Evidence map›Paper›PMID 38393437›Full record

ArticleJournal of autism and developmental disorders2025

Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths.

Sultan Mohammad Manjur, Luis Roberto Mercado Diaz, Irene O Lee, David H Skuse, Dorothy A Thompson, Fernando Marmolejos-Ramos, Paul A Constable, Hugo F Posada-Quintero

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of autism and developmental disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
4.2field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. A practical introduction to wavelet analysis in electroretinography.Documenta ophthalmologica. Advances in ophthalmology · 2026
    Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 5 institutions in 3 countries.

Sultan Mohammad ManjurDepartment of Biomedical Engineering, University of Connecticut, 06269, Storrs, CT, USA.
Luis Roberto Mercado DiazDepartment of Biomedical Engineering, University of Connecticut, 06269, Storrs, CT, USA.
Irene O LeeBehavioral and Brain Sciences Unit, Population Policy and Practice Program, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.
David H SkuseBehavioral and Brain Sciences Unit, Population Policy and Practice Program, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.
Dorothy A ThompsonTony Kriss Visual Electrophysiology Unit, Clinical and Academic Department of Ophthalmology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
Fernando Marmolejos-RamosCentre for Change and Complexity in Learning, University of South Australia, Adelaide, Australia.
Paul A ConstableCollege of Nursing and Health Sciences, Flinders University, Caring Futures Institute, Adelaide, Australia.
Hugo F Posada-QuinteroDepartment of Biomedical Engineering, University of Connecticut, 06269, Storrs, CT, USA. hugo.posada-quintero@uconn.edu.ORCID http://orcid.org/0000-0003-4514-4772
University of Connecticut · USUniversity College London · GBFlinders University · AUGreat Ormond Street Hospital · GBUniversity of South Australia · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAutism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are conditions that similarly alter cognitive functioning ability and challenge the social interaction, attention, and communication skills of affected individuals. Yet these are distinct neurological conditions that can exhibit diverse characteristics which require different management strategies. It is desirable to develop tools to assist with early distinction so that appropriate early interventions and support may be tailored to an individual's specific requirements. The current diagnostic procedures for ASD and ADHD require a multidisciplinary approach and can be lengthy. This study investigated the potential of electroretinogram (ERG), an eye test measuring retinal responses to light, for rapid screening of ASD and ADHD.

methodsPrevious studies identified differences in ERG amplitude between ASD and ADHD, but this study explored time-frequency analysis (TFS) to capture dynamic changes in the signal. ERG data from 286 subjects (146 control, 94 ASD, 46 ADHD) was analyzed using two TFS techniques.

resultsKey features were selected, and machine learning models were trained to classify individuals based on their ERG response. The best model achieved 70% overall accuracy in distinguishing control, ASD, and ADHD groups.

conclusionThe ERG to the stronger flash strength provided better separation and the high frequency dynamics (80-300 Hz) were more informative features than lower frequency components. To further improve classification a greater number of different flash strengths may be required along with a discrimination comparison to participants who meet both ASD and ADHD classifications and carry both diagnoses.

Indexed as

Attention Deficit Disorder with HyperactivityAutism Spectrum DisorderElectroretinographyMachine LearningAdolescentAdultChildFemaleHumansMalePhotic StimulationRetinaYoung AdultAttention deficit hyperactivity disorderAutism spectrum disorderElectroretinogramMachine learningTime-frequency analysis

Identifiers

PMID38393437
OpenAlexW4392110189

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.