Identification of Attention-Deficit-Hyperactivity Disorder Subtypes Based on Structural MRI Grey Matter Volume and
1Department of Electronics and Communication Engineering, SRM Institute of Science and Technology College of Engineering, Kattankulathur-603203, Tamil Nadu, India.
Background:
One of the neurodevelopmental disorders widely affecting school-aged children in recent years is attention deficit hyperactivity disorder (ADHD). In many neurodevelopmental disorders, grey matter may be used as a clinical indicator by looking at MRIs.
Objective:
The study aimed to segment grey matter from brain MRI using a proposed fuzzy c-means clustering-based technique for the detection of ADHD and its subtypes (ADHD-Inattentive, ADHDHyperactive, and ADHD-Combined). The grey matter volume, age, gender, and medication status of the subjects were investigated to identify ADHD subtypes.
Methods:
A modified fuzzy c-means with an elbow approach has been proposed to overcome the drawbacks of previous fuzzy c-means methods and improve segmentation performance. The volume of segmented grey matter was included with the phenotypic information of the ADHD-200 dataset for data analysis of typically developing (TD) and ADHD subtypes.
Results:
The proposed segmentation exhibited a dice similarity index of 95%. ADHD-Inattentive exhibited a loss of grey matter in the prefrontal cortex, while ADHD-hyperactive exhibited a loss of grey matter in the cerebellum when compared to TD. The analysis of ADHD subtypes based on age and gender showed that children transitioning to adolescence are mostly affected by ADHD-inattentive and female kids are less prone to ADHD-hyperactive. The whole grey matter volume of ADHD-inattentive children, on average, was found to be approximately 4% less than ADHD-combined. Furthermore, the whole grey matter volume was less in non-medication naive children.
Conclusion:
This study may support healthcare providers in giving appropriate occupational therapy based on the identification of different ADHD subtypes.
