<scp>Corticostriatal</scp> causality analysis in children and adolescents with attention‐deficit/hyperactivity disorder

Background Extensive research has been dedicated to exploring the neurobiological underpinnings of schizophrenia, yet there has been comparatively less emphasis on investigating specific markers associated with violent behaviors within this condition. Aim of the study This study aimed to compare gray matter thickness and volume and investigate clinical markers for assessing violence risk in schizophrenia. Results We enrolled a total of 36 participants, comprising 19 patients with a documented history of violent behaviors in schizophrenia and 17 patients with no such history. Schizophrenia patients with a history of violence exhibited a significant reduction in cortical thickness in the left caudal-middle-frontal gyrus (2.540 ± 0.146 mm) compared to those without such history (2.757 ± 0.126 mm) (P = 0.0301). Similarly, the cortical volume in the right middle temporal gyrus was decreased in patients with a history of violence (15.579 ± 8.790 mm3) compared to non-violent patients (29.941 ± 13.585 mm3) (P = 0.0071). ROC analysis demonstrated promising diagnostic efficiency, with an AUC of 0.876 for left caudal-middle-frontal gyrus thickness (P = 0.000) and 0.794 for right middle temporal gyrus volume (P = 0.003). Conclusions Our findings highlight brain cortical assessments as potential diagnostic biomarkers to identify individuals at risk of violent behavior within the schizophrenia population.

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Retinal Fundus Imaging as a Biomarker for Attention-Deficit/Hyperactivity Disorder: Machine Learning for Screening and Visual Attention Stratification

Choi,

Hong,

Kang

et al. 2024

Preprint

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Attention-deficit/hyperactivity disorder (ADHD), characterized by diagnostic complexity and symptom heterogeneity, is a prevalent neurodevelopmental disorder. In this study, we explored the machine learning analysis of retinal fundus photographs as a novel, noninvasive biomarker for ADHD screening and stratification of executive function (EF) deficits. Children and adolescents (<19 years) with ADHD (n=323) were recruited from two tertiary South Korean hospitals between April and October 2022. Retinal photographs of age- and sex-matched individuals with typical development were retrospectively collected. Individuals with major psychiatric disorders, neurological illnesses, and eye diseases affecting the retinal fundus were excluded. Retinal features were extracted using AutoMorph. Machine learning models for ADHD screening and EF subdomain prediction were developed using the comprehensive attention test. Model performance was evaluated via five-fold cross-validation with metrics including the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Feature importance was assessed using the Shapely additive explanation method. We analyzed 1,108 fundus photographs from 648 participants (mean age 9.5 years, 77.1% boys). ADHD screening models achieved 0.955–0.969 AUROC values, with vessel density as the most important feature. For EF function stratification, the visual and auditory subdomains showed strong (AUROC >0.85) and poor performances, respectively. Machine learning analysis of retinal fundus photographs demonstrated potential as a noninvasive biomarker for ADHD screening and EF deficit stratification in the visual attention domain. Furthermore, alterations in the retinal vascular structure and optic disc characteristics indicate a neurodevelopmental process affecting both cerebral and retinal structures, potentially reflecting systemic changes in ADHD. Our findings may guide the development of innovative screening tools and personalized treatment approaches. However, further research across diverse populations and age groups is necessary to validate these findings before clinical implementation.

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Corticostriatal causality analysis in children and adolescents with attention‐deficit/hyperactivity disorder

Cited by 3 publications

References 55 publications

Noninvasive brain–computer interfaces for children with neurodevelopmental disorders: Attention deficit hyperactivity disorder and autism spectrum disorder

Noninvasive brain–computer interfaces for children with neurodevelopmental disorders: Attention deficit hyperactivity disorder and autism spectrum disorder

Quantitative gray matter volumetric analysis in schizophrenia: investigating the risk of violent behaviors through structural MRI

Retinal Fundus Imaging as a Biomarker for Attention-Deficit/Hyperactivity Disorder: Machine Learning for Screening and Visual Attention Stratification

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