PurposeThe input function (IF) is a core element in the quantification of Translocator protein 18 kDa with positron emission tomography (PET), as no suitable reference region with negligible binding has been identified. Arterial blood sampling is indeed needed to create the IF (ASIF). In the present manuscript we study individualization of a population based input function (PBIF) with a single arterial manual sample to estimate total distribution volume (VT) for [18F]FEPPA and to replicate previously published clinical studies in which the ASIF was used.MethodsThe data of 3 previous [18F]FEPPA studies (39 of healthy controls (HC), 16 patients with Parkinson’s disease (PD) and 18 with Alzheimer’s disease (AD)) was reanalyzed with the new approach. PBIF was used with the Logan graphical analysis (GA) neglecting the vascular contribution to estimate VT. Time of linearization of the GA was determined with the maximum error criteria. The optimal calibration of the PBIF was determined based on the area under the curve (AUC) of the IF and the agreement range of VT between methods. The shape of the IF between groups was studied while taking into account genotyping of the polymorphism (rs6971).ResultsPBIF scaled with a single value of activity due to unmetabolized radioligand in arterial plasma, calculated as the average of a sample taken at 60 min and a sample taken at 90 min post-injection, yielded a good interval of agreement between methods and optimized the area under the curve of IF. In HC, gray matter VTs estimated by PBIF highly correlated with those using the standard method (r2 = 0.82, p = 0.0001). Bland-Altman plots revealed PBIF slightly underestimates (~1 mL/cm3) VT calculated by ASIF (including a vascular contribution). It was verified that the AUC of the ASIF were independent of genotype and disease (HC, PD, and AD). Previous clinical results were replicated using PBIF but with lower statistical power.ConclusionA single arterial blood sample taken 75 minute post-injection contains enough information to individualize the IF in the groups of subjects studied; however, the higher variability produced requires an increase in sample size to reach the same effect size.

Converging evidence points to the significant involvement of the immune system in autism spectrum disorders (ASD). Positron emission tomography (PET) can quantify translocator protein 18 kDa (TSPO), a marker with increased expression mainly in microglia and, to some extent astroglia during neuropsychiatric diseases with inflammation. This preliminary analysis explored, for the first time, whether TSPO binding was altered in male and female participants with ASD in vivo using full kinetic quantification. Thirteen individuals with ASD (IQ > 70 [n = 12], IQ = 62 [n = 1]), 5 F, 25 ± 5 years) were scanned with [ 18 F]FEPPA PET. Data from 13 typically developing control participants with matching age and TSPO rs6971 polymorphism (9 F, age 24 ± 5 years) were chosen from previous studies for comparison. The two tissue compartment model (2TCM) was used to determine the total volume of distribution ([ 18 F]FEPPA V T ) in four previously identified regions of interest (ROI): prefrontal, temporal, cerebellar, and anterior cingulate cortices. We observe no significant difference in [ 18 F]FEPPA V T relative to controls (F (1,26) = 1.74, p = 0.20). However, 2 ASD participants with higher V T had concurrent major depressive episodes (MDE), which has been consistently reported during MDE. After excluding those 2 ASD participants, in a post-hoc analysis, our results show lower [ 18 F]FEPPA V T in ASD participants compared to controls (F (1,24) = 6.62, p = 0.02). This preliminary analysis provides evidence suggesting an atypical neuroimmune state in ASD.

Autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD) are common neurodevelopmental disorders (NDDs) that may impact brain maturation. A number of studies have examined cortical gyrification morphology in both NDDs. Here we review and when possible pool their results to better understand the shared and potentially disorder-specific gyrification features. We searched MEDLINE, PsycINFO, and EMBASE databases, and 24 and 10 studies met the criteria to be included in the systematic review and meta-analysis portions, respectively. Meta-analysis of local Gyrification Index (lGI) findings across ASD studies was conducted with SDM software adapted for surface-based morphometry studies. Meta-regressions were used to explore effects of age, sex, and sample size on gyrification differences. There were no significant differences in gyrification across groups. Qualitative synthesis of remaining ASD studies highlighted heterogeneity in findings. Large-scale ADHD studies reported no differences in gyrification between cases and controls suggesting that, similar to ASD, there is currently no evidence of differences in gyrification morphology compared with controls. Larger, longitudinal studies are needed to further clarify the effects of age, sex, and IQ on cortical gyrification in these NDDs.