Decoding human lifespan from neural noise and explaining age-related changes in fractal dimension and gamma oscillations using fractional harmonic oscillator

Abstract

Predicting human lifespan is a longstanding objective of biomedical research. Traditional statistical models estimate mortality risk or biological age but not lifespan. We propose a two-parameter model based on stochastic fractional harmonic oscillator for neural signals. The model computes maximum human lifespan using 1/f slope, the measure of neural power decay with frequency, which is indicative of neural noise. Using slope rate from electroencephalographic and electrocorticographic datasets, we estimate the mean lifetimes of healthy adults and epileptic patients as 76.9 and 69.7 years, resulting in 89.4% and 96.9% accuracy respectively. Additionally, the present model resolves the inconsistency in age-related changes of fractal dimension (FD) and captures naturally the non-monotonic variation of stimulus-induced gamma power. Thus, the present model provides a simple way to estimate lifespan while explaining age-related changes in slope, FD and power simultaneously, thereby, paving the way for individualized lifetime measurements and unveiling fundamental principles governing life. One line SummaryWe build a two-parameter model for neural signals that predicts mean human lifespan through a single neural observable, that may lead to future individualized lifetime predictions along with unveiling fundamental principles of life.