Alteriis Giuseppe de, Sherwood Oliver, Ciaramella Alessandro, Leech Robert, Cabral Joana, Turkheimer Federico E, Expert Paul
Institute of Psychiatry, Psychology and Neuroscience (IoPPN) King's College London, London, United Kingdom.
DII, University of Pisa, Pisa, Italy.
PLoS Comput Biol. 2025 Mar 7;21(3):e1012795. doi: 10.1371/journal.pcbi.1012795. eCollection 2025 Mar.
A crucial challenge in neuroscience involves characterising brain dynamics from high-dimensional brain recordings. Dynamic Functional Connectivity (dFC) is an analysis paradigm that aims to address this challenge. dFC consists of a time-varying matrix (dFC matrix) expressing how pairwise interactions across brain areas change over time. However, the main dFC approaches have been developed and applied mostly empirically, lacking a common theoretical framework and a clear view on the interpretation of the results derived from the dFC matrices. Moreover, the dFC community has not been using the most efficient algorithms to compute and process the matrices efficiently, which has prevented dFC from showing its full potential with high-dimensional datasets and/or real-time applications. In this paper, we introduce the Dynamic Symmetric Connectivity Matrix analysis framework (DySCo), with its associated repository. DySCo is a framework that presents the most commonly used dFC measures in a common language and implements them in a computationally efficient way. This allows the study of brain activity at different spatio-temporal scales, down to the voxel level. DySCo provides a single framework that allows to: (1) Use dFC as a tool to capture the spatio-temporal interaction patterns of data in a form that is easily translatable across different imaging modalities. (2) Provide a comprehensive set of measures to quantify the properties and evolution of dFC over time: the amount of connectivity, the similarity between matrices, and their informational complexity. By using and combining the DySCo measures it is possible to perform a full dFC analysis. (3) Leverage the Temporal Covariance EVD algorithm (TCEVD) to compute and store the eigenvectors and values of the dFC matrices, and then also compute the DySCo measures from the EVD. Developing the framework in the eigenvector space is orders of magnitude faster and more memory efficient than naïve algorithms in the matrix space, without loss of information. The methodology developed here is validated on both a synthetic dataset and a rest/N-back task experimental paradigm from the fMRI Human Connectome Project dataset. We show that all the proposed measures are sensitive to changes in brain configurations and consistent across time and subjects. To illustrate the computational efficiency of the DySCo toolbox, we performed the analysis at the voxel level, a task which is computationally demanding but easily afforded by the TCEVD.
神经科学中的一个关键挑战涉及从高维脑记录中表征脑动力学。动态功能连接性（dFC）是一种旨在应对这一挑战的分析范式。dFC由一个随时间变化的矩阵（dFC矩阵）组成，该矩阵表示脑区之间的成对相互作用如何随时间变化。然而，主要的dFC方法大多是凭经验开发和应用的，缺乏一个共同的理论框架，对从dFC矩阵得出的结果的解释也缺乏清晰的认识。此外，dFC领域尚未使用最有效的算法来高效地计算和处理矩阵，这使得dFC无法在高维数据集和/或实时应用中充分发挥其潜力。在本文中，我们介绍了动态对称连接矩阵分析框架（DySCo）及其相关存储库。DySCo是一个框架，它以通用语言呈现最常用的dFC测量方法，并以计算高效的方式实现这些方法。这使得能够在不同的时空尺度上研究脑活动，直至体素水平。DySCo提供了一个单一的框架，允许：（1）将dFC用作一种工具，以一种易于在不同成像模态之间转换的形式捕捉数据的时空相互作用模式。（2）提供一套全面的测量方法，以量化dFC随时间的特性和演变：连接量、矩阵之间的相似性及其信息复杂性。通过使用和组合DySCo测量方法，可以进行完整的dFC分析。（3）利用时间协方差特征值分解算法（TCEVD）来计算和存储dFC矩阵的特征向量和特征值，然后也从特征值分解中计算DySCo测量值。在特征向量空间中开发该框架比在矩阵空间中使用朴素算法快几个数量级，并且内存效率更高，且不会丢失信息。这里开发的方法在一个合成数据集以及功能磁共振成像人类连接组计划数据集中的静息态/N-回溯任务实验范式上都得到了验证。我们表明，所有提出的测量方法对脑配置的变化都很敏感，并且在时间和受试者之间是一致的。为了说明DySCo工具箱的计算效率，我们在体素水平上进行了分析，这是一项计算要求很高但TCEVD能够轻松完成的任务。
