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.

Scarciglia, A., Catrambone, V., Bianco, M., Bonanno, C., Toschi, N., Valenza, G. (2024). Stochastic brain dynamics exhibits differential regional distribution and maturation-related changes. NEUROIMAGE, 290 [10.1016/j.neuroimage.2024.120562].

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

Toschi, N;
2024-01-01

Abstract

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.
2024
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore PHYS-06/A - Fisica per le scienze della vita, l'ambiente e i beni culturali
English
Aging
Noise
fMRI
Scarciglia, A., Catrambone, V., Bianco, M., Bonanno, C., Toschi, N., Valenza, G. (2024). Stochastic brain dynamics exhibits differential regional distribution and maturation-related changes. NEUROIMAGE, 290 [10.1016/j.neuroimage.2024.120562].
Scarciglia, A; Catrambone, V; Bianco, M; Bonanno, C; Toschi, N; Valenza, G
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/403486
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