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Related Experiment Video

Updated: Jul 12, 2026

An Investigation of the Effects of Sports-related Concussion in Youth Using Functional Magnetic Resonance Imaging and the Head Impact Telemetry System
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Linking a Deep Learning Model for Concussion Classification with Reorganization of Large-Scale Brain Networks in

Julianne McLeod1, Karun Thanjavur2, Sahar Sattari3

  • 1Department of Rehabilitation Sciences, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.

Bioengineering (Basel, Switzerland)
|September 27, 2025
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Summary

Deep learning accurately detects concussion in females using resting-state EEG data. This approach shows promise for objective diagnosis, complementing brain network analysis for concussion insights.

Keywords:
EEGcausal connectivityclassificationconcussiondeep learninginformation flow

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Machine Learning

Background:

  • Concussion (mild traumatic brain injury) is a major health issue, especially for females who often have prolonged symptoms.
  • Objective diagnostic tools are crucial for pediatric concussion, where symptom reporting can be unreliable.

Purpose of the Study:

  • To develop and evaluate a deep learning model for accurate concussion detection using resting-state EEG.
  • To investigate brain network connectivity changes associated with concussion in females.

Main Methods:

  • Collected 5-minute resting-state (RS) electroencephalography (EEG) data from concussed and non-concussed females (ages 15-24).
  • Applied a deep learning Long Short-Term Memory (LSTM) recurrent neural network to raw EEG data for classification.
  • Analyzed source-level causal connectivity using information flow rate to assess network changes.

Main Results:

  • The LSTM model achieved 84.2% accuracy and an AUC of 0.904 in classifying concussion.
  • Concussed females exhibited altered brain connectivity patterns (posterior and left-lateralized) compared to non-concussed individuals (symmetric, central-parietal midline).
  • Significantly higher connection magnitudes were observed in the concussed group (p < 0.001).

Conclusions:

  • Deep learning models can effectively detect concussion from resting-state EEG data with high accuracy.
  • Connectivity analyses reveal brain reorganization post-concussion, offering mechanistic insights.
  • Further research with larger datasets is needed to refine models and explore influencing factors.