Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data

Article · August 2006with98 Reads
DOI: 10.1016/j.neuroimage.2006.02.024 · Source: PubMed
Abstract
There has been much recent interest in using magnetic resonance diffusion imaging to provide information about anatomical connectivity in the brain, by measuring the anisotropic diffusion of water in white matter tracts. One of the measures most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies how strongly directional the local tract structure is. Many imaging studies are starting to use FA images in voxelwise statistical analyses, in order to localise brain changes related to development, degeneration and disease. However, optimal analysis is compromised by the use of standard registration algorithms; there has not to date been a satisfactory solution to the question of how to align FA images from multiple subjects in a way that allows for valid conclusions to be drawn from the subsequent voxelwise analysis. Furthermore, the arbitrariness of the choice of spatial smoothing extent has not yet been resolved. In this paper, we present a new method that aims to solve these issues via (a) carefully tuned non-linear registration, followed by (b) projection onto an alignment-invariant tract representation (the "mean FA skeleton"). We refer to this new approach as Tract-Based Spatial Statistics (TBSS). TBSS aims to improve the sensitivity, objectivity and interpretability of analysis of multi-subject diffusion imaging studies. We describe TBSS in detail and present example TBSS results from several diffusion imaging studies.
    • CA methods involving alignment between multiple images for voxel-wise analysis have been extensively applied in the fields of neuroimaging and radiation therapy. Examples include voxel-based morphometry for comparison of local concentration of gray matter between subjects[3], analysis of multi-subject diffusion data for studying brain connectivity[4], and adaptive radiotherapy[5]. These studies have demonstrated that CA alignment of medical images can aid clinicians with automated biomarker quantification and treatment replanning based on anatomical changes that occur over time.
    [Show abstract] [Hide abstract] ABSTRACT: Background Evaluating inflammatory changes over time on MR images of the spine in patients with suspected axial Spondyloarthritis (axSpA) can be a labor-intensive task, requiring readers to manually search for and perceptually align a set of vertebrae between two scans. The purpose of this study was to assess the feasibility of computer-aided (CA) evaluation of such inflammatory changes in a framework where scans from two time points are fused into a single color-encoded image integrated into an interactive scoring tool. Methods For 30 patients from the SPondyloArthritis Caught Early (SPACE) cohort (back pain ≥ 3 months, ≤ 2 years, onset < 45 years), baseline and follow-up MR scans acquired 9–12 months apart were fused into a single color-encoded image through locally-rigid image registration to evaluate inflammatory changes in 23 vertebral units (VUs). Scoring was performed by two expert readers on a (−2, 2) scale using an interactive scoring tool. For comparison of direction of change (increase/decrease) indicated by an existing reference, Berlin method scores ((−3, 3) scale) of the same MR scans from a different ongoing study were used. The distributions of VU-level differences between CA readers and between the CA and Berlin methods (sign of change scores) across patients were analyzed descriptively. Patient-level agreement between CA readers was assessed by intraclass correlation coefficient (ICC). Results Five patients were excluded from evaluation due to failed vertebrae segmentation. Patient-level inter-reader agreement ICC was 0.56 (95% CI: 0.22 to 0.78). Mean VU-level inter-reader differences across 25 patients ranged (−0.04, 0.12) with SD range (0, 0.45). Across all VUs, inter-reader differences ranged (−1, 1) in 573/575 VUs (99.7%). Mean VU-level inter-method differences across patients ranged (−0.04, 0.08) with SD range (0, 0.61). Across all VUs, inter-method differences ranged (−1, 1) in 572/575 VUs (99.5%). Conclusions Fusion of MR scans of the spine from two time points into a single color-encoded image allows for direct visualization and measurement of inflammatory changes over time in patients with suspected axSpA.
    Full-text · Article · Dec 2017
    • The crucial contribution of the CC to bimanual coordination has been widely demonstrated [for a review seeGooijers and Swinnen, 2014]. Using Tract-Based Spatial Statistics of FSL[Smith et al., 2004[Smith et al., , 2006, FA and MD images were created by fitting a tensor model to the raw diffusion data using FMRIB's Diffusion Toolbox, and then brain-extracted using the Brain Extraction Tool[Smith, 2002]. The FA and MD values of all participants were then aligned into a common space (FMRIB58_FA) using the non-linear registration tool FNIRT (http://www.fmrib.ox.ac.uk/analysis/techrep/ tr07ja2/tr07ja2.pdf),
    [Show abstract] [Hide abstract] ABSTRACT: For successful motor control, the central nervous system is required to combine information from the environment and the current body state, which is provided by vision and proprioception respectively. We investigated the relative contribution of visual and proprioceptive information to upper limb motor control and the extent to which structural brain measures predict this performance in youth (n = 40; age range 9-18 years). Participants performed a manual tracking task, adopting in- phase and anti-phase coordination modes. Results showed that, in contrast to older participants, younger participants performed the task with lower accuracy in general and poorer performance in anti-phase than in-phase modes. However, a proprioceptive advantage was found at all ages, i.e., tracking accuracy was higher when proprioceptive information was available during both in- and anti- phase modes at all ages. The microstructural organization of interhemispheric connections between homologous dorsolateral prefrontal cortices, and the cortical thickness of the primary motor cortex were associated with sensory-specific accuracy of tracking performance. Overall, the findings suggest that manual tracking performance in youth does not only rely on brain regions involved in sensorimotor processing, but also on prefrontal regions involved in attention and working memory.
    Article · Nov 2017
    • The Tract-Based Spatial Statistics (TBSS) method resolves these issues by estimating a thinned, thresholded mean FA "skeleton" representing the center of the most prominent white matter tracts, common to all subjects. Individual DTI metrics are then projected onto the skeleton so that skeleton voxels represent the local center of the nearest tract ( Smith et al., 2006). The TBSS method thus trades some anatomical coverage and specificity for increased normalization validity ( Bach et al., 2014).
    Full-text · Thesis · Oct 2017 · Molecular Genetics and Metabolism Reports
    • The DTI-USP-131 template proposed in this study should be interpreted as a statistical framework that could be applied to a patient-specific evaluation approach, where a voxel-wise statistical inference can be calculated over the entire brain volume. Hence, our strategy could be thought as a TBSS (Smith et al., 2006) or TSA (Zhang et al., 2010) computational tools generalization, which, in our case, offer a patient-specific evaluation instead of a group analysis. The main new possibility that is added with our DTI template is the patient-specific evaluation on DTI data, which greatly improve the clinical evaluation, e.g.
    [Show abstract] [Hide abstract] ABSTRACT: Introduction: The search for human brain templates has been progressing in the past decades and in order to understand disease patterns a need for a standard diffusion tensor imaging (DTI) dataset was raised. For this purposes, some DTI templates were developed which assist group analysis studies. In this study, complementary information to the most commonly used DTI template is proposed in order to offer a patient-specific statistical analysis on diffusion-weighted data. Methods: 131 normal subjects were used to reconstruct a population-averaged template. After image pre processing, reconstruction and diagonalization, the eigenvalues and eigenvectors were used to reconstruct the quantitative DTI maps, namely fractional anisotropy (FA), mean diffusivity (MD), relative anisotropy (RA), and radial diffusivity (RD). The mean absolute error (MAE) was calculated using a voxel-wise procedure, which informs the global error regarding the mean intensity value for each quantitative map. Results: The MAE values presented a low MAE estimate (max(MAE) = 0.112), showing a reasonable error measure between our DTI-USP-131 template and the classical DTI-JHU-81 approach, which also shows a statistical equivalence (p<0.05) with the classical DTI template. Hence, the complementary standard deviation (SD) maps for each quantitative DTI map can be added to the classical DTI-JHU-81 template. Conclusion: In this study, variability DTI maps (SD maps) were reconstructed providing the possibility of a voxel-wise statistical analysis in patient-specific approach. Finally, the brain template (DTI-USP-131) described here was made available for research purposes on the web site (http://dx.doi.org/10.17632/br7bhs4h7m.1), being valuable to research and clinical applications. © 2017, Brazilian Society of Biomedical Engineering. All rights reserved.
    Article · Sep 2017
    • DTI images were pre-processed to generate fractional antisotropy (FA) maps using FSL's Diffusion Toolbox[30]. Each participant's brain was extracted and corrected for eddy current distortion.
    [Show abstract] [Hide abstract] ABSTRACT: This study aims to provide convergent understanding of the neural basis of auditory word processing efficiency using a multimodal imaging. We investigated the structural and functional correlates of word processing efficiency in healthy individuals. We acquired two structural imaging (T1-weighted imaging and diffusion tensor imaging) and functional magnetic resonance imaging (fMRI) during auditory word processing (phonological and semantic tasks). Our results showed that better phonological performance was predicted by the greater thalamus activity. In contrary, better semantic performance was associated with the less activation in the left posterior middle temporal gyrus (pMTG), supporting the neural efficiency hypothesis that better task performance requires less brain activation. Furthermore, our network analysis revealed the semantic network including the left anterior temporal lobe (ATL), dorsolateral prefrontal cortex (DLPFC) and pMTG was correlated with the semantic efficiency. Especially, this network acted as a neural efficient manner during auditory word processing. Structurally, DLPFC and cingulum contributed to the word processing efficiency. Also, the parietal cortex showed a significate association with the word processing efficiency. Our results demonstrated that two features of word processing efficiency, phonology and semantics, can be supported in different brain regions and, importantly, the way serving it in each region was different according to the feature of word processing. Our findings suggest that word processing efficiency can be achieved by in collaboration of multiple brain regions involved in language and general cognitive function structurally and functionally.
    Full-text · Article · Sep 2017
    • Parametric maps were subsequently generated for mean diffusivity (MD), the DTI component of interest in this study. MD was compared across study groups using region of interest (ROI) and tract based spatial statistics (TBSS) analyses (FSL software, Oxford, UK; [39]). We did not control for age because groups were statistically equivalent on this metric, and previous analyses of DTI and cognition did not indicate significant interactions between age and group [3].
    [Show abstract] [Hide abstract] ABSTRACT: Sapropterin dihydrochloride (BH4) reduces phenylalanine (Phe) levels and improves white matter integrity in a subset of individuals with phenylketonuria (PKU) known as “responders.” Although prior research has identified biochemical and genotypic differences between BH4 responders and non-responders, cognitive and neural differences remain largely unexplored. To this end, we compared intelligence and white matter integrity prior to treatment with BH4 in 13 subsequent BH4 responders with PKU, 16 subsequent BH4 non-responders with PKU, and 12 healthy controls. Results indicated poorer intelligence and white matter integrity in non-responders compared to responders prior to treatment. In addition, poorer white matter integrity was associated with greater variability in Phe across the lifetime in non-responders but not in responders. These results underscore the importance of considering PKU as a multi-faceted, multi-dimensional disorder and point to the need for additional research to delineate characteristics that predict response to treatment with BH4.
    Full-text · Article · Sep 2017
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