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Record W4414207792 · doi:10.1192/j.eurpsy.2025.1668

Cortical alterations in patients with mild cognitive impairment who converted to Alzheimer disease compared to non-converters and correlations of these alterations with mild behavioral impairment

2025· article· en· W4414207792 on OpenAlexaboutno aff
A. S. Tomyshev, N. S. Cherkasov, Anna V. Komarova, Е. Г. Абдуллина, И. В. Колыхалов, И. С. Лебедева

Bibliographic record

VenueEuropean Psychiatry · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive impairmentAlzheimer's diseaseTemporal lobeMontreal Cognitive AssessmentCognitionHealthy agingParietal lobeDisease

Abstract

fetched live from OpenAlex

Introduction There is a growing evidence that a presence of mild behavioral impairment (MBI) in geriatric patients with mild cognitive impairment (MCI) increases the risk of Alzheimer disease. However, neurobiological patterns underling such additional risk remains unclear. Objectives We aimed to investigate structural cortical patterns in MCI patients that differentiate converters from non-converters to Alzheimer disease (AD) and to explore correlations of such patterns with mild behavioral impairment. Methods Thirty five right-handed geriatric patients with amnestic type of MCI (aMCI) were followed up during the period of 11.3±6.9 months and divided into converters to AD (n=11, mean age 74.6±7.5, 10 females) and non-converters (n=24, mean age 72.6±8.1, 18 females). Patients and matched healthy controls (n=17, mean age 72.3±7.2 years, 14 females) underwent structural 3T MRI at baseline. MRI images were processed via FreeSurfer 6.0 to quantify gray matter thickness for 68 cortical areas according to Desikan atlas. Cognitive status was assessed using the Montreal Cognitive Assessment (MoCA) scale, and the severity of mild behavioral impairment was assessed using the MBI-C (Mild Behavioral Impairment Checklist) at baseline. Results Cortical thickness in the left inferior parietal lobe (IPL) and left middle temporal gyrus (MTG) were decreased in converters compared to both non-converters (IPL: F(1,30)=12.8, p=0.0012, Cohen’s d=−1.30; MTG: F(1,30)=12.8, p=0.0012; Cohen’s d=−1.30) and healthy controls (IPL: F(1,24)=11.4, p=0.0025, Cohen’s d=−1.33; MTG: F(1,24)=8.3, p=0.008; Cohen’s d=−1.15) (Image 1A,B). Converters also showed larger baseline MBI-C scores compared to non-converters (13.8±12.0 vs 7.8±6.6; GML t=2.1, p=0.045) and no difference in baseline MoCA (21.8±4.0 vs 23.2±2.8; GML t=−1.4, p=0.19). Baseline MBI-C scores in the whole aMCI group correlated negatively with cortical thickness both in the left IPL (R=−0.53, p=0.0011) and left MTG (R=−0.48, p=0.004) (Image 1C). No correlations between MoCA scores and cortical thickness were observed. Image 1. A: Clusters of decreased cortical thickness according to atlas of Desikan et al. (2006) in converters compared to non-converters and healthy controls. B: Box-plots of cortical thickness in the left inferior parietal lobe and left middle temporal gyrus. C: Scatter plots of MBI-C scores and gray matter thickness in the left inferior parietal lobe and left middle temporal gyrus in the whole aMCI group (n = 35). Image 1: Conclusions The findings suggest that the decreased baseline cortical thickness in the left inferior parietal lobe and middle temporal gyrus with associated greater severity of mild behavioral impairment could be potential marker of increased risk of conversion to AD in aMCI patients. The study was supported by RSF grant 24-15-00220 Disclosure of Interest None Declared

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)0.001
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.303
Teacher spread0.289 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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