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Record W6889197511 · doi:10.25384/sage.c.6707457

Functional Near-Infrared Spectroscopy of English-Speaking Adults With Attention-Deficit/Hyperactivity Disorder During a Verbal Fluency Task

2023· other· en· W6889197511 on OpenAlexaff

Bibliographic record

VenueSage Journals Data · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsVerbal fluency testHemodynamicsPrefrontal cortexHaemodynamic responseTask (project management)NeurophysiologyFluencyWorking memory

Abstract

fetched live from OpenAlex

Objective:Functional near-infrared spectroscopy (fNIRS) provides direct and quantitative assessment of cortical hemodynamic response. It has been used to identify neurophysiological alterations in medication-naïve adults with attention-deficit/hyperactivity disorder (ADHD). Hence, this study aimed to distinguish both medication-naïve and medicated adults with ADHD from healthy controls (HC).Method:75 HCs, 75 medication-naïve, and 45 medicated patients took part in this study. fNIRS signals during a verbal fluency task (VFT) were acquired using a 52-channel system and relative oxy-hemoglobin changes in the prefrontal cortex were quantified.Results:Prefrontal cortex hemodynamic response was lower in patients than HCs (p ≤ ≤.001). Medication-naïve and medicated patients did not differ in hemodynamic response or symptom severity (p > .05). fNIRS measurements were not associated with any clinical variables (p > .05). 75.8% patients and 76% HCs were correctly classified using hemodynamic response.Conclusion:fNIRS may be a potential diagnostic tool for adult ADHD. These findings need to be replicated in larger validation studies.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.234
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0020.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0160.002

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 head0.020
GPT teacher head0.273
Teacher spread0.253 · 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; both teacher heads agree on what is shown here.

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
Published2023
Admission routes1
Has abstractyes

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Same venueSage Journals DataFrench-language works237,207