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Record W2998674938 · doi:10.1080/00207454.2019.1709843

A spatial profile difference in electrical distribution of resting-state EEG in ADHD children using sLORETA

2020· article· en· W2998674938 on OpenAlexaff
Mojtaba Jouzizadeh, Reza Khanbabaie, Amir Ghaderi

Bibliographic record

VenueInternational Journal of Neuroscience · 2020
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsYork UniversityUniversity of Ottawa
Fundersnot available
KeywordsElectroencephalographyAbnormalityPsychologyAudiologyResting state fMRIParietal lobeAttention deficit hyperactivity disorderAlpha (finance)Developmental psychologyNeuroscienceMedicinePsychiatryPsychometrics

Abstract

fetched live from OpenAlex

Purpose:In this article, we propose current source density (CSD) as a marker for diagnosis of Attention Deficit and Hyperactivity Disorder (ADHD) children for the first time.Materials and methods: A source localization method (sLORETA) was used to find the source of abnormality in the CSD in electrical distribution of different frequency bands in resting state EEG for the ADHD children in comparison to the normal children using statistical nonparametric mapping (SnPM) test. Resting-state EEG in eye-open (EO) condition was recorded from 13 ADHD and 15 age-matched normal children (aged between 6 and 13).Results: Significant differences were found in the CSD of three frequency bands: delta, theta, and alpha in the parietal lobe, between ADHD and normal groups.Conclusions: Higher CSD in the parietal lobe for ADHD children was found which suggests that an abnormality exists in the parietal lobe of children with ADHD which can be related to the attention shifting problem in these children.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.063
GPT teacher head0.348
Teacher spread0.285 · 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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Citations22
Published2020
Admission routes1
Has abstractyes

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Same venueInternational Journal of NeuroscienceSame topicAttention Deficit Hyperactivity DisorderFrench-language works237,207