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Record W4312087329 · doi:10.1002/alz.064727

Associations between psychological factors, grey matter volume, and resistance and resilience to tau pathology in cognitively unimpaired older adults

2022· article· en· W4312087329 on OpenAlexaff
Cherie Strikwerda‐Brown, Frédéric St‐Onge, Yara Yakoub, Hazal Ozlen, Alexa Pichet Binette, John C.S. Breitner, Sylvia Villeneuve

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsPsychologyGrey matterPersonality pathologyNeuroticismEntorhinal cortexCognitionVoxel-based morphometryPsychological resilienceClinical psychologyBig Five personality traitsPersonalityOptimismPsychiatryNeuroscienceWhite matterMedicinePersonality disordersMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Abstract Background Modifiable factors may influence risk for Alzheimer’s disease (AD) via two distinct, though not mutually exclusive, pathways: resistance and resilience to pathology. Whereas cognitive ‘resilience’ is defined as the maintenance of better‐than‐expected cognitive performance in the face of Aβ and tau pathology, ‘resistance’ refers to an absence or delaying of the pathology itself. We explored the potential influence of psychological factors on resistance and resilience to tau pathology in older adults at risk of AD dementia, and whether grey matter volume mediates these relationships. Method Measures of psychological factors (including mindfulness, optimism, purpose in life, Big‐5 personality traits, and affective symptoms), longitudinal cognitive assessments, and structural MRI scans were collected in 259 nondemented older adults, along with Aβ‐ and tau‐ positron emission tomography (PET) scans in a subset of 156 individuals. Relationships between psychological factors and entorhinal tau pathology, and between psychological factors and cognitive resilience to entorhinal tau pathology (quantified using the residuals method), were explored, controlling for age, sex, and global Aβ burden. Principal components analysis was then performed to reduce the psychological factors that were associated with resistance/resilience down to their underlying components. Relationships between the psychological components and grey matter volume were explored using voxel‐based morphometry analyses. Finally, the potentially mediating effect of grey matter volume on the relationships between the psychological components and resistance/resilience to pathology were examined. Result One principal component was associated with resistance to pathology, comprising mindful acting with awareness, perseverative thinking, conscientiousness, neuroticism, depression, purpose in life, and optimism. Two principal components were associated with resilience to pathology, the first comprising mindful nonjudgment, anxiety, and stress, and the second comprising extraversion and openness. These three principal components were associated with partially distinct patterns of grey matter volume, which overlapped in medial prefrontal and posterior cingulate cortices (Figure 1). Grey matter volume partially mediated the relationship between psychological factors and resistance, but not resilience, to pathology. Conclusion Psychological functioning may influence resistance and resilience to AD pathology in cognitively unimpaired older adults. Increased grey matter associated with different combinations of protective psychological factors may assist in the delaying or prevention of AD pathology.

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 score0.003
Threshold uncertainty score0.005

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.346
Teacher spread0.314 · 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
Published2022
Admission routes1
Has abstractyes

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