Webinar Review: Influence of Motion and Physiological noise on fMRI: QC, solutions, and challenges

Over the course of the semester, our trainees are reviewing webinars in their given fields and preparing abstracts to help colleagues outside their discipline make an informed choice about watching them. As our program bridges diverse disciplines, these abstracts are beneficial for our own group in helping one another gain key knowledge in each other’s fields. We are happy to share these here for anyone else who may find them helpful.

Influence of Motion & Physiological noise on fMRI: QC, solutions, and challenges

Dr. Rasmus Birn

September 14, 2021

Open Minds @ Pitt

Olawale SalaudeenAnalysis by Olawale Salaudeen:

This webinar is a comprehensive overview of the influence of motion and physiological noise on fMRI. As the title says, Dr. Birn presents an end-to-end synopsis of the problem, from introducing the BOLD signal to current solutions to further challenges.

The BOLD signal (Blood Oxygenation Level Dependent), which aims to measure a proxy of neuronal activity, is tiny and therefore susceptible to being influenced by artifacts from motion and physiological noise. When interested in uncovering functional connectivity, connections between brain regions, these artifacts can lead to false conclusions. For instance, when looking at child vs. adult brain in fMRI, a significant confound is that children tend to move more than adults, leading to many more false positives in non-artifactual activations.

Current techniques to mitigate this problem include measuring physiological confounds via external devices, such as a heart monitor, and regressing out the measurements from the scan, measuring frame to frame affine changes in the position of the skull, and regressing out the positional changes from the scan. Despite the availability of techniques used to remove artifacts, it remains a challenge to know if they worked. QC-FC vs. Distance is one attempt to address this challenge for motion. Distances between networks are correlated with the correlations between strengths between pairs of networks and estimated motion parameters. There is no correlation in the ideal case, which implies that the method applied for motion correction ‘worked.’ However, even this method has limitations, which Dr. Birn explains.

This webinar is impressive because of its accessibility. Viewers with no understanding of fMRI can follow along while developing experts can acquire new knowledge and understanding.