Abstract Details

Title Prediction of Headache Improvement Using Multimodal Machine Learning in Patients with Acute Post-traumatic Headache

Topic Headache

Presentation(s) P5 - Poster Session 5 (5:30 PM-6:30 PM)

Poster/Presentation
Number 12-002

Objective 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.

Background

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."

Design/Methods 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.

Results 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.

Conclusions

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, FAAN (Mayo Clinic)	Dr. Schwedt has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Eli Lilly. Dr. Schwedt has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Lundbeck. 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 Linpharma. Dr. Schwedt has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Theranica. Dr. Schwedt has received personal compensation in the range of $0-$499 for serving as a Consultant for Amgen. Dr. Schwedt has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Scilex. Dr. Schwedt has stock in Aural Analytics. Dr. Schwedt has stock in Nocira. Dr. Schwedt has 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 SPARK Neuro. 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. 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, FAAN	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.

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