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Record W1976432361 · doi:10.1016/j.jalz.2008.05.2501

IC‐P1‐059: Activation of dorsolateral prefrontal cortex during rest in early Alzheimer's disease: A preliminary study using 4 Tesla fMRI

2008· article· en· W1976432361 on OpenAlexaff
Xiaowei Song, Ryan C.N. D’Arcy, Alma Major, John D. Fisk, Sultan Darvesh, Janet Marshall, James Rioux, Chris V. Bowen, Steven Beyea, Kenneth Rockwood

Bibliographic record

VenueAlzheimer s & Dementia · 2008
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsCapital District Health AuthorityDalhousie UniversityCanadian Orthopaedic Trauma SocietyNational Research Council CanadaNational Research Council Institute for Biodiagnostics
Fundersnot available
KeywordsWorking memoryDorsolateral prefrontal cortexResting state fMRINeurosciencePsychologyPrefrontal cortexFunctional magnetic resonance imagingAudiologyBrain activity and meditationCognitionPosterior parietal cortexMedicineElectroencephalography

Abstract

fetched live from OpenAlex

Functional MRI studies on Alzheimer's disease (AD) often examine task-related activation, for which images taken during one cognitive state are compared with those during a reference state. When the resting state serves as this reference, inter-group differences in fMRI activation patterns thus may result from differences between the groups in baseline neural activity. We examined fluctuations of fMRI activation at rest in mild AD patients and in cognitively intact older adults. We began with a focus on the dorsolateral prefrontal cortex (DLPFC) as a region of interest because of its role in executive functions and working memory, and because of its frequent involvement in fMRI studies of cognitive tasks. Four patients with mild AD and eight healthy older adults were scanned on a 4-Tesla Varian-Oxford human imaging system. The subjects were instructed to remain relaxed while focusing their eyes on a central fixation for over 60 seconds. Functional images were acquired using two-shot spiral readout; 22 axial slices of 5.5+0.5mm. Data were processed using independent component analysis and artifacts attributed to physical and physiological sources were filtered. The time course of the representative resting state components (ICs) that were common across AD and healthy subjects were identified and analyzed applying GLM (p=0.001, uncorrected, extent=6). Signal changes, measured as the percentage difference between the highest and lowest of three consecutive fMRI data points during a scan were calculated. The fMRI signals filtered for known imaging signal to noise ratio and physiological variations fluctuated during the resting phase. Three ICs were identified across subjects with each being characterized as a low frequency response (<0.08Hz). Within the DFPLC, both the mean percentage change of the signal and the statistics in the contrast maps (i.e., for an IC) were greater in AD patients than in cognitively healthy older subjects. Regular patterns of fluctuations in fMRI indices of neural activity exist during rest and patients with mild AD demonstrated increased resting-state DLPFC activity. These resting state differences may reflect an early neurocompensatory response to AD.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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 head0.057
GPT teacher head0.279
Teacher spread0.222 · 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
Published2008
Admission routes1
Has abstractyes

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