4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Tract-specific statistics based on diffusion-weighted probabilistic tractography

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Diffusion-weighted neuroimaging approaches provide rich evidence for estimating the structural integrity of white matter in vivo, but typically do not assess white matter integrity for connections between two specific regions of the brain. Here, we present a method for deriving tract-specific diffusion statistics, based upon predefined regions of interest. Our approach derives a population distribution using probabilistic tractography, based on the Nathan Kline Institute (NKI) Enhanced Rockland sample. We determine the most likely geometry of a path between two regions and express this as a spatial distribution. We then estimate the average orientation of streamlines traversing this path, at discrete distances along its trajectory, and the fraction of diffusion directed along this orientation for each participant. The resulting participant-wise metrics (tract-specific anisotropy; TSA) can then be used for statistical analysis on any comparable population. Based on this method, we report both negative and positive associations between age and TSA for two networks derived from published meta-analytic studies (the “default mode” and “what-where” networks), along with more moderate sex differences and age-by-sex interactions. The proposed method can be applied to any arbitrary set of brain regions, to estimate both the spatial trajectory and DWI-based anisotropy specific to those regions.

          Abstract

          Andrew Reid et al. use publicly available data to present a method for deriving tract-specific statistics based on diffusion-weighted MRI, based upon arbitrarily-defined regions of interest. Their approach enables them to report both negative and positive associations between age and tract-specific anisotropy along with more moderate sex differences and age-by-sex interactions.

          Related collections

          Most cited references85

          • Record: found
          • Abstract: found
          • Article: not found

          Advances in functional and structural MR image analysis and implementation as FSL.

          The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

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

            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.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Unbiased nonlinear average age-appropriate brain templates from birth to adulthood

                Bookmark

                Author and article information

                Contributors
                andrew.reid@nottingham.ac.uk
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                17 February 2022
                17 February 2022
                2022
                : 5
                : 138
                Affiliations
                [1 ]GRID grid.4563.4, ISNI 0000 0004 1936 8868, School of Psychology, , University of Nottingham, ; Nottingham, United Kingdom
                [2 ]GRID grid.8385.6, ISNI 0000 0001 2297 375X, Institute for Neuroscience and Medicine (INM-7), , Jülich Research Center, ; Jülich, Germany
                [3 ]GRID grid.411327.2, ISNI 0000 0001 2176 9917, Institute of Systems Neuroscience, Medical Faculty, , Heinrich Heine University, ; Düsseldorf, Germany
                Author information
                http://orcid.org/0000-0002-0523-926X
                http://orcid.org/0000-0001-7163-3110
                Article
                3073
                10.1038/s42003-022-03073-w
                8854429
                35177755
                7d6ade6d-d63f-46b2-96c1-c768404a7422
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 May 2021
                : 24 January 2022
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                myelin biology and repair,neural ageing
                myelin biology and repair, neural ageing

                Comments

                Comment on this article