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Record W2053770492 · doi:10.1177/1087054712439418

Neural Correlates of Forethought in ADHD

2012· article· en· W2053770492 on OpenAlexafffund
Hélène Poissant, Adrianna Mendrek, Noureddine Senhadji

Bibliographic record

VenueJournal of Attention Disorders · 2012
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversité de MontréalInstitut universitaire en santé mentale de MontréalInstitut Universitaire de Gériatrie de MontréalUniversité du Québec à Montréal
FundersErasmus+Universitat de BarcelonaSocial Sciences and Humanities Research Council of CanadaUniversity of British ColumbiaUniversité du Québec à Montréal
KeywordsPsychologyFunctional magnetic resonance imagingNeural correlates of consciousnessCerebellar vermisAttention deficit hyperactivity disorderPrefrontal cortexNeuroscienceNeural activityCerebellumDevelopmental psychologyAudiologyClinical psychologyCognitionMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of the present investigation was to delineate the neural correlates of forethought in the ADHD children relative to typically developing (TD) children. METHOD: In all, 21 TD and 23 ADHD adolescents underwent functional magnetic resonance imaging (fMRI) while performing a forethought task. The participants had to identify congruent and incongruent stimuli from cartoon stories representing sequences of action. RESULTS: The findings revealed significantly greater activation in the bilateral prefrontal cortex (PFC) in TD versus ADHD children, and more activation in the cerebellar vermis in the adolescents with ADHD versus TD, during performance of the incongruent relative to congruent condition. CONCLUSION: The inverse pattern of activation of the PFC and the cerebellar vermis in both groups could reflect a compensatory role played by the cerebellum or suggest the malfunction of the neural network between those regions in ADHD. Further research of the neural correlates of forethought in ADHD is warranted.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.006
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.331
Teacher spread0.296 · 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 teacher head, 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

Citations12
Published2012
Admission routes2
Has abstractyes

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