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
Sitemap
A list of all the pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Yearly Publications
List of Javier Gonzalez-Castillo’s scientific peer-reviewed publications sorted by year Read more
Talks and presentations
Talks and presentations given by Javier Gonzalez-Castillo Read more
Talk Recodrdings
Links to pre-recorded talks Read more
talks
What is the ultimate sensitivity of fMRI: does the whole brain activate?
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
</article> </div>Realtime fMRI/fMRI Neurofeedback
Published:
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
</article> </div>Multi-echo EPI for resting state and activaton based fMRI
Published:
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
</article> </div>Uncovering hidden activations using model-free analysis
Published:
Here I describe our work looking at unconventional BOLD responses such as negative and transient responses that accompany block task designs. Read more
</article> </div>Unconventional fMRI methodology: multi-echo fMRI, connectivity dynamics, and fMRI neurofeedback
Published:
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
</article> </div>Machine Learning and fMRI
Published:
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
</article> </div>Quantitative deconvolution of neuronal-related BOLD events with multi-echo sparse free paradigm mapping
Published:
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
</article> </div>Multi-echo EPI for task-based and resting-state fMRI
Published:
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
</article> </div>Periods of discernible cognition contribute to dynamic functional connectivity during rest
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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
</article> </div>Dynamic connectivity: is it real? is it useful? how do we extract information?
Published:
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
</article> </div>Cognitive correlates of BOLD resting-state dynamic functional connectivity
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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
</article> </div>Individual variability in resting-state fMRI [VIDEO AVAILABLE]
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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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