AI Insight
Researchers developed the Neural Health Index (NHI), a single numerical measure that quantifies brain health by analyzing energy distribution patterns in resting-state brain activity. The metric is based on five properties of energy organization including lognormal distributions, spatial balance, regional independence, low variance, and temporal stability, and can be calculated from multiple brain imaging modalities including EEG, MEG, and fMRI. In a study of 808 participants with various neurological, psychiatric, neurodevelopmental, and neurodegenerative conditions, the NHI distinguished healthy individuals from clinical populations with over 95% accuracy, correlated with disease severity, responded to treatment changes, and generalized to previously unseen diagnoses.
Why it matters
This disease-agnostic biomarker could provide a standardized tool for assessing brain health across different conditions and imaging technologies, potentially enabling earlier detection of neurological disorders, objective monitoring of treatment effectiveness, and improved patient stratification in clinical trials without requiring diagnosis-specific tests.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Quantifying neural health with a single measure remains a major unmet challenge. Here, we report a physiological marker based on the spatial distribution of signal-derived energy correlates in the resting brain. We hypothesize that energy organization in the healthy brain approaches an entropy-maximizing, homeostatically regulated state characterized by five properties: lognormal energy distributions, balanced spatial allocation, statistical independence between regions, low energy variance, and temporal stability. Together, these properties define an energetic profile that the Neural Health Index (NHI) summarizes as a single score, computable from (scalp/intracranial) electroencephalography, magnetoencephalography, or functional magnetic resonance imaging. Across 808 participants spanning neurological, psychiatric, neurodevelopmental, and neurodegenerative disorders, NHI distinguished healthy from clinical populations with sensitivity and specificity exceeding 95% in internal and external validation, scaled with disease severity, tracked pharmacological treatment exposure, and generalized to unseen diagnoses. These findings support NHI as a disease-agnostic, modality-invariant candidate biomarker for disease stratification and treatment monitoring.