Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson?s disease

Parkinson's disease (PD) is a highly heterogeneous disorder that is difficult to diagnose. There-fore, reliable biomarkers are needed. We implemented a method constructing a regional radio-mics similarity network (R2SN) based on the amplitude of low-frequency fluctuation (ALFF). We classified patients with PD and healthy individuals by using a machine learning approach in accordance with the R2SN connectome. The ALFF-based R2SN exhibited great reproducibility with different brain atlases and datasets. Great classification performances were achieved both in primary (AUC = 0.85 +/- 0.02 and accuracy = 0.81 +/- 0.03) and independent external validation (AUC = 0.77 and accuracy = 0.70) datasets. The discriminative R2SN edges correlated with the clinical evaluations of patients with PD. The nodes of discriminative R2SN edges were primarily located in the default mode, sensorimotor, executive control, visual and frontoparietal network, cerebellum and striatum. These findings demonstrate that ALFF-based R2SN is a robust potential neuroimaging biomarker for PD and could provide new insights into connectome reorganization in PD.

Automated summary

Parkinson's disease (PD) is a challenging disorder to diagnose, but reliable biomarkers are necessary. Researchers developed a regional radio-mic similarity network (R2SN) using the amplitude of low-frequency fluctuation (ALFF) and successfully classified PD patients and healthy individuals. The ALFF-based R2SN showed reproducibility and achieved good classification performance, demonstrating its potential as a robust neuroimaging biomarker for PD.