OBJECTIVE: To present a remodeling of the electroretinogram waveform using a covariance matrix to identify regions of interest and distinction between a control and attention deficit/hyperactivity disorder (ADHD) group. Electroretinograms were recorded in n = 25 ADHD (16 male; age 11.9 ± 2.7 years) and n = 38 (8 male; age 10.4 ± 2.8 years neurotypical control participants as part of a broad study into the determining if the electroretinogram could be a biomarker for ADHD. Flash strengths of 0.6 and 1.2 log cd.s.m- 2 on a white 40 cd.m- 2 background were used. Averaged waveforms from each eye and flash strength were analyzed with Bayesian regularization of the covariance matrices using 100 equal length time intervals. The eigenvalues of the covariance matrices were ranked for each group to indicate the degree of complexity within the regularized waveforms. RESULTS: The correlation matrices indicated less correlation within the waveforms for the ADHD group in time intervals beyond 70 msec. The eigenvalue plots suggest more complexity within the ADHD group compared to the control group. Consideration of the correlation structure between ERG waveforms from different populations may reveal additional features for identifying group differences in clinical populations.

• Keywords
• Attention deficit hyperactivity disorder, Neurodevelopment, Retina, Time-domain ERG trajectory,
• MeSH
• Bayes Theorem MeSH
• Child MeSH
• Electroretinography * methods   MeSH
• Attention Deficit Disorder with Hyperactivity * physiopathology   diagnosis   MeSH
• Humans MeSH
• Adolescent MeSH
• Retina * physiopathology   MeSH
• Case-Control Studies MeSH
• Photic Stimulation MeSH
• Light * MeSH
• Check Tag
• Child MeSH
• Humans MeSH
• Adolescent MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Electroretinograms (ERGs) show differences between typically developing populations and those with a diagnosis of autism spectrum disorder (ASD) or attention deficit/hyperactivity disorder (ADHD). In a series of ERGs collected in ASD (n = 77), ADHD (n = 43), ASD + ADHD (n = 21), and control (n = 137) groups, this analysis explores the use of machine learning and feature selection techniques to improve the classification between these clinically defined groups. Standard time domain and signal analysis features were evaluated in different machine learning models. For ASD classification, a balanced accuracy (BA) of 0.87 was achieved for male participants. For ADHD, a BA of 0.84 was achieved for female participants. When a three-group model (ASD, ADHD, and control) the BA was lower, at 0.70, and fell further to 0.53 when all groups were included (ASD, ADHD, ASD + ADHD, and control). The findings support a role for the ERG in establishing a broad two-group classification of ASD or ADHD, but the model's performance depends upon sex and is limited when multiple classes are included in machine learning modeling.

• Keywords
• attention deficit hyperactivity disorder, autism, biomarker, feature selection, medication, retina, sex,
• Publication type
• Journal Article MeSH