Amide proton transfer (APT) imaging has shown its diagnostic and predictive superiority in PD in our previous studies using 2D APT imaging based on deep nuclei. We hypothesized that the pathophysiological abnormality of PD will change the APT-related parameters in the cerebral cortex, and the signal changes can contribute to accurate diagnosis of Parkinson’s disease.
34 patients with sporadic Parkinson’s disease (IPD) and 29 age- and sex-matched normal controls (NC) were enrolled in this prospective study. 3D-APT imaging and 3D-T1WI was performed in our participants. A volume-based morphometry algorithm was used and get automated cortical segmentations. Quantitative parameter maps of APT-related metrics were calculated by using SPM and MATLAB. The unpaired Student’s t-test or Mann-Whitney U test was used for comparison of these values between IPD and NC groups. The associations between APT-related metrics and clinical assessments were investigated by Spearman correlation analysis. The receiver-operating characteristic (ROC) analysis was used to assess the diagnostic performances. The binary logistic regression model was used to combine the imaging parameters.
There wasn’t any correlations between cortical APT-related signals and clinical assessment, including the H&Y scale, the disease duration, the UPDRS III scores and the MMSE scores. The MTRasym, CESTRnr and MTRRex had significantly higher values (p <0.001, corrected by Bonferroni methods) in the IPD group than NC groups in the region of bilateral and total temporal grey matter. The single parameters achieved the best diagnostic performance among all APT-related metrics was MTRRex on the right temporal grey matter, with an area under the ROC curve (AUC) of 0.865. The combined parameters achieved the highest diagnostic performance (AUC: 0.932).
3D-APT imaging could identify the changes of the cerebral cortex in Parkinson’s disease. The cortical changes of APT-related parameters could potentially serve as imaging biomarkers to aid in the non-invasive diagnosis of PD.
Copyright © 2023. Published by Elsevier Inc.
