Farahibozorg S Rezvan, Harrison Samuel J, Bijsterbosch Janine D, Woolrich Mark W, Smith Stephen M
FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Dept. of Clinical Neuroscience, Oxford University, Oxford, UK.
Department of Radiology, Washington University in St Louis, St. Louis, USA.
bioRxiv. 2024 Jun 1:2024.05.28.596120. doi: 10.1101/2024.05.28.596120.
Information processing in the brain spans from localised sensorimotor processes to higher-level cognition that integrates across multiple regions. Interactions between and within these subsystems enable multiscale information processing. Despite this multiscale characteristic, functional brain connectivity is often either estimated based on 10-30 distributed modes or parcellations with 100-1000 localised parcels, both missing -scale functional interactions. We present Multiscale Probabilistic Functional Modes (mPFMs), a new mapping which comprises modes over various scales of granularity, thus enabling direct estimation of functional connectivity within- and across-scales. Crucially, mPFMs emerged from data-driven multilevel Bayesian modelling of large functional MRI (fMRI) populations. We demonstrate that mPFMs capture both distributed brain modes and their co-existing subcomponents. In addition to validating mPFMs using simulations and real data, we show that mPFMs can predict ~900 personalised traits from UK Biobank more accurately than current standard techniques. Therefore, mPFMs can offer a paradigm shift in functional connectivity modelling and yield enhanced fMRI biomarkers for traits and diseases.
大脑中的信息处理涵盖从局部感觉运动过程到跨多个区域整合的高级认知。这些子系统之间以及内部的相互作用实现了多尺度信息处理。尽管具有这种多尺度特征，但功能性脑连接性通常要么基于10 - 30种分布式模式进行估计，要么基于100 - 1000个局部脑区划分进行估计，这两种方法都忽略了跨尺度的功能相互作用。我们提出了多尺度概率功能模式（mPFM），这是一种新的图谱，它包含了各种粒度尺度上的模式，从而能够直接估计尺度内和跨尺度的功能连接性。至关重要的是，mPFM是从对大量功能性磁共振成像（fMRI）人群进行数据驱动的多级贝叶斯建模中产生的。我们证明mPFM既捕获了分布式脑模式及其共存的子成分。除了使用模拟和真实数据验证mPFM外，我们还表明mPFM能够比当前标准技术更准确地从英国生物银行预测约900种个性化特征。因此，mPFM可以在功能连接性建模方面带来范式转变，并为特征和疾病产生增强的fMRI生物标志物。
