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.

中文翻译：

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随机大脑动力学表现出不同的区域分布和成熟相关的变化

功能磁共振成像 (fMRI) 是一种强大的非侵入性方法，通过分析血氧水平依赖性 (BOLD) 信号来研究大脑功能。这些信号源自确定性和随机生物元素的复杂相互作用。量化随机部分具有挑战性，因为它依赖于关于确定性部分的假设。我们提出了一个方法框架来估计功能磁共振成像数据中的内在随机大脑动力学，而不假设确定性动力学。我们的方法利用近似熵及其在噪声序列中的行为来识别和表征不可观察的功能磁共振成像动力学中的动态噪声。应用于广泛的功能磁共振成像数据集（645 个 Cam-CAN、1086 个人类连接组项目受试者），我们探索了内在大脑噪声的终生成熟。研究结果表明，10% 到 60% 的 fMRI 信号功率是由于大脑固有的随机元素造成的，且随年龄的不同而变化。这些成分展示了神经噪声的生理作用，神经噪声在大脑区域中表现出明显的分布，并在成熟过程中线性增加。