Showing papers by "Alexander Leemans published in 2014"

Altered structural networks and executive deficits in traumatic brain injury patients

Abstract: Recent research on traumatic brain injury (TBI) has shown that impairments in cognitive and executive control functions are accompanied by a disrupted neural connectivity characterized by white matter damage. We constructed binary and weighted brain structural networks in 21 patients with chronic TBI and 17 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Executive function was assessed with the local global task and the trail making task, requiring inhibition, updating, and switching. The results revealed that TBI patients were less successful than controls on the executive tasks, as shown by the higher reaction times, higher switch costs, and lower accuracy rates. Moreover, both TBI patients and controls exhibited a small world topology in their white matter networks. More importantly, the TBI patients demonstrated increased shortest path length and decreased global efficiency of the structural network. These findings suggest that TBI patients have a weaker globally integrated structural brain network, resulting in a limited capacity to integrate information across brain regions. Furthermore, we showed that the white matter networks of both groups contained highly connected hub regions that were predominately located in the parietal cortex, frontal cortex, and basal ganglia. Finally, we showed significant correlations between switching performance and network property metrics within the TBI group. Specifically, lower scores on the switching tasks corresponded to a lower global efficiency. We conclude that analyzing the structural brain network connectivity provides new insights into understanding cognitive control changes following brain injury.

Hemispheric lateralization of topological organization in structural brain networks

Abstract: The study on structural brain asymmetries in healthy individuals plays an important role in our understanding of the factors that modulate cognitive specialization in the brain. Here, we used fiber tractography to reconstruct the left and right hemispheric networks of a large cohort of 346 healthy participants (20-86 years) and performed a graph theoretical analysis to investigate this brain laterality from a network perspective. Findings revealed that the left hemisphere is significantly more "efficient" than the right hemisphere, whereas the right hemisphere showed higher values of "betweenness centrality" and "small-worldness." In particular, left-hemispheric networks displayed increased nodal efficiency in brain regions related to language and motor actions, whereas the right hemisphere showed an increase in nodal efficiency in brain regions involved in memory and visuospa- tial attention. In addition, we found that hemispheric networks decrease in efficiency with age. Finally, we observed significant gender differences in measures of global connectivity. By analyzing the struc- tural hemispheric brain networks, we have provided new insights into understanding the neuroana- tomical basis of lateralized brain functions. Hum Brain Mapp 00:000-000, 2014. V C 2014 Wiley Periodicals, Inc.

Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas

Abstract: Poster: "ECR 2014 / C-1540 / Integrating Diffusion Kurtosis Imaging, Dynamic Susceptibility-Weighted MR imaging and short echo time Chemical Shift Imaging for grading gliomas." by: "S. Van Cauter1, F. De Keyzer1, D. Sima1, U. Himmelreich1, S. Sunaert1, S. Van Huffel2, R. Peeters1, A. Croitor Sava2; 1Leuven/BE, 2Leuven - Heverlee/BE"

Isotropic non-white matter partial volume effects in constrained spherical deconvolution.

Abstract: Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVE) are present in the DW signal due to relatively large voxel sizes. These PVEs can be caused by both non-WM tissue, such as gray matter (GM) and cerebrospinal fluid (CSF), and by multiple nonparallel WM fiber populations. High angular resolution diffusion imaging (HARDI) methods have been developed to correctly characterize complex WM fiber configurations, but to date, many of the HARDI methods do not account for non-WM PVEs. In this work, we investigated the isotropic PVEs caused by non-WM tissue in WM voxels on fiber orientations extracted with constrained spherical deconvolution (CSD). Experiments were performed on simulated and real DW-MRI data. In particular, simulations were performed to demonstrate the effects of varying the diffusion weightings, signal-to-noise ratios (SNR), fiber configurations, and tissue fractions.Our results show that the presence of non-WM tissue signal causes a decrease in the precision of the detected fiber orientations and an increase in the detection of false peaks in CSD. We estimated 35-50 % of WM voxels to be affected by non-WM PVEs. For HARDI sequences, which typically have a relatively high degree of diffusion weighting, these adverse effects are most pronounced in voxels with GM PVEs. The non-WM PVEs become severe with 50 % GM volume for maximum spherical harmonics orders of 8 and below, and already with 25 % GM volume for higher orders. In addition, a low diffusion weighting or SNR increases the effects. The non-WM PVEs may cause problems in connectomics, where reliable fiber tracking at the WM-GM interface is especially important. We suggest acquiring data with high diffusion-weighting 2500-3000 s/mm2, reasonable SNR (~30) and using lower SH orders in GM contaminated regions to minimize the non-WM PVEs in CSD.

Distribution of tract deficits in schizophrenia

Abstract: Background: Gray and white matter brain changes have been found in schizophrenia but the anatomical organizing process underlying these changes remains unknown. We aimed to identify gray and white matter volumetric changes in a group of patients with schizophrenia and to quantify the distribution of white matter tract changes using a novel approach which applied three complementary analyses to diffusion imaging data. Methods: 21 patients with schizophrenia and 21 matched control subjects underwent brain magnetic resonance imaging. Gray and white matter volume differences were investigated using Voxel-based Morphometry (VBM). White matter diffusion changes were located using Tract Based Spatial Statistics (TBSS) and quantified within a standard atlas. Tracts where significant regional differences were located were examined using fiber tractography. Results: No significant differences in gray or white matter volumetry were found between the two groups. Using TBSS the schizophrenia group showed significantly lower fractional anisotropy (FA) compared to the controls in regions (false discovery rate <0.05) including the genu, body and splenium of the corpus callosum and the left anterior limb of the internal capsule (ALIC). Using fiber tractography, FA was significantly lower in schizophrenia in the corpus callosum genu (p= 0.003). Conclusions: In schizophrenia, white matter diffusion deficits are prominent in medial frontal regions. These changes are consistent with the results of previous studies which have detected white matter changes in these areas. The pathology of schizophrenia may preferentially affect the prefrontal-thalamic white matter circuits traversing these regions.

White matter microstructural abnormalities in families multiply affected with bipolar I disorder: a diffusion tensor tractography study

Abstract: BACKGROUND: White matter (WM) abnormalities are proposed as potential endophenotypic markers of bipolar disorder (BD). In a diffusion tensor imaging (DTI) voxel-based analysis (VBA) study of families multiply affected with BD, we previously reported that widespread abnormalities of fractional anisotropy (FA) are associated with both BD and genetic liability for illness. In the present study, we further investigated the endophenotypic potential of WM abnormalities by applying DTI tractography to specifically investigate tracts implicated in the pathophysiology of BD. METHOD: Diffusion magnetic resonance imaging (MRI) data were acquired from 19 patients with BD type I from multiply affected families, 21 of their unaffected first-degree relatives and 18 healthy volunteers. DTI tractography was used to identify the cingulum, uncinate fasciculus (UF), arcuate portion of the superior longitudinal fasciculus (SLF), inferior longitudinal fasciculus (ILF), corpus callosum, and the anterior limb of the internal capsule (ALIC). Regression analyses were conducted to investigate the effect of participant group and genetic liability on FA and radial diffusivity (RD) in each tract. RESULTS: We detected a significant effect of group on both FA and RD in the cingulum, SLF, callosal splenium and ILF driven by reduced FA and increased RD in patients compared to controls and relatives. Increasing genetic liability was associated with decreased FA and increased RD in the UF, and decreased FA in the SLF, among patients. CONCLUSIONS: WM microstructural abnormalities in limbic, temporal and callosal pathways represent microstructural abnormalities associated with BD whereas alterations in the SLF and UF may represent potential markers of endophenotypic risk.

Evaluating contextual processing in diffusion MRI: application to optic radiation reconstruction for epilepsy surgery.

Abstract: Diffusion MRI and tractography allow for investigation of the architectural configuration of white matter in vivo, offering new avenues for applications like presurgical planning. Despite the promising outlook, there are many pitfalls that complicate its use for (clinical) application. Amongst these are inaccuracies in the geometry of the diffusion profiles on which tractography is based, and poor alignment with neighboring profiles. Recently developed contextual processing techniques, including enhancement and well-posed geometric sharpening, have shown to result in sharper and better aligned diffusion profiles. However, the research that has been conducted up to now is mainly of theoretical nature, and so far these techniques have only been evaluated by visual inspection of the diffusion profiles. In this work, the method is evaluated in a clinically relevant application: the reconstruction of the optic radiation for epilepsy surgery. For this evaluation we have developed a framework in which we incorporate a novel scoring procedure for individual pathways. We demonstrate that, using enhancement and sharpening, the extraction of an anatomically plausible reconstruction of the optic radiation from a large amount of probabilistic pathways is greatly improved in three healthy controls, where currently used methods fail to do so. Furthermore, challenging reconstructions of the optic radiation in three epilepsy surgery candidates with extensive brain lesions demonstrate that it is beneficial to integrate these methods in surgical planning.

White matter organization in relation to upper limb motor control in healthy subjects: exploring the added value of diffusion kurtosis imaging

Abstract: Diffusion tensor imaging (DTI) characterizes white matter (WM) microstructure. In many brain regions, however, the assumption that the diffusion probability distribution is Gaussian may be invalid, even at low b values. Recently, diffusion kurtosis imaging (DKI) was suggested to more accurately estimate this distribution. We explored the added value of DKI in studying the relation between WM microstructure and upper limb coordination in healthy controls (N = 24). Performance on a complex bimanual tracking task was studied with respect to the conventional DTI measures (DKI or DTI derived) and kurtosis metrics of WM tracts/regions carrying efferent (motor) output from the brain, corpus callosum (CC) substructures and whole brain WM. For both estimation models, motor performance was associated with fractional anisotropy (FA) of the CC-genu, CC-body, the anterior limb of the internal capsule, and whole brain WM (rs range 0.42–0.63). Although DKI revealed higher mean, radial and axial diffusivity and lower FA than DTI (p < 0.001), the correlation coefficients were comparable. Finally, better motor performance was associated with increased mean and radial kurtosis and kurtosis anisotropy (rs range 0.43–0.55). In conclusion, DKI provided additional information, but did not show increased sensitivity to detect relations between WM microstructure and bimanual performance in healthy controls.

Choice of diffusion tensor estimation approach affects fiber tractography of the fornix in preterm brain.

Abstract: BACKGROUND AND PURPOSE: Neonatal DTI enables quantitative assessment of microstructural brain properties. Although its use is increasing, it is not widely known that vast differences in tractography results can occur, depending on the diffusion tensor estimation methodology used. Current clinical work appears to be insufficiently focused on data quality and processing of neonatal DTI. To raise awareness about this important processing step, we investigated tractography reconstructions of the fornix with the use of several estimation techniques. We hypothesized that the method of tensor estimation significantly affects DTI tractography results. MATERIALS AND METHODS: Twenty-eight DTI scans of infants born RESULTS: With nonlinear least squares and robust estimation of tensors by outlier rejection, significantly lower mean fractional anisotropy values were obtained than with linear least squares and weighted linear least squares. Visualized quality of tract reconstruction was significantly higher by use of robust estimation of tensors by outlier rejection and correlated with quality of DTI data. CONCLUSIONS: Quality assessment and choice of processing methodology have considerable impact on neonatal DTI analysis. Dedicated acquisition, quality assessment, and advanced processing of neonatal DTI data must be ensured before performing clinical analyses, such as associating microstructural brain properties with patient outcome.

Non-rigid Groupwise Image Registration for Motion Compensation in Quantitative MRI

Abstract: Quantitative magnetic resonance imaging (qMRI) aims to extract quantitative parameters representing tissue properties from a series of images by modeling the image acquisition process. This requires the images to be spatially aligned but, due to patient motion, anatomical structures in the consecutive images may be misaligned. In this work, we propose a groupwise non-rigid image registration method for motion compensation in qMRI. The method minimizes a dissimilarity measure based on principal component analysis (PCA), exploiting the fact that intensity changes can be described by a low-dimensional acquisition model. Using an unbiased groupwise formulation of the registration problem, there is no need to choose a reference image as in conventional pairwise approaches. The method was evaluated on three applications: modified Look-Locker inversion recovery T 1 mapping in a porcine myocardium, black-blood variable flip-angle T 1 mapping in the carotid artery region, and apparent diffusion coefficient (ADC) mapping in the abdomen. The method was compared to a conventional pairwise alignment that uses a mutual information similarity measure. Registration accuracy was evaluated by computing precision of the estimated parameters of the qMRI model. The results show that the proposed method performs equally well or better than an optimized pairwise approach and is therefore a suitable motion compensation method for a wide variety of qMRI applications.

The Added Value of Diffusion Tensor Imaging for Automated White Matter Hyperintensity Segmentation

Abstract: Automated white matter hyperintensity (WMH) segmentation techniques for brain MRI often employ voxel-wise classifiers, trained on traditional features such as: multi-spectral MR image intensities, spatial location, texture, or shape. Recent studies show that diffusion tensor imaging (DTI) provides a measure for WMH, independent from the commonly used FLAIR images. Hence, we hypothesized that adding features derived from DTI to a voxel-wise classifier for WMH segmentation may have added value and improve segmentation results.A k nearest neighbour (kNN) classifier was implemented and trained on various combinations of features. Manual delineations of WMH were available for 20 subjects. Classifiers trained with diffusion features, such as fractional anisotropy and mean diffusivity, are compared to an equivalent classifier without diffusion features. Evaluation measures are sensitivity and Dice similarity coefficient (SI).Adding diffusion features to a kNN classifier significantly (Student’s t-test, p < 0. 0001) improved the quality of the segmentation. Depending on the chosen kNN parameters and features, improvements in sensitivity ranged from 2.4 to 13.5 % and in SI from 4.7 to 18.0 %.In conclusion, adding diffusion features derived from DTI to a voxel-wise classifier for WMH segmentation significantly improves the quality of the segmentation.

Groupwise Registration for Correcting Subject Motion and Eddy Current Distortions in Diffusion MRI Using a PCA Based Dissimilarity Metric

Abstract: Before starting a diffusion-weighted MRI analysis, it is important to correct any misalignment between the diffusion-weighted images (DWIs) that was caused by subject motion and eddy current induced geometric distortions. Conventional methods adopt a pairwise registration approach, in which the non-DWI, a.k.a. the b = 0 image, is used as a reference. In this work, a groupwise affine registration framework, using a global dissimilarity metric, is proposed, which eliminates the need for selecting a reference image and which does not rely on a specific method that models the diffusion characteristics. The dissimilarity metric is based on principal component analysis (PCA) and is ideally suited for DWIs, in which the signal contrast varies drastically as a function of the applied gradient orientation. The proposed method is tested on synthetic data, with known ground-truth transformation parameters, and real diffusion MRI data, resulting in successful alignment.

Towards quantification of the brain's sheet structure : evaluation of the discrete Lie bracket

Abstract: TOWARDS QUANTIFICATION OF THE BRAIN'S SHEET STRUCTURE: EVALUATION OF THE DISCRETE LIE BRACKET Chantal M.W. Tax, Tom C.J. Dela Haije, Andrea Fuster, Remco Duits, Max A. Viergever, Luc M.J. Florack, and Alexander Leemans Image Sciences Institute, University Medical Center Utrecht, Utrecht, Utrecht, Netherlands, Imaging Science & Technology, Eindhoven University of Technology, Eindhoven, Noord-Brabant, Netherlands

Non-rigid Groupwise Image Registration for Motion Compensation in Quantitative MRI

Abstract: Quantitative magnetic resonance imaging (qMRI) aims to extract quantitative parameters representing tissue properties from a series of images by modeling the image acquisition process. This requires the images to be spatially aligned but, due to patient motion, anatomical structures in the consecutive images may be misaligned. In this work, we propose a groupwise non-rigid image registration method for motion compensation in qMRI. The method minimizes a dissimilarity measure based on principal component analysis (PCA), exploiting the fact that intensity changes can be described by a low-dimensional acquisition model. Using an unbiased groupwise formulation of the registration problem, there is no need to choose a reference image as in conventional pairwise approaches. The method was evaluated on three applications: modified Look-Locker inversion recovery T 1 mapping in a porcine myocardium, black-blood variable flip-angle T 1 mapping in the carotid artery region, and apparent diffusion coefficient (ADC) mapping in the abdomen. The method was compared to a conventional pairwise alignment that uses a mutual information similarity measure. Registration accuracy was evaluated by computing precision of the estimated parameters of the qMRI model. The results show that the proposed method performs equally well or better than an optimized pairwise approach and is therefore a suitable motion compensation method for a wide variety of qMRI applications.

Investigating the long-term effects of systemic chemotherapy on brain white matter using multi-shell diffusion MRI and myelin water imaging

Abstract: Investigating the long-term effects of systemic chemotherapy on brain white matter using multi-shell diffusion MRI and myelin water imaging Thibo Billiet, Sabine Deprez, Burkhard Maedler, Ronald Peeters, Hui Zhang, Alexander Leemans, Thijs Dhollander, Daan Christiaens, Frederic Amant, Ann Smeets, Bea Van den Bergh, Mathieu Vandenbulcke, Eric Legius, Stefan Sunaert, and Louise Emsell Translational MRI, Imaging & Pathology, KU Leuven & Radiology, University Hospitals Leuven, Leuven, Belgium, Medical Imaging Research Center, Leuven, Belgium, Stereotaxis and MR-based Intervention, Department of Neurosurgery, Bonn University Hospital, Bonn, Germany, Computer Science, University College London, London, United Kingdom, University Medical Center, Utrecht, Netherlands, ESAT-PSI Processing Speech and Images, KU Leuven, Leuven, Belgium, Leuven Cancer Institute (LKI), Multidisciplinary Breast Cancer Unit, KU Leuven & University Hospitals Leuven, Belgium, Developmental Psychology, Tilburg University, Tilburg, Netherlands, Psychiatry, KU Leuven & University Hospitals Leuven, Leuven, Belgium, Human Genetics, KU Leuven & University Hospitals Leuven, Leuven, Belgium Target audience: Basic scientists and clinicians with an interest in diffusion MRI, neuroscience, neuroimaging and oncology. Purpose: Systemic chemotherapy treatment for breast cancer has been associated with cognitive dysfunction [1]. Furthermore, the dysfunction has been shown to correlate with decreased fractional anisotropy (FA) and mean diffusivity (MD) in white matter [2]. The underlying pathology and evolution of systemic chemotherapy-related neurobiological effects is unknown. The purpose of this follow-up study is to investigate brain microstructural changes three years post-chemotherapy using multi-shell diffusion MRI and myelin water imaging. Methods: Subjects: Recovering female breast cancer patients who received chemotherapy (C+, n=25) or did not receive chemotherapy (C-, n=14) and healthy controls (HC, n=12). Imaging: DWIs were acquired (3T Philips Achieva) with b-values 700, 1000 and 2800 s/mm2 along 25, 40 and 75 directions, respectively. Multicomponent T2-relaxation data were acquired using a modified GraSE sequence [3]. Measures: Conventional diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) parameters were estimated using ExploreDTI [4]. The fraction of isotropic fluid (FISO), the neurite density index (NDI) and the orientation dispersion index (ODI) were calculated using the NODDI toolbox [5]. Relaxometry data yielded the myelin water fraction (MWF), the intraand extracellular water fraction (IEWF), geometric mean T2 time of the general T2 distribution (G-gmT2) and the IE water (IEW-gmT2) and the width of the IEW-peak (IEW-pw) [6]. A population-based FA-template was constructed [7], to which all parameter maps were registered. Fiber orientation distribution profiles (FODs) were calculated using MRtrix [8], and reoriented to the FA-template [9]. Tracts previously associated with chemotherapy-related changes were delineated using constrained spherical deconvolution (CSD) [10]: cingulum, forceps minor, forceps major and the superior longitudinal fasciculus (SLF). Parameter values of each subject were calculated in the tract masks in template space. Statistical analysis: Whole tract: one-way ANOVA was used to investigate the main effect of group membership. Local difference: the group:position interaction term in a 2-way ANOVA was used. A permutation method was applied to correct significant (p<0.05) findings for multiple comparisons [11]. To identify which groups or positions caused significant differences in these F-tests, post-hoc t-tests were performed and corrected using Tukey’s honestly significant difference criterion.

Hoe magnetisme en diffusie kunnen bijdragen aan hersenonderzoek: toepassingen van diffusie-MRI-tractografie

Abstract: Met MRI kunnen zachte weefsels in het lichaam afgebeeld worden op een niet-invasieve manier. MRI is daardoor onmisbaar bij de diagnose van veel ziektes. Diffusie-MRI is een relatief nieuwe techniek die, in tegenstelling tot conventionele MRI-technieken, ook de architectuur van weefsels in kaart kan brengen. Hiertoe wordt de MRI-sequentie gevoelig gemaakt voor de willekeurige beweging van deeltjes (diffusie). Watermoleculen bewegen zich in weefsels voort op een dergelijke willekeurige wijze, maar worden daar ook gehinderd door verschillende microstructuren. De eigenschappen van onderliggend weefsel bepalen dus de grootte van diffusie, wat met diffusie-MRI-scans in kaart kan worden gebracht. Veel lichaamsweefsels zijn opgebouwd uit vezelachtige structuren, bijvoorbeeld zenuwvezels in de witte stof van de hersenen. Diffusie-MRI-tractografie is het reconstrueren en visualiseren van vezelpaden die geassocieerd kunnen worden met deze onderliggende zenuwbanen. Hierdoor is het mogelijk om specifieke verbindingen tussen verschillende hersengebieden af te beelden en kwantitatief te bestuderen. Dit artikel gaat in op de werking van diffusie-MRI-tractografie en bespreekt (klinische) toepassingen, valkuilen, en beperkingen die gerelateerd zijn aan deze techniek.