Transdiagnostic mapping of corticostriatal circuit for behavior and cognition in autism and ADHD

Poster No:


Submission Type:

Abstract Submission 


Han Byul Cho1, Hyunhoe An1,2, Shinwon Park3, Seok-Jun Hong1,2,4,5


1Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea, 2Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea, 3Autism Center, Child Mind Institute, New York, United States, 4Life-inspired Neural network for Prediction and Optimization Research Group, Suwon, Republic of Korea, 5Center for the Developing Brain, Child Mind Institute, New York, United States

First Author:

Han Byul Cho  
Center for Neuroscience Imaging Research, Institute for Basic Science
Suwon, Republic of Korea


Hyunhoe An  
Center for Neuroscience Imaging Research, Institute for Basic Science|Department of Biomedical Engineering, Sungkyunkwan University
Suwon, Republic of Korea|Suwon, Republic of Korea
Shinwon Park  
Autism Center, Child Mind Institute
New York, United States
Seok-Jun Hong  
Center for Neuroscience Imaging Research, Institute for Basic Science|Department of Biomedical Engineering, Sungkyunkwan University|Life-inspired Neural network for Prediction and Optimization Research Group|Center for the Developing Brain, Child Mind Institute
Suwon, Republic of Korea|Suwon, Republic of Korea|Suwon, Republic of Korea|New York, United States


Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are the two highly comorbid developmental conditions that are characterized by heterogeneous behavioral phenotypes1,2. Previous studies targeting both ASD and ADHD suggested that some of their behavioral symptoms are commonly linked to the altered function of the corticostriatal circuit-a core structure for executive function3-6. However, those studies frequently relied on comparisons between case-control groups informed by clinically diagnosed labels, which leaves space for further exploration into the true essence of 'transdiagnostic research'. Here, we address this issue by performing a fully dimensional approach, where we pool the data of all individuals with ASD or/and ADHD and comprehensively phenotype the relationship between their behavioral spectrum and corticostriatal functional connectivity (FC), regardless of their labels.


We analyzed the male-only data of 23 ASD, 111 ADHD, 61 comorbid ASD+ADHD and 35 neurotypical (NT) individuals derived from Healthy Brain Network7 (age: 6-21 years). The two dimensionality reduction techniques were employed: First, we performed a factor analysis to identify the bases underlying common variance of behavioral symptoms across all subjects (Fig.1A). Second, the Partial least squares (PLS) analysis investigated the correlation between resting-state FC of corticostriatal circuits and behavior symptoms (Fig.1B). We employed a permutation test (1,000 iterations) for the significance of the PLS analysis. Moreover, the composite scores from PLS (for both FC and behavioral symptoms) were sorted out into three different bins (bottom 20%, middle 60%, top 20%) to quantitatively assess the gradual changes on this brain-behavior axis (Fig.2A). After these analyses, we further performed ANOVA to identify the differences of factor scores and cognitive performance (assessed by NIH toolbox cognition battery)8 between the diagnostic labeled groups as well as between the three binned groups based on the PLS scores above.


Factor analysis identified four behavioral bases (i.e., 'social problems', 'impulsive behavior', 'emotional problems', and 'repetitive behavior'; Fig.1A), each providing a component score across all individuals. The following PLS analysis revealed a significant association between these behavioral component scores and corticostriatal FC (permutation p = 0.05; Fig.1B,C) across individuals, regardless of the diagnostic groups. We also checked the effect of diagnosis by extracting the PLS component scores for both corticostriatal FC and behavior symptoms and comparing them between the clinically labeled groups including NT. In this analysis, the comorbid group (ASD+ADHD) showed the most negative PLS score, suggesting their severe symptoms and corticostriatal connectivity abnormalities (Fig.1D). In the one way ANOVA with clinical diagnosis as an independent variable, each behavior score in ASD and ADHD exhibited significant impairment compared to other groups (Fig.1E). In particular, the ASD group showed a significantly lower score for cognitive flexibility, corroborating previous findings9 (Fig.1F). The same analysis based on the PLS-score based three binned groups revealed significant differences for the behavior scores (Fig.2B). In the assessment of cognitive performance, however, the score showed only a trend of positive correlation, except for the working memory (Fig.2C).
Supporting Image: fig_1_rev3.png
Supporting Image: fig_2_rev3.png


In this study, we found compelling evidence of a common biological axis that transgresses the boundaries of clinically diagnosed ASD, ADHD and their comorbid groups. This axis revealed the spectrum of transdiagnostic pathogenicity on brain-behavior relationships as well as on cognitive performance, potentially indicating their shared developmental etiology and manifestation. Our findings can provide a novel insight to a neuroimaging-based disease modeling in the future RDoC research10.

Disorders of the Nervous System:

Neurodevelopmental/ Early Life (eg. ADHD, autism) 1

Higher Cognitive Functions:

Executive Function, Cognitive Control and Decision Making

Modeling and Analysis Methods:

fMRI Connectivity and Network Modeling 2
Task-Independent and Resting-State Analysis


Attention Deficit Disorder
Basal Ganglia
Computational Neuroscience

1|2Indicates the priority used for review

Provide references using author date format

1. Sinzig J et al (2009) ‘Attention deficit/hyperactivity disorder in children and adolescents with autism spectrum disorder’ Journal of Attention Disorders 13(2):117-2
2. Simonoff E. et al., (2008) ‘Psychiatric disorders in children with autism spectrum disorders: prevalence, comorbidity, and associated factors in a population-derived sample’ Journal of the American Academy of Child and Adolescent Psychiatry 47, 921–929.
3. Di Martino A et al (2011) ‘Aberrant striatal functional connectivity in children with autism’ Biological Psychiatry 69(9): 847–56.
4. Tomasi D et al., (2012) ‘Abnormal functional connectivity in children with Attention-Deficit/Hyperactivity Disorder’ Biological Psychiatry 71 (5): 443–50.
5. Uddin LQ, (2021) ‘Cognitive and behavioural flexibility: neural mechanisms and clinical considerations’ Nature Reviews Neuroscience 22(3): 167–79.
6. Westbrook A et al.,(2021) ‘A mosaic of cost-benefit control over cortico-striatal circuitry’ Trends in Cognitive Sciences 25(8): 710–21.
7. Alexander LM et al., (2017) ‘An open resource for transdiagnostic research in pediatric mental health and learning disorders’ Scientific data 4, 170181
9. Cho HB et al., Transdiagnostic mapping of striatal connectivity and behavior-circuit modeling in autism and ADHD. Canada. 2023.07.22-26. OHBM 2023.
10. RDoC Matrix.