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Abstract Full quantification of Positron Emission Tomography (PET) requires an arterial input function (AIF) for measurement of certain targets, or using particular radiotracers, or for the quantification of specific outcome measures. The AIF represents the measurement of radiotracer concentrations in the arterial blood plasma over the course of the PET examination. Measurement of the AIF is prone to error as it is a composite measure created from the combination of multiple measurements of different samples with different equipment, each of which can be sources of measurement error. Moreover, its measurement requires a high degree of temporal granularity for early time points, which necessitates a compromise between quality and quantity of recorded samples. For these reasons, it is often desirable to fit models to this data in order to improve its quality before using it for quantification of radiotracer binding in the tissue. The raw observations of radioactivity in arterial blood and plasma samples are derived from radioactive decay, which is measured as a number of recorded counts. Count data have several specific properties, including the fact that they cannot be negative as well as a particular mean-variance relationship. Poisson regression is the most principled modelling strategy for working with count data, as it both incorporates and exploits these properties. However, no previous studies to our knowledge have taken this approach, despite the advantages of greater efficiency and accuracy which result from using the appropriate distributional assumptions. Here, we implement a Poisson regression modelling approach for the AIF as proof-of-concept of its application. We applied both parametric and non-parametric models for the input function curve. We show that a negative binomial distribution is a more appropriate error distribution for handling overdispersion. Furthermore, we extend this approach to a hierarchical non-parametric model which is shown to be highly resilient to missing data. We thus demonstrate that Poisson regression is both feasible and effective when applied to AIF data, and propose that this is a promising strategy for modelling blood count data for PET in future.

Abstract Purpose In positron emission tomography quantification, multiple pharmacokinetic parameters are typically estimated from each time activity curve. Conventionally all but the parameter of interest are discarded before performing subsequent statistical analysis. However, we assert that these discarded parameters also contain relevant information which can be exploited to improve the precision and power of statistical analyses on the parameter of interest. Properly taking this into account can thereby draw more informative conclusions without collecting more data. Methods By applying a hierarchical multifactor multivariate Bayesian approach, all estimated parameters from all regions can be analysed at once. We refer to this method as Parameters undergoing Multivariate Bayesian Analysis (PuMBA). We simulated patient–control studies with different radioligands, varying sample sizes and measurement error to explore its performance, comparing the precision, statistical power, false positive rate and bias of estimated group differences relative to univariate analysis methods. Results We show that PuMBA improves the statistical power for all examined applications relative to univariate methods without increasing the false positive rate. PuMBA improves the precision of effect size estimation, and reduces the variation of these estimates between simulated samples. Furthermore, we show that PuMBA yields performance improvements even in the presence of substantial measurement error. Remarkably, owing to its ability to leverage information shared between pharmacokinetic parameters, PuMBA even shows greater power than conventional univariate analysis of the true binding values from which the parameters were simulated. Across all applications, PuMBA exhibited a small degree of bias in the estimated outcomes; however, this was small relative to the variation in estimated outcomes between simulated datasets. Conclusion PuMBA improves the precision and power of statistical analysis of PET data without requiring the collection of additional measurements. This makes it possible to study new research questions in both new and previously collected data. PuMBA therefore holds great promise for the field of PET imaging.

The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets, serving not only to facilitate the process of data sharing and aggregation, but also to simplify the application and development of new methods and software for working with neuroimaging data. Here, we present an extension of BIDS to include positron emission tomography (PET) data, also known as PET-BIDS, and share several open-access datasets curated following PET-BIDS along with tools for conversion, validation and analysis of PET-BIDS datasets.

Positron emission tomography (PET) is an in vivo imaging method essential for studying the neurochemical pathophysiology of psychiatric and neurological disease. However, its high cost and exposure of participants to radiation make it unfeasible to employ large sample sizes. The major shortcoming of PET imaging is therefore its lack of power for studying clinically-relevant research questions. Here, we introduce a new method for performing PET quantification and analysis called SiMBA, which helps to alleviate these issues by improving the efficiency of PET analysis by exploiting similarities between both individuals and regions within individuals. In simulated [11C]WAY100635 data, SiMBA greatly improves both statistical power and the consistency of effect size estimation without affecting the false positive rate. This approach makes use of hierarchical, multifactor, multivariate Bayesian modelling to effectively borrow strength across the whole dataset to improve stability and robustness to measurement error. In so doing, parameter identifiability and estimation are improved, without sacrificing model interpretability. This comes at the cost of increased computational overhead, however this is practically negligible relative to the time taken to collect PET data. This method has the potential to make it possible to test clinically-relevant hypotheses which could never be studied before given the practical constraints. Furthermore, because this method does not require any additional information over and above that required for traditional analysis, it makes it possible to re-examine data which has already previously been collected at great expense. In the absence of dramatic advancements in PET image data quality, radiotracer development, or data sharing, PET imaging has been fundamentally limited in the scope of research hypotheses which could be studied. This method, especially combined with the recent steps taken by the PET imaging community to embrace data sharing, will make it possible to greatly improve the research possibilities and clinical relevance of PET neuroimaging.

Abstract Background Reliable quantification of dopamine transporter (DAT), a biomarker for Parkinson’s disease (PD), is essential for diagnostic purposes as well as for evaluation of potential disease-modifying treatment. Due to degeneration of dopaminergic neurons and thus lower expected radioligand binding to DAT, higher measurement variability in PD patients might be expected than earlier reproducibility results in healthy controls. Therefore, we aimed to examine the test-retest properties of [18F]FE-PE2I-PET in PD patients. Methods Nine patients with PD (Hoehn and Yahr stage < 3) were included (men/women 6/3; mean age 65.2 ± 6.8 years). Each patient underwent two [18F]FE-PE2I-PET measurements within 7–28 days. The outcome measure was non-displaceable binding potential generated using wavelet-aided parametric imaging with cerebellum as reference region. We assessed test-retest performance using estimates of reliability and repeatability. Regions for primary analysis were caudate, putamen, ventral striatum, and substantia nigra. Exploratory analysis was performed for functional subdivisions of the striatum. We also compared the more vs. less affected side. Results [18F]FE-PE2I showed absolute variability estimates of 5.3–7.6% in striatal regions and 11% in substantia nigra and ICCs of 0.74–0.97 (median 0.91). The absolute variability for functional striatal subdivisions was 6.0–9.6% and ICCs of 0.76–0.91 (median 0.91). The less affected substantia nigra exhibited greater consistency than the more affected side. According to power calculations based on the current sample size, DAT changes of 5–11% in the striatum and 28% in the substantia nigra can be detected with a power of 0.8 (p < 0.0125). Conclusion DAT-PET measurements with [18F]FE-PE2I in PD patients showed good repeatability and reliability. The slightly lower reliability in the substantia nigra in patients may be explained by lower DAT density and smaller anatomical size. Power calculations suggest that [18F]FE-PE2I PET is a suitable marker for longitudinal DAT decline in PD. Trial registration EudraCT 2017-003327-29

Psychotic disorders as well as psychosis proneness in the general population have been associated with perceptual instability, suggesting weakened predictive processing. Sleep disturbances play a prominent role in psychosis and schizophrenia, but it is unclear whether perceptual stability diminishes with sleep deprivation, and whether the effects of sleep deprivation differ as a function of psychosis proneness. In the current study, we aimed to clarify this matter. In this preregistered study, 146 participants successfully completed an intermittent version of the random dot kinematogram (RDK) task and the 21-item Peters Delusion Inventory (PDI-21) to assess perceptual stability and psychosis proneness, respectively. Participants were randomized to sleep either as normal (8 to 9 h in bed) (n = 72; Mage = 24.7, SD = 6.2, 41 women) or to stay awake through the night (n = 74; Mage = 24.8, SD = 5.1, 44 women). Sleep deprivation resulted in diminished perceptual stability, as well as in decreases in perceptual stability over the course of the task. However, we did not observe any association between perceptual stability and PDI-21 scores, nor a tendency for individuals with higher PDI-21 scores to be more vulnerable to sleep-deprivation-induced decreases in perceptual stability. The present study suggests a compromised predictive processing system in the brain after sleep deprivation, but variation in psychosis trait is not related to greater vulnerability to sleep deprivation in our dataset. Further studies in risk groups and patients with psychosis are needed to evaluate whether sleep loss plays a role in the occurrence of objectively measured perceptual-related clinical symptoms.

Dopamine D2 receptors (D2-R) in extrastriatal brain regions are of high interest for research in a wide range of psychiatric and neurologic disorders. Pharmacological competition studies and test–retest experiments have shown high validity and reliability of the positron emission tomography (PET) radioligand [11C]FLB 457 for D2-R quantification in extrastriatal brain regions. However, this radioligand is not available at most research centers. Instead, the medium affinity radioligand [11C]raclopride, which has been extensively validated for quantification of D2-R in the high-density region striatum, has been applied also in studies on extrastriatal D2-R. Recently, the validity of this approach has been questioned by observations of low occupancy of [11C]raclopride in extrastriatal regions in a pharmacological competition study with quetiapine. Here, we utilise a data set of 16 healthy control subjects examined with both [11C]raclopride and [11C]FLB 457 to assess the correlation in binding potential (BPND) in extrastriatal brain regions. BPND was quantified using the simplified reference tissue model with cerebellum as reference region. The rank order of mean regional BPND values were similar for both radioligands, and corresponded to previously reported data, both post-mortem and using PET. Nevertheless, weak to moderate within-subject correlations were observed between [11C]raclopride and [11C]FLB 457 BPND extrastriatally (Pearson's R: 0.30–0.56), in contrast to very strong correlations between repeated [11C]FLB 457 measurements (Pearson's R: 0.82–0.98). In comparison, correlations between repeated [11C]raclopride measurements were low to moderate (Pearson's R: 0.28–0.75). These results are likely related to low signal to noise ratio of [11C]raclopride in extrastriatal brain regions, and further strengthen the recommendation that extrastriatal D2-R measures obtained with [11C]raclopride should be interpreted with caution.

Abstract Background The D1-dopamine receptor radioligand [11C]SCH23390 has been frequently used in PET studies. In drug-naïve patients with schizophrenia, the findings have been inconsistent, with decreases, increases, and no change in the frontal cortex D1-dopamine receptors. While these discrepancies are likely primarily due to a lack of statistical power in these studies, we speculated that an additional explanation may be the differences due to methods of image analysis between studies, affecting reliability as well as bias between groups. Methods Fifteen healthy subjects underwent two PET measurements with [11C]SCH23390 on the same day. The binding potential (BPND) was compared using a 95% confidence interval following manual and automated delineation of a region of interest (ROI) as well as with and without frame-by-frame realignment. Results Automated target region delineation produced lower BPND values, while automated delineation of the reference region yielded higher BPND values. However, no significant differences were observed for repeatability using automated and manual delineation methods. Frame-by-frame realignment generated higher BPND values and improved repeatability. Conclusions The results suggest that the choice of ROI delineation method is not an important factor for reliability, whereas the improved results following movement correction confirm its importance in PET image analysis. Realignment is therefore especially important for measurements in patient populations such as schizophrenia or Parkinson’s disease, where motion artifacts may be more prevalent.

Abstract Purpose Kinetic modelling with metabolite-corrected arterial plasma is considered the gold standard for quantification of [11C]PBR28 binding to the translocator protein (TSPO), since there is no brain region devoid of TSPO that can serve as reference. The high variability in binding observed using this method has motivated the use of simplified ratio-based approaches such as standardised uptake value ratios (SUVRs) and distribution volume (VT) ratios (DVRs); however, the reliability of these measures and their relationship to VT have not been sufficiently evaluated. Methods Data from a previously published [11C]PBR28 test-retest study in 12 healthy subjects were reanalysed. VT was estimated using a two-tissue compartment model. SUVR and DVR values for the frontal cortex were calculated using the whole brain and cerebellum as denominators. Test-retest reliability was assessed for all measures. Interregional correlations were performed for SUV and VT, and principal component analysis (PCA) was applied. Lastly, correlations between ratio-based outcomes and VT were assessed. Results Reliability was high for VT, moderate to high for SUV and SUVR, and poor for DVR. Very high interregional correlations were observed for both VT and SUV (all R 2 > 85%). The PCA showed that almost all variance (>98%) was explained by a single component. Ratio-based methods correlated poorly with VT (all R 2

Neuroimaging, in addition to many other fields of clinical research, is both time-consuming and expensive, and recruitable patients can be scarce. These constraints limit the possibility of large-sample experimental designs, and often lead to statistically underpowered studies. This problem is exacerbated by the use of outcome measures whose accuracy is sometimes insufficient to answer the scientific questions posed. Reliability is usually assessed in validation studies using healthy participants, however these results are often not easily applicable to clinical studies examining different populations. I present a new method and tools for using summary statistics from previously published test-retest studies to approximate the reliability of outcomes in new samples. In this way, the feasibility of a new study can be assessed during planning stages, and before collecting any new data. An R package called relfeas also accompanies this article for performing these calculations. In summary, these methods and tools will allow researchers to avoid performing costly studies which are, by virtue of their design, unlikely to yield informative conclusions.

Objective A putative relationship between markers for the serotonin system and the personality scale self-transcendence (ST) and its subscale spiritual acceptance (SA) has been demonstrated in a previous PET study of 5-HT1A receptor binding in healthy control subjects. The results could however not be replicated in a subsequent PET study at an independent centre. In this study, we performed a replication of our original study in a larger sample using Bayesian hypothesis testing to evaluate relative evidence both for and against this hypothesis. Methods Regional 5-HT1A receptor binding potential (BPND) was examined in 50 healthy male subjects using PET with the radioligand [11C]WAY100635. 5-HT1Aavailability was calculated using the simplified reference tissue model (SRTM) yielding regional BPND. ST and SA were measured using the Temperament and Character Inventory (TCI) questionnaire. Correlations between ST/SA scores and 5-HT1ABPND in frontal cortex, hippocampus and raphe nuclei were examined by calculation of default correlation Bayes factors (BFs) and replication BFs. Results There were no significant correlations between 5-HT1A receptor binding and ST/SA scores. Rather, five of six replication BFs provided moderate to strong evidence for no association between 5-HT1A availability and ST/SA, while the remaining BF provided only weak evidence. Conclusion We could not replicate our previous findings of an association between 5-HT1A availability and the personality trait ST/SA. Rather, the Bayesian analysis provided evidence for a lack of correlation. Further research should focus on whether other components of the serotonin system may be related to ST or SA. This study also illustrates how Bayesian hypothesis testing allows for greater flexibility and more informative conclusions than traditional p-values, suggesting that this approach may be advantageous for analysis of molecular imaging data.