Hierarchical modelling of functional brain networks in population and individuals from big fMRI data.
Hierarchical modelling of functional brain networks in population and individuals from big fMRI data.
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DOI：
10.1016/j.neuroimage.2021.118513
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发表时间：
2021-11
期刊：
影响因子：
5.7
通讯作者：
Woolrich MW
中科院分区：
文献类型：
--
作者：
Farahibozorg SR;Bijsterbosch JD;Gong W;Jbabdi S;Smith SM;Harrison SJ;Woolrich MW
我们引入了随机普罗富莫(SPROFUMO)来从大数据中推断功能脑网络。SPROFUMO分层地评估人群和每个人的fMRI网络。我们描述了来自UK Biobank的高维静息状态fMRI网络。在估计特定个体的网络拓扑结构方面，模型优于独立分量分析和双重回归。我们展示了该模型在预测认知特征和捕获网络拓扑结构与连通性之间的受试者变异性方面的有效性。大规模脑成像数据集的一个主要目标是为研究不同类型的人群提供资源。从这些数据集中对单个受试者的大脑功能网络进行表征，将具有预测认知或临床特征的巨大潜力。我们首次提出了一种名为随机概率功能模式(SPROFUMO)的技术，该技术可扩展到英国生物库(UKB)，预计有100,000名参与者，并分层估计个人和人群中的功能大脑网络，同时允许两者之间的信息双向流动。通过仿真，我们展示了该模型的实用性，特别是在涉及显著的跨主题可变性的场景中，或者需要描述网络之间的细粒度差异。随后，通过将该模型应用于4999名UKB受试者的静息状态功能磁共振成像，我们在单个受试者中绘制了比以前在UKB中更详细的静息状态网络(RSNs)(>；100 RSNs)，并证明这些RSNs可以预测一系列感觉运动和更高水平的认知功能。此外，我们还展示了该模型相对于独立成分分析结合双重回归(ICA-DR)的几个优势，特别是在估计RSN的空间构型和对认知特征的预测能力方面。提出的模型和结果可以为未来从大数据中研究大脑功能的个性化特征打开一扇新的大门。
We introduce stochastic PROFUMO (sPROFUMO) for inferring functional brain networks from big data. sPROFUMO hierarchically estimates fMRI networks for the population and every individual. We characterised high dimensional resting state fMRI networks from UK Biobank. Model outperforms ICA and dual regression for estimation of individual-specific network topography. We demonstrate the model's utility for predicting cognitive traits, and capturing subject variability in network topographies versus connectivity. A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data.
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影响因子：
64.8
作者：
Elliott LT;Sharp K;Alfaro-Almagro F;Shi S;Miller KL;Douaud G;Marchini J;Smith SM
通讯作者：
Smith SM
DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
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作者：
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