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Cited by 26Advanced Neuroimaging Techniques and ApplicationsDementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Surface-based analysis on shape and fractional anisotropy of white matter tracts in Alzheimer’s disease

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Qiu, Anqi, Oishi, Kenichi, Miller, Michael I., Lyketsos, Constantine G., Mori, Susumu, Albert, Marilyn·2010·PLoS ONE
DOI 10.1371/journal.pone.0009811PMC PMC2842443
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Abstract

BACKGROUND: White matter disruption has been suggested as one of anatomical features associated with Alzheimer's disease (AD). Diffusion tensor imaging (DTI), which has been widely used in AD studies, obtains new insights into the white matter structure. METHODS: We introduced surface-based geometric models of the deep white matter tracts extracted from DTI, allowing the characterization of their shape variations relative to an atlas as well as fractional anisotropy (FA) variations on the atlas surface through large deformation diffeomorphic metric mapping (LDDMM). We applied it to assess local shapes and FA variations of twenty-three deep white matter tracts in 13 patients with AD and 19 healthy control subjects. RESULTS: Our results showed regionally-specific shape abnormalities and FA reduction in the cingulum tract and the sagittal stratum tract in AD, suggesting that disruption in the white matter tracts near the temporal lobe may represent the secondary consequence of the medial temporal lobe pathology in AD. Moreover, the regionally-specific patterns of FA and shape of the white matter tracts were shown to be of sufficient sensitivity to robustly differentiate patients with AD from healthy comparison controls when compared with the mean FA and volumes within the regions of the white matter tracts. Finally, greater FA or deformation abnormalities of the white matter tracts were associated with lower MMSE scores. CONCLUSION: The regionally-specific shape and FA patterns could be potential imaging markers for differentiating AD from normal aging.

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  • Other approaches fit a medial surface representation to the pathway, so sampled data can be represented in two dimensions and analyzed along the pathway and across its cross-section.

    “Other approaches fit a medial surface representation to the pathway, such that sampled data can be represented in two dimensions and can be analyzed not only along the pathway but also across its cross-section (Chen et al., 2016b; Qiu et al., 2010; Yushkevich et al., 2009).”

    From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Qiu et al., 2010

    Methods §4.3 defines the tract surface as the boundary of its volume, not a medial surface. Methods §§4.4–4.5 describe a two-dimensional surface and mapping three-dimensional FA data onto it, supporting the dimensionality-reduction part of the claim. The cited paper does not describe analysis along the pathway and across its cross-section in those terms. Page numbers are unavailable in our copy. (LLM, conf=0.88)

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  • Additional studies have analyzed tracts represented as surfaces or performed data reduction techniques to reduce dimensionality of point-wise measurements along tracts.

    “Additional studies have analyzed tracts represented as surfaces (Chen et al., 2016b; Qiu et al., 2010; Yushkevich et al., 2009) or performed data reduction techniques to reduce the dimensionality of the point-wise measurements along tracts (Ceschin et al., 2015; Chamberland et al., 2019; Geeraert et al., 2020).”

    From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Qiu et al., 2010

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Cited by

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Authors
Qiu, A., Oishi, K., Miller, M. I., Lyketsos, C. G., Mori, S., & Albert, M.
Affiliations
  • Agency for Science, Technology and Research (SG)
  • Johns Hopkins University (US)
Corresponding author
Qiu, Anqi
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2010-03-22
Volume
5
Issue
3
Pages
e9811
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0009811
PMC
PMC2842443
OpenAlex
W1979075889
Cited by
26
References
4
Citations by year
2025: 1 · 2024: 1 · 2023: 1 · 2022: 2 · 2020: 1 · 2019: 1 · 2018: 1 · 2017: 1 · 2014: 4 · 2013: 3 · 2012: 5
FWCI
3.12
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • National University of Singapore (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Agency for Science, Technology and Research (SICS-09/1/1/001)
  • National University of Singapore (R-397-000-058-133)
  • Singapore Institute for Clinical Sciences
  • National Institute on Aging (P50‐AG021334, P50AG005146, P30AG021334)
  • National Institute of Biomedical Imaging and Bioengineering (R01EB008171, R01EB000975)
  • National Center for Research Resources (P41RR15241, RR-15241, P41RR015241)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
Keywords
Alzheimer's disease, white matter tracts, fractional anisotropy, deep white matter, sagittal stratum, LDDMM, diffusion tensor imaging, imaging biomarkers, normal aging, Mini-Mental State Examination
MeSH terms
Aged, Algorithms, Alzheimer Disease, Brain, Brain Mapping, Humans, Middle Aged, Surface Properties, Reproducibility of Results, Models, Statistical, Case-Control Studies, Anisotropy, Principal Component Analysis, Diffusion Tensor Imaging

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