Year: 2026
Type: preprint
Study design: Observational Study
Abstract: 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.
Source: medRxiv
Institutions: Johns Hopkins University, Johns Hopkins Medicine
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: EFRI BRAID: Using Proto-Object Based Saliency Inspired By Cortical Local Circuits to Limit the Hypothesis Space for Deep Learning Models, EEG Biomarkers Derived from Dynamical Network Models Enable Rapid Paths to Accurate Diagnosis and Effective Treatment of Epilepsy, Establishing novel properties of dynamic systems models to identify epileptogenic networks in patients with drug resistant epilepsy
OpenAlex ID: W7220690564