Human behavior comprises many aspects that stand out by their dynamic nature. To quantify its neural underpinnings, time-resolved fMRI methods have blossomed over the past decade. In this review we conceptually organize a broad repertoire of dynamic analytical pipelines and extract general observations on their application to the study of behavior and brain disorders. We aim to provide an extensive overview instead of examining only selected methodological families or specific behavioral domains. We consider behavioral aspects with distinct long-term stability (e.g., physiological state versus personality), and also address selected brain disorders with complementary genetics and symptomatology. This synthesis exposes the somewhat limited consistency of dynamic findings in the literature, as well as the unbalanced application of the multitude of available approaches which would, owing to their technical specificities, have potential to reveal distinct aspects of dynamics. We call for further comparative and collaborative efforts in the future. Brain Dynamics Inferred from Functional Neuroimaging Are Relevant to the Study of Human BehaviorPerhaps the most remarkable feature of humanity is the profound behavioral diversity across different individuals, which pertains to all factors involved in interactions with the physical and social environment. This diversity underlies variability in personality, physiology, and mental capacity, which in turn are not only constituted by biological influences (e.g., fatigue, the influence of drugs, genetic makeup) but also shaped by experience (e.g., social learning, trauma). Arguably, the brain is the most complex system known to humankind, and understanding this organ is crucial for explaining behavior. Studying the brain at rest has demonstrated that, although the environment has an influence on it, the brain operates intrinsically and is modulated rather than controlled by the environment [1]. This modulation is a recursive process between the brain and the environment mediated by perception and action [2]. Evidently, this process is highly dynamic, as are the environment and the brain [3].Neuroscience, in particular neuroimaging research, aims to relate variability in behavior to changes in the brain. Since its discovery in the early 1990s, functional magnetic resonance imaging (fMRI; see Glossary) has become one of the most prominent methods to this end. fMRI is a non-invasive tool to probe whole-brain activity and enables the study of sophisticated processes that involve functional integration and segregation of different brain areas over time. The study of brain signals during task or other forms of stimulation has been a productive way to decode the representation of specific processes in the brain; however, studies on the intrinsic organization of the brain at rest are equally valuable, and have been shown to predict behavior and psychopathology [4,5].

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:

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