In this map you can see a geographic depiction of where science has taken me over the years. There are a lot of gratifying things associated with being a scientist, and one of them is the unique opportunity to travel around the world and meet people from all nationalities and backgrounds.
NIH Summer Course (2026), National Institutes of Health, Bethesda, MD
This talk introduces the unique features of real-time fMRI in comparison with conventional offline fMRI experimentation. It discusses the differences between real-time and offline preprocessing, reviews common applications of real-time fMRI, and highlights several real-time fMRI and neurofeedback studies that serve as exemplary models for conducting this type of research.
Association for the Scientific Study of Consciousness (2026), Casa Central (Pontificia Universidad Catolica de Chile), Santiago de Chile, Chile
Resting-state fMRI (rsfMRI) is widely used to identify altered functional connectivity (FC) in clinical populations, yet studies rarely consider how conscious experience during scanning influences FC. In this talk, I present evidence from over 400 annotated resting-state scans showing that differences in conscious experience systematically shape FC and can themselves be predicted from FC. These findings highlight the importance of accounting for in-scanner experience when interpreting rsfMRI data and demonstrate the value of annotating resting-state datasets with experiential information.
Organization for Human Brain Mapping (2026), Palais Atlantic, Bordeaux, France
Multi-edho data are best known for enabling the automatic identification and removal of artifactual non-BOLD components, thereby improving data quality. However, the richer information provided by ME acquisitions can also be leveraged to enhance the sensitivity of deconvolution algorithms, assess data quality via functional connectivity across echoes, and estimate the BOLD-likelihood of signals of interest, such as the global signal. In this talk, I will highlight these lesser-known applications of multi-echo data.
NIMH Center for Multimodal Imaging - Workshop on Naturalistic Stimuli and Individual Differences, National Institute of Mental Health, NIH (Online), Bethesda, MD, USA
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. []
Brain Functional Organization, Connectivity and Behavior, Westing Hotel, Whistler, CA
Overview of recent work looking at the cognitive correlates of time varying functional connectivity, as well as some thoughts on future directions.
NIH Summer Course (2019), National Institutes of Health, Bethesda, MD
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.
ISMRM 27th Annual Meeting, Palais de Congres, Montreal, QC, Canada
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.
NIH Summer Course (2018), National Institutes of Health, Bethesda, MD
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).
ISMRM 27th Annual Meeting, Paris Expo Porte de Versailles, Paris, France
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.
NIH Summer Course (2017), National Institutes of Health, Bethesda, MD
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.
Invited Talk, Centro Integral de Neurociencias, Hospital de Madrid, Madrid, Spain
Overview of alternative ways to acquire and analyze fMRI data other than typical task-based experiments. Note that this talk is in Spanish language.
ISMRM 24th Annual Meeting, Suntec Singapore Convention & Exhibition Center, Singapore, Singapore
Here I describe our work looking at unconventional BOLD responses such as negative and transient responses that accompany block task designs.
TTU Neuroimaging Symposium, Texas Tech University, Lubbock, TX
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.
Invited Talk, Basque Center on Cognition, Brain and Language, San Sebastian, Spain
Invited Talk, Max Planck Institute, Leipzig, Germany
NIH Summer Course (2014), National Institutes of Health, Bethesda, MD
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.
Invited Talk, 1st International Conference on Brain Development, Beijing, China
Invited Talk, Chinese Academy of Sciences, Beijing, China
ISMRM 20th Annual Meeting, Melbourne Convention & Exhibition Center, Melbourne, Australia
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.