Brain iron deficiency in children with attention deficit hyperactivity disorder using iterAtive magnetic suscePtibility sources sepARaTion (APART-QSM)
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 (ORCID: https://orcid.org/0000-0002-3960-6101)
- Xin Lan (ORCID: https://orcid.org/0000-0001-5895-9343)
- Yan Dai (ORCID: https://orcid.org/0000-0002-5347-5136)
- Zhiyun Yang (ORCID: https://orcid.org/0000-0002-6139-9049)
- Boyan Xu (ORCID: https://orcid.org/0000-0003-1327-4740)
- Hongyu Zhang (ORCID: https://orcid.org/0000-0001-7152-001X)
- Huan Wang (ORCID: https://orcid.org/0000-0001-8419-8402)
- Yingqian Chen (ORCID: https://orcid.org/0000-0002-5924-5593)
- Shu Su (ORCID: https://orcid.org/0000-0003-2395-8978)
- Hong-Guang Cai (ORCID: https://orcid.org/0000-0001-7575-3603)
Institutions
- Sun Yat-sen University (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-07-01
- DOI
- https://doi.org/10.21037/qims-2024-2800
- Citations
- 2
- Primary Topic
- Diet and metabolism studies
- Type
- article
- Field-Weighted Citation Impact
- 2.33