ORCID Profile
0000-0001-8323-5451
Current Organisation
KU Leuven
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Publisher: Cold Spring Harbor Laboratory
Date: 15-02-2019
DOI: 10.1101/551739
Abstract: MRtrix3 is an open-source, cross-platform software package for medical image processing, analysis and visualization, with a particular emphasis on the investigation of the brain using diffusion MRI. It is implemented using a fast, modular and flexible general-purpose code framework for image data access and manipulation, enabling efficient development of new applications, whilst retaining high computational performance and a consistent command-line interface between applications. In this article, we provide a high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software.
Publisher: Proceedings of the National Academy of Sciences
Date: 10-05-2021
Abstract: This work uses state-of-the-art acquisition and analysis methods developed specifically for fetal MRI to delineate the developing brain’s association, projection, and callosal white matter pathways. We describe unique, heterogenous maturational trajectories for different tracts, suggesting that regionally distinct biological mechanisms are at play in building the structural connectome in utero.
Publisher: Springer Science and Business Media LLC
Date: 19-01-2017
DOI: 10.1007/S11682-016-9665-8
Abstract: In a previous longitudinal diffusion tensor imaging (DTI) study, we observed cerebral white matter (WM) alterations (reduced fractional anisotropy (FA)) related to decreased cognitive performance 3-5 months after chemotherapy-treatment (t2) when compared to baseline (t1) (Deprez et al. in Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 30(3), 274-281. doi:10.1200/JCO.2011.36.8571, 2012). The current study investigates the evolution and the nature of these previously observed microstructural changes. Twenty-five young women with early-stage breast cancer who received chemotherapy treatment (C+), 14 who did not receive chemotherapy (C-) and 15 healthy controls (HC) previously studied, underwent reassessment 3-4 years after treatment (t3). We assessed (1) longitudinal changes of cognitive performance and FA and (2) cross-sectional group differences in myelin-water-imaging and multishell diffusion MRI metrics at t3. MRI metrics were assessed on a voxel-by-voxel basis and in regions-of-interest (ROI) in which previous WM injury was detected. Longitudinal results: Mixed-effects modeling revealed significant group-time interactions for verbal memory and processing speed (p < 0.05) reflecting regained performance in the C+ group at t3. Furthermore, in chemotherapy-treated patients, FA returned to baseline levels at t3 in all ROIs (p < 0.002), whereas no FA changes were seen in controls. Additionally, FA increase from t2 to t3 correlated with time since treatment in two of the four regions (r = 0.40, p < 0.05). Cross-sectional results: Advanced diffusion MRI and myelin-water imaging metrics in the ROIs did not differ between groups. Similarly, no whole-brain voxelwise differences were detected. Initial WM alterations and reduced cognitive performance following chemotherapy-treatment were found to recover in a group of young breast cancer survivors three to four years after treatment.
Publisher: Cold Spring Harbor Laboratory
Date: 21-10-2020
DOI: 10.1101/2020.10.20.347518
Abstract: MRI scanner and sequence imperfections and advances in reconstruction and imaging techniques to increase motion robustness can lead to inter-slice intensity variations in Echo Planar Imaging. Leveraging deep convolutional neural networks as universal image filters, we present a data-driven method for the correction of acquisition artefacts that manifest as inter-slice inconsistencies, regardless of their origin. This technique can be applied to motion- and dropout-artefacted data by embedding it in a reconstruction pipeline. The network is trained in the absence of ground-truth data on, and finally applied to, the reconstructed multi-shell high angular resolution diffusion imaging signal to produce a corrective slice intensity modulation field. This correction can be performed in either motion-corrected or scattered source-space. We focus on gaining control over the learned filter and the image data consistency via built-in spatial frequency and intensity constraints. The end product is a corrected image reconstructed from the original raw data, modulated by a multiplicative field that can be inspected and verified to match the expected features of the artefact. In-plane, the correction approximately preserves the contrast of the diffusion signal and throughout the image series, it reduces interslice inconsistencies within and across subjects without biasing the data. We apply our pipeline to enhance the super-resolution reconstruction of neonatal multi-shell high angular resolution data as acquired in the developing Human Connectome Project.
Publisher: Elsevier BV
Date: 08-2022
DOI: 10.1016/J.NEUROIMAGE.2022.119319
Abstract: The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. Diffusion MRI allows the characterisation of subtle inter-in idual differences in structural brain connectivity, though in idual brain connectivity maps (connectomes) are by nature high in dimensionality and complex to interpret. Machine learning methods are a powerful tool to uncover properties of the connectome which are not readily visible and can give us clues as to how and why in idual developmental trajectories differ. In this manuscript we used Deep Neural Networks and Random Forests to predict demographic and neurodevelopmental characteristics from neonatal structural connectomes in a large s le of babies (n = 524) from the developing Human Connectome Project. We achieved an accurate prediction of postmenstrual age (PMA) at scan in term-born infants (mean absolute error (MAE) = 0.72 weeks, r = 0.83 and p < 0.001). We also achieved good accuracy when predicting gestational age at birth in a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p < 0.001). We subsequently used sensitivity analysis to obtain feature relevance from our prediction models, with the most important connections for prediction of PMA and GA found to predominantly involve frontal and temporal regions, thalami, and basal ganglia. From our models of PMA at scan for infants born at term, we computed a brain maturation index (predicted age minus actual age) of in idual preterm neonates and found a significant correlation between this index and motor outcome at 18 months corrected age. Our results demonstrate the applicability of machine learning techniques in analyses of the neonatal connectome and suggest that a neural substrate of brain maturation with implications for future neurodevelopment is detectable at term equivalent age from the neonatal connectome.
Publisher: Wiley
Date: 06-07-2020
DOI: 10.1002/NBM.4348
Publisher: Cold Spring Harbor Laboratory
Date: 11-06-2019
DOI: 10.1101/661348
Abstract: Diffusion MRI has the potential to provide important information about the connectivity and microstructure of the human brain during normal and abnormal development, non-invasively and in vivo. Recent developments in MRI hardware and reconstruction methods now permit the acquisition of large amounts of data within relatively short scan times. This makes it possible to acquire more informative multi-shell data, with diffusion-sensitisation applied along many directions over multiple b -value shells. Such schemes are characterised by the number of shells acquired, and the specific b -value and number of directions s led for each shell. However, there is currently no clear consensus as to how to optimise these parameters. In this work, we propose a means of optimising multi-shell acquisition schemes by estimating the information content of the diffusion MRI signal, and optimising the acquisition parameters for sensitivity to the observed effects, in a manner agnostic to any particular diffusion analysis method that might subsequently be applied to the data. This method was used to design the acquisition scheme for the neonatal diffusion MRI sequence used in the developing Human Connectome Project, which aims to acquire high quality data and make it freely available to the research community. The final protocol selected by the algorithm, and currently in use within the dHCP, consists of b = 0, 400, 1000, 2600 s/mm 2 with 20, 64, 88 & 128 DW directions per shell respectively. A data driven method is presented to design multi-shell diffusion MRI acquisition schemes ( b -values and no. directions). This method optimises the multi-shell scheme for maximum sensitivity to the information content in the signal. When applied in neonates, the data suggest that a b =0 + 3 shell strategy is appropriate
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Publisher: Cold Spring Harbor Laboratory
Date: 29-09-2020
DOI: 10.1101/2020.09.28.317180
Abstract: The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. Diffusion MRI allows the characterisation of subtle inter-in idual differences in structural brain connectivity. In idual brain connectivity maps (connectomes) are by nature high in dimensionality and are complex to interpret. Machine learning methods are a powerful tool to uncover properties of the connectome which are not readily visible, and can give us clues as to how and why in idual developmental trajectories differ. In this manuscript we used Deep Neural Networks and Random Forests to predict demographic and neurodevelopmental characteristics from neonatal structural connectomes in a large s le of babies (n = 524) from the developing Human Connectome Project. We achieved an accurate prediction of post menstrual age (PMA) at scan in term-born infants (Mean absolute error (MAE) = 0.72 weeks, r = 0.83 and p .001). We also achieved good accuracy when predicting gestational age at birth in a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p .001). We subsequently used sensitivity analysis to obtain feature relevance from our prediction models, with the most important connections for prediction of PMA and GA found to be predominantly thalamocortical. From our models of PMA at scan for infants born at term, we computed a brain maturation index ( predicted age minus actual age ) of in idual preterm neonates and found a significant correlation between this index and motor outcome at 18 months corrected age. Our results demonstrate the applicability of machine learning techniques in analyses of the neonatal connectome, and suggest that a neural substrate for later developmental outcome is detectable at term equivalent age.
Publisher: Elsevier BV
Date: 12-2015
DOI: 10.1016/J.NEUROIMAGE.2015.08.008
Abstract: Diffusion-weighted imaging and tractography provide a unique, non-invasive technique to study the macroscopic structure and connectivity of brain white matter in vivo. Global tractography methods aim at reconstructing the full-brain fiber configuration that best explains the measured data, based on a generative signal model. In this work, we incorporate a multi-shell multi-tissue model based on spherical convolution, into a global tractography framework, which allows to deal with partial volume effects. The required tissue response functions can be estimated from and hence calibrated to the data. The resulting track reconstruction is quantitatively related to the apparent fiber density in the data. In addition, the fiber orientation distribution for white matter and the volume fractions of gray matter and cerebrospinal fluid are produced as ancillary results. Validation results on simulated data demonstrate that this data-driven approach improves over state-of-the-art streamline and global tracking methods, particularly in the valid connection rate. Results in human brain data correspond to known white matter anatomy and show improved modeling of partial voluming. This work is an important step toward detecting and quantifying white matter changes and connectivity in healthy subjects and patients.
Publisher: Elsevier BV
Date: 11-2019
DOI: 10.1016/J.NEUROIMAGE.2019.116137
Abstract: MRtrix3 is an open-source, cross-platform software package for medical image processing, analysis and visualisation, with a particular emphasis on the investigation of the brain using diffusion MRI. It is implemented using a fast, modular and flexible general-purpose code framework for image data access and manipulation, enabling efficient development of new applications, whilst retaining high computational performance and a consistent command-line interface between applications. In this article, we provide a high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software.
Publisher: Cold Spring Harbor Laboratory
Date: 29-04-2022
DOI: 10.1101/2022.04.28.22274095
Abstract: Maternal prenatal depression is associated with increased likelihood of neurodevelopmental and psychiatric conditions in offspring. The relationship between maternal depression and offspring outcome may be mediated by in-utero changes in brain development. Recent advances in magnetic resonance imaging (MRI) have enabled in vivo investigations of neonatal brains, minimising the effect of postnatal influences. The aim of this study was to examine associations between maternal prenatal depressive symptoms, infant white matter, and toddler behaviour. 413 mother-infant dyads enrolled in the developing Human Connectome Project. Mothers completed the Edinburgh Postnatal Depression Scale (median = 5, range = 0-28, n=52 scores ≥ 11). Infants (n=223 male) (median gestational age at birth=40 weeks, range 32.14-42.29) underwent MRI (median postmenstrual age at scan=41.29 weeks, range 36.57-44.71). Fixel-based fibre metrics (mean fibre density, fibre cross- section, and fibre density modulated by cross-section) were calculated from diffusion imaging data in the left and right uncinate fasciculi and cingulum bundle. For n=311, internalizing and externalizing behaviour, and social-emotional abilities were reported at a median corrected age of 18 months (range 17-24). Statistical analysis used multiple linear regression and mediation analysis with bootstrapping. Maternal depressive symptoms were positively associated with infant fibre density in the left (B =.0005, p=.003, q=.027) and right (B=.0006, p=.003, q=.027) uncinate fasciculus, with the left uncinate fasciculus, in turn, positively associated with social-emotional abilities in toddlerhood (B =105.70, p=.0007, q=.004). In a mediation analysis, higher maternal depressive symptoms predicted toddler social-emotional difficulties (B=.342, t(307)=3.003, p=.003), but this relationship was not mediated by fibre density in the left uncinate fasciculus (Sobel test p=.143, bootstrapped indirect effect=.035, SE=.02, 95%CI [-.01,.08]). There was no evidence of an association between maternal depressive and cingulum fibre properties. These findings suggest that maternal perinatal depressive symptoms are associated with neonatal uncinate fasciculi microstructure, but not fibre bundle size, and toddler behaviour.
Publisher: Elsevier BV
Date: 08-2021
Publisher: Elsevier BV
Date: 11-2017
DOI: 10.1016/J.JAD.2017.06.063
Abstract: Differences in corpus callosum (CC) morphology and microstructure have been implicated in late-life depression and may distinguish between late and early-onset forms of the illness. However, a multimodal approach using complementary imaging techniques is required to disentangle microstructural alterations from macrostructural partial volume effects. 107 older adults were assessed: 55 currently-depressed patients without dementia and 52 controls without cognitive impairment. We investigated group differences and clinical associations in 7 sub-regions of the mid-sagittal corpus callosum using T1 anatomical data, white matter hyperintensity (WMH) quantification and two different diffusion MRI (dMRI) models (multi-tissue constrained spherical deconvolution, yielding apparent fibre density, AFD and diffusion tensor imaging, yielding fractional anisotropy, FA and radial diffusivity, RD). Callosal AFD was lower in patients compared to controls. There were no group differences in CC thickness, surface area, FA, RD, nor whole brain or WMH volume. Late-onset of depression was associated with lower FA, higher RD and lower AFD. There were no associations between any imaging measures and psychotic features or depression severity as assessed by the geriatric depression scale. WMH volume was associated with lower FA and AFD, and higher RD in patients. Patients were predominantly treatment-resistant. Measurements were limited to the mid-sagittal CC. dMRI analysis was performed on a smaller cohort, n=77. AFD was derived from low b-value data. Callosal structure is largely preserved in LLD. WMH burden may impact on CC microstructure in late-onset depression suggesting vascular pathology has additional deleterious effects in these patients.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2019
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Daan Christiaens.