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Record W4210714808 · doi:10.1002/alz.055894

Cognitive function and neuroimaging correlates in a subjective cognitive decline population within a multimodal intervention supplemented with green tea extract (PENSA Study): Preliminary results of a voxel‐based morphometry study

2021· article· en· W4210714808 on OpenAlexaboutno aff
Thaïs Lorenzo, Greg Operto, Carles Falcón, Aida Cuenca‐Royo, Laura Forcano, Iris Piera, Albert Puig‐Pijoan, Natalia Soldevila‐Domenech, Anna Boronat, Iva Knezevic, Karine Fauria, José Luís Molinuevo, Juan Domingo Gispert, Rafael de la Torre

Bibliographic record

VenueAlzheimer s & Dementia · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsNeuroimagingCognitionCognitive declineIntervention (counseling)VoxelPsychologyVoxel-based morphometryPopulationCognitive InterventionNeuroscienceMedicineDementiaPsychiatryMagnetic resonance imagingPathologyEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Background Multicomponent interventions including diet, physical activity and cognitive training have shown great potential to prevent cognitive decline in individuals at risk of developing the clinical stages of Alzheimer’s disease (AD). The PENSA study is a clinical trial focused on the prevention of cognitive decline in individuals with subjective cognitive decline (SCD) who are also APOE ε4 carriers. It is based on a 12‐month multimodal intervention (nutritional, physical and cognitive) combined with epigallocatechin‐3‐gallate (EGCG), a natural flavanol and known synaptic plasticity booster. In this study, we sought for neuroimaging correlates of cognitive performance of the PENSA participants at baseline. Method Twenty‐five (N=25) SCD participants of the PENSA Study at baseline (Table 1) with valid T1w and T2w MRI scans were included. They also underwent a neuropsychological assessment battery consisting of the modified Alzheimer Disease Cooperative Study Preclinical Alzheimer Cognitive Composite (ADCS‐PACC‐like) and additional cognitive performance scores obtained from (i) semantic verbal fluency, (ii) Boston Naming Test, (iii) digit span subtest, (iv) visual puzzle subtest and (v) the Montreal Cognitive Assessment. In addition, measurements of mental health, functionality, quality of sleep and quality of life perception have been assessed. T1w and T2w MRI scans were used to perform a multimodal segmentation of grey matter volumes (GMv) entered in the SPM12 voxel‐based morphometry (VBM) pipeline. Associations between test scores and GMv were sought (p<0.001) including age, sex, education and total intracranial volume as confounders. Result A negative correlation was found between the Free and Cued Selective Reminding Test (FCSRT) performance and GMv in the left anterior cingulate gyrus, left medial temporal lobe, right and middle temporal gyrus and left medial orbital gyrus (Figure 1). The rest of comparisons did not survive the statistical significance threshold. Conclusion Preliminary results show the expected pattern of negative associations between GMv and episodic memory performance, therefore suggesting that the PENSA study is recruiting the intended population of SCD individuals at risk of developing clinical stages of AD who could benefit from multimodal preventive interventions.

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)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.039
GPT teacher head0.306
Teacher spread0.267 · 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
Published2021
Admission routes1
Has abstractyes

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