Resting-state fMRI noise regression in multi-site aging studies
Creators: name: Scheel, Norman
name: Keller, Jeffrey N
name: Binder, Ellen F
name: Vidoni, Eric D
name: Burns, Jeffrey
name: Binu Thomas
name: Kerwin, Diana R
name: Wanpen Vongpatanasin
name: C. Munro Callum
name: Zhang, Rong
name: Zhu, David C.
Publisher: name: F1000 Research Limited
Year: 2021

description: Resting-state fMRI noise regression in multi-site aging studies<br> Norman Scheel<sup>1</sup>, Jeffrey N. Keller<sup>4</sup>, Ellen F. Binder<sup>5</sup>, Eric D. Vidoni<sup>3</sup>, Jeffrey Burns<sup>3</sup>, Binu Thomas<sup>2</sup>, Diana R. Kerwin<sup>6</sup>, Wanpen Vongpatanasin<sup>2</sup>, C. Munro Cullum<sup>2</sup>, Rong Zhang<sup>2,6</sup>, David C. Zhu<sup>1</sup><br> <sup>1</sup>Michigan State University, East Lansing, MI, USA,<br> <sup>2</sup>UT Southwestern Medical Center, Dallas, TX, USA,<br> <sup>3</sup>University of Kansas Alzheimer's Disease Center, Fairway, KS, USA,<br> <sup>4</sup>Pennington Biomedical Research Center, Baton Rouge, LA, USA,<br> <sup>5</sup>Washington University School of Medicine, St. Louis, MO, USA,<br> <sup>6</sup>Texas Health Presbyterian Hospital, Dallas, TX, USA<br> <br> Introduction<br> Risk reduction for Alzheimer’s Disease (rrAD) is an on-going trial designed to assess the effects of aerobic exercise and pharmacological cardio-vascular control on cognitive function in older adults who are at a higher risk of developing Alzheimer’s Disease (Szabo-Reed et al. 2019). As part of the neuroimaging protocol, resting-state fMRI (rs-fMRI) is obtained at baseline and again after two years of intervention. 434 hypertensive older subjects (60 to 85 years) were scanned on five different 3T MRI scanners (2x Siemens, 2x GE, 1x Philips). Our aim was to find the most efficient rs-fMRI noise regression technique across scanners and rrAD subjects, as noise regression techniques are still diverse and much debated. Also, emerging methods like AROMA (Pruim et al. 2015a) and SOCK (Bhaganagarapu et al. 2013) have only been evaluated on young subjects (Pruim et al. 2015b). Here, we compared the performance of six common techniques.<br> <br> Methods<br> With eyes fixed on a cross, rs-fMRI was acquired for 12min on each subject using a 2.5s TR, 28ms TE, 64x64 matrix size with 3.4x3.4mm pixel size, and 3-3.4mm slice thickness. The data were first processed via AFNI (Cox J.S. 1996) to detect outliers, de-spike, correct slice timing, co-register functional to anatomical images, correct subject motion and carry out a 4mm smoothing. Then, we compared the following six noise regression techniques:<br> <b>(A)</b> <b><i>Reference technique</i></b>: noise parameters derived from 24 motion parameters (24MP),<i> sudden motion scrubbing regressors<b> </b></i>(FD&lt;0.5mm)<i>,</i> white matter (WM), cerebrospinal fluid (CSF), bandpass filter (0.009-0.08Hz) and third order detrend were combined for a one-step regression using AFNI’s “3dDeconvolve” software.<br> <b>(B)</b> Added the calculated <b><i>global mean</i></b> <b><i>signal</i></b> to the regression matrix of (A).<br> <b>(C)</b> Performed <b><i>aggressive AROMA</i></b> first, before using 3dDeconvolve to regress bandpass filter and detrend parameters.<br> <b>(D)</b> As in (C), but with <b><i>non-aggressive AROMA</i></b>.<br> <b>(E)</b> <b><i>One-step AROMA</i></b> regression: combined AROMA noise independent components (ICs) with bandpass and detrend parameters as one regression matrix for 3dDeconvolve.<br> <b>(F)</b> Combined noise ICs from <b><i>SOCK</i></b>, 24MP, WM/CSF signals, bandpass and detrend parameters for 3dDeconvolve regression.<br> The performance of each technique was evaluated based on resting-state network (RSN) reproducibility, (true positive) identifiability (Pruim et al. 2015b) as well as false positives, loss of temporal degrees of freedom (tDOF), spatial smoothness, false edge and CSF activity (Bhaganagarapu et al. 2013) and temporal signal-to-noise ratios (tSNR).<br> <br> Results<br> For techniques A to F (as above) z-scores for <b>RSN reproducibility</b> were 5.8±1.2, 5.3±0.7, 6.9±1.2, 4.5±1.4, 6.4±1.3, 6.4±1.3, for <b>RSN identifiability </b>(<i>true positive</i>) 3.5±1.1, 3.4±1, 3.6±1, 1.6±1.2, 3.5+1.2, 4.1±1, and for <i>false positive</i>0.4±0.2, 0.5±0.2, 0.6±0.2, 0.5±0.2, 0.4±0.1, 0.4±0.1 (Fig. 1). <b>Edge activity</b> was at 11±9%, 8±4%, 10±5%, 26±19%, 6±4%, 6±2%, <b>CSF activity</b> is at 17±5%, 15±4%, 20±6%, 25±11%, 12±4%, 13±3% and <b>spatial</b> <b>smoothness</b> fractions were at 0.028±0.004, 0.023±0.004, 0.031±0.007, 0.03±0.007, 0.022±0.006, 0.023±0.003 (Fig. 2). Motion scrubbing regression (A,B) had a <b>loss of tDOF</b> of 7±4%, AROMA (C,D,E) lost 15±4% and SOCK (F) lost 10±2%. Global mean regression shows unbalanced anterior to posterior changes in <b>tSNR</b>. On the contrary, aggressive AROMA enhances tSNR in grey matter and reduces tSNR in WM and CSF regions uniformly across the brain. The performance of all techniques on the data of each scanner separately was comparable with all subjects’ data combined.<br> <br> Conclusions<br> Comparing across all data quality metrics, most techniques performed satisfyingly with marginal differences, except technique D (non-aggressive AROMA), which performed worst. Considering reproducibility as the most important factor for longitudinal studies, technique C (aggressive AROMA) appears to be the most favorable noise regression technique across scanners and for older hypertensive subject populations as in the rrAD trial.<br> <br> References:<br> Bhaganagarapu, Kaushik, Graeme D Jackson, and David F Abbott. 2013. “An Automated Method for Identifying Artifact in Independent Component Analysis of Resting-State FMRI.” <i>Frontiers in Human Neuroscience</i> 7 (July): 343. https://doi.org/10.3389/fnhum.2013.00343.<br> Cox J.S., R.W.; Hyde. 1996. “AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages.” <i>Computers and Biomedical Research</i> 29 (29): 162–73. https://ac-els-cdn-com.ezp-prod1.hul.harvard.edu/S0010480996900142/1-s2.0-S0010480996900142-main.pdf?_tid=c22bae7a-b8f5-4a8b-9dac-91c1ed841d53&amp;acdnat=1549393398_e37181b8933a2ac88c2d7dc0eab14413.<br> Pruim, Raimon H.R., Maarten Mennes, Jan K. Buitelaar, and Christian F Beckmann. 2015 (b). “Evaluation of ICA-AROMA and Alternative Strategies for Motion Artifact Removal in Resting-State FMRI.” <i>NeuroImage</i>. https://doi.org/10.1016/j.neuroimage.2015.02.063.<br> Pruim, Raimon H.R., Maarten Mennes, Daan van Rooij, Alberto Llera Arenas, Jan K. Buitelaar, and Christian F Beckmann. 2015 (a). “ICA-AROMA: A Robust ICA-Based Strategy for Removing Motion Artifact from FMRI Data.” <i>NeuroImage</i>. https://doi.org/10.1016/j.neuroimage.2015.02.064.<br> Szabo-Reed, Amanda N., Eric Vidoni, Ellen F. Binder, Jeffrey Burns, C. Munro Cullum, William P. Gahan, Aditi Gupta, et al. 2019. “Rationale and Methods for a Multicenter Clinical Trial Assessing Exercise and Intensive Vascular Risk Reduction in Preventing Dementia (RrAD Study).” <i>Contemporary Clinical Trials</i> 79 (January): 44–54. https://doi.org/10.1016/j.cct.2019.02.007.