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1H-MRS detection of metabolites in posterior cingulate gyrus of Parkinson disease with cognitive impairment patients / 中国医学影像技术
Article de Zh | WPRIM | ID: wpr-706234
Bibliothèque responsable: WPRO
ABSTRACT
Objective To investigate the value of 1H-MRS technology combined with linear combination model (LCmodel) software in diagnosis of Parkinson disease (PD) cognitive impairment.Methods Thirty-five PD patients (PD group) and 22 matched healthy subjects (control group) were collected.Patients in PD group were divided into PDN and PDMCI subgroups according to whether having cognitive impairment or not.The concentration of metabolites of posterior cingulate gyrus (PCG)was applied with 1H-MRS technology combined with LCmodel software.The differences of metabolites were compared between the two groups,and the correlations between metabolites level and cognitive status were analyzed.Results The absolute concentrations of metabolites in PDN subgroup were not significantly different from those in control group (all P>0.05).The absolute concentrations of total creatine (tCr),N-acetyl aspartate (NAA),myo-inositol (mI) and glycerophosphocholine+ phosphocholine (tCho) in PDMCI subgroup were lower than those in control group (all P<0.05).The absolute concentration of tCr in PDMCI subgroup was lower than that in PDN subgroup (P<0.05).There was positive correlation among the absolute concentration of tCr (r=0.444,P=0.01),glutathione (GSH;r=0.393,P=0.024) and MMSE scores,as well as among the absolute concentration of tCr (r=0.367,P=0.035),GSH (r=0.376,P=0.031),tCho (r=0.375,P=0.031) and MoCA scores.Conclusion 1 H-MRS technology combined with LCmodel software can quantitatively analyze the changes of metabolites in PCG,therefore being helpful to evaluating PD cognitive impairment.
Mots clés
Texte intégral: 1 Base de données: WPRIM Type d'étude: Diagnostic_studies / Prognostic_studies Langue: Zh Journal: Chinese Journal of Medical Imaging Technology Année: 2018 Type de document: Article
Texte intégral: 1 Base de données: WPRIM Type d'étude: Diagnostic_studies / Prognostic_studies Langue: Zh Journal: Chinese Journal of Medical Imaging Technology Année: 2018 Type de document: Article