Characteristics of brain glucose metabolism in patients with anti- N-methyl- D-aspartate receptor encephalitis underlying different inducing factors / 中华核医学与分子影像杂志

ABSTRACT

Objective:To investigate characteristics and differences of cerebral glucose metabolism in patients with anti- N-methyl- D-aspartate receptor (NMDAR) encephalitis from the perspective of different trigger factors of antibodies. Methods:A total of 15 patients (8 males, 7 females, age (30.5±17.7) years) with anti-NMDAR encephalitis between January 2016 and January 2019 in Huashan Hospital, Fudan University were recruited retrospectively. All patients underwent resting state cerebral 18F-FDG PET imaging. The characteristics of brain glucose metabolism were analyzed, and the SUV ratio (SUVR) was semi-quantitatively compared with that in 12 healthy subjects (HS; 7 males, 5 females, age (51.5±9.6) years). Independent-sample t test was used to analyze the data. Results:Among 15 patients, 5 patients were viral encephalitis-related anti-NMDAR encephalitis, showing focal decreased metabolism in unilateral temporal lobe or basal ganglia (SUVR: patients: 0.659±0.219; HS: 1.754±0.203; t=-9.58, P<0.001), with increased metabolism in contralateral temporal lobe or basal ganglia (SUVR: patients: 2.275±0.244; HS: 1.960±0.227; t=2.55, P=0.022) in 18F-FDG PET imaging. Six patients were cryptogenic anti-NMDAR encephalitis, showing asymmetric increased metabolism in frontal, temporal, parietal and basal ganglia (SUVR: patients: 2.482±0.395; HS: 1.754±0.203; t=5.23, P<0.001), with decreased metabolism in bilateral occipital lobes. The remaining 4 cases were paraneoplastic origin accompanied by teratoma, showing increased metabolism in bilateral temporal and basal ganglia (SUVR: patient: 2.359±0.181; HS: 1.960±0.227; t=3.16, P=0.007), with mild decreased metabolism in bilateral occipital lobe. Conclusions:The abnormal changes of cerebral glucose metabolism in patients with anti-NMDAR encephalitis can be divided into at least three patterns according to different trigger factors. A comprehensive understanding of these characteristic metabolic changes is helpful for detecting disease, and may provide potential value in indicating different causes.