Light-Adapted Electroretinogram and Oscillatory Potentials (LEOPs) Dataset for Autism Spectrum Disorder and Typically Developing Individuals

Paul A. Constable; Dorothy A. Thompson; Irene O. Lee; Lynne Loh; Aleksei Zhdanov; Mikhail Kulyabin; Andreas Maier

physics.med-phApr 18, 2026

Light-Adapted Electroretinogram and Oscillatory Potentials (LEOPs) Dataset for Autism Spectrum Disorder and Typically Developing Individuals

Authors: Paul A. Constable, Dorothy A. Thompson, Irene O. Lee, Lynne Loh, Aleksei Zhdanov, Mikhail Kulyabin, Andreas Maier

Organizations: Flinders University, College of Nursing and Health Sciences, Caring Futures Institute, Adelaide, Australia · The Tony Kriss Visual Electrophysiology Unit, Clinical and Academic Department of Ophthalmology, Great Ormond Street Hospital for Children NHS Trust, London, United Kingdom · UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom · Behavioural and Brain Sciences Unit, Population Policy and Practice Programme, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom · Visiomed.AI, Moscow, Russia · Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany

Abstract

The LEOPs (Light-ERG-Oscillatory Potentials) dataset provides light-adapted (LA) electroretinogram (ERG) and Oscillatory Potentials (OPs) waveforms for typically developing Control, Autism Spectrum Disorder (ASD) and ASD + Attention Deficit Hyperactivity Disorder (ADHD) childhood and adolescent populations. The ERGs were recorded in the Right And Left eyes with skin electrodes using the handheld RETeval device at two sites in Australia and the United Kingdom. The LEOPs dataset includes 5309 single flash ERG and 4434 OPs waveforms as well as images selected from each participant showing the position of the skin electrode. The LEOPs dataset is constructed from recordings using a 9 step randomized flash series from $-0.37$ to $1.20$ ~~$Td.s$ , a 2 step at 113 and 446 $Td.s$ flash strengths (2500 Control, 1730 ASD and 451 ASD + ADHD samples), as well as the $85$~~ $Td.s$ (Light Adapted 3 $cd.s.m^{-2}$ (LA3)) equivalent International Society of Clinical Electrophysiology of Vision (ISCEV) Standard flash with 435 Control, 176 ASD and 37 ASD + ADHD waveform samples. Code for the stimulus is provided along with participant demographics, date and time of testing, and where available diagnostic scores for the ASD and ASD + ADHD groups, alongside iris color, electrode position with image files and time domain values for the ERG and summed values for the OPs. The repository contains excel file, exported JSON files on the patient level that are more suitable for machine learning tasks, images of electrode position for each recording and the protocol files for use with the RETeval.

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Aug 3, 2026 · cs.CVJ/K move · Enter open · S save

Temporal Generalization in fNIRS-Based Autism Classification: A Cross-Time-Window Transfer Benchmark

Marios Petrov, Sahana Vinayak, Targol Bakhtiarvand, Moses Smith Guddah+3

Department of Computer Science, University of Colorado Colorado Springs, Colorado Springs, CO 80918, USA · Center for Child Health, Behavior and Development, Seattle Children’s Research Institute, Seattle, WA, USA · Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA

Functional near-infrared spectroscopy (fNIRS) is a promising modality for autism spectrum disorder (ASD) classification, yet existing approaches assume temporally aligned evaluation. In practice, the optimal observation window varies across subjects due to differences in hemodynamic delay and neurovascular coupling, creating a temporal distribution shift that degrades performance. We formalize this as a \textit{cross-time-window transfer problem}, introducing a protocol that varies window length (2.5--10,s) and offset within biological motion trials. Using topographic map representations of fNIRS recordings, we benchmark three vision architectures under two zero-shot baselines and eight adaptation strategies under leave-one-subject-out cross-validation (

N{=}124

). Key findings: (1) zero-shot cross-window accuracy is near chance (54--69%); (2)

{\approx}5\%

subject-specific fine-tuning recovers 90--96%, while a subject-specific upper bound reaches 97--100%, identifying inter-subject variability as the dominant barrier; (3) domain-adversarial and self-supervised strategies achieve 78--90% without target-subject data; and (4) discriminative information is recoverable from windows as short as 2.5,s. These findings provide a practical roadmap for deploying fNIRS-based ASD classifiers under realistic temporal variability.

Autism Spectrum Disorder Inter-Subject Variability Cross-Dataset Benchmark Neuroimaging Models Infrared

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Abstract

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