Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative

Abstract

The Italian Neuroimaging Network Initiative (INNI) is an expanding repository of brain MRI data from multiple sclerosis (MS) patients recruited at four Italian MRI research sites. We describe the raw data quality of resting-state functional MRI (RS-fMRI) time-series in INNI and the inter-site variability in functional connectivity (FC) features after unified automated data preprocessing. MRI datasets from 489 MS patients and 246 healthy control (HC) subjects were retrieved from the INNI database. Raw data quality metrics included temporal signal-to-noise ratio (tSNR), spatial smoothness (FWHM), framewise displacement (FD), and differential variation in signals (DVARS). Automated preprocessing integrated white-matter lesion segmentation (SAMSEG) into a standard fMRI pipeline (fMRIPrep). FC features were calculated on pre-processed data and harmonized between sites (Combat) prior to assessing general MS-related alterations. Across centers (both groups), median tSNR and FWHM ranged from 47 to 84 and from 2.0 to 2.5, and median FD and DVARS ranged from 0.08 to 0.24 and from 1.06 to 1.22. After preprocessing, only global FC-related features were significantly correlated with FD or DVARS. Across large-scale networks, age/sex/FD-adjusted and harmonized FC features exhibited both inter-site and site-specific inter-group effects. Significant general reductions were obtained for somatomotor and limbic networks in MS patients (vs. HC). The implemented procedures provide technical information on raw data quality and outcome of fully automated preprocessing that might serve as reference in future RS-fMRI studies within INNI. The unified pipeline introduced little bias across sites and appears suitable for multisite FC analyses on harmonized network estimates.

Implementing ABCD studyⓇ MRI sequences for multi-site cohort studies: Practical guide to necessary steps, preprocessing methods, and challenges

“… ReHo mean difference calculated with each scanner's fMRI were 0.0039, 0.0005, −0.0007 and −0.01 for Siemens Skyra Fit, Philips Elition X, GE Premier, and Siemens Vida, respectively. The variability of ReHo values as a function of functional time points has also been reported in previous studies [ 49 ] and explains why ReHo values in present study for Siemens Skyra (time points = 200) was less as compared to other scanners (time points = 380). ALFF values showed significantly larger variations amongst all the scanners with ALFF mean of 476, 210,304, 1480, 41.76 for Siemens Skyra Fit, Philips Elition X, GE Premier and Siemens Vida respectively.…”

2.5-Year changes of connectivity dynamism are relevant for physical and cognitive deterioration in multiple sclerosis

“…After RS fMRI pre-processing (Supplementary methods), TVFC was assessed basing on the calculation of degree centrality, 26 a measure quantifying the relative importance of individual gray matter (GM) voxels over the whole-brain networks that was already used in multicenter studies of MS 26 and showed minor scanner-related effects, easy to be controlled using scanner-adjusted statistical models. 27 Using DPABI software (http://rfmri.org/dpabi), we splitted RS fMRI scans into sliding windows of 22 time points (Scanner 1) or 42 time points (Scanner 2), to take into account for different repetition times/acquisition lengths (but at the same time maintaining an equal window length in seconds across scanners), with a shift length of one volume across windows. Degree centrality maps were computed for each window.…”

Functional gradients reveal cortical hierarchy changes in multiple sclerosis

“…MRI data were preprocessed using a fully automated pipeline specifically assembled for the serial extraction of FC features from rs‐fMRI data acquired in large samples of subjects including MS patients (De Rosa et al, 2023 ). In detail, brain tissue segmentation was performed with FreeSurfer v7.1.1 (Fischl, 2012 ), employing 3D T1w and co‐registered FLAIR scans with the sequence adaptive multimodal segmentation (SAMSEG) (Cerri et al, 2021 ) procedure to automatically and simultaneously perform whole‐brain tissue segmentation, including white matter (WM), gray matter, cerebrospinal fluid (CSF) volumes, and MS lesion segmentation for lesion load (LL) estimation.…”