Brain iron deficiency in children with attention deficit hyperactivity disorder using iterAtive magnetic suscePtibility sources sepARaTion (APART-QSM)
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background: IterAtive magnetic suscePtibility sources sepARaTion (APART-QSM), a recently proposed susceptibility source separation method, can differentiate paramagnetic and diamagnetic susceptibility distributions related to iron and myelin, respectively. This study aimed to investigate whether paramagnetic susceptibility values of deep gray matter structures combined with machine learning algorithms could be used to identify individuals with attention-deficit/hyperactivity disorder (ADHD) and to further explore ADHD-related pathogenesis. Methods: Thirty-six ADHD and 35 age, sex-matched healthy controls (HCs) were recruited. The paramagnetic susceptibility mapping obtained by using APART-QSM method was normalized and the positive susceptibility values of deep gray matter structures, including the bilateral caudate nucleus, putamen, pallidum, and thalamus, were extracted. Random forest (RF) and support vector machine (SVM) were adopted to build machine learning models based on regional positive susceptibility values. The accuracy, sensitivity, specificity and the area under the curve (AUC) were used to evaluate the classification performance. Results: 0.0488±0.0120, P<0.05, uncorrected). However, no significant correlations were found between decreased iron levels and attention performance. Both classifiers achieved good performance, particularly the RF model with an AUC of 0.756, sensitivity of 77.8%, specificity of 68.6% and accuracy of 73.2%. Conclusions: Our findings revealed iron deficiency of deep gray matter nuclei in children with ADHD, and machine learning models combined with APART-QSM could be used to distinguish ADHD from HCs, providing a potential biomarker for further understanding of ADHD pathophysiology and facilitating early diagnosis.
Authors: Yufen Li, Huan Wang, Huasong Cai, Shasha Lan, Yan Dai, Boyan Xu, Shu Su, Hongyu Zhang, Zhiyun Yang, Yingqian Chen
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4411901751