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Updated: Sep 13, 2025
Assessing Iron Deposition in the Brains of 5xFAD Mice by Perls'/DAB Staining
Published on: May 23, 2025
Brain iron deficiency in children with attention deficit hyperactivity disorder using iterAtive magnetic
Yufen Li1, Huan Wang1, Huasong Cai1
1Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
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:
Lower positive susceptibility values of the left caudate nucleus and bilateral pallidum were found (Caudate_L: 0.0231±0.0045 vs. 0.0261±0.0051, Pallidum_L: 0.0431±0.0114 vs. 0.0503±0.0141, Pallidum_R: 0.0426±0.0119 vs. 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.
