Journal Article

Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson’s disease diagnosis

Xu, Xiaojun, Wen, Jiaqi, Yuan, Weijin, Qin, Jianmei, Chen, Jingwen, Liu, Chunlei, Wu, Jingjing, Ma, Yiran, Zhang, Minming, Tan, Sijia, Zhou, Cheng, Wu, Chenqing, Zhu, Zihao, Guan, Xiaojun, Duanmu, Xiaojie, Wu, Haoting, He, Chenyu, Guo, Tao, Zheng, Qianshi

2025

Overview

•Modifying intermediate steps in the rs-fMRI processing pipeline, such as incorporating QSM for better subcortical nucleus visualization and registration, could have important practical consequences.•Difference in RSFC between PD and normal controls was more stable and reliable revealed by the QSM-guided method.•Machine learning models utilized QSM-guided RSFC features persistently showed better performance in diagnosing Parkinson’s Disease. Previous resting state functional MRI (rs-fMRI) analyses of the basal ganglia in Parkinson’s disease heavily relied on T1-weighted imaging (T1WI) atlases. However, subcortical structures are characterized by subtle contrast differences, making their accurate delineation challenging on T1WI. In this study, we aimed to introduce and validate a method that incorporates quantitative susceptibility mapping (QSM) into the rs-fMRI analytical pipeline to achieve precise subcortical nuclei segmentation and improve the stability of RSFC measurements in Parkinson’s disease. A total of 321 participants (148 patients with Parkinson’s Disease and 173 normal controls) were enrolled. We performed cross-modal registration at the individual level for rs-fMRI to QSM (FUNC2QSM) and T1WI (FUNC2T1), respectively.The consistency and accuracy of resting state functional connectivity (RSFC) measurements in two registration approaches were assessed by intraclass correlation coefficient and mutual information. Bootstrap analysis was performed to validate the stability of the RSFC differences between Parkinson’s disease and normal controls. RSFC-based machine learning models were constructed for Parkinson’s disease classification, using optimized hyperparameters (RandomizedSearchCV with 5-fold cross-validation). The consistency of RSFC measurements between the two registration methods was poor, whereas the QSM-guided approach showed better mutual information values, suggesting higher registration accuracy. The disruptions of RSFC identified with the QSM-guided approach were more stable and reliable, as confirmed by bootstrap analysis. In classification models, the QSM-guided method consistently outperformed the T1WI-guided method, achieving higher test-set ROC-AUC values (FUNC2QSM: 0.87–0.90, FUNC2T1: 0.67–0.70). The QSM-guided approach effectively enhanced the accuracy of subcortical segmentation and the stability of RSFC measurement, thus facilitating future biomarker development in Parkinson’s disease.

• Publisher: Elsevier Inc,Elsevier Limited,Elsevier
• Subject: Aged / Basal ganglia / Basal Ganglia - diagnostic imaging / Basal Ganglia - physiopathology / Brain Mapping - methods / Brain research / Classification / Connectome - methods / Data processing / Female / Functional magnetic resonance imaging / Humans / Image processing / Machine Learning / Magnetic resonance imaging / Magnetic Resonance Imaging - methods / Male / Mapping / Medical imaging / Middle Aged / Neurodegenerative diseases / Neuroimaging / Parkinson Disease - diagnostic imaging / Parkinson Disease - physiopathology / Parkinson's disease / Quantitative susceptibility mapping / Radiology/Diagnostic Imaging / Registration / Resting-state functional connectivity / Segmentation / Subcortical nuclei