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Record W2423712451 · doi:10.18632/oncotarget.10091

White matter degeneration in subjective cognitive decline: a diffusion tensor imaging study

2016· article· en· W2423712451 on OpenAlexaboutno aff
Xuanyu Li, Zhenchao Tang, Yu Sun, Jie Tian, Zhenyu Liu, Ying Han

Bibliographic record

VenueOncotarget · 2016
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsDiffusion MRIWhite matterFractional anisotropyMedicineBeijingCognitive declineNeurologyMontreal Cognitive AssessmentChinaCognitive impairmentDiseaseInternal medicineDementiaPsychiatryMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

// Xuan-yu Li 1, * , Zhen-chao Tang 3, * , Yu Sun 1 , Jie Tian 2 , Zhen-yu Liu 2 , Ying Han 1, 4 1 Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, 100053, China 2 Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China 3 School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, Shandong Province, 264209, China 4 Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China * These authors contributed equally to this work and should be considered co-first authors Correspondence to: Ying Han, email: 13621011941@163.com Zhen-yu Liu, email: zhenyu.liu@ia.ac.cn Keywords: subjective cognitive decline, diffusion tensor imaging, preclinical Alzheimer’s disease, white matter, tract-based spatial statistics Received: November 09, 2015 Accepted: May 17, 2016 Published: June 15, 2016 ABSTRACT Subjective cognitive decline (SCD) may be an at-risk stage of Alzheimer’s disease (AD) occurring prior to amnestic mild cognitive impairment (aMCI). To examine white matter (WM) defects in SCD, diffusion images from 27 SCD (age=65.3±8.0), 35 aMCI (age=69.2±8.6) and 25 AD patients (age=68.3±9.4) and 37 normal controls (NC) (age=65.1±6.8) were compared using Tract-Based Spatial Statistics (TBSS). WM impairments common to the three patient groups were extracted, and fractional anisotropy (FA) values were averaged in each group. As compared to NC subjects, SCD patients displayed widespread WM alterations represented by decreased FA (p<0.05), increased mean diffusivity (MD; p<0.05), and increased radial diffusivity (RD; p<0.05). In addition, localized WM alterations showed increased axial diffusivity (AxD; p<0.05) similar to what was observed in aMCI and AD patients (p<0.05). In the shared WM impairment tracts, SCD patients had FA values between the NC group and the other two patient groups. In the NC and SCD groups, the AVLT-delayed recall score correlated with higher AxD (r=-0.333, p=0.045), MD (r=-0.351, p=0.03) and RD (r=-0.353, p=0.025). In both the aMCI and AD groups the diffusion parameters were highly correlated with cognitive scores. Our study suggests that SCD patients present with widespread WM changes, which may contribute to the early memory decline they experience.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.348
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations67
Published2016
Admission routes1
Has abstractyes

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