Stochastic brain dynamics exhibits differential regional distribution and maturation-related changes

Andrea Scarciglia; Vincenzo Catrambone; Martina Bianco; Claudio Bonanno; Nicola Toschi; Gaetano Valenza

NeuroImage (Apr 2024)

Stochastic brain dynamics exhibits differential regional distribution and maturation-related changes

Andrea Scarciglia,
Vincenzo Catrambone,
Martina Bianco,
Claudio Bonanno,
Nicola Toschi,
Gaetano Valenza

Affiliations

Andrea Scarciglia: Department of Information Engineering, School of Engineering, University of Pisa, Italy; Bioengineering and Robotics Research Center E.Piaggio, School of Engineering, University of Pisa, Italy; Corresponding author at: Department of Information Engineering, School of Engineering, University of Pisa, Italy.
Vincenzo Catrambone: Department of Information Engineering, School of Engineering, University of Pisa, Italy; Bioengineering and Robotics Research Center E.Piaggio, School of Engineering, University of Pisa, Italy
Martina Bianco: Department of Information Engineering, School of Engineering, University of Pisa, Italy; Department of Mathematics, University of Pisa, Italy
Claudio Bonanno: Department of Mathematics, University of Pisa, Italy
Nicola Toschi: Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy; A.A. Martinos Center for Biomedical Imaging, MGH and Harvard Medical School, Boston, MA, USA
Gaetano Valenza: Department of Information Engineering, School of Engineering, University of Pisa, Italy; Bioengineering and Robotics Research Center E.Piaggio, School of Engineering, University of Pisa, Italy

Journal volume & issue: Vol. 290
p. 120562

Abstract

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Functional magnetic resonance imaging (fMRI) is a powerful non-invasive method for studying brain function by analyzing blood oxygenation level-dependent (BOLD) signals. These signals arise from intricate interplays of deterministic and stochastic biological elements. Quantifying the stochastic part is challenging due to its reliance on assumptions about the deterministic segment. We present a methodological framework to estimate intrinsic stochastic brain dynamics in fMRI data without assuming deterministic dynamics. Our approach utilizes Approximate Entropy and its behavior in noisy series to identify and characterize dynamical noise in unobservable fMRI dynamics. Applied to extensive fMRI datasets (645 Cam-CAN, 1086 Human Connectome Project subjects), we explore lifelong maturation of intrinsic brain noise. Findings indicate 10% to 60% of fMRI signal power is due to intrinsic stochastic brain elements, varying by age. These components demonstrate a physiological role of neural noise which shows a distinct distributions across brain regions and increase linearly during maturation.

Published in NeuroImage

ISSN: 1053-8119 (Print); 1095-9572 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry
Website: https://www.journals.elsevier.com/neuroimage

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