MétaCan
Menu
← Back to cohort
Record W2040290889 · doi:10.1016/j.jalz.2009.04.330

P2‐021: Detection of β‐amyloid plaques in human AD brain tissue specimen using MR imaging

2009· article· en· W2040290889 on OpenAlexaff
Hagen H. Kitzler, John A. Ronald, Yuanxin Chen, Robert Hammond, Brian K. Rutt

Bibliographic record

VenueAlzheimer s & Dementia · 2009
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsMagnetic resonance imagingPathologyAmyloid (mycology)Human brainNuclear magnetic resonanceParenchymaIn vivoMaterials scienceBiomedical engineeringNuclear medicineMedicineBiologyRadiologyPhysics

Abstract

fetched live from OpenAlex

There are no human in vivo magnetic resonance (MR) imaging techniques that specifically demonstrate the formation of β-amyloid plaques of Alzheimer's Disease (AD). High field strength MR studies of human specimens and in vivo AD animal model brain tissue have shown evidence that MR can detect β-amyloid plaques on the basis of iron co-localization with plaques. Here we adapt and exploit an optimized form of the highly SNR-efficient and iron-sensitive Fast Imaging Employing STeady State Acquisition (FIESTA) MR sequence for the detection of AD β-amyloid plaques, using a clinical 3T system supported by a gradient insert in order to provide the spatial resolution for high-resolution imaging of brain tissue specimens. MRI was performed on a 3T scanner (GE Signa HD) interfaced with an insertable gradient coil [peak strength: 500 mT/m, peak slew rate: 3200 T/m/s] and 3.5cm diameter solenoidal RF coil. Isotropically resolved FIESTA images of n=5 autopsy AD brain tissue sets (CERAD definite AD) were acquired in 218 minutes [(100μm); TR/TE 21/11 ms; FA 20°; BW ±8 kHz; phase cycling number/recon: 8/sum-of-squares]-. Datasets were analyzed for the presence of focal signal voids. Subsequent histological sections were stained for β-amyloid protein. High-resolution imaging revealed spherical signal voids throughout the specimen and we observed correlations between patterns found in MRI and histology. A subset of about 10% of plaques in immunohistochemistry sections stained for β-amyloid correlated to spherical signal voids throughout the brain specimen parenchyma on matched high-resolution 3DFIESTA images. However the task of β-amyloid plaque detection was complicated by a second paramagnetic intraparenchymal deposition because post-mortem formalin fixed human AD brain tissue specimens feature intravascular blood products caused by perimortal stasis. Using Minimum Intensity Projection (MinIP) spherical intra-parenchymal and tubular intravascular signal voids could be distinguished. This study provides evidence that clinical field strength MR has the ability to detect microscopic iron associated with human β-amyloid plaques although our gradient insert permits gradient amplitudes and slew rates that are presently not available for human studies with clinical MR systems. We will continue to explore this MR imaging tool for further specific cross-sectional analyses of AD brain.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.356
Teacher spread0.327 · 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 designBench or experimental
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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueAlzheimer s & Dementia→Same topicAdvanced MRI Techniques and Applications→French-language works237,207→