Houston Daily

A University of Houston researcher has found that recording brain activity while a person listens to a story may help diagnose primary progressive aphasia (PPA), a rare neurodegenerative syndrome that impairs language skills.

The findings, published August 12 in Scientific Reports, indicate that this method was up to 75% effective in classifying the three subtypes of PPA by using brain activity data and machine-learning algorithms. The underlying cause of PPA is often Alzheimer’s disease or frontotemporal lobar degeneration. Diagnosing PPA can be challenging because current methods require two to four hours of cognitive testing and sometimes brain scans, which can be emotionally taxing for patients.

“Our thought with this project was, can we do something different that takes less time, that helps with diagnosis?” said Heather Dial, lead author and assistant professor in UH’s department of communication sciences and disorders.

The non-invasive approach could lead to faster, more patient-friendly assessments for PPA and other language-affecting disorders such as Alzheimer’s dementia and stroke. Dial worked with researchers from University of Wisconsin-Madison, The University of Texas at Austin, and Rice University. They used electroencephalography (EEG) to record electrical activity in participants’ brains as they listened to a story. The EEG tracked how the brain processed different levels of language, from acoustic features to syntactic structure.

“If this method is reliable and valid, then we can feel confident in physicians using it to assess change in patient response to treatment and for diagnosis,” said Dial.

Machine-learning models analyzed the data. The most effective model reached nearly 75% accuracy in classifying PPA subtype. This suggests potential for future diagnostic tools but the method is not yet ready for clinical use.

“This suggests it’s worth pursing further and trying to find the optimal parameters,” Dial said. “What are the best modeling approaches? What are the best features? How can we use this to improve the tools that a clinician has access to for diagnosis?”

The research team plans to refine the algorithm to improve diagnostic accuracy and reliability. In 2024, Dial’s team received a $375,000 grant from the National Institutes of Health to apply the same story-listening technique to studying stroke-induced language deterioration. That project will continue through 2026.

“If this method is reliable and valid, then we can feel confident in physicians using it to assess change in patient response to treatment and for diagnosis,” she said.