Same Brain, Different Look? – the Impact of Scanner, Sequence and Preprocessing on Diffusion Imaging Outcome Parameters

Abstract

In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or masking pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from e.g. diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19-54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyrafit). After differing preprocessing steps, 3D-fractional anisotropy (FA) maps obtained by tensor fitting were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Preprocessing including unringing reduced the Gibbs ringing artefact, and head motion estimates were significantly lower at Skyra. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.

Power in network neuroscience

“…Conversely, the lower the measurement variance and the more consistent observations can be made between repeated measurements, the higher the statistical power to detect effects in a study [14]. Studies examining the quality of connectome mappings by means of neuroimaging data have pointed out that connectome reconstructions can vary significantly from study to study due to motion artefacts [19], scanner quality [80] and non-neural physiological processes [81] as well as different approaches in the analysis such as atlas choice [76,82] (Figure 3e) and thresholding [23,75] (Figure 3f). Test-retest studies have reported strong differences in measurement reliability for a wide range of connectome variables, including cortical thickness between brain areas [83]; fractional anisotropy between white-matter voxels [84] (Figure 3g); structural connectivity between edges [22]; functional connectivity between resting state networks [21]; and for example local graph metrics of structural [20] and function connectivity [85].…”

High-frequency longitudinal white matter diffusion- & myelin-based MRI database: reliability and variability

“…To date, consistency of MRI-based WM measurements has been evaluated through numerous studies, especially for the DWI technique (Boekel et al, 2017; Grech-Sollars et al, 2015; Hakulinen et al, 2021; Magnotta et al, 2012; Teipel et al, 2011; Thieleking et al, 2021; Veenith et al, 2013). These studies reported moderate to high reliability in the WM using Intraclass correlation coefficient (ICC) or Pearson’s correlation ranging from 0.5 to >0.8 as well as within- and between-subject coefficients of variation (CV) ranging from 1 to 8% and 1 to 15% respectively.…”

“…These studies reported moderate to high reliability in the WM using Intraclass correlation coefficient (ICC) or Pearson’s correlation ranging from 0.5 to >0.8 as well as within- and between-subject coefficients of variation (CV) ranging from 1 to 8% and 1 to 15% respectively. Among DTI-derived measures, Fractional anisotropy (FA) and Mean Diffusivity (MD) generally show the highest reliability across different WM regions (Acheson et al, 2017; Hakulinen et al, 2021; Luque Laguna et al, 2020; Palacios et al, 2017; Shahim et al, 2017; Thieleking et al, 2021; Zhou et al, 2018). For NODDI-derived measures, studies reported similar (intracellular volume fraction, ICvf) or higher (orientation dispersion, OD) reliability compared to DTI measures, while isotropic volume fraction (ISOvf) showed the poorest reliability (ICC<0.6) (Andica et al, 2020; Chung et al, 2016; Granberg et al, 2017; Lucignani et al, 2021; Tariq, 2013).…”

“…Nevertheless, several important issues that remain to be addressed : (1) although the recent studies include a reasonably large sample size n ≥ 20 (Boekel et al, 2017; Hakulinen et al, 2021; Lehmann et al, 2021; Thieleking et al, 2021), most of them are based on limited data with sample sizes of n ≥ 10 (Andica et al, 2020; Chung et al, 2016; Granberg et al, 2017; Koller et al, 2020; Tariq, 2013; L. Zhang et al, 2019); (2) most of the previous studies are focused on a short-period (scan-rescan within a week or with 2-4 weeks intervals) rather than longer time intervals. Indeed, longitudinal neuroimaging studies or clinical trials are typically separated by a few weeks (> 3 weeks) to several months; (3) only a few studies include more than two or three-time points (Cai et al, 2021; Schwartz et al, 2019), thus not generating enough data per subject to assess relevant reliabilities; and (4) few studies have directly compared multiple WM microstructural from several MRI techniques in the same population (Koller et al, 2020; Schwartz et al, 2019).…”