Cited by 42Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsAdvanced Neuroimaging Techniques and Applications

Discriminative Analysis of Mild Alzheimer’s Disease and Normal Aging Using Volume of Hippocampal Subfields and Hippocampal Mean Diffusivity: An In Vivo Magnetic Resonance Imaging Study

Open access
Li, Ya-Di, Dong, Hai-Bo, Xie, Guo-Ming, Zhang, Ling-jun·2013·American Journal of Alzheimer's Disease & Other Dementias®

Abstract

Background: Studies discovered that the hippocampal subfields are differentially affected by pathological damage, and magnetic resonance (MR) diffusion tensor imaging parameters might be more sensitive measures of early degeneration in Alzheimer’s disease (AD) than conventional MR imaging techniques. The purpose of this study was to evaluate the significance of the volume of hippocampal subfields and the mean diffusivity (MD) value of hippocampus in discrimination between mild AD and normal aging. Methods: A total of 29 patients with mild AD and 30 normal aging were scanned. Binary logistic regression analysis was applied to assess the diagnostic significance of the volumes of hippocampal subfields and the MD value of hippocampus. Results: All hippocampal subfields except right tail atrophied significantly in the mild AD group ( P < .05). The relative volumes of right CA1 and subiculum subfields entered the binary logistic regression model. The accuracy was 91.8%, which was improved to 93.9% as the MD value of right hippocampus entered the model. Conclusion: Atrophy was present in almost all hippocampal subfields at mild AD stage. The volumes of CA1 and subiculum were of the most diagnostic significance in discrimination of mild AD, which can be improved by the combination of volume and diffusivity analysis.

Integrity signals

Registry

Crossref · 10.1177/1533317513494452

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

The citation lists haven’t been fetched yet

References 45 · Cited by 42

Authors
Li, Y.-D., Dong, H.-B., Xie, G.-M., & Zhang, L.-J.
Affiliations
  • Ningbo University (CN)
  • Department of Radiology, Lihuili Hospital of Ningbo Medical Center, Ningbo University, Ningbo, Zhejiang, China
  • College of Science & Technology, Ningbo University, Ninbo, Zhejiang, China
Venue
American Journal of Alzheimer's Disease & Other Dementias®
Published
2013-06-29
Volume
28
Issue
6
Pages
627-633
ISSN
  • 0888-4897 (ISSN-L)
  • 1533-3175
  • 1938-2731
OpenAlex
W2121935833
Cited by
42
References
45
Citations by year
2025: 1 · 2024: 1 · 2023: 2 · 2022: 1 · 2021: 5 · 2020: 5 · 2019: 4 · 2018: 3 · 2017: 4 · 2016: 5 · 2015: 6 · 2014: 5
FWCI
3.78
Type
journal article
Language
en
Open access
Locations
  • American Journal of Alzheimer s Disease & Other Dementias® (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords
mild Alzheimer's disease, normal aging, hippocampal subfields, CA1, subiculum, hippocampal atrophy, diffusion tensor imaging, differential diagnosis, early detection of Alzheimer's disease
MeSH terms
Aged, Aged, 80 and over, Aging, Alzheimer Disease, Atrophy, Cognition Disorders, Female, Hippocampus, Humans, Male, Middle Aged, ROC Curve, Severity of Illness Index, Diffusion Tensor Imaging, CA1 Region, Hippocampal

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 ›

Are you an author, or do you see a problem with this page? Request a review