Multiparametrik MRI kullanarak Parkinson hastalığında motor olmayan semptomların multiplex dizisi ile değerlendirilmesi.

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BackgroundNon-motor symptoms (NMS) in Parkinson's disease (PD) often precede motor manifestations and are challenging to detect with conventional MRI. This study investigates the use of the Multi-Flip-Angle and Multi-Echo Gradient Echo Sequence (MULTIPLEX) in MRI to detect previously undetectable microstructural changes in brain tissue associated with NMS in PD.MethodsA prospective study was conducted on 37 patients diagnosed with PD. Anxiety and depression levels were assessed using the Hamilton Anxiety Scale (HAMA) and Hamilton Depression Scale (HAMD), respectively. MRI techniques, including 3D T1-weighted imaging (3D T1WI) and MULTIPLEX - which encompasses T2*-mapping, T1-mapping, proton density-mapping, and quantitative susceptibility mapping (QSM)-were performed. Brain subregions were automatically segmented using deep learning, and their volume and quantitative parameters were correlated with NMS-related assessment scales using Spearman's rank correlation coefficient.ResultsCorrelations were observed between QSM and T2* values of certain subregions within the left frontal and bilateral temporal lobes and both anxiety and depression (absolute r-values ranging from 0.358 to 0.480, p r-values ranging from 0.354 to 0.658, p ConclusionQuantitative parameters derived from MULTIPLEX MRI show significant associations with clinical evaluations of NMS in PD. Multiparametric MR neuroimaging may serve as a potential early diagnostic tool for PD.

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