This lightning talk describes an automated pipline for positron emission tomography (PET) data. 

This session introduces the PET-to-BIDS (PET2BIDS) library, a toolkit designed to simplify the conversion and preparation of PET imaging datasets into BIDS-compliant formats. It supports multiple data types and formats (e.g., DICOM, ECAT7+, nifti, JSON), integrates seamlessly with Excel-based metadata, and provides automated routines for metadata updates, blood data conversion, and JSON synchronization. PET2BIDS improves human readability by mapping complex reconstruction names into standardized, descriptive labels and offers extensive documentation, examples, and video tutorials to make adoption easier for researchers.

This session introduces the PET-to-BIDS (PET2BIDS) library, a toolkit designed to simplify the conversion and preparation of PET imaging datasets into BIDS-compliant formats. It supports multiple data types and formats (e.g., DICOM, ECAT7+, nifti, JSON), integrates seamlessly with Excel-based metadata, and provides automated routines for metadata updates, blood data conversion, and JSON synchronization. PET2BIDS improves human readability by mapping complex reconstruction names into standardized, descriptive labels and offers extensive documentation, examples, and video tutorials to make adoption easier for researchers.

This session dives into practical PET tooling on BIDS data—showing how to run motion correction, register PET↔MRI, extract time–activity curves, and generate standardized PET-BIDS derivatives with clear QC reports. It introduces modular BIDS Apps (head-motion correction, TAC extraction), a full pipeline (PETPrep), and a PET/MRI defacer, with guidance on parameters, outputs, provenance, and why Dockerized containers are the reliable way to run them at scale.

This session introduces two PET quantification tools—bloodstream for processing arterial blood data and kinfitr for kinetic modeling and quantification—built to work with BIDS/BIDS-derivatives and containers. Bloodstream fuses autosampler and manual measurements (whole blood, plasma, parent fraction) using interpolation or fitted models (incl. hierarchical GAMs) to produce a clean arterial input function (AIF) and whole-blood curves with rich QC reports ready. TAC data (e.g., from PETPrep) and blood (e.g., from bloodstream) can be ingested using kinfitr to run reproducible, GUI-driven analyses: define combined ROIs, calculate weighting factors, estimate blood–tissue delay, choose and chain models (e.g., 2TCM → 1TCM with parameter inheritance), and export parameters/diagnostics. Both are available as Docker apps; workflows emphasize configuration files, reports, and standard outputs to support transparency and reuse.

This lecture covers positron emission tomography (PET) imaging and the Brain Imaging Data Structure (BIDS), and how they work together within the PET-BIDS standard to make neuroscience more open and FAIR.

This module covers many of the types of non-invasive neurotech and neuroimaging devices including electroencephalography (EEG), electromyography (EMG), electroneurography (ENG), magnetoencephalography (MEG), and more. 

The goal of computational modeling in behavioral and psychological science is using mathematical models to characterize behavioral (or neural) data. Over the past decade, this practice has revolutionized social psychological science (and neuroscience) by allowing researchers to formalize theories as constrained mathematical models and test specific hypotheses to explain unobservable aspects of complex social cognitive processes and behaviors. This course is composed of 4 modules in the format of Jupyter Notebooks. This course comprises lecture-based, discussion-based, and lab-based instruction. At least one-third of class sessions will be hands-on. We will discuss relevant book chapters and journal articles, and work with simulated and real data using the Python programming language (no prior programming experience necessary) as we survey some selected areas of research at the intersection of computational modeling and social behavior. These selected topics will span a broad set of social psychological abilities including (1) learning from and for others, (2) learning about others, and (3) social influence on decision-making and mental states. Rhoads, S. A. & Gan, L. (2022). Computational models of human social behavior and neuroscience - An open educational course and Jupyter Book to advance computational training.  ​​​Journal of Open Source Education, 5(47), 146. https://doi.org/10.21105/jose.00146

This lesson contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below. 

This is an introductory lecture on whole-brain modelling, delving into the various spatial scales of neuroscience, neural population models, and whole-brain modelling. Additionally, the clinical applications of building and testing such models are characterized. 

This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).

This lesson corresponds to slides 65-90 of the PDF below. 

This lecture discusses what defines an integrative approach regarding research and methods, including various study designs and models which are appropriate choices when attempting to bridge data domains; a necessity when whole-person modelling. 

Similarity Network Fusion (SNF) is a computational method for data integration across various kinds of measurements, aimed at taking advantage of the common as well as complementary information in different data types. This workshop walks participants through running SNF on EEG and genomic data using RStudio.

In this lesson, you will learn about one particular aspect of decision making: reaction times. In other words, how long does it take to take a decision based on a stream of information arriving continuously over time?

This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.

This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.

This lecture covers an Introduction to neuron anatomy and signaling, as well as different types of models, including the Hodgkin-Huxley model.

This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.

This lecture provides an introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.

This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.