Assessing chance in neuro-vascular interactions.

ABSTRACT: Interpreting correlations between neuronal activity and hemodynamic signals is complicated by their inherently strong autocorrelation. Standard parametric tests underestimate false positives, creating the appearance of widespread neurovascular coupling during rest. Here we present a surrogate-based statistical framework designed to calibrate inference in autocorrelated physiological signals. Using simultaneous recordings of cortical oxygen tension (PO₂), single-unit firing, and local field potentials (LFP) in awake rabbits, we applied amplitude-adjusted Fourier surrogates to generate null distributions that preserve temporal structure but remove cross-dependence. This workflow embeds lag optimization, controls for multiple comparisons across windows and units, and scales to population-leve

SUBMITTER: Aksenov DP 

PROVIDER: S-EPMC12863138 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature