Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson’s disease diagnosis
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...
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| Language: | English |
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Elsevier
2025-07-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811925002599 |
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| author | Jianmei Qin Haoting Wu Chenqing Wu Tao Guo Cheng Zhou Xiaojie Duanmu Sijia Tan Jiaqi Wen Qianshi Zheng Weijin Yuan Zihao Zhu Jingwen Chen Jingjing Wu Chenyu He Yiran Ma Chunlei Liu Xiaojun Xu Xiaojun Guan Minming Zhang |
| author_facet | Jianmei Qin Haoting Wu Chenqing Wu Tao Guo Cheng Zhou Xiaojie Duanmu Sijia Tan Jiaqi Wen Qianshi Zheng Weijin Yuan Zihao Zhu Jingwen Chen Jingjing Wu Chenyu He Yiran Ma Chunlei Liu Xiaojun Xu Xiaojun Guan Minming Zhang |
| author_sort | Jianmei Qin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ca64586e4334465baac78be7cec21279 |
| institution | OA Journals |
| issn | 1095-9572 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj-art-ca64586e4334465baac78be7cec212792025-08-20T01:52:55ZengElsevierNeuroImage1095-95722025-07-0131412125610.1016/j.neuroimage.2025.121256Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson’s disease diagnosisJianmei Qin0Haoting Wu1Chenqing Wu2Tao Guo3Cheng Zhou4Xiaojie Duanmu5Sijia Tan6Jiaqi Wen7Qianshi Zheng8Weijin Yuan9Zihao Zhu10Jingwen Chen11Jingjing Wu12Chenyu He13Yiran Ma14Chunlei Liu15Xiaojun Xu16Xiaojun Guan17Minming Zhang18Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR ChinaDepartment of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR ChinaDepartment of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR ChinaDepartment of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR ChinaDepartment of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR ChinaDepartment of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR ChinaDepartment of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR ChinaDepartment of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR ChinaDepartment of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR ChinaDepartment of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR ChinaDepartment of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR ChinaDepartment of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR ChinaDepartment of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR ChinaState Key Laboratory of Computer-aided Design & Computer Graphics, Zhejiang University College of Computer Science and technology, Hangzhou, PR ChinaState Key Laboratory of Industrial Control Technology, Zhejiang University College of Control Science and Engineering, Hangzhou, PR ChinaDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USADepartment of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR ChinaDepartment of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Correspondence authors at: Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 31009 PR China.Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, PR China; Correspondence authors at: Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 31009 PR China.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.http://www.sciencedirect.com/science/article/pii/S1053811925002599Parkinson’s diseaseResting-state functional connectivityQuantitative susceptibility mappingSubcortical nuclei |
| spellingShingle | Jianmei Qin Haoting Wu Chenqing Wu Tao Guo Cheng Zhou Xiaojie Duanmu Sijia Tan Jiaqi Wen Qianshi Zheng Weijin Yuan Zihao Zhu Jingwen Chen Jingjing Wu Chenyu He Yiran Ma Chunlei Liu Xiaojun Xu Xiaojun Guan Minming Zhang Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson’s disease diagnosis NeuroImage Parkinson’s disease Resting-state functional connectivity Quantitative susceptibility mapping Subcortical nuclei |
| title | Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson’s disease diagnosis |
| title_full | Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson’s disease diagnosis |
| title_fullStr | Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson’s disease diagnosis |
| title_full_unstemmed | Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson’s disease diagnosis |
| title_short | Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson’s disease diagnosis |
| title_sort | robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping an application in parkinson s disease diagnosis |
| topic | Parkinson’s disease Resting-state functional connectivity Quantitative susceptibility mapping Subcortical nuclei |
| url | http://www.sciencedirect.com/science/article/pii/S1053811925002599 |
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