MétaCan
Menu
← Back to cohort
Record W3112025802 · doi:10.1002/alz.041245

Alterations of cortical thickness and gray‐white matter contrast in Alzheimer’s disease and Lewy body‐related cognitive impairment

2020· article· en· W3112025802 on OpenAlexaff
Seun Jeon, Hosung Kim, Byoung Seok Ye, Alan C. Evans

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsWhite matterGray (unit)PsychologyGrey matterTemporal lobePathologyNeuroscienceMagnetic resonance imagingMedicineEpilepsyNuclear medicineRadiology

Abstract

fetched live from OpenAlex

Abstract Background Gray and white matter alteration has been known in patients with neurodegenerative diseases including Alzheimer’s disease‐ (ADCI) and Lewy body‐related cognitive impairment (LBCI). We examined a distinct pattern of cortical thickness (CTH) and gray‐white matter contrast (GWC) alterations in these degenerative neurological diseases. Since the T1‐weighted MRI signal is closely related to myelin content, the GWC may reflect disease‐related alterations of myeloarchitecture along the cortical surface. Methods T1‐weighted MRIs were obtained from patients (demo‐matched, CDR‐SOB≤9) with ADCI (n=97), LBCI (n=93), and healthy controls (n=37). We measured CTH using the CIVET processing pipeline. GWC was calculated at each vertex as the difference between gray matter (sampled at 50% CTH) and white matter intensity (sampled at 1mm subcortical white matter), then this was normalized by their average. Group differences (controls vs. patients) and correlations (with neuropsychological scores) were examined using general linear models covarying for age, sex, education, intracranial volume, and several vascular risk factors. Results were corrected for multiple comparisons using random field theory p<0.05 (cluster‐forming threshold p=0.001). Results Patients with ADCI showed altered CTH in the brain regions typically affected in AD including the bilateral medial temporal, lateral temporo‐parietal, and posterior cingulate cortices. The pattern of GWC alteration was smaller and no change was shown in the lateral temporal lobe (Figure 1A). LBCI patients showed altered CTH in superior frontal, lateral temporal, basal frontal and occipital cortices. The GWC alteration pattern was shown in larger area, notably in the limbic and occipital cortices (Figure 1B). Memory function, in the AD‐spectrum, was more closely associated with CTH, while in the LB‐spectrum, it was more closely with GWC (Figure 2). Frontal/executive function has stronger association with CTH than with GWC in the AD‐spectrum, while the LB‐spectrum showed independent patterns of association in those measures (Figure 3). The visuospatial function was only associated with CTH in AD‐spectrum, and in LB‐spectrum, it was greater in CTH than in GWC (Figure 4). Conclusions We demonstrated distinct patterns of CTH and GWC alteration in ADCI and LBCI. Consideration of heterogeneous atrophy patterns may be important when planning prevention and treatment strategies, as they may have different responses to treatment in the disease progression.

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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.321
Teacher spread0.281 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

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