Retinal Thickness and Its Interocular Asymmetry Between Parkinson’s Disease and Drug-Induced Parkinsonism

Background@#Drug-induced parkinsonism (DIP) is common, but diagnosis is challenging.Although dopamine transporter imaging is useful, the cost and inconvenience are problematic, and an easily accessible screening technique is needed. We aimed to determine whether optical coherence tomography (OCT) findings could differentiate DIP from Parkinson’s disease (PD). @*Methods@#We investigated 97 de novo PD patients and 27 DIP patients using OCT and [ 18 F] N-(3-fluoropropyl)-2b-carbon ethoxy-3b-(4-iodophenyl) nortropane (FP-CIT) positron emission tomography. We compared peripapillary retinal nerve fiber layer thickness (pRNFLT) and macular retinal thickness (mRT) between PD and DIP patients as well as interocular differences in the pRNFLT and the mRT. Asymmetric index (%) for retinal thickness (AIRT) was calculated to measure the interocular differences between pRNFLT and mRT. The correlation between AIRT and total striatal specificon-specific binding ratio asymmetry index (SNBRAI) was investigated in PD and DIP patients. @*Results@#No significant differences in pRNFLT and mRT values were observed between PD and DIP patients (all Pvalues > 0.090). The mean SNBRAI was significantly higher in PD than in DIP (P = 0.008) patients; however, AIRT did not differ between PD and DIP patients in pRNFLT and mRT (all P values > 0.100). SNBRAI did not correlate with AIRT of pRNFL or mRT in PD and DIP patients (all P values > 0.060). @*Conclusion@#Our study showed no benefit of retinal thickness and interocular asymmetry measurements using OCT for distinguishing PD from DIP in the early stages. Additional investigations are needed for confirmation.

MR Template‑Based Individual Brain PET Volumes‑of‑Interest Generation Neither Using MR nor Using Spatial Normalization / 대한핵의학회잡지

For more anatomically precise quantitation of mouse brain PET, spatial normalization (SN) of PET onto MR template and subsequent template volumes-of-interest (VOIs)-based analysis are commonly used. Although this leads to dependency on the corresponding MR and the process of SN, routine preclinical/clinical PET images cannot always afford corresponding MR and relevant VOIs. To resolve this issue, we propose a deep learning (DL)-based individual-brain-specific VOIs (i.e., cortex, hippocampus, striatum, thalamus, and cerebellum) directly generated from PET images using the inverse-spatialnormalization (iSN)-based VOI labels and deep convolutional neural network model (deep CNN). Our technique was applied to mutated amyloid precursor protein and presenilin-1 mouse model of Alzheimer’s disease. Eighteen mice underwent T2-weighted MRI and 18 F FDG PET scans before and after the administration of human immunoglobin or antibody-based treatments. To train the CNN, PET images were used as inputs and MR iSN-based target VOIs as labels. Our devised methods achieved decent performance in terms of not only VOI agreements (i.e., Dice similarity coefficient) but also the correlation of mean counts and SUVR, and CNN-based VOIs was highly accordant with ground-truth (the corresponding MR and MR template-based VOIs). Moreover, the performance metrics were comparable to that of VOI generated by MR-based deep CNN. In conclusion, we established a novel quantitative analysis method both MR-less and SN-less fashion to generate individual brain space VOIs using MR template-based VOIs for PET image quantification.

Effect of Nutrition Counseling Program on Weight Control in Obese University Students

This study was conducted to investigate the effect of nutrition counseling program and related factors on weight control for obese university students. Subjects were 24 students with a body mass index (BMI) of 25 or above. The program was conducted from September 16th to November 18th, 2015. Change of body composition, blood index and nutrient intake were observed in subjects before and after the program. The average age of the subjects was 23.2 years old and the percentage of male and female was 66.7% and 33.0%, respectively. There were tendencies of decrease in weight, amount of body fat, BMI, and body fat percentage. The blood test showed that values of all biochemical parameters were in the normal range before and after the program. When the change of the nutrient intake was examined and compared with the Dietary Reference Intakes for Koreans (KDRIs), there was a tendency of decreased intake in most of the nutrients including protein. However, the nutrient quality index showed increasing tendency, which implies that the intake of micronutrients was getting balances simultaneously with the decrease of calorie intake.