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P417. In Vivo Evidence of Microstructural Hypo-Connectivity of Brain White Matter in 22q11.2 Deletion Syndrome [Meeting Abstract]
Raven, E; Veraart, J; Kievit, R; Genc, S; Ward, I; Cunningham, A; Doherty, J; van, den Bree M; Jones, D
Background: 22q11.2 Deletion Syndrome, or 22q11.2DS, is a genetic syndrome associated with high rates of schizophrenia, autism, and attention deficit hyperactivity disorder, in addition to widespread structural and functional abnormalities throughout the brain. Experimental animal models have identified neuronal connectivity deficits, e.g., decreased axonal length and complexity of axonal branching, as a primary mechanism underlying atypical brain development in 22q11.2DS. However, it is still unclear whether deficits in axonal morphology can also be observed in people with 22q11.2DS.
Method(s): To explore axonal morphology in depth, it is necessary to move beyond current state-of-the-art MRI techniques to achieve enhanced cellular specificity in developmental populations. Here, we present an in-depth characterization of white matter microstructure in both typically developing (n=92) and 22q11.2DS (n=6) participants using ultra-strong gradients and a multi-shell diffusion MRI acquisition, including b-values up to 6000 s/mm2. The scanner and unique high-b shell protocol enable for the first time sensitivity to axon morphology. We then conducted a novel multi-parametric analysis to probe microstructural properties underlying disrupted axonal morphology, to better describe previous observations of white matter hypo-connectivity in 22q11.2DS.
Result(s): We observed increased diffusion anisotropy and reduced water mobility across all white matter tracts, in addition to identifying deficits in axonal morphology. This, in combination with reduced tract volume measurements, supports the hypothesis that microstructural connectivity in 22q11.2DS is mediated by densely packed axons with disproportionately small diameters.
Conclusion(s): Our findings provide insight into the in vivo mechanistic features of 22q11.2DS, and promote further investigation of shared features in neurodevelopmental and psychiatric disorders. Supported By: ER: UK Marshall-Sherfield Fellowship; JV: NIH P41 EB-017183; R01 NS088040); RK: SUAG/047 G101400; JD: Wellcome Trust 102003/Z/13/Z; MvdB: MRC MR/T033045/1, NIMH U01 MH119738-01, Wellcome Trust ISSF; DKJ: Wellcome Trust 096646/Z/11/Z and Wellcome Trust 104943/Z/14/Z. Keywords: 22q11 Deletion Syndrome, Diffusion MRI, Developmental Neuroimaging, Structural MRI
Copyright
EMBASE:2017551985
ISSN: 1873-2402
CID: 5240622
Improved diffusion parameter estimation by incorporating T2 relaxation properties into the DKI-FWE model
Anania, Vincenzo; Collier, Quinten; Veraart, Jelle; Buikema, Annemieke E; Vanhevel, Floris; Billiet, Thibo; Jeurissen, Ben; den Dekker, Arnold J; Sijbers, Jan
The free water elimination (FWE) model and its kurtosis variant (DKI-FWE) can separate tissue and free water signal contributions, thus providing tissue-specific diffusional information. However, a downside of these models is that the associated parameter estimation problem is ill-conditioned, necessitating the use of advanced estimation techniques that can potentially bias the parameter estimates. In this work, we propose the T2-DKI-FWE model that exploits the T2 relaxation properties of both compartments, thereby better conditioning the parameter estimation problem and providing, at the same time, an additional potential biomarker (the T2 of tissue). In our approach, the T2 of tissue is estimated as an unknown parameter, whereas the T2 of free water is assumed known a priori and fixed to a literature value (1573 ms). First, the error propagation of an erroneous assumption on the T2 of free water is studied. Next, the improved conditioning of T2-DKI-FWE compared to DKI-FWE is illustrated using the Cramér-Rao lower bound matrix. Finally, the performance of the T2-DKI-FWE model is compared to that of the DKI-FWE and T2-DKI models on both simulated and real datasets. The error due to a biased approximation of the T2 of free water was found to be relatively small in various diffusion metrics and for a broad range of erroneous assumptions on its underlying ground truth value. Compared to DKI-FWE, using the T2-DKI-FWE model is beneficial for the identifiability of the model parameters. Our results suggest that the T2-DKI-FWE model can achieve precise and accurate diffusion parameter estimates, through effective reduction of free water partial volume effects and by using a standard nonlinear least squares approach. In conclusion, incorporating T2 relaxation properties into the DKI-FWE model improves the conditioning of the model fitting, while only requiring an acquisition scheme with at least two different echo times.
PMID: 35447354
ISSN: 1095-9572
CID: 5218532
In Vivo Evidence of Microstructural Hypo-Connectivity of Brain White Matter in 22q11.2 Deletion Syndrome [Meeting Abstract]
Raven, Erika; Veraart, Jelle; Kievit, Rogier; Genc, Sila; Ward, Isobel; Cunningham, Adam; Doherty, Joanne; van den Bree, Marianne; Jones, Derek
ISI:000789022201004
ISSN: 0006-3223
CID: 5499322
What's New and What's Next in Diffusion MRI Preprocessing
Tax, Chantal M W; Bastiani, Matteo; Veraart, Jelle; Garyfallidis, Eleftherios; Okan Irfanoglu, M
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews or have only gained attention in recent years: brain/skull extraction, B-matrix flips w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on "what's new" since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on "Mapping the Connectome" in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on "what's next" in dMRI preprocessing.
PMID: 34965454
ISSN: 1095-9572
CID: 5108252
Toward more robust and reproducible diffusion kurtosis imaging
Henriques, Rafael N; Jespersen, Sune N; Jones, Derek K; Veraart, Jelle
PURPOSE/OBJECTIVE:The general utility of diffusion kurtosis imaging (DKI) is challenged by its poor robustness to imaging artifacts and thermal noise that often lead to implausible kurtosis values. THEORY AND METHODS/UNASSIGNED:A robust scalar kurtosis index can be estimated from powder-averaged diffusion-weighted data. We introduce a novel DKI estimator that uses this scalar kurtosis index as a proxy for the mean kurtosis to regularize the fit. RESULTS:The regularized DKI estimator improves the robustness and reproducibility of the kurtosis metrics and results in parameter maps with enhanced quality and contrast. CONCLUSION/CONCLUSIONS:Our novel DKI estimator promotes the wider use of DKI in clinical research and potentially diagnostics by improving the reproducibility and precision of DKI fitting and, as such, enabling enhanced visual, quantitative, and statistical analyses of DKI parameters.
PMID: 33829542
ISSN: 1522-2594
CID: 4839472
Nanostructure-specific X-ray tomography reveals myelin levels, integrity and axon orientations in mouse and human nervous tissue
Georgiadis, Marios; Schroeter, Aileen; Gao, Zirui; Guizar-Sicairos, Manuel; Liebi, Marianne; Leuze, Christoph; McNab, Jennifer A; Balolia, Aleezah; Veraart, Jelle; Ades-Aron, Benjamin; Kim, Sunglyoung; Shepherd, Timothy; Lee, Choong H; Walczak, Piotr; Chodankar, Shirish; DiGiacomo, Phillip; David, Gergely; Augath, Mark; Zerbi, Valerio; Sommer, Stefan; Rajkovic, Ivan; Weiss, Thomas; Bunk, Oliver; Yang, Lin; Zhang, Jiangyang; Novikov, Dmitry S; Zeineh, Michael; Fieremans, Els; Rudin, Markus
Myelin insulates neuronal axons and enables fast signal transmission, constituting a key component of brain development, aging and disease. Yet, myelin-specific imaging of macroscopic samples remains a challenge. Here, we exploit myelin's nanostructural periodicity, and use small-angle X-ray scattering tensor tomography (SAXS-TT) to simultaneously quantify myelin levels, nanostructural integrity and axon orientations in nervous tissue. Proof-of-principle is demonstrated in whole mouse brain, mouse spinal cord and human white and gray matter samples. Outcomes are validated by 2D/3D histology and compared to MRI measurements sensitive to myelin and axon orientations. Specificity to nanostructure is exemplified by concomitantly imaging different myelin types with distinct periodicities. Finally, we illustrate the method's sensitivity towards myelin-related diseases by quantifying myelin alterations in dysmyelinated mouse brain. This non-destructive, stain-free molecular imaging approach enables quantitative studies of myelination within and across samples during development, aging, disease and treatment, and is applicable to other ordered biomolecules or nanostructures.
PMID: 34011929
ISSN: 2041-1723
CID: 4877382
The variability of MR axon radii estimates in the human white matter
Veraart, Jelle; Raven, Erika P; Edwards, Luke J; Weiskopf, Nikolaus; Jones, Derek K
The noninvasive quantification of axonal morphology is an exciting avenue for gaining understanding of the function and structure of the central nervous system. Accurate non-invasive mapping of micron-sized axon radii using commonly applied neuroimaging techniques, that is, diffusion-weighted MRI, has been bolstered by recent hardware developments, specifically MR gradient design. Here the whole brain characterization of the effective MR axon radius is presented and the inter- and intra-scanner test-retest repeatability and reproducibility are evaluated to promote the further development of the effective MR axon radius as a neuroimaging biomarker. A coefficient-of-variability of approximately 10% in the voxelwise estimation of the effective MR radius is observed in the test-retest analysis, but it is shown that the performance can be improved fourfold using a customized along-tract analysis.
PMID: 33576105
ISSN: 1097-0193
CID: 4780142
Improved Task-based Functional MRI Language Mapping in Patients with Brain Tumors through Marchenko-Pastur Principal Component Analysis Denoising
Ades-Aron, Benjamin; Lemberskiy, Gregory; Veraart, Jelle; Golfinos, John; Fieremans, Els; Novikov, Dmitry S; Shepherd, Timothy
Background Functional MRI improves preoperative planning in patients with brain tumors, but task-correlated signal intensity changes are only 2%-3% above baseline. This makes accurate functional mapping challenging. Marchenko-Pastur principal component analysis (MP-PCA) provides a novel strategy to separate functional MRI signal from noise without requiring user input or prior data representation. Purpose To determine whether MP-PCA denoising improves activation magnitude for task-based functional MRI language mapping in patients with brain tumors. Materials and Methods In this Health Insurance Portability and Accountability Act-compliant study, MP-PCA performance was first evaluated by using simulated functional MRI data with a known ground truth. Right-handed, left-language-dominant patients with brain tumors who successfully performed verb generation, sentence completion, and finger tapping functional MRI tasks were retrospectively identified between January 2017 and August 2018. On the group level, for each task, histograms of z scores for original and MP-PCA denoised data were extracted from relevant regions and contralateral homologs were seeded by a neuroradiologist blinded to functional MRI findings. Z scores were compared with paired two-sided t tests, and distributions were compared with effect size measurements and the Kolmogorov-Smirnov test. The number of voxels with a z score greater than 3 was used to measure task sensitivity relative to task duration. Results Twenty-three patients (mean age ± standard deviation, 43 years ± 18; 13 women) were evaluated. MP-PCA denoising led to a higher median z score of task-based functional MRI voxel activation in left hemisphere cortical regions for verb generation (from 3.8 ± 1.0 to 4.5 ± 1.4; P < .001), sentence completion (from 3.7 ± 1.0 to 4.3 ± 1.4; P < .001), and finger tapping (from 6.9 ± 2.4 to 7.9 ± 2.9; P < .001). Median z scores did not improve in contralateral homolog regions for verb generation (from -2.7 ± 0.54 to -2.5 ± 0.40; P = .90), sentence completion (from -2.3 ± 0.21 to -2.4 ± 0.37; P = .39), or finger tapping (from -2.3 ± 1.20 to -2.7 ± 1.40; P = .07). Individual functional MRI task durations could be truncated by at least 40% after MP-PCA without degradation of clinically relevant correlations between functional cortex and functional MRI tasks. Conclusion Denoising with Marchenko-Pastur principal component analysis led to higher task correlations in relevant cortical regions during functional MRI language mapping in patients with brain tumors. © RSNA, 2020 Online supplemental material is available for this article.
PMID: 33289611
ISSN: 1527-1315
CID: 4708782
Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: algorithms and results
Ning, Lipeng; Bonet-Carne, Elisenda; Grussu, Francesco; Sepehrband, Farshid; Kaden, Enrico; Veraart, Jelle; Blumberg, Stefano B; Khoo, Can Son; Palombo, Marco; Kokkinos, Iasonas; Alexander, Daniel C; Coll-Font, Jaume; Scherrer, Benoit; Warfield, Simon K; Karayumak, Suheyla Cetin; Rathi, Yogesh; Koppers, Simon; Weninger, Leon; Ebert, Julia; Merhof, Dorit; Moyer, Daniel; Pietsch, Maximilian; Christiaens, Daan; Gomes Teixeira, Rui Azeredo; Tournier, Jacques-Donald; Schilling, Kurt G; Huo, Yuankai; Nath, Vishwesh; Hansen, Colin; Blaber, Justin; Landman, Bennett A; Zhylka, Andrey; Pluim, Josien; Parker, Greg; Rudrapatna, Umesh; Evans, John; Charron, Cyril; Jones, Derek K; Tax, Chantal M W
Cross-scanner and cross-protocol variability of diffusion magnetic resonance imaging (dMRI) data are known to be major obstacles in multi-site clinical studies since they limit the ability to aggregate dMRI data and derived measures. Computational algorithms that harmonize the data and minimize such variability are critical to reliably combine datasets acquired from different scanners and/or protocols, thus improving the statistical power and sensitivity of multi-site studies. Different computational approaches have been proposed to harmonize diffusion MRI data or remove scanner-specific differences. To date, these methods have mostly been developed for or evaluated on single b-value diffusion MRI data. In this work, we present the evaluation results of 19 algorithms that are developed to harmonize the cross-scanner and cross-protocol variability of multi-shell diffusion MRI using a benchmark database. The proposed algorithms rely on various signal representation approaches and computational tools, such as rotational invariant spherical harmonics, deep neural networks and hybrid biophysical and statistical approaches. The benchmark database consists of data acquired from the same subjects on two scanners with different maximum gradient strength (80 and 300 mT/m) and with two protocols. We evaluated the performance of these algorithms for mapping multi-shell diffusion MRI data across scanners and across protocols using several state-of-the-art imaging measures. The results show that data harmonization algorithms can reduce the cross-scanner and cross-protocol variabilities to a similar level as scan-rescan variability using the same scanner and protocol. In particular, the LinearRISH algorithm based on adaptive linear mapping of rotational invariant spherical harmonics features yields the lowest variability for our data in predicting the fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK) and the rotationally invariant spherical harmonic (RISH) features. But other algorithms, such as DIAMOND, SHResNet, DIQT, CMResNet show further improvement in harmonizing the return-to-origin probability (RTOP). The performance of different approaches provides useful guidelines on data harmonization in future multi-site studies.
PMID: 32673745
ISSN: 1095-9572
CID: 4528402
The diagnostic role of diffusional kurtosis imaging in glioma grading and differentiation of gliomas from other intra-axial brain tumours: a systematic review with critical appraisal and meta-analysis
Abdalla, Gehad; Dixon, Luke; Sanverdi, Eser; Machado, Pedro M; Kwong, Joey S W; Panovska-Griffiths, Jasmina; Rojas-Garcia, Antonio; Yoneoka, Daisuke; Veraart, Jelle; Van Cauter, Sofie; Abdel-Khalek, Ahmed M; Settein, Magdy; Yousry, Tarek; Bisdas, Sotirios
PURPOSE/OBJECTIVE:We aim to illustrate the diagnostic performance of diffusional kurtosis imaging (DKI) in the diagnosis of gliomas. METHODS:A review protocol was developed according to the (PRISMA-P) checklist, registered in the international prospective register of systematic reviews (PROSPERO) and published. A literature search in 4 databases was performed using the keywords 'glioma' and 'diffusional kurtosis'. After applying a robust inclusion/exclusion criteria, included articles were independently evaluated according to the QUADAS-2 tool and data extraction was done. Reported sensitivities and specificities were used to construct 2 × 2 tables and paired forest plots using the Review Manager (RevMan®) software. A random-effect model was pursued using the hierarchical summary receiver operator characteristics. RESULTS: = 73.8%). CONCLUSION/CONCLUSIONS:DKI shows good diagnostic accuracy in the differentiation of high- and low-grade gliomas further supporting its potential role in clinical practice. Further exploration of DKI in differentiating IDH status and in characterising non-glioma CNS tumours is however needed.
PMID: 32367349
ISSN: 1432-1920
CID: 4430012