A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol

Abstract

Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations.

Comparison of heritability estimates on resting state fMRI connectivity phenotypes using the ENIGMA analysis pipeline

“…The site-to-site variability in the quality of the T1w data and the variance in registration quality between T1w and rsfMRI images is likely be more prominent for ENIGMA studies and therefore poses a risk of influencing the results of the overall rsfMRI analysis. Likewise, we showed that the use of a deformable template improved registration for individual EPI images, including ventricular overlap, when compared to the standard ICBM-152 template (Adhikari, et al, 2017). …”

“…It includes the application of principal components analysis (PCA)-based denoising (Veraart, et al, 2016a; Veraart, et al, 2016b). The denoising is the first step in this analysis pipeline to improve signal-to noise ratio (SNR) and temporal SNR (tSNR) properties of the time series data (Adhikari, et al, 2017), without losing image spatial resolution, and avoids introducing of additional partial volume effects that complicate further analyses. The MP-PCA approach does not alter the resting state network activation patterns, whereas spatial smoothing using a Gaussian kernel leads to partial voxel averaging, spreading the activations across gray and white matter regions and removing smaller nodes.…”

“…Here, we implemented the spatial distortion corrections in the HCP dataset but distortion correction data was not collected in GOBS dataset. In the next step, a transformation is computed registering the base volume to the ENIGMA EPI template that was derived from 1,100 datasets collected across 22 sites (Adhikari, et al, 2017) to develop a spatial template and spatial atlas. This atlas has a dual purpose: it is used for regression of the global signal, but it also offers a common anatomical spatial reference frame.…”

“…Multi-site studies can collect larger and more representative samples but require pre-processing steps to address site-specific sources of methodological variance. The ENIGMA consortium developed an rsfMRI analysis pipeline to perform consistent analysis and extraction of resting state connectivity measures across data collected using diverse protocols (Adhikari, et al, 2017). Here, we used this pipeline to (i) replicate findings in the first report that demonstrated significant additive genetic contribution to the inter-subject variance in the default mode network using the original (N=334) Genetics of Brain Structure (GOBS) cohort (Glahn, et al, 2010); (ii) extend this analysis to other resting state networks and (iii) replicate these findings in an independent dataset from the Human Connectome Project (HCP (Van Essen, et al, 2013)).…”

“…Many fMRI analysis pipelines require a structural T1-weighted (T1w) brain MRI scan to regress out signal measured in the cerebrospinal fluid (CSF) and cerebral white matter and for registration of all data to a common atlas space (Jenkinson, et al, 2012; Smith, et al, 2004). In the spirit of ENIGMA, this pipeline is a single-modality and uses a deformable template created from 1,100 individual images provided by ENIGMA sites to incorporate and compensate for shape distortions common to fMRI images (Adhikari, et al, 2017). This alleviates potential pitfalls of site-to-site variance in the quality of T1w data and coregistration biases that may influence extraction of rsfMRI phenotypes.…”