TbCAPs: A ToolBox for CoActivation pattern analysis Abstract   Functional magnetic resonance imaging provides rich spatiotemporal data of human brain activity during task and rest. Many recent efforts have focussed on characterising dynamics of brain activity. One notable instance is coactivation pattern (CAP) analysis, a framewise analytical approach that disentangles the different functional brain networks interacting with a userdefined 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 at  https:c4science.chsourceCAP_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 restingstate fMRI using as seed the 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 (Rxe2x80xafxe2x80xaf0.59).

Overlapping attentional networks yield divergent behavioral predictions across tasks: Neuromarkers for diffuse and focused attention? Abstract   Attention is a critical cognitive function, allowing humans to select, enhance, and sustain focus on information of behavioral relevance. Attention contains dissociable neural and psychological components. Nevertheless, some brain networks support multiple attentional functions. In this study, we used the visual attentional blink (VAB) as a test of the functional generalizability of the brainu0027s attentional networks. In a VAB task, attention devoted to a target often causes a subsequent item to be missed. Although frequently attributed to limitations in attentional capacity or selection, VAB deficits attenuate when participants are distracted or deploy attention diffusely. The VAB is also behaviorally and theoretically dissociable from other attention tasks. Here we used Connectomebased Predictive Models (CPMs), which associate individual differences in task performance with functional connectivity patterns, to test their ability to predict performance for multiple attentional tasks. We constructed visual attentional blink (VAB) CPMs, and then used them and a sustained attention network model (saCPM; Rosenberg et al., 2016a,b) to predict performance. The latter model had been previously shown to successfully predict performance across tasks involving selective attention, inhibitory control, and even reading recall. Participants (nxe2x80xafxe2x80xaf73; 24 males) underwent fMRI while performing the VAB task and while resting. Outside the scanner, they completed other cognitive tasks over several days. A vabCPM constructed from VAB task data (behavior and fMRI) successfully predicted VAB performance. Strikingly, the network edges that predicted better VAB performance (positive edges) predicted worse performance for selective and sustained attention tasks, and vice versa. Predictions from applying the saCPM to the data mirrored these results, with the networku0027s negative edges predicting better VAB performance. The vabCPMu0027s positive edges partially yet significantly overlapped with the saCPMu0027s negative edges, and vice versa. Many positive edges from the vabCPM involved the default mode network, whereas many negative edges involved the salienceventral attention network. We conclude that the vabCPM and saCPM networks reflect general attentional functions that influence performance on many tasks. The networks may indicate an individualu0027s propensity to deploy attention in a more diffuse or a more focused manner.