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Record W2766868522 · doi:10.1016/j.jalz.2017.06.445

[P1–429]: FUNCTIONAL MRI OF TABLET‐BASED COMPUTERIZED PAIRED ASSOCIATE LEARNING (PAL) WITH IMPROVED ECOLOGICAL VALIDITY

2017· article· en· W2766868522 on OpenAlexaff
Mahta Karimpoor, Nathan W. Churchill, Corinne E. Fischer, Tom A. Schweizer, Simon J. Graham

Bibliographic record

VenueAlzheimer s & Dementia · 2017
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsSt. Michael's HospitalHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsDorsolateral prefrontal cortexPsychologyNeuropsychologyAnalysis of variancePrefrontal cortexCognitive psychologyNeuroscienceComputer scienceCognitionMachine learning

Abstract

fetched live from OpenAlex

Administered with a computerized touch-screen display, PAL assists in characterizing memory and learning deficits in patients with probable early Alzheimer's disease (AD) (Blackwell Dement-Geriatr-Cogn-Disord 2003). FMRI may clarify the relationship between PAL performance and brain activity, but ecological validity is a concern during complex motor tasks. Previously, we developed an fMRI-compatible tablet including visual feedback of hand position (VFHP) and an augmented reality display (Tam HBM-2011; Karimpoor Frontiers-2015) to address this issue for AD patients with impaired motor planning (Ghilardi BrainRes-2000). The purpose of the present work is to characterize brain activity for an ecologically valid fMRI computerized PAL test. Prior to testing AD patients, initial work involved young adults. The PAL test involves making associative judgements of an increasing number of patterns (1–6 patterns) and their locations on the screen (Fig.1). Each task is repeated twice in a mixed fMRI design interspersed with baseline visual fixation. Ten young healthy right-handed adults performed PAL at 3T. The PRONTO toolkit was used to optimize fMRI preprocessing pipelines (Churchill PloSone-2015), and obtain Z-scored maps of retrieval phase vs. baseline, using a uni-variate Gauss Naïve Base (GNB) model. Principal Component Analysis (PCA) was performed on the Z-scored activation maps to obtain brain patterns explaining maximum variance across the group. Fig 2.a shows group maps during retrieval of 3 patterns (3pat) vs. baseline. Fig 2.b shows 6 patterns (6pat) vs. baseline. Increased activity in medial temporal lobe and dorsolateral prefrontal cortex is observed during 6pat compared to 3pat, consistent with the different difficulty levels. Activation of left ventral premotor cortex is consistent with visually-guided hand actions. Extensive activation of the left middle frontal gyrus, superior parietal lobule, and basal ganglia is observed during 6pat, compared to 3pat; consistent with differences in complex motor planning and visuospatial understanding. Results suggest that the fMRI-compatible tablet with VFHP enables PAL tests with good ecological validity involving complex fine motor movements. These methods and findings are relevant to future fMRI studies of AD patients; involving batteries of tasks required to probe different cognitive domains during limited scan time. fMRI-compatible PAL test. The first principal component depicting brain activity for 10 subjects performing PAL. a) Retrieval 3 patterns vs. baseline. b) Retrieval 6 patterns vs. baseline.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.096

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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0290.004

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 head0.051
GPT teacher head0.280
Teacher spread0.229 · 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
Published2017
Admission routes1
Has abstractyes

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