Institute of Bioengineering, Center for Neuroprosthetics, Center for Biomedical Imaging, EPFL, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia.
Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, Australia; Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia.
Neuroimage. 2021 Apr 15;230:117760. doi: 10.1016/j.neuroimage.2021.117760. Epub 2021 Jan 22.
It has been hypothesized that resting state networks (RSNs), extracted from resting state functional magnetic resonance imaging (rsfMRI), likely display unique temporal complexity fingerprints, quantified by their multiscale entropy patterns (McDonough and Nashiro, 2014). This is a hypothesis with a potential capacity for developing digital biomarkers of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of McDonough and Nashiro (2014) was that rsfMRI data from only 20 healthy individuals was used for the analysis. To validate this hypothesis in a larger cohort, we used rsfMRI datasets of 987 healthy young adults from the Human Connectome Project (HCP), aged 22-35, each with four 14.4-min rsfMRI recordings and parcellated into 379 brain regions. We quantified multiscale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We performed effect-size analysis on the data in 8 RSNs. Given that the morphology of multiscale entropy is affected by the choice of its tolerance parameter (r) and embedding dimension (m), we repeated the analyses at multiple values of r and m including the values used in McDonough and Nashiro (2014). Our results reinforced high temporal complexity in the default mode and frontoparietal networks. Lowest temporal complexity was observed in the subcortical areas and limbic system. We investigated the effect of temporal resolution (determined by the repetition time T) after downsampling of rsfMRI time series at two rates. At a low temporal resolution, we observed increased entropy and variance across datasets. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially when the tolerance parameter r is equal to 0.5. The results confirmed that the relationship between functional brain connectivity strengths and rsfMRI temporal complexity changes over time scales. Finally, a non-random correlation was observed between temporal complexity of RSNs and fluid intelligence suggesting that complex dynamics of the human brain is an important attribute of high-level brain function.
据推测，从静息态功能磁共振成像(rsfMRI)中提取的静息态网络(rsn)可能具有独特的时间复杂度特征，这些特征可以通过其多尺度熵模式来量化(McDonough 和 Nashiro，2014)。这一假说具有发展正常大脑功能和病理性大脑功能障碍的数字生物标志物的潜力。然而， McDonough 和 Nashiro (2014) 的一个局限性是，该分析仅使用了 20 名健康个体的 rsfMRI 数据。为了在更大的队列中验证这一假说，我们使用了来自人类连接体计划(HCP)的 987 名健康年轻成年人的 rsfMRI 数据集，年龄在 22-35 岁之间，每个人都有 4 次 14.4 分钟的 rsfMRI 记录，并分为 379 个脑区。我们量化了不同皮质和皮质下区域 rsfMRI 时间序列的多尺度熵。我们对 8 个 rsn 中的数据进行了效应大小分析。由于多尺度熵的形态受到其容忍参数(r)和嵌入维度(m)的选择的影响，我们在多个 r 和 m 值上重复了分析，包括 McDonough 和 Nashiro (2014) 中使用的值。我们的结果强化了默认模式和额顶叶网络中的高时间复杂度。在皮质下区域和边缘系统中观察到最低的时间复杂度。我们研究了在两种速率下对 rsfMRI 时间序列进行下采样后的时间分辨率(由重复时间 T 决定)的影响。在较低的时间分辨率下，我们观察到跨数据集的熵和方差增加。测试-重测分析表明，在四个 rsfMRI 运行中，个体之间的发现很可能具有可重复性，特别是当容忍参数 r 等于 0.5 时。结果证实，功能脑连接强度与 rsfMRI 时间复杂度随时间尺度的变化之间存在关系。最后，观察到 rsn 的时间复杂度与流体智力之间存在非随机相关性，这表明人类大脑的复杂动态是高级大脑功能的一个重要属性。
