Poster No:
292
Submission Type:
Abstract Submission
Authors:
Yi-An Hung1, Chung-Yuan Cheng2, Yen-Chin Wang3, Susan Shur-Fen Gau1
Institutions:
1National Taiwan University Hospital, Taipei, Taiwan, 2National Taiwan University College of Medicine, Taipei City, Taipei City, 3National Taiwan University Hospital and College of Medicine, Department of Psychiatry, Taipei City, None
First Author:
Yi-An Hung
National Taiwan University Hospital
Taipei, Taiwan
Co-Author(s):
Chung-Yuan Cheng
National Taiwan University College of Medicine
Taipei City, Taipei City
Yen-Chin Wang
National Taiwan University Hospital and College of Medicine, Department of Psychiatry
Taipei City, None
Introduction:
Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by core symptoms of inattention, hyperactivity, and impulsivity (Kieling & Rohde, 2012). It is one of the most prevalent psychiatric disorders in children and adolescents (Chen et al., 2019; Drechsler et al., 2020), profoundly affecting their quality of life (Erskine et al., 2016) and has a significant and lasting impact on various aspects of life quality in adulthood (Henning et al., 2024). While significant progress has been made, the precise brain areas and mechanisms underlying ADHD remain incompletely understood (Cortese et al., 2023; Samea et al., 2019). Our preliminary study used machine learning to identify key cortical features and brain regions in ADHD. This study further analyzes the correlation between these regions and clinical symptoms assessed by the Swanson, Nolan, and Pelham Version IV Rating Scale (SNAP-IV), in the hope to identify brain regions linked to underlying pathophysiological mechanisms and with potential clinical implications.
Methods:
425 ADHD subjects (102 females, 24.0%, aged 15.64 ± 9.05 years, age range 5-49) and 543 typically developing controls (TDC) (204 females, 37.6%, aged 17.65 ± 8.44 years, age range 4-54) were included in this study. Clinical symptoms were rated by SNAP-IV. All participants received T1-weighted MRI scans, and cortical parcellation was conducted using the Destrieux Atlas with FreeSurfer (Destrieux et al., 2010; Fischl, 2012). From each parcellated region, three cortical features (folding index, gray volume, surface area) were derived and further evaluated and selected by machine learning models (Lundberg & Lee, 2017). The Shapiro-Wilk test was applied to assess normality. A chi-square test was used to evaluate gender differences between groups, while the Mann-Whitney U test was employed to compare age and SNAP-IV scores across the two groups. Generalized linear models were performed to analyze the correlation between brain regions shape values and SNAP-IV scores, with age and gender included as covariates.
Results:
Overall, left inferior part of the precentral sulcus, right precuneus, right paracentral lobule and sulcus, right anterior occipital sulcus and preoccipital notch, left supramarginal gyrus, and right posterior transverse collateral sulcus were identified with importance in ADHD. Correlation between the brain regions shape values with scores in SNAP-IV (inattention, hyperactivity and impulsivity, oppositional) were evaluated, showing left supramarginal gyrus with negative correlation (estimate=-39.917, Pr(>|t|)<0.05) and right posterior transverse collateral sulcus with positive correlation (estimate=3.0829, Pr(>|t|)<0.05) with scores in SNAP-IV inattention subscale.
.jpg ·Correlation between brain region signal intensity and scores in SNAP-IV.
Conclusions:
Our study identified two brain regions significantly correlated with inattention symptoms in ADHD. Future research should focus on validating these findings and exploring underlying mechanisms.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
Classification and Predictive Modeling
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 2
Perception, Attention and Motor Behavior:
Perception and Attention Other
Keywords:
Attention Deficit Disorder
Autonomics
Machine Learning
Modeling
MRI
Univariate
1|2Indicates the priority used for review
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
I accept
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information.
Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
I do not want to participate in the reproducibility challenge.
Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Structural MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Free Surfer
Provide references using APA citation style.
Chen, Y. L., Chen, W. J., Lin, K. C., Shen, L. J., & Gau, S. S. (2019). Prevalence of DSM-5 mental disorders in a nationally representative sample of children in Taiwan: methodology and main findings. Epidemiol Psychiatr Sci, 29, e15.
Cortese, S., Solmi, M., Michelini, G., Bellato, A., Blanner, C., Canozzi, A., Eudave, L., Farhat, L. C., Højlund, M., Köhler-Forsberg, O., Leffa, D. T., Rohde, C., de Pablo, G. S., Vita, G., Wesselhoeft, R., Martin, J., Baumeister, S., Bozhilova, N. S., Carlisi, C. O., . . . Correll, C. U. (2023). Candidate diagnostic biomarkers for neurodevelopmental disorders in children and adolescents: a systematic review. World Psychiatry, 22(1), 129-149.
Destrieux, C., Fischl, B., Dale, A., & Halgren, E. (2010). Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage, 53(1), 1-15.
Drechsler, R., Brem, S., Brandeis, D., Grünblatt, E., Berger, G., & Walitza, S. (2020). ADHD: Current Concepts and Treatments in Children and Adolescents. Neuropediatrics, 51(5), 315-335.
Erskine, H. E., Norman, R. E., Ferrari, A. J., Chan, G. C., Copeland, W. E., Whiteford, H. A., & Scott, J. G. (2016). Long-Term Outcomes of Attention-Deficit/Hyperactivity Disorder and Conduct Disorder: A Systematic Review and Meta-Analysis. J Am Acad Child Adolesc Psychiatry, 55(10), 841-850.
Fischl, B. (2012). FreeSurfer. Neuroimage, 62(2), 774-781.
Henning, C. T., Summerfeldt, L. J., & Parker, J. D. A. (2024). Longitudinal Associations Between Symptoms of ADHD and Life Success: From Emerging Adulthood to Early Middle Adulthood. J Atten Disord, 28(7), 1139-1151.
Kieling, R., & Rohde, L. A. (2012). ADHD in children and adults: diagnosis and prognosis. Curr Top Behav Neurosci, 9, 1-16.
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, California, USA.
Samea, F., Soluki, S., Nejati, V., Zarei, M., Cortese, S., Eickhoff, S. B., Tahmasian, M., & Eickhoff, C. R. (2019). Brain alterations in children/adolescents with ADHD revisited: A neuroimaging meta-analysis of 96 structural and functional studies. Neurosci Biobehav Rev, 100, 1-8.
No