About me
Javier Gonzalez-Castillo is a Senior Associate Scientist at the NIH conducting research on fMRI methods, resting state functional connectivity, realtime fMRI, the neural correlates of internally driven cognition Read more
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Javier Gonzalez-Castillo is a Senior Associate Scientist at the NIH conducting research on fMRI methods, resting state functional connectivity, realtime fMRI, the neural correlates of internally driven cognition Read more
Neuroimaging Analysis Methods Research Portfolio Read more
Clinical Research Portfolio Read more
Cognitive Neuroscience Research Portfolio Read more
Neuroimaging Acquisition Methods Research Portfolio Read more
List of Javier Gonzalez-Castillo’s scientific peer-reviewed publications sorted by year Read more
Talks and presentations given by Javier Gonzalez-Castillo Read more
Links to pre-recorded talks Read more
Published:
Original description of our work looking at the true extent of fMRI activity when signal-to-noise ratio is sufficiently high and we also take into account transitory and negative BOLD responses. Read more
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Introductory talk to realtime fMRI and fMRI-based neurofeedback. Here I describe what is realtime fMRI, the details of the realtime fMRI system that we have running at the NIH, and a series of studies that takes advantage of this unique technology. Read more
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Here I give an overview on our efforts at the time in developing methods for multi-echo fMRI, and how you can use multi-echo fMRI to perform automatic denoising. Read more
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Here I describe our work looking at unconventional BOLD responses such as negative and transient responses that accompany block task designs. Read more
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Overview of alternative ways to acquire and analyze fMRI data other than typical task-based experiments. Note that this talk is in Spanish language. Read more
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Introductory talk to machine learning (ML) for neuroimagers. Here I introduce basic concepts such as supervised and unsupervised learning, regression, classification, gradient descent, overfitting and testing. I next discuss a few examples of how ML can be applied to functional neuroimaing data. Read more
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Here I describe our work on the design and evaluation of a multi-echo based formulation of the hemodynamic deconvolution algorithm called free paradigm mapping. Read more
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Introductory talk to multi-echo fMRI. Here I discuss what is multi-echo fmri, basic operations you can do with multi-echo data (e.g., optimal combination), and more advanced approaches such as me-based denoising (ME-ICA) and multi-ech based deconvolution (ME-SPFM). Read more
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This conference talk describes our work looking at how to extract information about on-going cognition during rest using time varying functional connectivity and hemodynamic deconvolution. Read more
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Introductory talk to dynamic functional connectivity. Here I discuss what is dynamic functional connectivity, how do we typically measure it. It then goes into what it is known today about how to interpret results. Read more
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Overview of recent work looking at the cognitive correlates of time varying functional connectivity, as well as some thoughts on future directions. Read more
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Overview of state-of-the-art regarding our understanding of sources of individual differences in resting-state fMRI. This talk first describes the primary sources of variability, and then provide some thoughts on how to address those sources depending on whether a given study targets “state-level” or “trait-level” properties of the brain. [] Read more
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