Tools of the trade: Estimating time-varying connectivity patterns from fMRI data

Abstract

Given the dynamic nature of the brain, there has always been a motivation to move beyond “static” functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain’s dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity (dFNC) at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.

Stable Meta-Networks, Noise, and Artifacts in the Human Connectome: Low- to High-Dimensional Independent Components Analysis as a Hierarchy of Intrinsic Connectivity Networks

“…This hierarchical structure is broadly similar to many network features observed in other analyses (Kiviniemi et al, 2009;Abou−Elseoud et al, 2010). Multi-model order ICA was recently extended to investigate dynamic interactions between multiple scales (Iraji et al, 2021). Additionally, highdimensional ICA can reliably estimate neural processing and connectivity across a wide range of spatial scales, well beyond the limits of other hierarchical methods.…”

“…Multi-scale processing can be investigated using multiple techniques, such as hierarchical clustering (Doucet et al, 2011;Yeo et al, 2011;Thirion et al, 2014;Gotts et al, 2020), hierarchical modularity (Meunier et al, 2009), fuzzy-c-means clustering (Lee et al, 2012), multi-level k-means clustering (Bellec et al, 2010), gradient-weighted Markov random field models (Schaefer et al, 2018), non-negative matrix factorization (Li et al, 2018), or multi-scale ICA (Iraji et al, 2021). Similarly, multigranularity analyses segment the brain into interrelated spatial scales by applying multiple gray matter atlases (Arslan et al, 2018;Gong et al, 2018).…”

Further results on why a point process is effective for estimating correlation between brain regions

“…Relevance of the current results for functional connectivity of the brain: Since its introduction, it has been suggested that the PP (or its variants) contains dynamical information, in the sense that it is potentially able to identify the timing and the location of fluctuating epochs of high correlations among brain regions. This identification has recently acquired relevance in the context of what is now dubbed "dynamical functional connectivity", a very active area of research in the neuro-imaging community (see for instance the reviews by Keilholz et al [25] and Iraji et al [27]. In line with this, the recent report of Esfahlani et al [26] emphasizes the fact that few events of co-activation can estimate the functional connectivity architecture of a system, a finding which is in full agreement with our arguments.…”

Tapping into Multi-Faceted Human Behavior and Psychopathology Using fMRI Brain Dynamics

“…As a solution, we believe that current efforts towards a more comparative investigation of dFC tools, which so far concern specific subfamilies of approaches [86][87][88][89], should be further pushed to a larger scale, complemented by attempts to more thoroughly mathematically characterize the relationships between different dynamic methodologies [90]. To this end, the increasing availability of publicly released tools to apply different families of dynamic approaches [91][92][93][94] is reassuring. In addition, a possibly fruitful way to encourage research laboratories to leverage their own methodologies towards such comparative insight may be via dedicated competitions (e.g., https://www.…”