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ObjectivesAbnormal brain function in ASD patients changes dynamically across developmental stages. However, no one has studied the brain function of prepubertal children with ASD. Prepuberty is an important stage for children’s socialization. This study aimed to investigate alterations in local spontaneous brain activity in prepubertal boys with ASD.Materials and MethodsMeasures of the amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) acquired from resting-state functional magnetic resonance imaging (RS-fMRI) database, including 34 boys with ASD and 49 typically developing (TD) boys aged 7 to 10 years, were used to detect regional brain activity. Pearson correlation analyses were conducted on the relationship between abnormal ALFF and ReHo values and Autism Diagnostic Observation Schedule (ADOS) and Autism Diagnostic Interview-Revised (ADI-R) scores.ResultsIn the ASD group, we found decreased ALFF in the left inferior parietal lobule (IPL) and decreased ReHo in the left lingual gyrus (LG), left superior temporal gyrus (STG), left middle occipital gyrus (MOG), and right cuneus (p < 0.05, FDR correction). There were negative correlations between ReHo values in the left LG and left STG and the ADOS social affect score and a negative correlation between ReHo values in the left STG and the calibrated severity total ADOS score.ConclusionBrain regions with functional abnormalities, including the left IPL, left LG, left STG, left MOG, and right cuneus may be crucial in the neuropathology of prepubertal boys with ASD. Furthermore, ReHo abnormalities in the left LG and left STG were correlated with sociality. These results will supplement the study of neural mechanisms in ASD at different developmental stages, and be helpful in exploring the neural mechanisms of prepubertal boys with ASD.

Introduction11C-labeled Pittsburgh compound B (11C-PiB) PET imaging can provide information for the diagnosis of Alzheimer's disease (AD) by quantifying the binding of PiB to β-amyloid deposition in the brain. Quantification index, such as standardized uptake value ratio (SUVR) and distribution volume ratio (DVR), has been exploited to effectively distinguish between healthy and subjects with AD. However, these measures require a long wait/scan time, as well as the selection of an optimal reference region. In this study, we propose an alternate measure named amyloid quantification index (AQI), which can be obtained with the first 30-min scan without the selection of the reference region.Methods11C-labeled Pittsburgh compound B PET scan data were obtained from the public dataset “OASIS-3”. A total of 60 mild subjects with AD and 60 healthy controls were included, with 50 used for training and 10 used for testing in each group. The proposed measure AQI combines information of clearance rate and mid-phase PIB retention in featured brain regions from the first 30-min scan. For each subject in the training set, AQI, SUVR, and DVR were calculated and used for classification by the logistic regression classifier. The receiver operating characteristic (ROC) analysis was performed to evaluate the performance of these measures. Accuracy, sensitivity, and specificity were reported. The Kruskal–Wallis test and effect size were also performed and evaluated for all measures. Then, the performance of three measures was further validated on the testing set using the same method. The correlations between these measures and clinical MMSE and CDR-SOB scores were analyzed.ResultsThe Kruskal–Wallis test suggested that AQI, SUVR, and DVR can all differentiate between the healthy and subjects with mild AD (p < 0.001). For the training set, ROC analysis showed that AQI achieved the best classification performance with an accuracy rate of 0.93, higher than 0.88 for SUVR and 0.89 for DVR. The effect size of AQI, SUVR, and DVR were 2.35, 2.12, and 2.06, respectively, indicating that AQI was the most effective among these measures. For the testing set, all three measures achieved less superior performance, while AQI still performed the best with the highest accuracy of 0.85. Some false-negative cases with below-threshold SUVR and DVR values were correctly identified using AQI. All three measures showed significant and comparable correlations with clinical scores (p < 0.01).ConclusionAmyloid quantification index combines early-phase kinetic information and a certain degree of β-amyloid deposition, and can provide a better differentiating performance using the data from the first 30-min dynamic scan. Moreover, it was shown that clinically indistinguishable AD cases regarding PiB retention potentially can be correctly identified.