stream.

Conclusions:

The developed classifier offers a highly accurate and generalizable method for concussion diagnosis, complementing current clinical approaches.
The identified salient brain connections highlight a network vulnerable to concussion, explaining frequent cognitive disturbances.
This tool can provide valuable independent information in clinical settings for patients with equivocal presentations and near-normal cognition.