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Record W2609197151 · doi:10.1016/j.jalz.2017.06.448

[P1–432]: ANTERIOR CINGULATE CORTEX EXHIBITS AGE‐RELATED METABOLIC CHANGES: CORRELATION WITH BEHAVIORAL PERFORMANCE IN ATTENTION TASK

2017· article· en· W2609197151 on OpenAlexaboutno aff
Pui Wai Chiu, Hui Zhang, Savio W.H. Wong, Tianyan Liu, Gloria Hoi Yan Wong, Terry Yat Sang Lum, Leung‐Wing Chu, Henry Ka‐Fung Mak

Bibliographic record

VenueAlzheimer s & Dementia · 2017
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsStroop effectAnterior cingulate cortexPsychologyCreatineGrey matterWhite matterCingulate cortexAudiologyInternal medicineCognitionNeuroscienceMedicineMagnetic resonance imagingRadiologyCentral nervous system

Abstract

fetched live from OpenAlex

The anterior cingulate cortex(ACC) has become a focus for aging research because of its implicated role in cognition. Furthermore, a prior fMRI study in older adults had demonstrated an increase in blood oxygen level-dependent(BOLD) signal in the ACC compared to young adults during Stroop task, suggesting compensation. In this study, we investigated the metabolic changes during aging in the dorsal ACC of a local Chinese cohort using quantitative proton magnetic resonance spectroscopy(1H-MRS). In addition, relationship between metabolite concentrations and performance from an attention task (numerical Stroop) will be assessed. 36 cognitively normal (Mini-mental State Examination≥28; Montreal Cognitive Assessment≥26) subjects (mean=49.3±17.5years, age range 24–84years) underwent MR scan using 3.0T Philips scanner. A PRESS(TR/TE=2000/39 ms) single voxel of 2x2x2cm3 was placed in the dorsal ACC. Choline(Cho),creatine (Cr),N-acetyl aspartate(NAA), myo-inositol(mI), and summation of glutamate and glutamine complex(Glx), were measured and quantified using internal water as reference by QUEST in jMRUI 4.0(Figure 1). Cerebrospinal fluid(CSF) normalization, water content correction for grey matter, white matter and CSF, and correction factors for T1 and T2 relaxations were implemented. Also, the subjects took part in a numerical Stroop task (Figure 2) inside the scanner(fMRI data was currently undergoing analysis). Pearson correlation coefficient(r) was calculated to assess any correlation between 1) absolute metabolite concentrations ([met]abs) and age in the ACC, and 2) [met]abs and behavioral performance. SPSS version 20.0 was used for statistical analysis and level of significance was set at 0.05. Mean [met]abs and behavioral performance were shown in Tables 1 and 2, respectively. In the ACC, both [Cr]abs(r = 0.336; p = 0.045) and [NAA]abs(r = 0.354; p = 0.034) showed significant positive correlation with age. Only [Cho]abs showed significant positive correlation with both Stroop effect (incongruent-congruent)(r = 0.378; p = 0.023) and Stroop interference (incongruent-neutral)(r = 0.549, p = 0.001) after age-effect adjustment (Figure 3). Age-related increase in [Cr]abs might imply altered energy metabolism[1], while increase in [NAA]abs with age might indicate compensation activity[2]. For the positive correlation between [Cho]abs with Stroop effect, and Stroop interference, higher [Cho]abs could signify glial proliferation which might in turn lead to deficits on executive function[3]. [1]Charlton et al., 2007,[2]Milham et al., 2000,[3]Russell et al., 2006. (a) Position of voxel placed in the anterior cingulatecortex, (b) Simulated spectrum using QUEST in jMRUI, and (c) Simulated spectrum (blue) fitted on spectrum from subject (red). Trial conditions shown in numerical Stroop Task. Scatter plots of [Cho]abs with behavioral performance of (a) Stroop effect, and (b) Stroop Interference.

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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.029
GPT teacher head0.316
Teacher spread0.287 · 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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Citations1
Published2017
Admission routes1
Has abstractyes

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