MICRA: Microstructural image compilation with repeated acquisitions

Koller, Kristin; Rudrapatna, Umesh; Chamberland, Maxime; Raven, Erika P.; Parker, Greg D.; Tax, Chantal M. W.; Drakesmith, Mark; Fasano, Fabrizio; Owen, David; Hughes, Garin; Charron, Cyril; Evans, John; Jones, Derek K.

doi:10.1016/j.neuroimage.2020.117406

Cited by 30 publications

(44 citation statements)

References 53 publications

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“…For instance, there are different methods for fitting tensors (Chang et al, 2005;Cook et al, 2006;Hernandez-Fernandez et al, 2019), for identifying regions (Desikan et al, 2006;Figley et al, 2017;Hansen et al, 2020;Volz et al, 2018) and bundles (Warrington et al, 2020;Yeh, 2020;Yeh et al, 2013;Yendiki et al, 2011), for comparing bundles (Rheault et al, 2020), and for configuring and representing connectomes (Hagmann et al, 2008;Roine et al, 2019;Rubinov and Sporns, 2010;Sporns et al, 2005). Additionally, there are a number of other microstructural measures that can be characterized as well (Koller et al, 2020). Thus, the goal of the present study was not to provide an analysis between different processing toolboxes, parameters, or analysis approaches.…”

Section: Discussionmentioning

confidence: 99%

“…Another reason for this is the low number of properly configured publicly available datasets. Some of the few that exist that allow for investigations of DWI variability are the MASSIVE dataset (Froeling et al, 2017), the Human Connectome Project (HCP) 3T dataset (Van Essen et al, 2013), the MICRA dataset (Koller et al, 2020), the SIMON dataset (Duchesne et al, 2019), and the multisite dataset published by Tong et al (Tong et al, 2020). MASSIVE consists of one subject scanned repeatedly on one scanner (Froeling et al, 2017); HCP consists of multiple subjects with multiple acquisitions per session all on one scanner (Van Essen et al, 2013); MICRA consists of multiple subjects scanned repeatedly on one scanner (Koller et al, 2020); SIMON consists of one subject scanned at over 70 sites (Duchesne et al, 2019), and the Tong dataset consists of multiple subjects each scanned on multiple scanners (Tong et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging

Cai

Yang

Kanakaraj

et al. 2020

Preprint

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Diffusion weighted imaging (DWI) allows investigators to identify microstructural differences between subjects, but variability due to session and scanner biases is still a challenge. To investigate DWI variability, we present MASiVar, a multisite dataset consisting of 319 diffusion scans acquired at 3T from b = 1000 to 3000 s/mm2 across 97 different healthy subjects and four different scanners as a publicly available, preprocessed, and de-identified dataset. With these data we characterize variability on the intrasession intrascanner (N = 158), intersession intrascanner (N = 328), intersession interscanner (N = 53), and intersubject intrascanner (N = 80) levels. Our baseline analysis focuses on four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy (FA), mean diffusivity, and principal eigenvector; region-wise cerebral spinal fluid volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, volume, length and FA; and connectome correlation and maximized modularity, global efficiency, and characteristic path length. We plot the scan/re-scan discrepancies in these measures at each level and find that variability generally increases with intrasession to intersession to interscanner to intersubject effects and that sometimes interscanner variability can approach intersubject variability. This baseline study suggests harmonization between scanners for multisite analyses is critical prior to inference of group differences on subjects and demonstrates the potential of MASiVar to investigate DWI variability across multiple levels and processing approaches simultaneously.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging

Cai

Yang

Kanakaraj

et al. 2020

Preprint

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“…To assess repeatability, we employed the microstructural image compilation with repeated acquisitions dataset (MICRA, 23 ), which comprises 5 repeated sets of microstructural imaging in 6 healthy human participants (3 female, age 24-30 years). Each participant was scanned five times in the span of two weeks on a 3.0T Siemens Connectom system with ultra-strong (300 mT/m) gradients.…”

Section: Methodsmentioning

confidence: 99%

Detecting microstructural deviations in individuals with deep diffusion MRI tractometry

Chamberland

Genc

Tax

et al. 2021

Preprint

Self Cite

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Most diffusion MRI (dMRI) studies of disease rely on statistical comparisons between large groups of patients and healthy controls to infer altered tissue state. Such studies often require data from a significant number of patients before robust inferences can be made, and clinical heterogeneity can greatly challenge their discriminative power. Moreover, for clinicians and researchers studying small datasets, rare cases, or individual patients, this approach is clearly inappropriate. There is a clear and unmet need to shift away from the current standard approach of group-wise comparisons to methods with the sensitivity for detection of altered tissue states at the individual level. This would ultimately enable the early detection and interpretation of microstructural abnormalities in individual patients, an important step towards personalised-medicine in translational imaging. To this end, Detect was developed to advance dMRI-based Tractometry towards single-subject analysis. By: 1) operating on the manifold of white matter pathways; and 2) learning normative microstructural features to better discriminate patients from controls, our framework captures idiosyncrasies in patterns along brain white matter pathways in the individual. This novel approach paves the way from traditional group-based comparisons to true personalised radiology, taking microstructural imaging from the bench to the bedside.

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“…It is worth investigating the potentially large array of automated bundle segmentation methods that exist, as some are likely more/less appropriate when comparing or combining datasets with different confounds. Additionally, as alternative segmentation methods, or even whole-brain connectome analysis pipelines, are proposed, the use of open-source multi-site multi-subject datasets [91][92][93] should be encouraged to investigate the successes and limitations of new approaches. As along-fiber quantification [6,7] has proven valuable in the research setting, it would be worthwhile to perform investigations which parallel the current study in order to ask how and where along the bundle differences occur due to different effects.…”

Section: Future Studies and Limitationsmentioning

confidence: 99%

Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow

Rheault

et al. 2021

Preprint

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When investigating connectivity and microstructure of white matter pathways of the brain using diffusion tractography bundle segmentation, it is important to understand potential confounds and sources of variation in the process. While cross-scanner and cross-protocol effects on diffusion microstructure measures are well described (in particular fractional anisotropy and mean diffusivity), it is unknown how potential sources of variation effect bundle segmentation results, which features of the bundle are most affected, where variability occurs, nor how these sources of variation depend upon the method used to reconstruct and segment bundles. In this study, we investigate four potential sources of variation, or confounds, for bundle segmentation: variation (1) across scan repeats, (2) across scanners, (3) across acquisition protocol, and (4) across diffusion sensitization. We employ four different bundle segmentation workflows on two benchmark multi-subject cross-scanner and cross-protocol databases, and investigate reproducibility and biases in volume overlap, shape geometry features of fiber pathways, and microstructure features within the pathways. We find that the effects of acquisition protocol, in particular acquisition resolution, result in the lowest reproducibility of tractography and largest variation of features, followed by scanner-effects, and finally b-value effects which had similar reproducibility as scan-rescan variation. However, confounds varied both across pathways and across segmentation workflows, with some bundle segmentation workflows more (or less) robust to sources of variation. Despite variability, bundle dissection is consistently able to recover the same location of pathways in the deep white matter, with variation at the gray matter/ white matter interface. Next, we show that differences due to the choice of bundle segmentation workflows are larger than any other studied confound, with low-to-moderate overlap of the same intended pathway when segmented using different methods. Finally, quantifying microstructure features within a pathway, we show that tractography adds variability over-and-above that which exists due to noise, scanner effects, and acquisition effects. Overall, these confounds need to be considered when harmonizing diffusion datasets, interpreting or combining data across sites, and when attempting to understand the successes and limitations of different methodologies in the design and development of new tractography or bundle segmentation methods.

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MICRA: Microstructural image compilation with repeated acquisitions

Cited by 30 publications

References 53 publications

MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging

MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging

Detecting microstructural deviations in individuals with deep diffusion MRI tractometry

Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow

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