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Abstract Details

Prediction of Headache Improvement Using Multimodal Machine Learning in Patients with Acute Post-traumatic Headache
Headache
P5 - Poster Session 5 (5:30 PM-6:30 PM)
12-002
To develop a machine learning method on multimodal data for predicting headache improvement in patients with acute post-traumatic headache (aPTH) attributed to mild traumatic brain injury.

To assess headache improvement in aPTH patients, we used multimodal data including brain MRI T2* imaging and resting-state functional connectivity among 63 pain-processing areas. Speech samples over 12 weeks were collected. Patients are deemed "improved" if post-injury excess headaches in the third month decrease compared to the first month or are under 2.5; otherwise, they are labeled "not-improved."
We analyzed 43 aPTH patients (27 females/16 males) within 0-59 days post-mild TBI, comparing them to 61 healthy controls (39 females/22 males). Our multimodal approach combined T2* features and speech data with an SVM classifier. Key predictive features for headache improvement were selected using Scikit-learn's SelectKBest method. Additionally, we explored combining functional connectivity and T2* data using a Graph Neural Network (GNN) and ensembled it with an SVM trained on speech data.
At the 3-month follow-up, 26 aPTH patients saw headache improvement, while 17 did not. SVM classifier for predicting headache improvement for single-modality data revealed an AUC of 0.658 using T2* features of five brain regions (right posterior insula, bilateral somatomotor, right spinal trigeminal nucleus, and right middle frontal) and an AUC of 0.749 using speech features including ratio of demonstrative words, jitter relative average perturbation, pitch perturbation factor, and recurrence period density entropy. Combining both modalities improved performance to an AUC of 0.866. A GNN combining T2* and fMRI data with speech data resulted in a lower AUC of 0.60, likely due to limited sample size and increased input dimensionality, affecting the GNN's ability to learn effective features.

PTH improvement at three months is accurately predicted by SVM on T2* and speech data.
Authors/Disclosures
Amogh M. Joshi (Brickyard School of Engineering, Arizona State University)
PRESENTER 		Mr. Joshi has received personal compensation for serving as an employee of Arizona State University.
Md Mahfuzur Rahman Siddiquee (Arizona State University) Md Mahfuzur Rahman Siddiquee has received personal compensation for serving as an employee of Meta.
Jay Shah, MD (Arizona State University) Mr. Shah has nothing to disclose.
Todd J. Schwedt, MD, FÂ鶹´«Ã½Ó³»­ (Mayo Clinic) Dr. Schwedt has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Lundbeck. The institution of Dr. Schwedt has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Abbvie. Dr. Schwedt has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Salvia. Dr. Schwedt has or had stock in Nocira.Dr. Schwedt has or had stock in Allevalux. The institution of Dr. Schwedt has received research support from National Institutes of Health. The institution of Dr. Schwedt has received research support from United States Department of Defense. The institution of Dr. Schwedt has received research support from Patient Centered Outcomes Research Institute. The institution of Dr. Schwedt has received research support from Henry Jackson Foundation. The institution of Dr. Schwedt has received research support from Pfizer. The institution of Dr. Schwedt has received research support from National Headache Foundation. The institution of Dr. Schwedt has received research support from American Heart Association. The institution of Dr. Schwedt has received research support from AbbVie. The institution of Dr. Schwedt has received research support from Flinn Foundation. Dr. Schwedt has received intellectual property interests from a discovery or technology relating to health care. Dr. Schwedt has received intellectual property interests from a discovery or technology relating to health care. Dr. Schwedt has received intellectual property interests from a discovery or technology relating to health care. Dr. Schwedt has received intellectual property interests from a discovery or technology relating to health care. Dr. Schwedt has received publishing royalties from a publication relating to health care.
Catherine D. Chong, PhD, FÂ鶹´«Ã½Ó³»­ Dr. Chong has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for HCOP.
Baoxin Li (Arizona State University, School of Computing and Augmented Intelligence) Baoxin Li has nothing to disclose.
Teresa Wu (Arizona State University) Teresa Wu has nothing to disclose.