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.
[1]
Cristiano Capone,et al.
Spontaneous activity emerging from an inferred network model captures complex spatio-temporal dynamics of spike data
,
2018,
bioRxiv.
[2]
R. Knabb,et al.
ANNALS OF THE NEW YORK ACADEMY OF SCIENCES
,
2014,
Annals of the New York Academy of Sciences.
[3]
Jürgen Kurths,et al.
Synchronization - A Universal Concept in Nonlinear Sciences
,
2001,
Cambridge Nonlinear Science Series.
[4]
J. Herskowitz,et al.
Proceedings of the National Academy of Sciences, USA
,
1996,
Current Biology.
[5]
Np Sheehy,et al.
Electroencephalography: Basic Principles, Clinical Applications and Related Fields
,
1984
.
[6]
J. Fermaglich.
Electric Fields of the Brain: The Neurophysics of EEG
,
1982
.