Tractography atlas-based spatial statistics: statistical analysis of diffusion tensor image along fiber pathways
Abstract
The quantitative analysis of diffusion tensor image (DTI) data has attracted increasing attention in recent decades for studying white matter (WM) integrity and development. Among the current DTI analysis methods, tract-based spatial statistics (TBSS), as a pioneering approach for the voxelwise analysis of DTI data, has gained a lot of popularity due to its user-friendly framework. However, in recent years, the reliability and interpretability of TBSS have been challenged by several works, and several improvements over the original TBSS pipeline have been suggested. In this paper, we propose a new DTI statistical analysis method, named tractography atlas-based spatial statistics (TABSS). It doesn't rely on the accurate alignment of fractional anisotropy (FA) images for population analysis and gets rid of the skeletonization procedures of TBSS, which have been indicated as the major sources of error. Furthermore, TABSS improves the interpretability of results by directly reporting the resulting statistics on WM tracts, waiving the need of a WM atlas in the interpretation of the results. The feasibility of TABSS was evaluated in an example study to show age-related FA alternation pattern of healthy human brain. Through this preliminary study, it is validated that TABSS can provide detailed statistical results in a comprehensive and easy-to-understand way.
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One popular approach is to average data across corresponding points along each streamline and leverage a prototype, core, or mean streamline to enable statistical analysis comparing subjects or groups.
“One popular approach is to average data across corresponding points along each streamline in the pathway and leverage a prototype, core, or mean streamline to enable statistical analysis comparing subjects or groups (Colby et al., 2012b; Wang et al., 2016; Yeatman et al., 2012).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Wang et al., 2016
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- Authors
- Wang, D., Luo, Y., Mok, V. C. T., Chu, W. C. W., & Shi, L.
- Affiliations
- Chinese University of Hong Kong (HK)
- Corresponding author
- Shi, Lin
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 125
- Pages
- 301-310
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1751817085
- Cited by
- 33
- References
- 56
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 1 · 2023: 3 · 2022: 3 · 2021: 2 · 2020: 4 · 2019: 4 · 2018: 5 · 2017: 4 · 2016: 1
- FWCI
- 2.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Research Grants Council of the Hong Kong Special Administrative Region, China (CUHK 416712, CUHK 14113214, CUHK 475711, CUHK 473012)
- Shenzhen Science and Technology Innovation Committee (CXZZ20140606164105361)
- National Natural Science Foundation of China (81271653, 81201157)
- Lui Che Woo Foundation
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Neurology › Cerebrovascular and genetic disorders
- Keywords
- tract-based spatial statistics, diffusion tensor imaging, white matter integrity, fractional anisotropy, white matter tracts, skeletonization
- MeSH terms
- Anatomy, Artistic, Atlases as Topic, Brain, Humans, Image Processing, Computer-Assisted, Neural Pathways, Diffusion Tensor Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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