Projection scrubbing: a more effective, data-driven fMRI denoising method

07/31/2021
by   Damon Pham, et al.
0

Functional MRI (fMRI) data are subject to artifacts from a myriad of sources which can have negative consequences on the accuracy and power of statistical analyses. Scrubbing is a technique for excluding fMRI volumes thought to be contaminated by artifacts. Here we present "projection scrubbing", a new data-driven scrubbing method based on a statistical outlier detection framework. Projection scrubbing consists of two main steps: projection of the data onto directions likely to represent artifacts, and quantitative comparison of each volume's association with artifactual directions to identify volumes exhibiting artifacts. We assess the ability of projection scrubbing to improve the reliability and predictiveness of functional connectivity (FC) compared with two popular scrubbing methods: motion scrubbing, a measure of subject head displacement, and DVARS, another data-driven measure based on signal intensity change in the fMRI scan. We perform scrubbing in conjunction with regression-based denoising through CompCor, which we found to outperform alternative methods. Projection scrubbing and DVARS were both substantially more beneficial to FC reliability than motion scrubbing, illustrating the advantage of data-driven measures over head motion-based measures for identifying contaminated volumes. The benefit of scrubbing was strongest for connections between subcortical regions and cortical-subcortical connections. Scrubbing with any method did not have a noticeable effect on prediction accuracy of sex or total cognition, suggesting that the ultimate effect of scrubbing on downstream analysis depends on a number of factors specific to a given analysis. To promote the adoption of effective fMRI denoising techniques, all methods are implemented in a user-friendly, open-source R package that is compatible with NIFTI- and CIFTI-format data.

READ FULL TEXT

page 10

page 11

page 17

page 20

page 21

page 22

page 24

page 41

research
04/28/2023

A robust multivariate, non-parametric outlier identification method for scrubbing in fMRI

Functional magnetic resonance imaging (fMRI) data contain high levels of...
research
07/19/2019

Generating fMRI volumes from T1-weighted volumes using 3D CycleGAN

Registration between an fMRI volume and a T1-weighted volume is challeng...
research
02/11/2022

Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging Data

Motion correction is an essential preprocessing step in functional Magne...
research
07/21/2017

Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis

We propose a novel denoising framework for task functional Magnetic Reso...
research
04/21/2010

Parcellation of fMRI Datasets with ICA and PLS-A Data Driven Approach

Inter-subject parcellation of functional Magnetic Resonance Imaging (fMR...
research
08/04/2023

A Parameter-efficient Multi-subject Model for Predicting fMRI Activity

This is the Algonauts 2023 submission report for team "BlobGPT". Our mod...

Please sign up or login with your details

Forgot password? Click here to reset