Correlation Analysis Of Multimodal MRI Parameters With Non-Motor Symptoms Of Parkinson’s Disease

PurposeMultimodal MRI has been proved to be helpful in the diagnosis of PD.However,most of its examinations are based on thick layer examination,and the scanning time is long.In addition,the research on PD by multimodal MRI have mainly focused on the basal ganglia structure related to motor symptoms,while the research on brain pathway and brain structure changes of non-motor symptoms is relatively small.In this study,MULTIPLEX MRI scanning technology was used to obtain a fully registered high-resolution multi-parameter MRI image in a single scan.Correlate multimodal MRI metrics with clinical indicators of nonmotor symptoms of PD,and explore the feasibility of using multimodal MRI to assess the disease progression of non-motor symptoms of PD.Materials and MethodsWe collected 33 patients with PD who were clinically diagnosed in the Chain-Japan Union Hospital of Jilin University from August 2021 to December 2021.The cognitive status,anxiety and depression of PD patients were evaluated by Simple Mental State Examination Scale(MMSE),Hamilton Anxiety Scale(HAMA)and Hamilton Depression Scale(HAMD).The 3D TIWI and MTP-T1 mapping,MTP-PD mapping,MTPQSM(Quantitative susceptibility mapping)were obtained by scanning with a 3.0T MRI scanner with the MULTIPLEX method based on MultiFlip-Angle and Multi-Echo Gradient Echo Sequence.According to the automatic brain segmentation program based on deep learning,the whole brain is divided into 106 brain subarea structures,and the volume of each rain sub-region on 3D-TIWI was obtained,along with the quantitative parameter values of each brain sub-region on T1 mapping,PD mapping and QSM.The Spearman’s rank correlation coefficient was used to analyze the correlation between the volume of each brain subarea,the quantitative parameter value and the non-motor symptom correlation assessment scale.Results1.In PD patients,MMSE was negatively correlated with the volume of eight brain subregions,including left frontal pole and right temporal pole;It was positively correlated with the T1 value of three brain subregions,such as inferior horns of the lateral ventricle;It was negatively correlated with the QSM values of the right superior frontal gyrus and the left nucleus accumbens,positively correlated with the QSM values of the left inferior horns of the lateral ventricle and the left calcarine,and positively correlated with the PD values of the cerebrospinal fluid.2.In PD patients,HAMD was negatively correlated with the volume of 12 brain subregions such as the right middle cingulate cortex and the right middle temporal gyrus;It was negatively correlated with QSM values in four brain subregions,including the left paracentral lobule and the right superior temporal gyrus.3.In PD patients,HAMA was negatively correlated with the volume of 9 brain subregions such as right insula lobe and right middle temporal gyrus;It was positively correlated with T1 value of the right postcentral gyrus;It was negatively correlated with the QSM values of five brain subregions such as precuneus on both sides.ConclusionThere is a certain correlation between the changes of brain subareas in multimodal MRI and some clinical evaluation indicators of non-motor symptoms.Multimodal neuroimaging measurement is helpful for targeted screening of non-motor symptoms,increasing the possibility of early detection and management,and providing new targets for treatment.