Mapping Resting-State Functional Connectivity Based on QSM in Parkinson’s Disease
Poster No:
214
Submission Type:
Abstract Submission
Authors:
Jianmei Qin1, Minming Zhang2
Institutions:
1The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 2Zhejiang University, Hangzhou, Zhejiang
First Author:
Jianmei Qin
The Second Affiliated Hospital of Zhejiang University School of Medicine
Hangzhou, Zhejiang
The Second Affiliated Hospital of Zhejiang University School of Medicine
Hangzhou, Zhejiang
Co-Author:
Introduction:
Despite numerous research efforts on the basal ganglia's functional status in Parkinson's disease (PD), the coherence of results remains poor. Typically, the RSFC (resting state functional connectivity) is measured using regions of interest (ROI) defined by T1-weighted imaging (T1WI) atlases, as T1WI is part of most established neuroimaging protocols and provides excellent contrast between cortical gray and white matter. However, at 3T MRI, subcortical nuclei exhibit low tissue contrast on T1WI, especially the basal ganglia and midbrain nuclei. Notably, Quantitative Susceptibility Mapping（QSM）not only excels in quantifying brain iron content (paramagnetic) and myelin (diamagnetic), but also providing high Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR) for subcortical nuclei (Liu, 2015). In this study, we proposed a strategy to integrate QSM into the rs-fMRI analysis pipeline for more accurate localization of subcortical nuclei. The hypothesis underlying our work is that the QSM-guided approach for RSFC has the potential to robustly improve PD diagnosis, thereby facilitating the clinical translation of this research.
Methods:
A total of 321 participants (148 patients with PD and 173 normal controls) were enrolled. All participants were scanned on a 3.0T MRI (Discovery 750). QSM reconstruction was performed in Susceptibility Tensor Imaging (STI) Suite (Liu, 2010). We performed cross-modal registration at the individual level for rs-fMRI to QSM and T1WI, respectively (See Figure 1. for the detailed flowchart). Region-of-interest (ROI) of subcortical nuclei were delineated on QSM by using a semi-automatic segmentation method on the ANTs-R language environment as shown in the previous study (Guan, 2020). The RSFC within bilateral subcortical nuclei (caudate, putamen, globus pallidus, red nucleus, and substantia nigra) were extracted from two registration methods creating FUNC2QSM and FUNC2T1 datasets.
1.The consistency and accuracy of RSFC measurements in two approaches were assessed by intraclass correlation coefficient (ICC) (Koo, 2016) and mutual information (Maes, 1988).
2.RSFC matrix differences between PD and normal controls across nuclei were compared on the two datasets. Bootstrap analysis was performed to validate the stability of the RSFC differences.
3.RSFC-based Machine learning models were constructed for PD classification, and the DeLong test (Delong, 1988) is used to compare the receiver operating characteristic-area under the curve (ROC-AUC) of models on two datasets.
1.The consistency and accuracy of RSFC measurements in two approaches were assessed by intraclass correlation coefficient (ICC) (Koo, 2016) and mutual information (Maes, 1988).
2.RSFC matrix differences between PD and normal controls across nuclei were compared on the two datasets. Bootstrap analysis was performed to validate the stability of the RSFC differences.
3.RSFC-based Machine learning models were constructed for PD classification, and the DeLong test (Delong, 1988) is used to compare the receiver operating characteristic-area under the curve (ROC-AUC) of models on two datasets.
·Flow Diagram of rs-fMRI registration to QSM and T1WI.
Results:
1.ICC ≤ 0.50 was observed in 75.6% of RSFC measurements (34 of 45) and none of one RSFC has an ICC greater than 0.75, indicating poor measurement consistency for RSFC between measures of the two registration methods was observed.
2.The proposed QSM-guided approach achieved a high mutual information value (0.85) compared to the FUNC2T1 dataset (0.73), signifying superior cross-modal registration accuracy.
3.The distribution patterns of significant FC from the two datasets varied greatly (Figure2-A), showing methodological heterogeneity. In the bootstrapping analysis (Figure2-B), all RSFCs with differences identified by the QSM-guided method all passed bootstrap analysis. However, the variation of inter-group differences obtained by the T1WI-guided method is relatively large.
4.All machine learning models exhibited better performance in the FUNC2QSM dataset than in the FUNC2T1 dataset (Figure2-Right). Particularly the support vector machines achieved an ROC-AUC of 0.77 with FUNC2QSM, demonstrating higher ROC-AUC values compared to FUNC2T1 (p = 0.001, DeLong's test).
2.The proposed QSM-guided approach achieved a high mutual information value (0.85) compared to the FUNC2T1 dataset (0.73), signifying superior cross-modal registration accuracy.
3.The distribution patterns of significant FC from the two datasets varied greatly (Figure2-A), showing methodological heterogeneity. In the bootstrapping analysis (Figure2-B), all RSFCs with differences identified by the QSM-guided method all passed bootstrap analysis. However, the variation of inter-group differences obtained by the T1WI-guided method is relatively large.
4.All machine learning models exhibited better performance in the FUNC2QSM dataset than in the FUNC2T1 dataset (Figure2-Right). Particularly the support vector machines achieved an ROC-AUC of 0.77 with FUNC2QSM, demonstrating higher ROC-AUC values compared to FUNC2T1 (p = 0.001, DeLong's test).
·Left: Inter-group comparisons (t-value matrix) of RSFC among the subcortical nuclei in the FUNC2QSM dataset and the FUNC2T1 dataset.Right: Receiver Operating Characteristic (ROC) curves and Area Under
Conclusions:
Our study demonstrated that incorporating QSM into the rs-fMRI analytical pipeline enhanced the accuracy of subcortical segmentation and the stability of RSFC measurement. The selection of registration standards could improve the discriminative power of diagnostic models.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Neuroinformatics and Data Sharing:
Workflows 2
Keywords:
Basal Ganglia
Data Registration
Degenerative Disease
Design and Analysis
FUNCTIONAL MRI
Workflows
1|2Indicates the priority used for review
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Provide references using APA citation style.
Liu, C. (2010). Susceptibility tensor imaging. Magnetic Resonance in Medicine, 63(6), 1471–1477.
Guan, X. (2020). Asymmetrical nigral iron accumulation in Parkinson’s disease with motor asymmetry: An explorative, longitudinal and test-retest study. Aging (Albany NY), 12(18), 18622–18634.
Koo. (2016). A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of Chiropractic Medicine, 15(2), 155–163.
Maes, F. (2003). Medical image registration using mutual information. Proceedings of the IEEE, 91(10), 1699–1722. Proceedings of the IEEE.
DeLong. (1988). Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach. Biometrics, 44(3), 837.