Evidence map›Paper›PMID 23644540›Full record

ArticleNeuroradiology2013

In vivo quantitative whole-brain diffusion tensor imaging analysis of APP/PS1 transgenic mice using voxel-based and atlas-based methods.

Yuan-Yuan Qin, Mu-Wei Li, Shun Zhang, Yan Zhang, Ling-Yun Zhao, Hao Lei, Kenichi Oishi, Wen-Zhen Zhu

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Article in Neuroradiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

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26citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  4. Diffusion MRI reveals amyloid plaque-associated cellular signatures through spatial transcriptomic integration.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
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  17. Manual for clinical language tractography.Acta neurochirurgica · 2019
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yuan-Yuan QinDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, China.
Mu-Wei LiThe Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 217D Traylor Building, 720 Rutland Avenue, Baltimore, MD, 21205, USA.
Shun ZhangDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, China.
Yan ZhangDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, China.
Ling-Yun ZhaoDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, China.
Hao LeiWuhan Center for Magnetic Resonance, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China.
Kenichi OishiThe Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 217D Traylor Building, 720 Rutland Avenue, Baltimore, MD, 21205, USA. koishi@mri.jhu.edu.
Wen-Zhen ZhuDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, China. zhuwenzhen@hotmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDiffusion tensor imaging (DTI) has been applied to characterize the pathological features of Alzheimer's disease (AD) in a mouse model, although little is known about whether these features are structure specific. Voxel-based analysis (VBA) and atlas-based analysis (ABA) are good complementary tools for whole-brain DTI analysis. The purpose of this study was to identify the spatial localization of disease-related pathology in an AD mouse model.

methodsVBA and ABA quantification were used for the whole-brain DTI analysis of nine APP/PS1 mice and wild-type (WT) controls. Multiple scalar measurements, including fractional anisotropy (FA), trace, axial diffusivity (DA), and radial diffusivity (DR), were investigated to capture the various types of pathology. The accuracy of the image transformation applied for VBA and ABA was evaluated by comparing manual and atlas-based structure delineation using kappa statistics. Following the MR examination, the brains of the animals were analyzed for microscopy.

resultsExtensive anatomical alterations were identified in APP/PS1 mice, in both the gray matter areas (neocortex, hippocampus, caudate putamen, thalamus, hypothalamus, claustrum, amygdala, and piriform cortex) and the white matter areas (corpus callosum/external capsule, cingulum, septum, internal capsule, fimbria, and optic tract), evidenced by an increase in FA or DA, or both, compared to WT mice (p < 0.05, corrected). The average kappa value between manual and atlas-based structure delineation was approximately 0.8, and there was no significant difference between APP/PS1 and WT mice (p > 0.05). The histopathological changes in the gray matter areas were confirmed by microscopy studies. DTI did, however, demonstrate significant changes in white matter areas, where the difference was not apparent by qualitative observation of a single-slice histological specimen.

conclusionThis study demonstrated the structure-specific nature of pathological changes in APP/PS1 mouse, and also showed the feasibility of applying whole-brain analysis methods to the investigation of an AD mouse model.

Indexed as

Disease Models, AnimalAlgorithmsAlzheimer DiseaseAmyloid beta-Protein PrecursorAnimalsBrainDiffusion Tensor ImagingHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalMiceMice, TransgenicReproducibility of ResultsSensitivity and SpecificityAmyloid beta-Protein Precursor

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.