Transformer-based structural connectivity networks for ADHD-related connectivity alterations.
Authors
Affiliations (8)
Affiliations (8)
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
- Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
- The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, China.
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, China. [email protected].
- School of Disaster and Emergency Medicine, Tianjin University, Tianjin, China. [email protected].
- Jinan Guoke Medical Engineering and Technology Development Co., Ltd., Jinan, China. [email protected].
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that affects behavior, attention, and learning. Current diagnoses rely heavily on subjective assessments, underscoring the need for objective imaging-based methods. This study aims to explore whether structural connectivity networks derived from MRI can reveal alterations associated with ADHD and support data-driven understanding. We collected brain MRI data from 947 individuals (aged 7-26 years; 590 males, 356 females, 1 unspecified) across eight centers, sourced from the Neuro Bureau ADHD-200 preprocessed dataset. Transformer-based deep learning models were used to learn relationships between different brain regions and construct structural connectivity networks. To prepare input for the model, each region was transformed into a standardized data sequence using four different strategies. The strength of connectivity between brain regions was then measured to identify structural differences related to ADHD. Five-fold cross-validation and statistical analyses were used to evaluate model robustness and group differences, respectively. Here we show that the proposed method performs well in distinguishing ADHD individuals from healthy controls, with accuracy reaching 71.9 percent and an area under curve of 0.74. The structural networks also reveal significant differences in connectivity patterns (paired t-test: P = 0.81 × 10<sup>-6</sup>), particularly involving regions responsible for motor and executive function. Notably, the importance rankings of several brain regions, including the thalamus and caudate, differ markedly between groups. This study shows that ADHD may be associated with connectivity alterations in multiple brain regions. Our findings suggest that brain structural connectivity networks built using Transformer-based methods offer a promising tool for both diagnosis and further research into brain structure.