Article

Deep Generative Analysis for Task-Based Functional MRI Experiments

2021-04-04

Abstract excerpt

While functional magnetic resonance imaging (fMRI) remains one of the most widespread and important methods in basic and clinical neuroscience, the data it produces—time series of brain volumes—continue to pose daunting analysis challenges. The current standard (“mass univariate”) approach involves constructing a matrix of task regressors, fitting a separate general linear model at each volume pixel (“voxel”), com...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 6c4c781b-2696-53fb-b945-6881beb49dcc
DOI: 10.1101/2021.04.04.438365

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Deep Generative Analysis for Task-Based Functional MRI ExperimentsDOI 10.1101/2021.04.04.438365Read

What Is a Generative Model? Definitions, Disagreements, and Evaluation in Human NeuroimagingSemantic proximity Read

GenBrain: A Generative Foundation Model of Multimodal Brain ImagingSemantic proximity Read

Multimodal image fusion via deep generative modelsSemantic proximity Read

Encoding models in functional magnetic resonance imaging: the Voxelwise Encoding Model frameworkSemantic proximity Read

Brain Diffusion Transformer for Personalized Neuroscience and PsychiatrySemantic proximity Read

TS-AI: A deep learning pipeline for multimodal subject-specific parcellation with task contrasts synthesisSemantic proximity Read

MRecover: A Conditional Generative Model for Recovering Motion- Corrupted MR images Using AI Generated ContrastSemantic proximity Read

GAN-MAT: Generative Adversarial Network-based Microstructural Profile Covariance Analysis ToolboxSemantic proximity Read