A deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mapping

Caballero-Gaudes, César; Moia, Stefano; Panwar, Puja; Bandettini, Peter A.; Gonzalez‐Castillo, Javier

doi:10.1016/j.neuroimage.2019.116081

Cited by 30 publications

(45 citation statements)

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“…Second, there is no empirically tested best set of parameters for a ME fMRI scan, and systematically testing different combinations of parameters was outside of the scope of the present study. ME denoising itself is an active area of research and development (Caballero-Gaudes et al, 2019;Kundu et al, 2012), and the algorithms used here will likely be improved upon in the near future by other investigators. Third, we are not advocating for ME fMRI scans as a panacea for the challenges inherent to obtaining accurate descriptions of an individual's functional brain organization.…”

Section: Discussionmentioning

confidence: 99%

Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI

et al. 2020

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“…Second, through multi-echo acquisition, physiology-, and motion-induced confounds are further attenuated [104,105]. Dynamic analytical pipelines tailored to exploit the benefits provided by such data are emerging [106].…”

Section: Box 1 Non-neural Contributions To Dynamic Fmri-based Brain/behavior Analysesmentioning

confidence: 99%

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

Bolton

Morgenroth

Preti

et al. 2020

Trends in Neurosciences

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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].

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“…Future developments should help address many of these limitations. For example, multi-echo fMRI can improve the accuracy of the deconvolution (Caballero-Gaudes et al, 2019); and improved probabilistic decoding frameworks may increase the specificity of the inferences (Rubin et al, 2017) by providing researchers with the ability to generate context-sensitive interpretations of whole-brain activity maps. In addition, it will be valuable to develop systems able to directly map FC matrices into cognitive states in an open-ended fashion, erasing the need to generate representative "activity" maps for each FC state as an intermediate step.…”

Section: Open-ended Cognitive Inference For Fmri Scansmentioning

confidence: 99%

Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest

et al. 2019

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Brain functional connectivity (FC) changes have been measured across seconds using fMRI. This is true for both rest and task scenarios. Moreover, it is well accepted that task engagement alters FC, and that dynamic estimates of FC during and before task events can help predict their nature and performance. Yet, when it comes to dynamic FC (dFC) during rest, there is no consensus about its origin or significance. Some argue that rest dFC reflects fluctuations in ongoing cognition, or is a manifestation of intrinsic brain maintenance mechanisms, which could have predictive clinical value. Conversely, others have concluded that rest dFC is mostly the result of sampling variability, head motion or fluctuating sleep states. Here, we present novel analyses suggesting that rest dFC is influenced by short periods of spontaneous cognitive-task-like processes, and that the cognitive nature of such mental processes can be inferred blindly from the data. As such, several different behaviorally relevant whole-brain FC configurations may occur during a single rest scan even when subjects were continuously awake and displayed minimal motion. In addition, using low dimensional embeddings as visualization aids, we show how FC states-commonly used to summarize and interpret resting dFC-can accurately and robustly reveal periods of externally imposed tasks; however, they may be less effective in capturing periods of distinct cognition during rest.

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A deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mapping

Cited by 30 publications

References 90 publications

Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI

Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI

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

Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest

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