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Record W4390194776 · doi:10.1002/alz.078785

The effect of Alzheimer’s disease pathology in corticobasal syndrome and progressive supranuclear palsy

2023· article· en· W4390194776 on OpenAlexaffabout
Indira García‐Cordero, Chloe Anastassiadis, Abeer Khoja, Alonso Morales‐Rivero, Anna Vasilevskaya, Simrika Thapa, Carly Davenport, Vishaal Sumra, David F. Tang‐Wai, Blas Couto, Namita Multani, Foad Taghdiri, Cassandra Jessica Anor, Brenda Varriano, Karen Misquitta, Susan H. Fox, Gábor G. Kovács, Adam L. Boxer, Lawren VandeVrede, Anthony E. Lang, Maria Carmela Tartaglia

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsOntario Brain InstituteUniversity of TorontoUniversity Health NetworkToronto Western HospitalParkinson's Clinic of Eastern Toronto & Movement Disorders CentreOccupational Cancer Research Centre
Fundersnot available
KeywordsProgressive supranuclear palsyCorticobasal degenerationPathologyPsychologyAtrophyClinical Dementia RatingGrey matterMontreal Cognitive AssessmentAlzheimer's diseaseMedicineNeuroscienceDementiaWhite matterDiseaseMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Abstract Background Corticobasal syndrome (CBS) and progressive supranuclear palsy (PSP) display phenotypic heterogeneity. CBS pathology can include 4‐repeat (4‐R) tau and Alzheimer’s disease (AD) pathology/co‐pathology; while PSP involves predominantly 4‐R tau with AD co‐pathology. We evaluated whether in vivo biomarkers of AD pathology in CBS and PSP are associated with specific brain atrophy and default mode network (DMN) alterations and relates to specific clinical manifestations. Method We analyzed MRIs of 110 patients with a clinical diagnosis of CBS and PSP from 4RTNI‐1, 4RTNI‐2 and the Toronto Western Hospital and 30 healthy controls. Patients were separated according to AD biomarker positivity (CBS/PSP‐AD, n = 23) and negativity (CBS/PSP‐noAD, n = 87) based on blood and cerebrospinal fluid AD biomarkers. Patients’ cortical and subcortical volumes were obtained using Freesufer segmentation and compared against controls. The DMN connectivity was compared across groups using two‐sample t‐tests. In a subgroup of patients, we analyzed Montreal Cognitive Assessment (MoCA), Clinical Dementia Rating (CDR), PSP Rating Scale (PSPRS) and delayed memory on the California Verbal Learning Test (CVLT). Spearman partial correlations were performed to relate the PSPRS and CVLT scores with whole‐brain grey matter volumes. Neuroimaging analyses were controlled for age, site and total intracranial volume. Result CBS/PSP‐AD displayed atrophy in temporal and parietal regions and hippocampus; while CBS/PSP‐noAD, in frontal and temporal regions, basal ganglia and thalamus (p<0.05, Bonferroni corrected). Reduced connectivity within the DMN was found in frontal‐temporal regions for CBS/PSP‐AD, and in frontal‐parietal regions for CBS/PSP‐noAD versus controls; and in temporal regions for CBS/PSP‐AD versus CBS/PSP‐noAD (p<0.001, uncorrected). No differences were obtained for MoCA, CDR and CVLT. The CBS/PSP‐AD group showed lower scores in the PSPRS in comparison to the CBS/PSP‐noAD [t(81) = 2.39, p = 0.02)]. Left thalamus grey matter volume negatively correlated with PSPRS scores [r:‐0.44, p = 0.02, Bonferroni corrected] in CBS/PSP‐noAD but no associations were found in CBS/PSP‐AD. Conclusion Distinct patterns of atrophy and connectivity were observed in CBS/PSP‐AD compared to CBS/PSP‐noAD, suggesting selective vulnerability to AD pathology. As well, severity of disease was related to thalamic atrophy only in CBS/PSP‐noAD. Selective vulnerability to diverse proteinopathies, leading to different atrophy and connectivity patterns, may help explain phenotypic heterogeneity in CBS and PSP.

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)0.002
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.286
Teacher spread0.269 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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