# Diagnostic Machine Learning Algorithm to Identify MEG Features of Mild TBI and Comorbid PTSD

> **NIH VA I01** · VA SAN DIEGO HEALTHCARE SYSTEM · 2022 · —

## Abstract

Mild traumatic brain injury (mTBI) is a leading cause of sustained physical, cognitive, emotional, and
behavioral deficits in OEF/OIF/OND Veterans and the general public. However, the underlying pathophysiology
and recovery mechanisms, especially those associated with cognitive functioning in mTBI, are not completely
understood. The neuronal mechanisms for the increased risk of PTSD after an mTBI are even less clear.
Conventional MRI and CT images are generally negative even in patients with persistent post-concussive
symptoms (PCS) and/or PTSD symptoms. Diffusion-based MRI techniques have been developed to identify
abnormalities in white-matter tracts, owing to the major role of diffuse axonal injury (DAI) in mTBI. Yet even
sophisticated diffusion-based MRI techniques are not sufficiently sensitive for reliable clinical applications.
Recent animal studies indicate that gray matter is also vulnerable to DAI, which leads to abnormal
electromagnetic signals from the injured regions. In this regard, support is mounting for the sensitivity of
resting-state magnetoencephalography (rs-MEG) source imaging markers for detecting neuronal abnormalities
in mTBI. We demonstrated that rs-MEG delta-wave (1-4 Hz) markers were very sensitive in distinguishing
mTBI patients with persistent PCS from neurologically intact individuals. We also found that rs-MEG gamma-
band (30-80 Hz) markers show marked hyperactivity in mTBI, possibly due to injury of GABA-ergic
parvalbumin-positive (PV+) interneurons. In addition, we found that task-evoked MEG (te-MEG) recordings
during working memory (WM) task detected abnormal signals throughout the brain in mTBI that were related to
poorer cognitive functioning. A main goal of this application is to develop highly sensitive diagnostic algorithms
to differentiate Veterans with mTBI from those with comorbid mTBI and PTSD, and those healthy control
Veterans. The new approaches will use artificial neural network based machine-learning techniques to
integrate rs-MEG and te-MEG imaging makers. We will study three groups of Veterans (N=75 per group): 1)
individuals with mTBI and persistent PCS (mTBI-only group); 2) individuals with comorbid mTBI and PTSD
who have persistent PCS and PTSD symptoms; 3) healthy controls (HC). Aim 1 will establish a machine-
learning based MEG diagnostic algorithm for mTBI that optimally integrates three MEG regional imaging
markers (i.e., delta-band and gamma-band rs-MEG; WM evoked MEG) to differentiate Veterans with mTBI
(mTBI-only and comorbid mTBI-PTSD) from HC Veterans with >90% accuracy. We predict that sensitive
features for mTBI classification will include abnormal increases in rs-MEG delta- and gamma-band activity in
prefrontal and posterior-parietal areas and aberrant WM evoked activity in the mTBI-only and comorbid groups
relative to the HC group. Aim 2 will develop a machine-learning MEG algorithm that integrates rs-MEG activity
and te-MEG responses evoked by a negative emotion processing pictur...

## Key facts

- **NIH application ID:** 10398791
- **Project number:** 5I01CX002035-03
- **Recipient organization:** VA SAN DIEGO HEALTHCARE SYSTEM
- **Principal Investigator:** MINGXIONG HUANG
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2022
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2020-01-01 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10398791

## Citation

> US National Institutes of Health, RePORTER application 10398791, Diagnostic Machine Learning Algorithm to Identify MEG Features of Mild TBI and Comorbid PTSD (5I01CX002035-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10398791. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*