Towards an optimised processing pipeline for diffusion MRI data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank

Maximov, Ivan I.; Alnæs, Dag; Westlye, Lars T.

doi:10.1101/511964

Cited by 6 publications

(2 citation statements)

References 57 publications

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“…Another problem is an image manipulation and data processing related to the brains with massive lesions and severe deformations [72]. Currently, there is no reliable method of DKI/DTI post-processing similar to reference [73], which allows one to validate and verify a robustness of numerical algorithms in the case of heavy brain deformation/deflection. Thus, it might introduce a larger variability in the results.…”

Section: Discussionmentioning

confidence: 99%

Feasibility of Non-Gaussian Diffusion Metrics in Chronic Disorders of Consciousness

et al. 2019

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Diagnostic accuracy of different chronic disorders of consciousness (DOC) can be affected by the false negative errors in up to 40% cases. In the present study, we aimed to investigate the feasibility of a non-Gaussian diffusion approach in chronic DOC and to estimate a sensitivity of diffusion kurtosis imaging (DKI) metrics for the differentiation of vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS) from a healthy brain state. We acquired diffusion MRI data from 18 patients in chronic DOC (11 VS/UWS, 7 MCS) and 14 healthy controls. A quantitative comparison of the diffusion metrics for grey (GM) and white (WM) matter between the controls and patient group showed a significant (p < 0.05) difference in supratentorial WM and GM for all evaluated diffusion metrics, as well as for brainstem, corpus callosum, and thalamus. An intra-subject VS/UWS and MCS group comparison showed only kurtosis metrics and fractional anisotropy differences using tract-based spatial statistics, owing mainly to macrostructural differences on most severely lesioned hemispheres. As a result, we demonstrated an ability of DKI metrics to localise and detect changes in both WM and GM and showed their capability in order to distinguish patients with a different level of consciousness.

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Section: Discussionmentioning

confidence: 99%

Feasibility of Non-Gaussian Diffusion Metrics in Chronic Disorders of Consciousness

et al. 2019

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“…Diffusion MRI processing was performed as follows: for each subject the two dMRI acquisitions were first concatenated and then denoised using a Marchenko-Pastur-PCA-based algorithm (Veraart et al, 2016; Veraart et al, 2016b); the eddy current, head movement and EPI geometric distortions were corrected using outlier replacement and slice-to-volume registration with TOPUP and EDDY (Andersson et al, 2003; Smith et al, 2004; Andersson and Sotiropoulos, 2016; Andersson et al, 2016; Andersson et al, 2017); bias field inhomogeneity correction was performed by calculating the bias field of the mean b0 volume and applying the correction to all the volumes (Tustison et al, 2010). This framework only differs from the optimal pipeline for diffusion preprocessing presented in Maximov et al (2019) in that we did not perform the final smoothing or the gibbs-ring removal (Kellner et al, 2016) due to the nature of the data (partial fourier space acquisition).…”

Section: Methodsmentioning

confidence: 99%

Neonatal morphometric similarity mapping for predicting brain age and characterizing neuroanatomic variation associated with preterm birth

Galdi

Blesa

Stoye

et al. 2019

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1Multi-contrast MRI captures information about brain macro-and micro-structure which can be combined in an inte-2 grated model to obtain a detailed "fingerprint" of the anatomical properties of an individual's brain. Inter-regional 3 similarities between features derived from structural and diffusion MRI, including regional volumes, diffusion tensor 4 metrics, neurite orientation dispersion and density imaging measures, can be modelled as morphometric similarity net-5 works (MSNs). Here, individual MSNs were derived from 105 neonates (59 preterm and 46 term) who were scanned 6 between 38 and 45 weeks postmenstrual age (PMA). Inter-regional similarities were used as predictors in a regression 7 model of age at the time of scanning and in a classification model to discriminate between preterm and term infant 8 brains. When tested on unseen data, the regression model predicted PMA at scan with a mean absolute error of 0.70 ± 9 0.56 weeks, and the classification model achieved 92% accuracy. We conclude that MSNs predict chronological brain 10 age accurately; and they provide a data-driven approach to identify networks that characterize typical maturation and 11 those that contribute most to neuroanatomic variation associated with preterm birth. 12 Significance Statement 13 Preterm birth affects 15 million deliveries each year and is closely associated with intellectual disability, educational 14under-performance and psychiatric disorders. Imaging studies reveal a cerebral signature of preterm birth that includes 15 alterations in brain structure and network connectivity, but there has not been a unified data-driven approach that 16 incorporates all available information from MRI. We report that morphometric similarity networks (MSNs), which 17 integrate information from structural MRI and diffusion MRI in a single model, accurately predict brain age. MSNs 18 reveal the networks that characterize maturation and those that contribute to neuroanatomic variation associated with 19 preterm birth. MSNs are extensible and offer a new approach for investigating early life origins of neurodevelopmental 20 and mental health disorders 21 1

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Impact of b-value on estimates of apparent fibre density

Genc

Tax

Raven

et al. 2020

Preprint

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Recent advances in diffusion magnetic resonance imaging (dMRI) analysis techniques have improved our understanding of fibre-specific variations in white matter microstructure.Increasingly, studies are adopting multi-shell dMRI acquisitions to improve the robustness of dMRI-based inferences. However, the impact of b-value choice on the estimation of dMRI measures such as apparent fibre density (AFD) derived from spherical deconvolution is not known. Here, we investigate the impact of b-value sampling scheme on estimates of AFD.First, we performed simulations to assess the correspondence between AFD and simulated intra-axonal signal fraction across multiple b-value sampling schemes. We then studied the impact of sampling scheme on the relationship between AFD and age in a developmental population (n=78) aged 8-18 (mean=12.4, SD=2.9 years) using hierarchical clustering and whole brain fixel-based analyses. Multi-shell dMRI data were collected at 3.0T using ultrastrong gradients (300 mT/m), using 6 diffusion-weighted shells ranging from 0 -6000 s/mm 2 . Simulations revealed that the correspondence between estimated AFD and simulated intra-axonal signal fraction was improved with high b-value shells due to increased suppression of the extra-axonal signal. These results were supported by in vivo data, as sensitivity to developmental age-relationships was improved with increasing bvalue (b=6000 s/mm 2 , median R 2 = .34; b=4000 s/mm 2 , median R 2 = .29; b=2400 s/mm 2 , median R 2 = .21; b=1200 s/mm 2 , median R 2 = .17) in a tract-specific fashion. Overall, estimates of AFD and age-related microstructural development were better characterised at high diffusion-weightings due to improved correspondence with intra-axonal properties.

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Towards an optimised processing pipeline for diffusion MRI data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank

Cited by 6 publications

References 57 publications

Feasibility of Non-Gaussian Diffusion Metrics in Chronic Disorders of Consciousness

Feasibility of Non-Gaussian Diffusion Metrics in Chronic Disorders of Consciousness

Neonatal morphometric similarity mapping for predicting brain age and characterizing neuroanatomic variation associated with preterm birth

Impact of b-value on estimates of apparent fibre density

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