Researchers have unveiled an innovative technique that significantly enhances the accuracy of brain-state classification using functional near-infrared spectroscopy (fNIRS). This non-invasive brain imaging method detects variations in blood flow and oxygen saturation, providing insights into neural activity by measuring the oxygen needs of active brain cells. Despite its advantages—such as portability, cost-effectiveness, and reliability even with patient movement—the analytical methods for fNIRS data have lagged behind other brain imaging technologies.
Harnessing the Unique Properties of fNIRS Signals
An international team of researchers has developed a method specifically designed to leverage the dual characteristics of fNIRS signals, resulting in more precise brain-state classification. Unlike other imaging techniques, fNIRS measures both oxygenated and deoxygenated blood, which exhibit contrasting patterns. According to Tim Näher, the first author of the study and a researcher at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany, “These two signals naturally show opposite patterns, but that doesn’t mean they are redundant. Instead, they give us complementary insights into brain activity.”
By employing advanced mathematical tools from Riemannian geometry, the team successfully classified brain states during various mental tasks. Participants were asked to perform activities such as imagining playing tennis or mentally rotating an object. The new computational framework resulted in an impressive accuracy rate, surpassing traditional classification methods.
Implications for Diagnosing Disorders of Consciousness
The implications of this research extend into the realm of diagnosing disorders of consciousness, which pose significant challenges due to patients’ limited ability to communicate or move. Accurate diagnosis is critical for effective treatment and reliable prognosis. To further explore this area, Näher collaborated with Lisa Bastian from the University of Tübingen on a second study conducted at Maastricht University. This study developed a novel fNIRS paradigm aimed at assessing consciousness levels in non-responsive patients.
In this assessment, healthy participants engaged in mental tasks simulating awareness or remained inactive. The combination of the innovative fNIRS paradigm and the Riemannian geometry-based analysis proved effective in distinguishing between responsive and unresponsive brain states. The method achieved perfect accuracy in identifying responsive states and recognized unresponsiveness in nine out of ten cases.
“So far, we provided a proof of concept that the new fNIRS framework can serve as a fast, objective, and accessible tool to support more reliable diagnoses and improve treatment decisions for disorders of consciousness,” Näher stated. The next step for the research team will be to test their method on actual patients.
The findings from both studies are set to be published in the journal Neurophotonics in March 2025. With this breakthrough, researchers hope to pave the way for more effective interventions and a deeper understanding of consciousness in patients suffering from severe incapacitation.
