TbCAPs: A toolbox for co-activation pattern analysis

Abstract

Functional magnetic resonance imaging provides rich spatio-temporal data of human brain activity during task and rest. Many recent efforts have focussed on characterising dynamics of brain activity. One notable instance is co-activation pattern (CAP) analysis, a frame-wise analytical approach that disentangles the different functional brain networks interacting with a user-defined seed region. While promising applications in various clinical settings have been demonstrated, there is not yet any centralised, publicly accessible resource to facilitate the deployment of the technique.Here, we release a working version of TbCAPs, a new toolbox for CAP analysis, which includes all steps of the analytical pipeline, introduces new methodological developments that build on already existing concepts, and enables a facilitated inspection of CAPs and resulting metrics of brain dynamics. The toolbox is available on a public academic repository https://c4science.ch/source/CAP_ Toolbox.git.In addition, to illustrate the feasibility and usefulness of our pipeline, we describe an application to the study of human cognition. CAPs are constructed from resting-state fMRI using as seed the arXiv:1910.06113v1 [q-bio.QM]A PREPRINT -OCTOBER 15, 2019 right dorsolateral prefrontal cortex, and, in a separate sample, we successfully predict a behavioural measure of continuous attentional performance from the metrics of CAP dynamics (R=0.59).Keywords dynamic functional connectivity · frame-wise analysis · co-activation pattern analysis · task-positive network · attention · continuous performance · open source software In this work, in addition to the above, we propose an extension in which more than one seed region can be considered: for each seed j, a set of time points T s,j is derived. Assuming J separate seeds, one can then consider the time points when all seed time courses jointly take extreme values:

Dynamic functional networks in idiopathic normal pressure hydrocephalus: Alterations and reversibility byCSFtap test

“…The relative temporal occurrence (defined as the number of fMRI volumes classified in one CAP‐cluster normalized by the number of volumes corresponding to precuneus activations), average duration (the average number of consecutive volumes classified in one CAP‐cluster) and frequency (the number of disjoint volume‐sets classified in one CAP‐cluster normalized by the recording time) of the three CAPs were computed for each subject. The choice of the number of clusters ( k = 3) was based on a consensus clustering assessment from data bootstrapping (Bolton et al, 2019; Monti, Tamayo, Mesirov, & Golub, 2003) and ensuring a minimum cluster expression across subjects (Figure S4). Analyses were replicated with an alternative number of clusters ( k = 4).…”

“…To identify the brain circuits affected in iNPH and characterize their spatial and temporal characteristics during resting-state, we developed a three-step methodological approach. (Bolton et al, 2019;Monti, Tamayo, Mesirov, & Golub, 2003) and ensuring a minimum cluster expression across subjects ( Figure S4). Analyses were replicated with an alternative number of clusters (k = 4).…”

Brain functional connectivity dynamics at rest in the aftermath of affective and cognitive challenges

“…(d) Retained frames across subjects (depicted by different shades of red) undergo k‐means clustering to be separated into K different CAPs, each defined as the arithmetic mean between the subset of frames denoting one particular network of regions (voxelwise), with which the seed was strongly co‐(de)active at the same time points. Adapted from reference Bolton et al (2020)…”

“…Three different metrics were computed to assess temporal fluctuations in the expression of each brain CAP across conditions. See Figure 2, and (Bolton et al, 2020) for a complete description of the CAPs pipeline. “Occurrences” corresponded to the sum of frames assigned to each CAP among all the retained frames, across the entire fMRI scanning runs.…”

Dynamic functional brain networks underlying the temporal inertia of negative emotions

“…S1. A more detailed description of our methodological procedure is provided elsewhere (47). Importantly, this approach yields a temporal metric to quantify dFC variability over time by computing the occurrences of each CAP in each condition.…”