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Record W3023334592 · doi:10.1016/j.bbr.2020.112632

Electrophysiological correlates of spontaneous mind wandering in attention-deficit/hyperactivity disorder

2020· article· en· W3023334592 on OpenAlexfundno aff
Natali Bozhilova, Ruth Cooper, Jonna Kuntsi, Philip Asherson, Giorgia Michelini

Bibliographic record

VenueBehavioural Brain Research · 2020
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsnot available
FundersMedical Research Council CanadaVifor PharmaMedical Research CouncilNational Institute for Health and Care ResearchNIHR Bristol Biomedical Research Centre
KeywordsNeurotypicalPsychologyElectroencephalographyCognitionAudiologyAttention deficit hyperactivity disorderEvent-related potentialElectrophysiologyNeuroscienceAssociation (psychology)Developmental psychologyClinical psychologyMedicine

Abstract

fetched live from OpenAlex

We recently hypothesised that increased spontaneous mind wandering (MW-S) reflects a core process underlying attention-deficit/hyperactivity disorder (ADHD). Previous studies show that individuals with ADHD and neurotypical individuals with increased MW-S display similar cognitive-performance and electrophysiological (EEG) impairments in attentional processes. However, the cognitive-EEG markers associated with increased MW-S in ADHD remain poorly understood. We therefore investigated such markers in a sample of 69 sex- and age-matched adults with ADHD and 29 controls during the Sustained Attention to Response Task. We compared task performance and EEG measures (P3, time-frequency brain-oscillations) of attentional processes between groups, and examined their association with a validated self-report questionnaire of MW-S. Finally, we tested the hypothesis that MW-S and ADHD diagnosis relate to the same cognitive-EEG impairments using a hierarchical regression model. Compared to controls, adults with ADHD showed attenuations in P3, event-related alpha and beta suppression during response inhibition (No-Go trials), and theta power activations during response execution (Go trials), as well as increased reaction time variability and more commission/omission errors. MW-S was also continuously associated with most cognitive-EEG measures related to ADHD. The hierarchical regressions on measures associated with both ADHD diagnosis and MW-S showed that MW-S did not explain additional variance in the cognitive-EEG markers (except for beta suppression) beyond ADHD diagnosis, and vice versa. These findings are consistent with our hypothesis that ADHD diagnosis and MW-S share common neural deficits, and that MW-S may reflect a core symptom of the disorder.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.822
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.136
GPT teacher head0.359
Teacher spread0.223 · 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

Citations32
Published2020
Admission routes1
Has abstractyes

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