Showing papers by "Alexander Leemans published in 2010"

Correlation of cognitive dysfunction and diffusion tensor MRI measures in patients with mild and moderate multiple sclerosis.

Abstract: scores. Materials and Methods: Fractional anisotropy (FA), and mean, longitudinal, and transverse diffusivity are compared between control subjects and MS patients, which were subdivided as mildly and moderately impaired. In addition, PASAT scores are correlated for both MS groups with the diffusion measures. An optimized voxel based analysis (VBA) method, in terms of coregistration, atlas construction, and image smoothing, was thereby used. Results:: Diffusion differences between the control subjects and the patients with MS were found in the corpus callosum, inferior longitudinal fasciculus, cortico spinal tracts, forceps major, superior longitudinal fasciculus, and cingulum. In addition, we observed significant correlations of the FA and PASAT scores in the left inferior longitudinal fasciculus, the forceps minor, the capsula interna and externa, the genu of the corpus callosum, the left cingulum, the superior longitudinal fasciculus, and the corona radiata. Conclusion:: Diffusion differences were observed between the mildly impaired MS patients and control subjects. In addition, different diffusion measures correlated with PASAT scores for cognitive decline in parietal, frontal, as well as temporal white matter (WM) regions.

Bimanual coordination and corpus callosum microstructure in young adults with traumatic brain injury

Abstract: Background and objectives: Hand function is frequently affected after moderate/severe traumatic brain injury (TBI) and is treated in rehabilitation settings [1]. Bimanual actions are ubiquitous in daily life. Many coordinated movements of the upper extremities rely on precise timing of movements and interhemispheric communication via the corpus callosum (CC) [2], which is especially vulnerable to TBI [3-4]. Diffusion tensor imaging (DTI) has been shown to be an effective tool in identifying reduced fractional anisotropy (FA) in TBI patients [5-7]. However, the relationship between this tissue damage and functional performance is not well understood. The aim of this study was to investigate the relationship between white matter integrity in specific regions of the CC (i.e. genu, body, splenium, as shown in Figure 1) and performance on a bimanual coordination task. Methods: 25 adults with TBI and 18 controls (mean age = 25 y 4 mo) made spatially and temporally coupled bimanual circular motions during eventrelated fMRI (see Figure 2). An auditory metronome was used for pacing. There were four possible bimanual movement patterns: inward circles, outward circles, clockwise circles, and counterclockwise circles. Visual cues were used to indicate the movement to be performed. A Siemens 3 T Magnetom Trio MRI scanner (Siemens, Erlangen, Germany) with standard head coil was used for image acquisition. For all subjects, a high resolution T1-weighted structural image was acquired using magnetization prepared rapid gradient echo (MPRAGE; TR = 2300 ms, echo time (TE) = 2.98 ms, 1 x 1 x 1.1 mm voxels, field of view (FOV): 240 x 256, 160 sagittal slices). The DTI images were acquired using a DTI SE-EPI (diffusion weighted single shot spin-echo echoplanar imaging) sequence (TR/TE 7916/68 ms; matrix 96×96; FOV 220×220 mm; parallel imaging factor 2.5; 56 contiguous sagittal slices; slice thickness 2.14 mm; voxel size 2.15×2.86×2.14 mm). Diffusion sensitizing gradients were applied at a bvalue of 1000 s/mm, along 65 non-collinear directions with one b0 image. The DTI data were analyzed and processed in ExploreDTI [8], using the following multi-step procedure: (a) Subject motion and eddy-current induced geometrical distortions were corrected [9], (b) the diffusion tensors and subsequently the FA, axial and radial diffusivity, were calculated using a non-linear regression procedure [10], (c) the DTI data were coregistered to MNI space for analyzing potential group differences [11-14], (d) a standard deterministic streamline tractography approach was used [15], to reconstruct the WM fiber pathways of interest, (e) the probabilistic cytoarchitectonic atlas [16], and a digitized version of the original Talairach atlas [17-18], both mapped in MNI space as provided by the FSL toolbox, were used to perform the correlation analysis [19]. For the kinematic and DTI measures, nonparametric Mann-Whitney U-tests were performed for comparing the group of TBI patients with the ageand gender matched control group. Results: Bimanual task performance was significantly lower in the TBI as compared to control subjects, as shown by the larger values of the reaction time and error scores (all p’s < .01, Figure 3). Furthermore, DTI revealed a significant reduction in FA in the CC compared with the control group for all three subregions (all p’s < .001) (see Figure 3). This decrease of FA was mediated by the combined effects of axial and radial diffusivity increase (Figure 4). Bimanual performance deficits corresponded well with reductions in anisotropy. For example, duration of the contralateral disruptions (when left hand direction was involuntarily reversed during the right hand switch) correlated with mean FA in the fibers passing through in the splenium of the CC (r = -0.53, p < .05)), in the TBI group (Figure 5). Conclusions: This correlation between brain white matter structure and behavior, and more specifically between fractional anisotropy and motor deficits in adults with TBI, is of major interest for improving prediction of motor outcome in TBI patients. Such a prognostic tool can be used to design focused intervention protocols for TBI patients during the first months following injury.

Estimating the number of fiber orientations in diffusion MRI voxels : a constrained spherical deconvolution study

Abstract: Introduction: It has long been recognized that the Gaussian diffusion tensor model is inappropriate for voxels with complex fiber architecture [1, 2]. Many groups have tried to classify voxels in terms of their diffusion complexity. The earliest studies distinguished between voxels with isotropic, single-fiber anisotropic, and multifiber anisotropic complexity and have reported clustered and symmetric regions of increased complexity, supporting genuine effects consistent with anatomical knowledge [3, 4]. However, they were not able to report the number of fiber orientations found in each voxel. Recent advances of high angular resolution diffusion imaging allow the extraction of multiple fiber orientations [5] and have spawned an interest for classification of voxels by the number of fiber orientations. Recently, a Bayesian Automatic Relevance Determination method was proposed to infer the number of fiber orientations in a multi-compartment model [6]. The study was able to find voxels with up to two fiber orientations and estimated the proportion of white matter fiber voxels that contain complex fiber architecture at approximately one third. In this work, we estimated the number of fiber orientations within each voxel using the constrained spherical deconvolution (CSD) method [7] with the residual bootstrap approach [8]. We showed that multiple-fiber profiles arise consistently in various regions of the human brain where complex tissue structure is known to exist. Moreover, we detect voxels with more than two fiber orientations and detect a much higher proportion of multi-fiber voxels than previously reported.

Theory and applications of diffusion MRI

Abstract: Boosted by the tremendous success of diffusion tensor MRI, which led to the first in vivo and non-invasive characterization of microstructural tissue properties, many advancements in diffusion MRI have been made during the last decade. With new methodological developments in data acquisition, modeling, pre-processing, analysis, and visualization, diffusion MRI is rapidly becoming a standard addition to clinical MRI, especially, for investigating brain white matter architecture. In this work, an overview of methods and applications in diffusion MRI is presented. In particular, concepts underlying diffusion tensor MRI are discussed, techniques for reconstructing fiber pathways (both deterministically and probabilistically) are described, and the “crossing fibers” issue is explained.