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Record W2142104298 · doi:10.3174/ajnr.a3709

Evaluation of Common Structural Brain Changes in Aging and Alzheimer Disease with the Use of an MRI-Based Brain Atrophy and Lesion Index: A Comparison Between T1WI and T2WI at 1.5T and 3T

2013· article· en· W2142104298 on OpenAlexafffund
Hui Guo, Xiaowei Song, Robert Vandorpe, Y. Zhang, Wei Chen, Nan Zhang, Matthias H. Schmidt, Kenneth Rockwood

Bibliographic record

VenueAmerican Journal of Neuroradiology · 2013
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsNational Research Council CanadaNational Research Council Institute for BiodiagnosticsDalhousie UniversityNova Scotia Health AuthorityIzaak Walton Killam Health Centre
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute on AgingNational Research Council CanadaFoundation for the National Institutes of HealthTianjin Medical UniversityDalhousie UniversityUniversity of California, San FranciscoUniversity of California, San DiegoNational Natural Science Foundation of ChinaUniversity of California, Los AngelesDalhousie Medical Research FoundationNorthern California Institute for Research and EducationNational Institutes of HealthCanadian Institutes of Health ResearchNational Science Foundation
KeywordsMedicineAtrophyLesionBrain agingAlzheimer's diseasePathologyMagnetic resonance imagingDegenerative diseaseNeuroscienceDiseaseRadiology

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: The Brain Atrophy and Lesion Index combines several common, aging-related structural brain changes and has been validated for high-field MR imaging. In this study, we evaluated measurement properties of the Brain Atrophy and Lesion Index by use of T1WI and T2WI at 1.5T and 3T MR imaging to comprehensively assess the usefulness of the lower field-strength testing. MATERIALS AND METHODS: Data were obtained from the Alzheimer's Disease Neuroimaging Initiative. Images of subjects (n = 127) who had T1WI and T2WI at both 3T and 1.5T on the same day were evaluated, applying the Brain Atrophy and Lesion Index rating. Criterion and construct validity and interrater agreement were tested for each field strength and image type. RESULTS: Regarding reliability, the intraclass correlation coefficients for the Brain Atrophy and Lesion Index score were consistently high (>0.81) across image type and field strength. Regarding construct validity, the Brain Atrophy and Lesion Index score differed among diagnostic groups, being lowest in people without cognitive impairment and highest in those with Alzheimer disease (F > 5.14; P < .007). Brain Atrophy and Lesion Index scores correlated with age (r > 0.37, P < .001) and cognitive performance (r > 0.38, P < .001) and were associated with positive amyloid-β test (F > 3.96, P < .050). The T1WI and T2WI Brain Atrophy and Lesion Index scores were correlated (r > 0.93, P < .001), with the T2WI scores slightly greater than the T1WI scores (F > 4.25, P < .041). Regarding criterion validation of the 1.5T images, the 1.5T scores were highly correlated with the 3T Brain Atrophy and Lesion Index scores (r > 0.93, P < .001). CONCLUSIONS: The higher field and T2WI more sensitively detect subtle changes in the deep white matter and perivascular spaces in particular. Even so, 1.5T Brain Atrophy and Lesion Index scores are similar to those obtained by use of 3T images. The Brain Atrophy and Lesion Index may have use in quantifying the impact of dementia on brain structures.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.075
GPT teacher head0.367
Teacher spread0.292 · 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".

Quick stats

Citations30
Published2013
Admission routes2
Has abstractyes

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