[Paper Review] State equation from the spectral structure of human brain activity

Trang-Anh E. Nghiem, Jean‐Marc Lina+5 more|arXiv (Cornell University)|Jun 19, 2018

Neural dynamics and brain function1 references3 citations

TL;DR

This study proposes a thermodynamics-inspired state equation linking spectral energy and entropy in human brain activity, derived from magnetoencephalography (MEG) data. It reveals a robust, linear energy-entropy relationship across resting and active states, with higher energy and lower entropy in rest, suggesting a macroscopic conservation rule for neural information processing governed by noise and coupling strength.

ABSTRACT

Neural electromagnetic (EM) signals recorded non-invasively from individual human subjects vary in complexity and magnitude. Nonetheless, variation in neural activity has been difficult to quantify and interpret, due to complex, broad-band features in the frequency domain. Studying signals recorded with magnetoencephalography (MEG) from healthy young adult subjects while in resting and active states, a systematic framework inspired by thermodynamics is applied to neural EM signals. Despite considerable inter-subject variation in terms of spectral entropy and energy across time epochs, data support the existence of a robust and linear relationship defining an effective state equation, with higher energy and lower entropy in the resting state compared to active, consistently across subjects. Mechanisms underlying the emergence of relationships between empirically measured effective state functions are further investigated using a model network of coupled oscillators, suggesting an interplay between noise and coupling strength can account for coherent variation of empirically observed quantities. Taken together, the results show macroscopic neural observables follow a robust, non-trivial conservation rule for energy modulation and information generation.

Motivation & Objective

• To develop a macroscopic, thermodynamics-inspired framework for quantifying human brain states using spectral features of neural electromagnetic signals.
• To identify robust, cross-subject relationships between energy and entropy in brain activity across resting and active states.
• To investigate the mechanisms underlying the observed energy-entropy relationship using a model of coupled oscillators.
• To bridge macroscopic neural observables with microscopic neural dynamics through statistical physics-inspired modeling.

Proposed method

• Spectral analysis of MEG signals from healthy young adults in resting and active states to compute power spectra.
• Calculation of spectral energy and entropy using defined equations (Eq. 1 and Eq. 2) across time epochs and subjects.
• Application of non-parametric statistical tests (Mann-Whitney U, paired t-tests) to assess differences in energy and entropy between brain states.
• Use of phase lag index (PLI) to quantify functional connectivity and coupling strength across frequency bands and states.
• Simulation of a Kuramoto model of coupled oscillators to explore the role of noise and coupling in generating the observed energy-entropy relationship.
• Fitting of linear models to energy-entropy scatter plots to quantify slope and intercept shifts between brain states.

Experimental results

Research questions

• RQ1 Is there a consistent, cross-subject relationship between spectral energy and entropy in human brain activity across different functional states?
• RQ2 How do noise fluctuations and coupling strength influence the observed energy-entropy relationship in neural signals?
• RQ3 Can a model of coupled oscillators reproduce the empirically observed energy-entropy dynamics in human brain states?
• RQ4 What is the macroscopic thermodynamic interpretation of neural energy and entropy in resting versus active states?
• RQ5 How do changes in functional connectivity (measured via PLI) relate to shifts in energy and entropy during brain state transitions?

Key findings

• A robust, linear energy-entropy relationship was observed across all subjects, with higher energy and lower entropy in the resting state compared to the active state.
• The energy-entropy relation exhibited a significant shift in slope and intercept between resting and active states, indicating a non-trivial, non-tautological relationship.
• The phase lag index (PLI) was significantly higher in the resting state, suggesting stronger functional coupling during rest.
• The Kuramoto model simulations demonstrated that noise and coupling strength can reproduce the observed energy-entropy dynamics, supporting their role in shaping macroscopic neural states.
• The results suggest that energy is used to generate information, with the 'gas-like' active state favoring information generation and the 'liquid-like' resting state favoring low-entropy, stable states.
• The findings support a conservation-like rule for energy modulation and information generation in macroscopic neural observables, independent of individual subject variability.