Computational Visual Neuroscience Laboratory at CMRR
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If you have any questions about the resources here, feel free to contact Kendrick.

TOOLS

We create a variety of software tools and share these freely with the community.

analyzeprfthumbnail analyzePRF

This is a toolbox for estimating population receptive fields from time-series data (or from single-response data). For more information, see the analyzePRF web site and the analyzePRF github repository. The toolbox is closely related to the compressive spatial summation Journal of Neurophysiology paper.

fracridgethumbnail fracridge

This is a toolbox that implements the fractional ridge regression technique as described in the GigaScience paper. The primary advantage of this implementation of ridge regression is that you automatically get correctly set hyperparameters that probe the relevant range for any regression problem. For more information, see the fracridge github repository.

glmdenoisethumbnail GLMdenoise

This is a toolbox for denoising task-based fMRI data. The basic principle is to derive nuisance regressors in a data-driven fashion and to use cross-validation to determine the optimal number of regressors (to avoid overfitting). It was originally introduced in this Frontiers paper and followed up in this NeuroImage paper. For more information, see the GLMdenoise web site and the GLMdenoise github repository. (GLMsingle is a replacement for GLMdenoise; see below.)

glmdenoisethumbnail GLMsingle

This toolbox provides new methods for improved single-trial GLM estimation in fMRI time-series data. GLMsingle can be viewed as a wholesale replacement of GLMdenoise. GLMsingle incorporates (1) HRF estimation per voxel (using a library-of-HRFs approach), (2) data-driven denoising (using GLMdenoise), and (3) regularized single-trial estimation using fractional ridge regression. The technique was introduced in the NSD data paper and is given a fuller treatment in this pre-print.  For more information, see glmsingle.org.

tdmthumbnail TDM

This is a toolbox that implements the Temporal Decomposition Method as described in the Nature Methods paper. This TDM technique provides a method for summarizing, visualizing, and extracting different response timecourses from fMRI data. These timecourses can then be used to isolate early and late components of fMRI responses associated with microvasculature and macrovasculature, respectively. For more information, see the TDM github repository.

IN-HOUSE TOOLS

These tools are primarily designed and intended for in-house projects, but might be useful more generally (depending on one's workflow).

alignvolumedatathumbnail alignvolumedata

This toolbox provides a convenient visual interface for achieving manual or automatic co-registration of two 3D volumes. Rigid-body and affine transformations are allowable. See the alignvolumedata github repository.

cvncodethumbnail cvncode

This collection of utilities handles various pre-processing procedures related to fMRI analysis. The most useful functions are ones that pertain to automatic generation of visualizations of surface-based data (cvnlookup) and to manual drawing of ROIs on surface data (cvndefinerois). See the cvncode github repository.

knkutilsthumbnail knkutils

Over the years, we have collected a large number of utility functions that facilitate computational analyses. Some of these utilities are incorporated into other of our repositories, but in some cases, the knkutils repository is a required dependency. See the knkutils github repository.

preprocessfmrithumbnail preprocessfmri

This toolbox implements volume- and surface-based pre-processing of fMRI data, including dynamic time-varying fieldmap-based corrections and generation of extensive visual inspections. Having been developed over many years, it is flexible, powerful, but a bit complex. See the preprocessfmri github repository.

DATASETS

We share data and code from our fMRI experiments.
 
nsdthumbnail Natural Scenes Dataset

This dataset consists of massive dense sampling of whole-brain 7T fMRI responses to color natural scenes in eight carefully screened observers. We also provide an archive of code used in this project. For more information, see the NSD web site
and the nsddatapaper github repository.
veinsthumbnail Ultra-high-resolution fMRI in visual cortex

We collected a number of high-resolution (0.8-mm) 7T fMRI datasets using basic visual manipulations. The data formed the basis of several papers: the veins NeuroImage paper, the TDM technique Nature Methods paper, and a ventral visual cortex Journal of Neuroscience paper. For more information, see this OSF web site.
hcp7tretthumbnail HCP7T Retinotopy Dataset

A large collection
(n = 181) of fMRI retinotopic mapping data collected as part of the Human Connectome Project. The dataset is formally described in the HCP7TRET Journal of Vision paper. For more information, see the HCP7TRET OSF site.
vtcipsthumbnail vtcipsmodel

This dataset is associated with the bottom-up/top-down eLife paper. The experiment involved a wide range of face and word stimuli under different stimulus manipulations and task manipulations. We also include the code we used to model ventral visual regions and their interaction with parietal cortex. For more information, see the vtcipsmodel github repository
vtcdatathumbnail vtcdata

This dataset is associated with the face pRF Current Biology paper. The experiment involved mapping population receptive fields in face-selective regions of human visual cortex under different behavioral states. For more information, see the vtcdata web site.
socmodelthumbnail socmodel

This dataset is associated with the second-order contrast (SOC) PLoS Computational Biology paper. The dataset includes fMRI responses in visual cortex to a wide range of synthetic stimuli, and also includes code implementing the SOC model described in the paper. For more information, see the socmodel web site.
vim1thumbnail vim-1

This dataset is associated with the encoding/decoding Nature paper, and consists of fMRI responses in occipital cortex to a large number of grayscale natural images. The data are hosted at crcns.org.

TEACHING

nsdabudhabithumbnail The Big Data Revolution in Neuroscience

A week-long workshop held at NYU Abu Dhabi in January 2020. Materials include lecture slides, videos, and code. The content largely revolves around the design, pre-processing, and analyses that can be performed on the very large Natural Scenes Dataset.
statsthumbnail Statistics and Data Analysis in MATLAB

A course that we developed to cover fundamental principles in statistics as well as how to implement and deal with these concepts effectively in code. Materials include lecture slides, videos, and code. Be sure to check out the figures and animations that illustrate core statistical concepts.
blogthumbnail Blog on statistical analyses

A series of short blog posts on some interesting observations in computational statistics. 
linksthumbnail Cognitive neuroscience links

A collection of links to demos and materials available on the web that help illustrate concepts in cognitive neuroscience.

SEMINARS / TALKS

talksthumbnail
Recorded talks

MRC-CBU Chaucer Club 2021 talk on "A data-driven approach to advancing cognitive neuroscience" (video)


Algonauts workshop 2019 talk on the NSD dataset (video | slides)


OSA Fall Vision Meeting 2017 talk on the bottom-up/top-down eLife paper (video | slides)


BrainHack 2017 talk on fMRI software tools (video | slides)

Neurohackweek 2016 talk on modeling fMRI data (video | slides)

VSS 2016 symposium talk on "what are deep neural networks and what are they good for?" (video | slides)


VSS 2014 symposium on "understanding representation in visual cortex: why are there so many approaches and which is best?" (videos)

OTHER

brainartthumbnail Brain art

In several MRI projects (especially those pitched at ultra-high spatial resolution fMRI), careful and informative visualizations play a key role (see the veins NeuroImage paper). Some of these explorations have generated some visually interesting figures, we have compiled these into a Google Photos album.
 
 


University of Minnesota | Department of Radiology | Center for Magnetic Resonance Research