Affiliation:
1. School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213159, China
2. College of Information Science and Engineering, Hohai University, Changzhou 213200, China
Abstract
<abstract><p>Attention deficit hyperactivity disorder (ADHD) is a common childhood developmental disorder. In recent years, pattern recognition methods have been increasingly applied to neuroimaging studies of ADHD. However, these methods often suffer from limited accuracy and interpretability, impeding their contribution to the identification of ADHD-related biomarkers. To address these limitations, we applied the amplitude of low-frequency fluctuation (ALFF) results for the limbic system and cerebellar network as input data and conducted a binary hypothesis testing framework for ADHD biomarker detection. Our study on the ADHD-200 dataset at multiple sites resulted in an average classification accuracy of 93%, indicating strong discriminative power of the input brain regions between the ADHD and control groups. Moreover, our approach identified critical brain regions, including the thalamus, hippocampal gyrus, and cerebellum Crus 2, as biomarkers. Overall, this investigation uncovered potential ADHD biomarkers in the limbic system and cerebellar network through the use of ALFF realizing highly credible results, which can provide new insights for ADHD diagnosis and treatment.</p></abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Reference63 articles.
1. G. Polanczyk, P. Jensen, Epidemiologic considerations in attention deficit hyperactivity disorder: A review and update, Child Adolesc. Psychiatr. Clin. N. Am., 17 (2008), 245–260. https://doi.org/10.1016/j.chc.2007.11.006
2. Z. Zhang, G. Li, Y. Xu, X. Tang, Application of artificial intelligence in the MRI classification task of human brain neurological and psychiatric diseases: A scoping review, Diagnostics, 11 (2021), 1402. https://doi.org/10.3390/diagnostics11081402
3. M. Quaak, L. Mortel, R. M. Thomas, G. V. Wingen, Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis, Neuroimage Clin., 30 (2021), 102584. https://doi.org/10.1016/j.nicl.2021.102584
4. L. Zou, J. Zheng, C. Miao, M. J. Mckeown, Z. J. Wang, 3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI, IEEE Access, 5 (2017), 23626–23636. https://doi.org/10.1109/ACCESS.2017.2762703
5. L. Su, S. I. Kamata, ADHD classification with low-frequency fluctuation feature map based on 3D CBAMe, in Proceedings of the 7th International Conference on Biomedical Signal and Image Processing, ACM, (2022), 74–79. https://doi.org/10.1145/3563737.3563749