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

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

doi:10.1007/s10803-024-06290-w

Cited by 3 publications

(3 citation statements)

References 90 publications

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“…However, test conditions such as the electrode position and type and subject parameters such as sex assigned at birth, age, and iris color may also influence the reference ERG parameters [ 6 – 8 ]. Having the ability to increase representative waveforms based on exemplars may help bolster the sample size of not only normative but also of case series in challenging to recruit or under-represented populations in clinical research, such as autism spectrum disorder (ASD) [ 9 – 11 ], rare inherited retinal dystrophies (IRDs) [ 12 ], Parkinson's disease [ 13 ], glaucoma [ 14 ] and attention-deficit/hyperactivity disorder (ADHD) [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Additional signal analytical methods using variable frequency complex demodulation (VFCDM) employs a series of bandpass filters to give a greater time-frequency resolution than DWT, but at the expense of detailed information concerning the cellular origins of the extracted signals [ 23 ]. Nonetheless, VFCDM has been applied to the ERG for the classification of ASD and ADHD based on the LA-ERGs [ 15 , 23 – 25 ]. In addition to these analytical methods, other developments based on functional data analysis that identify features of the waveform shape to classify groups have also been described for the ascending portion of the b-wave [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, it can be beneficial in neurodevelopmental and neurodegenerative conditions, where clinical phenotypes are diverse and can be influenced by medication interactions and disease progression. These conditions include ASD and ADHD [ 9 – 11 , 15 , 23 , 27 ] schizophrenia [ 28 , 29 ], Parkinson's disease [ 13 , 30 – 32 ], and Alzheimer's disease [ 33 – 35 ]. The variability and complexity of these disorders make traditional classification challenging, highlighting the need for advanced techniques such as machine learning to improve diagnosis and understanding.…”

Section: Introductionmentioning

confidence: 99%

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Generating Synthetic Light‐Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under‐Represented Populations

Kulyabin,

Zhdanov,

Maier

et al. 2024

Journal of Ophthalmology

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Visual electrophysiology is often used clinically to determine the functional changes associated with retinal or neurological conditions. The full‐field flash electroretinogram (ERG) assesses the global contribution of the outer and inner retinal layers initiated by the rods and cone pathways depending on the state of retinal adaptation. Within clinical centers, reference normative data are used to compare clinical cases that may be rare or underpowered within a specific demographic. To bolster either the reference dataset or the case dataset, the application of synthetic ERG waveforms may offer benefits to disease classification and case‐control studies. In this study and as a proof of concept, artificial intelligence (AI) to generate synthetic signals using generative adversarial networks is deployed to upscale male participants within an ISCEV reference dataset containing 68 participants, with waveforms from the right and left eye. Random forest classifiers further improved classification for sex within the group from a balanced accuracy of 0.72–0.83 with the added synthetic male waveforms. This is the first study to demonstrate the generation of synthetic ERG waveforms to improve machine learning classification modelling with electroretinogram waveforms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generating Synthetic Light‐Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under‐Represented Populations

Kulyabin,

Zhdanov,

Maier

et al. 2024

Journal of Ophthalmology

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Exploring autism via the retina: Comparative insights in children with autism spectrum disorder and typical development

Li,

Wang,

Gao

et al. 2024

Autism Research

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Autism spectrum disorder (ASD) is a widely recognized neurodevelopmental disorder, yet the identification of reliable imaging biomarkers for its early diagnosis remains a challenge. Considering the specific manifestations of ASD in the eyes and the interconnectivity between the brain and the eyes, this study investigates ASD through the lens of retinal analysis. We specifically examined differences in the macular region of the retina using optical coherence tomography (OCT)/optical coherence tomography angiography (OCTA) images between children diagnosed with ASD and those with typical development (TD). Our findings present potential novel characteristics of ASD: the thickness of the ellipsoid zone (EZ) with cone photoreceptors was significantly increased in ASD; the large‐caliber arteriovenous of the inner retina was significantly reduced in ASD; these changes in the EZ and arteriovenous were more significant in the left eye than in the right eye. These observations of photoreceptor alterations, vascular function changes, and lateralization phenomena in ASD warrant further investigation, and we hope that this work can advance interdisciplinary understanding of ASD.

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Attention to the Electroretinogram: Gated Multilayer Perceptron for ASD Classification

Kulyabin,

Constable,

Zhdanov

et al. 2024

IEEE Access

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The electroretinogram (ERG) is a clinical test that records the retina's electrical response to a brief flash of light as a waveform signal. Analysis of the ERG signal offers a promising non-invasive method for studying different neurodevelopmental and neurodegenerative disorders. Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by poor communication, reduced reciprocal social interaction, and restricted and/or repetitive stereotyped behaviors that should be detected as early as possible to ensure timely and appropriate intervention to support the individual and their family. In this study, we applied gated Multilayer Perceptron (gMLP) for the light-adapted ERG waveform classification as an effective alternative to Transformers. In this first reported application of this model to ASD classification which consisted of basic multilayer perceptrons, with fewer parameters than Transformers. We compared the performance of different time-series models on an ASD-Control dataset and found that the superiority of gMLP in classification accuracy was the best at 89.7% compared to alternative models and supports the use of gMLP in classification models based on ERG recordings involving case-control comparisons.

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

Cited by 3 publications

References 90 publications

Generating Synthetic Light‐Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under‐Represented Populations

Generating Synthetic Light‐Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under‐Represented Populations

Exploring autism via the retina: Comparative insights in children with autism spectrum disorder and typical development

Attention to the Electroretinogram: Gated Multilayer Perceptron for ASD Classification

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