Abstract #3434

QSM-Guided Functional Connectivity of Subcortical Nuclei: A Robust Approach for Parkinson's Disease Diagnosis

Jianmei Qin¹, Xiaojun Guan¹, Chenyu He², Chenqing Wu¹, Cheng Zhou¹, Haoting Wu¹, Tao Guo¹, Chunlei Liu³, Yong Zhang⁴, Xiaojun Xu¹, and Minming Zhang¹

¹Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China, ²State Key Laboratory of Computer-aided Design & Computer Graphics, Zhejiang University, Hangzhou, China, ³Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA., Berkeley, CA, United States, ⁴MR Research, GE Healthcare, Shanghai, China

Synopsis

Keywords: fMRI Analysis, Neurodegeneration, Quantitative Susceptibility Mapping;Subcortical Nuclei;Registration

Motivation: Conventional rs-fMRI analysis of basal ganglia in Parkinson's disease(PD) relies on T1WI atlases, posing challenges to localizing subcortical nuclei in fMRI.

Goal(s): This study aims to validate a novel method incorporating Quantitative Susceptibility Mapping(QSM) into fMRI analysis for precise subcortical nuclei segmentation.

Approach: fMRI registration to QSM and T1WI respectively in the study. Intraclass Correlation Coefficient and Mutual Information to assess the consistency and accuracy of the two registration approaches. Various Machine learning models utilized functional connectivity derived from two methods for PD classification.

Results: Two methods showed measurement inconsistency. The QSM-guided approach displayed superior accuracy and significantly outperformed in classification models.

Impact: Our study introduces a groundbreaking approach by incorporating QSM into the fMRI analysis of subcortical nuclei in Parkinson’s disease. Shedding light on the potential of the QSM-guided method in capturing meaningful alterations in Parkinson's disease-related neural networks.

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