Evaluation of noise regression techniques in resting-state fMRI studies using data of 434 older adults

Abstract

Subject motion is a well-known confound in resting-state functional MRI (rs-fMRI) and the analysis of functional connectivity. Consequently, several clean-up strategies have been established to minimize the impact of subject motion. Physiological signals in response to cardiac activity and respiration are also known to alter the apparent rs-fMRI connectivity. Comprehensive comparisons of common noise regression techniques showed that the “Independent Component Analysis based strategy for Automatic Removal of Motion Artifacts” (ICA-AROMA) was a preferred pre-processing technique for teenagers and adults. However, motion and physiological noise characteristics may differ substantially for older adults. Here, we present a comprehensive comparison of noise-regression techniques for older adults from a large multi-site clinical trial of exercise and intensive pharmacological vascular risk factor reduction. The Risk Reduction for Alzheimer’s Disease (rrAD) trial included hypertensive older adults (60–84 years old) at elevated risk of developing Alzheimer’s Disease (AD). We compared the performance of censoring, censoring combined with global signal regression, non-aggressive and aggressive ICA-AROMA, as well as the Spatially Organized Component Klassifikator (SOCK) on the rs-fMRI baseline scans from 434 rrAD subjects. All techniques were rated based on network reproducibility, network identifiability, edge activity, spatial smoothness, and loss of temporal degrees of freedom (tDOF). We found that non-aggressive ICA-AROMA did not perform as well as the other four techniques, which performed table with marginal differences, demonstrating the validity of these techniques. Considering reproducibility as the most important factor for longitudinal studies, given low false-positive rates and a better preserved, more cohesive temporal structure, currently aggressive ICA-AROMA is likely the most suitable noise regression technique for rs-fMRI studies of older adults.

Causalized convergent cross-mapping and its approximate equivalence with directed information in causality analysis

“…The approximate equivalence of cCCM and DI is demonstrated using resting-state fMRI data, which are often modeled as Gaussian random variables ( 51 , 52 ). For fMRI, we investigate the baseline data of 30 subjects from the risk reduction for Alzheimer's disease (rrAD) trial ( 53 , 54 ), where 18 common ROIs of the default mode network (DMN) were extracted and sorted in a descending order by their connection strength to the isthmus of the posterior cingulate cortex seed region time course. The fMRI data of each brain region is regarded as a dynamic manifold with a deterministic attractor but perturbed by random afferent input and noise.…”

“… Demonstration of the approximate equivalence of cCCM and DI using resting-state fMRI data of 30 subjects from the rrAD trials ( 53 , 54 ). A) The approximate log-relationship between estimated DI and cCCM.…”

“…Here, we investigate the baseline data of 30 subjects (age 60–83, 17 men, and 13 women) from the rrAD trial ( 53 , 54 ). All study procedures were approved by the institutional review board at University of Texas Southwestern Medical Center, University of Kansas Medical Center Research Institute, Pennington Biomedical Research Center, and Washington University in St Louis, and each participant provided written, informed consent.…”

Comparing data-driven physiological denoising approaches for resting-state fMRI: implications for the study of aging

“…For instance, Dipasquale et al (2017) compared regression of motion parameters, the WM-CSF regression method, ICA-FIX, ICA-AROMA and multi-echo ICA, and found multi-echo ICA to be best at decoupling motion and neuronal effects. Scheel et al compared censoring, GSR, ICA-AROMA (aggressive and non-aggressive) and SOCK (an AROMA variation), and found aggressive ICA-AROMA to provide the highest network reproducibility ( Scheel et al, 2022 ). Moreover, Kassinopoulos et al compared GSR and aCompCor in terms of a set of quality metrics including functional-connectivity repeatability and modularity ( Kassinopoulos and Mitsis, 2022 ), and found a combination of aCompCor and GSR to provide the best outcomes.…”

Functional connectivity patterns in parosmia

“…There is some growing criticism about the use of denoising techniques in resting state fMRI, especially related to AROMA. However, AROMA still is one of the reliable measures to denoise fMRI data and when comparing with other algorithms like ICA-FIX or aCompCor, AROMA seems to perform at par [ 18 , 19 ]. It must also be noted that as per the developers, AROMA must be implemented after performing pre-processing with FEAT FMRIB toolkit [ 20 ] for better ICA classification.…”