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Record W3020957608 · doi:10.1148/radiol.2020191904

Cerebral Microhemorrhage at MRI in Mild Cognitive Impairment and Early Alzheimer Disease: Association with Tau and Amyloid β at PET Imaging

2020· article· en· W3020957608 on OpenAlexafffund
Boris‐Stephan Rauchmann, Farhad Ghaseminejad, Shailaja Mekala, Robert Perneczky

Bibliographic record

VenueRadiology · 2020
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsUniversity of British Columbia
FundersJohnson and Johnson Pharmaceutical Research and DevelopmentNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institutes of HealthGenentechIXICOH. Lundbeck A/SServierEisaiNorthern California Institute for Research and EducationBioClinicaF. Hoffmann-La RocheUniversity of Southern CaliforniaBiogenU.S. Department of DefenseMeso Scale DiagnosticsAlzheimer's Disease Neuroimaging InitiativePfizerEli Lilly and CompanyBristol-Myers SquibbNational Institute on AgingAlzheimer's AssociationFoundation for the National Institutes of Health
KeywordsMedicineCognitive impairmentAlzheimer's diseaseMagnetic resonance imagingNeuroimagingAmyloid (mycology)Positron emission tomographyPet imagingPathologyDiseaseRadiologyPsychiatry

Abstract

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Background Growing evidence indicates an association between cerebral microhemorrhages (MHs) and amyloid β accumulation in Alzheimer disease (AD), but to the knowledge of the authors the association with tau burden is unknown. Purpose To investigate the association between cerebral MH load and tau pathologic structure measured in healthy older individuals and individuals along the AD spectrum, stratified by using the A (amyloid β)/T (tau)/N (neurodegeneration) biomarker classification system. Materials and methods In this prospective cohort study, participants from the AD Neuroimaging Initiative were included (healthy control participants, participants with mild cognitive impairment, and participants with AD dementia; data from October 2005 to January 2019). T2*-weighted gradient-echo MRI was performed to quantify MH, fluorine 18 (18F) flortaucipir (AV-1451) PET was performed to quantify tau, and 18F-florbetaben/18F- florbetapir (AV45) PET was performed to quantify amyloid β to study associations of MH with regional and global tau and amyloid β load. Associations with cerebrospinal fluid (CSF) biomarkers (amyloid β1–42, total tau, phosphorylated tau 181) were also assessed. Analysis of covariance and Spearman rank correlation test for cross-sectional analysis and Wilcoxon signed rank test for longitudinal analyses were used, controlling for multiple comparisons (Bonferroni significance threshold, P < .008). Results Evaluated were 343 participants (mean age, 75 years ± 7; 186 women), including 205 participants who were A−TN− (mean age, 73 years ± 7; 115 women), 80 participants who were A+TN− (mean age, 76 years ± 7; 38 women), and 58 participants who were A+TN+ (mean age, 77 ± 8; 34 women). MH count was associated with global (Spearman ρ = 0.27; P = .004) and frontal (ρ = 0.27; P = .005) amyloid β load and global tau load (ρ = 0.31; P = .001). In a longitudinal analysis, MH count increased significantly over approximately 5 years in the entire cohort (T-1, 81 [range, 0–6 participants]; T0, 214 [range, 0–58 participants]; P < .001), in A+TN+ (T-1, 20 [range, 0–5 participants]; T0, 119 [range, 1–58 participants]; P < .001), A+TN− (T-1, 31 [range, 0–6 participants]; T0, 43 [range, 0–8 participants]; P = .03), and A−TN− (T-1, 30 [range, 0–4 participants]; T0, 52 [range, 0–6 participants]; P = .007). A higher MH count was associated with higher future global (ρ = 0.29; P = .008) and parietal (ρ = 0.31; P = .005) amyloid β and parietal tau load (ρ = 0.31; P = .005). Conclusion Cerebral microhemorrhage load is associated spatially with tau accumulation, both cross-sectionally and longitudinally. © RSNA, 2020 Online supplemental material is available for this article.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.016
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.255
Teacher spread0.243 · 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 teacher head, 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

Citations15
Published2020
Admission routes2
Has abstractyes

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