Math-based tools to read how brain signals change over time and space
Official title: Using integral equations to capture spatiotemporal relations in the brain
Funded at Idaho State University · NIH-11170560 · Data updated 2026-11-12
Researchers will try math-driven machine learning to read brain recordings and produce continuous scores of cognitive function for people with memory, attention, or other thinking problems.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Idaho State University NIH-funded |
| Lab location | 1 site (Pocatello, United States) |
| Project start | August 15, 2024 |
| Project end | July 31, 2028 |
| Funding year | 2025 |
| Award (this year) | $174,534 |
| NIH project number | 5R16GM154734-02 |
| Project ID | NIH-11170560 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates would be people with memory, attention, or other cognitive complaints who can provide or share brain recording data as part of diagnostic testing or research.
✕Less likely to help
People without cognitive symptoms or those who cannot undergo or share brain recordings are unlikely to benefit directly from this project.
What this research studies
This project uses advanced math (integral and integro-differential equations) combined with machine learning to capture long-range time and space relationships in brain recordings. The team will train models to learn dynamic "fingerprints" and attention-like model states that reflect how the brain is working over time. Those learned states will be translated into continuous diagnostic scores that aim to reflect severity of cognitive problems rather than single snapshot measures. The work builds from the investigators' preliminary results and aims to handle complex brain dynamics missed by static methods.
Why it matters
Potential benefit: If successful, this could give more sensitive, continuous measures of cognitive impairment to help diagnose and track thinking problems over time.
How similar studies have gone: This integral-equation approach combined with machine learning is relatively novel, with only preliminary promising work from the investigators and limited prior clinical testing.
Where this research is happening
-
Idaho State University
Pocatello, United States Active
Researchers
- Principal investigator: Zappala, Emanuele — Idaho State University
- Study coordinator: Zappala, Emanuele
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.