Integrated Causal Learning Workflow of Brain Iron Deposition in Patients with PD based on QSM
6 affiliations
- 1Medical Digital Intelligence Innovation Center, Beijing Friendship Hospital, Capital Medical University, Beijing, No. 95, Yong An Road, Xicheng District, Beijing 100050, China
- 2Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, No. 95, Yong An Road, Xicheng District, Beijing 100050, China
- 3School of Computer Science, Beijing Institute of Technology, Beijing, No. 5, Zhongguancun South Street, Haidian District, Beijing 100081, China
- 4Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No. 119, South Fourth Ring West Road, Fengtai District, Beijing 100070, China
- 5Tangshan Research Institute, Beijing Institute of Technology, Beijing, No. 5, Zhongguancun South Street, Haidian District, Beijing 100081, China
- 6Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, No. 95, Yong An Road, Xicheng District, Beijing 100050, China
Abstract
Introduction: To investigate the model-inferred dependency pattern of iron deposition in the brain of patients with Parkinson's Disease (PD).
Materials and Methods: Clinical and imaging data of 25 patients with PD and 36 Healthy Controls (HCs) were collected between January and December 2021. Iron deposition in the brain was quantified using Quantitative Susceptibility Mapping (QSM). An integrated causal learning workflow, combining GES-based causal discovery, causal effect estimation, and refutation analysis, was used to explore the causal relationships between clinical data and image characteristics and estimate their causal effects.
Results: Beck Anxiety Inventory (BAI) and Beck Depression Inventory (BDI) scores were significantly greater in the PD group than the HC group (P = 0.003; P < 0.001). Timed Up and Go Test (TUG) and Berg Balance Scale (BBS) scores were lower in the PD group than in the HC group (P < 0.001; P < 0.001). The integrated causal learning workflow suggested a cross-sectional, model-inferred directional dependency structure with TH occupying an upstream position and downstream dependencies involving PUT, CN, RN, SN, and GP.
Discussion: The iron deposition tissues in different regions of the brains of patients with PD exhibit a structured dependency pattern, which is of great significance for proposing hypotheses regarding the mechanism of iron deposition related to PD.
Conclusion: The integrated causal learning workflow combined with QSM may help explore cross-sectional dependency patterns of brain iron deposition in patients with PD. These findings provide a biologically interpretable and hypothesis-generating workflow for understanding PD-related iron deposition patterns and may assist in early clinical detection of PD, rapid determination of its course, and timely administration of interventions and treatments.
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BibTeX
@article{wei2026integrated,
title = {{Integrated Causal Learning Workflow of Brain Iron Deposition in Patients with PD based on QSM}},
author = {Wei Wei and Wuyan Zhao and Ying Yan and Zheng Wang and Hanzhu Jia and Yunlin Lei and Pengzhao Quan and Jiayun Wang and Mingjun Shi and Xu Yang and Shengjun Sun and Zhenchang Wang and Xuan Wei},
journal = {Current Medical Imaging},
year = {2026},
month = sep,
doi = {10.2174/0115734056473262260817043352},
url = {https://doi.org/10.2174/0115734056473262260817043352},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Integrated Causal Learning Workflow of Brain Iron Deposition in Patients with PD based on QSM
AU - Wei Wei
AU - Wuyan Zhao
AU - Ying Yan
AU - Zheng Wang
AU - Hanzhu Jia
AU - Yunlin Lei
AU - Pengzhao Quan
AU - Jiayun Wang
AU - Mingjun Shi
AU - Xu Yang
AU - Shengjun Sun
AU - Zhenchang Wang
AU - Xuan Wei
PY - 2026
DA - 2026/09/11
JO - Current Medical Imaging
DO - 10.2174/0115734056473262260817043352
UR - https://doi.org/10.2174/0115734056473262260817043352
AB - Introduction: To investigate the model-inferred dependency pattern of iron deposition in the brain of patients with Parkinson's Disease (PD). Materials and Methods: Clinical and imaging data of 25 patients with PD and 36 Healthy Controls (HCs) were collected between January and December 2021. Iron deposition in the brain was quantified using Quantitative Susceptibility Mapping (QSM). An integrated causal learning workflow, combining GES-based causal discovery, causal effect estimation, and refutation analysis, was used to explore the causal relationships between clinical data and image characteristics and estimate their causal effects. Results: Beck Anxiety Inventory (BAI) and Beck Depression Inventory (BDI) scores were significantly greater in the PD group than the HC group (P = 0.003; P < 0.001). Timed Up and Go Test (TUG) and Berg Balance Scale (BBS) scores were lower in the PD group than in the HC group (P < 0.001; P < 0.001). The integrated causal learning workflow suggested a cross-sectional, model-inferred directional dependency structure with TH occupying an upstream position and downstream dependencies involving PUT, CN, RN, SN, and GP. Discussion: The iron deposition tissues in different regions of the brains of patients with PD exhibit a structured dependency pattern, which is of great significance for proposing hypotheses regarding the mechanism of iron deposition related to PD. Conclusion: The integrated causal learning workflow combined with QSM may help explore cross-sectional dependency patterns of brain iron deposition in patients with PD. These findings provide a biologically interpretable and hypothesis-generating workflow for understanding PD-related iron deposition patterns and may assist in early clinical detection of PD, rapid determination of its course, and timely administration of interventions and treatments.
SN - 1573-4056
ER -