Gaia Rizzo

Microglial activity in people at ultra high risk of psychosis and in schizophrenia; an [11C]PBR28 PET brain imaging study

OBJECTIVE The purpose of this study was to determine whether microglial activity, measured using translocator-protein positron emission tomography (PET) imaging, is increased in unmedicated persons presenting with subclinical symptoms indicating that they are at ultra high risk of psychosis and to determine whether microglial activity is elevated in schizophrenia after controlling for a translocator-specific genetic polymorphism. METHOD The authors used the second-generation radioligand [(11)C]PBR28 and PET to image microglial activity in the brains of participants at ultra high risk for psychosis. Participants were recruited from early intervention centers. The authors also imaged a cohort of patients with schizophrenia and matched healthy subjects for comparison. In total, 50 individuals completed the study. At screening, participants were genotyped to account for the rs6971 polymorphism in the gene encoding the 18Kd translocator protein. The main outcome measure was total gray matter [(11)C]PBR28 binding ratio, representing microglial activity. RESULTS [(11)C]PBR28 binding ratio in gray matter was elevated in ultra-high-risk participants compared with matched comparison subjects (Cohens d >1.2) and was positively correlated with symptom severity (r=0.730). Patients with schizophrenia also demonstrated elevated microglial activity relative to matched comparison subjects (Cohens d >1.7). CONCLUSIONS Microglial activity is elevated in patients with schizophrenia and in persons with subclinical symptoms who are at ultra high risk of psychosis and is related to at-risk symptom severity. These findings suggest that neuroinflammation is linked to the risk of psychosis and related disorders, as well as the expression of subclinical symptoms.

The methodology of TSPO imaging with positron emission tomography

The 18-kDA translocator protein (TSPO) is consistently elevated in activated microglia of the central nervous system (CNS) in response to a variety of insults as well as neurodegenerative and psychiatric conditions. It is therefore a target of interest for molecular strategies aimed at imaging neuroinflammation in vivo. For more than 20 years, positron emission tomography (PET) has allowed the imaging of TSPO density in brain using [11C]-(R)-PK11195, a radiolabelled-specific antagonist of the TSPO that has demonstrated microglial activation in a large number pathological cohorts. The significant clinical interest in brain immunity as a primary or comorbid factor in illness has sparked great interest in the TSPO as a biomarker and a surprising number of second generation TSPO radiotracers have been developed aimed at improving the quality of TSPO imaging through novel radioligands with higher affinity. However, such major investment has not yet resulted in the expected improvement in image quality. We here review the main methodological aspects of TSPO PET imaging with particular attention to TSPO genetics, cellular heterogeneity of TSPO in brain tissue and TSPO distribution in blood and plasma that need to be considered in the quantification of PET data to avoid spurious results as well as ineffective development and use of these radiotracers.

Kinetic modeling without accounting for the vascular component impairs the quantification of [11C]PBR28 brain PET data

The positron emission tomography radioligand [11C]PBR28 targets translocator protein (18 kDa) (TSPO) and is a potential marker of neuroinflammation. [11C]PBR28 binding is commonly quantified using a two-tissue compartment model and an arterial input function. Previous studies with [11C]HRJ-PK11195 demonstrated a slow irreversible binding component to the TSPO proteins localized in the endothelium of brain vessels, such as venous sinuses and arteries. However, the impact of this component on the quantification of [11C]PBR28 data has never been investigated. In this work we propose a novel kinetic model for [11C]PBR28. This model hypothesizes the existence of an additional irreversible component from the blood to the endothelium. The model was tested on a data set of 19 healthy subjects. A simulation was also performed to quantify the error generated by the standard two-tissue compartmental model when the presence of the irreversible component is not taken into account. Our results show that when the vascular component is included in the model the estimates that include the vascular component (2TCM-1K) are more than three-fold smaller, have a higher time stability and are better correlated to brain mRNA TSPO expression than those that do not include the model (2TCM).

The Predictive Power of Brain mRNA Mappings for in vivo Protein Density: A Positron Emission Tomography Correlation Study

Substantial efforts are being spent on postmortem mRNA transcription mapping on the assumption that in vivo protein distribution can be predicted from such data. We tested this assumption by comparing mRNA transcription maps from the Allen Human Brain Atlas with reference protein concentration maps acquired with positron emission tomography (PET) in two representative systems of neurotransmission (opioid and serotoninergic). We found a tight correlation between mRNA expression and specific binding with 5-HT1A receptors measured with PET, but for opioid receptors, the correlation was weak. The discrepancy can be explained by differences in expression regulation between the two systems: transcriptional mechanisms dominate the regulation in the serotoninergic system, whereas in the opioid system proteins are further modulated after transcription. We conclude that mRNA information can be exploited for systems where translational mechanisms predominantly regulate expression. Where posttranscriptional mechanisms are important, mRNA data have to be interpreted with caution. The methodology developed here can be used for probing assumptions about the relationship of mRNA and protein in multiple neurotransmission systems.

Multi-scale hierarchical generation of PET parametric maps: Application and testing on [11C]DPN study

We propose a general approach to generate parametric maps. It consists in a multi-stage hierarchical scheme where, starting from the kinetic analysis of the whole brain, we then cascade the kinetic information to anatomical systems that are akin in terms of receptor densities, and then down to the voxel level. A-priori classes of voxels are generated either by anatomical atlas segmentation or by functional segmentation using unsupervised clustering. Kinetic properties are transmitted to the voxels in each class using maximum a posteriori (MAP) estimation method. We validate the novel method on a [11C]diprenorphine (DPN) test-retest data-set that represents a challenge to estimation given [11C]DPNs slow equilibration in tissue. The estimated parametric maps of volume of distribution (VT) reflect the opioid receptor distributions known from previous [11C]DPN studies. When priors are derived from the anatomical atlas, there is an excellent agreement and strong correlation among voxel MAP and ROI results and excellent test-retest reliability for all subjects but one. Voxel level results did not change when priors were defined through unsupervised clustering. This new method is fast (i.e. 15 min per subject) and applied to [11C]DPN data achieves accurate quantification of VT as well as high quality VT images. Moreover, the way the priors are defined (i.e. using an anatomical atlas or unsupervised clustering) does not affect the estimates.

Multi-scale hierarchical approach for parametric mapping: assessment on multi-compartmental models.

This paper investigates a new hierarchical method to apply basis function to mono- and multi-compartmental models (Hierarchical-Basis Function Method, H-BFM) at a voxel level. This method identifies the parameters of the compartmental model in its nonlinearized version, integrating information derived at the region of interest (ROI) level by segmenting the cerebral volume based on anatomical definition or functional clustering. We present the results obtained by using a two tissue-four rate constant model with two different tracers ([(11)C]FLB457 and [carbonyl-(11)C]WAY100635), one of the most complex models used in receptor studies, especially at the voxel level. H-BFM is robust and its application on both [(11)C]FLB457 and [carbonyl-(11)C]WAY100635 allows accurate and precise parameter estimates, good quality parametric maps and a low percentage of voxels out of physiological bound (<8%). The computational time depends on the number of basis functions selected and can be compatible with clinical use (~6h for a single subject analysis). The novel method is a robust approach for PET quantification by using compartmental modeling at the voxel level. In particular, different from other proposed approaches, this method can also be used when the linearization of the model is not appropriate. We expect that applying it to clinical data will generate reliable parametric maps.

MENGA: A New Comprehensive Tool for the Integration of Neuroimaging Data and the Allen Human Brain Transcriptome Atlas

Introduction Brain-wide mRNA mappings offer a great potential for neuroscience research as they can provide information about system proteomics. In a previous work we have correlated mRNA maps with the binding patterns of radioligands targeting specific molecular systems and imaged with positron emission tomography (PET) in unrelated control groups. This approach is potentially applicable to any imaging modality as long as an efficient procedure of imaging-genomic matching is provided. In the original work we considered mRNA brain maps of the whole human genome derived from the Allen human brain database (ABA) and we performed the analysis with a specific region-based segmentation with a resolution that was limited by the PET data parcellation. There we identified the need for a platform for imaging-genomic integration that should be usable with any imaging modalities and fully exploit the high resolution mapping of ABA dataset. Aim In this work we present MENGA (Multimodal Environment for Neuroimaging and Genomic Analysis), a software platform that allows the investigation of the correlation patterns between neuroimaging data of any sort (both functional and structural) with mRNA gene expression profiles derived from the ABA database at high resolution. Results We applied MENGA to six different imaging datasets from three modalities (PET, single photon emission tomography and magnetic resonance imaging) targeting the dopamine and serotonin receptor systems and the myelin molecular structure. We further investigated imaging-genomic correlations in the case of mismatch between selected proteins and imaging targets.

SAKE: A new quantification tool for positron emission tomography studies

In dynamic positron emission tomography (PET) studies, spectral analysis (SA) refers to a data-driven quantification method, based on a single-input single-output model for which the transfer function is described by a sum of exponential terms. SA allows to quantify numerosities, amplitudes and eigenvalues of the transfer function allowing, in this way, to separate kinetic components of the tissue tracer activity with minimal model assumptions. The SA model can be solved with a linear estimator alone or with numerical filters, resulting in different types of SA approaches. Once estimated the number, amplitudes and eigenvalues of the transfer function, one can distinguish the presence in the system of irreversible and/or reversible components as well as derive parameters of physiological significance. These characteristics make it an appealing alternative method to compartmental models which are widely used for the quantitative analysis of dynamic studies acquired with PET. However, despite its applicability to a large number of PET tracers, its implementation is not straightforward and its utilization in the nuclear medicine community has been limited especially by the lack of an user-friendly software application. In this paper we proposed SAKE, a computer program for the quantitative analysis of PET data through the main SA methods. SAKE offers a unified pipeline of analysis usable also by people with limited computer knowledge but with high interest in SA.

Kinetic modelling of [11C]PBR28 for 18 kDa translocator protein PET data: A validation study of vascular modelling in the brain using XBD173 and tissue analysis

The 18 kDa translocator protein (TSPO) is a marker of microglia activation in the central nervous system and represents the main target of radiotracers for the in vivo quantification of neuroinflammation with positron emission tomography (PET). TSPO PET is methodologically challenging given the heterogeneous distribution of TSPO in blood and brain. Our previous studies with the TSPO tracers [11C]PBR28 and [11C]PK11195 demonstrated that a model accounting for TSPO binding to the endothelium improves the quantification of PET data. Here, we performed a validation of the kinetic model with the additional endothelial compartment through a displacement study. Seven subjects with schizophrenia, all high-affinity binders, underwent two [11C]PBR28 PET scans before and after oral administration of 90 mg of the TSPO ligand XBD173. The addition of the endothelial component provided a signal compartmentalization much more consistent with the underlying biology, as only in this model, the blocking study produced the expected reduction in the tracer concentration of the specific tissue compartment, whereas the non-displaceable compartment remained unchanged. In addition, we also studied TSPO expression in vessels using 3D reconstructions of histological data of frontal lobe and cerebellum, demonstrating that TSPO positive vessels account for 30% of the vascular volume in cortical and white matter.

Deriving physiological information from PET images: from SUV to compartmental modelling

Positron emission tomography (PET) imaging has made it possible to detect the in vivo concentration of positron-emitting compounds accurately and non-invasively. In order to relate the radioactivity concentration measured using PET to the underlying physiological or biochemical processes, the application of mathematical models to describe tracer kinetics within a particular region of interest is necessary. Image analysis can be performed both by visual interpretation and quantitative assessment and, depending on the ultimate purposes of the analysis, several alternatives are available. In clinical practice, PET quantification is routinely performed using the standard uptake value (SUV), a semi-quantitative index in use since the 1980s. Its computation is very simple since it requires only the PET measure at a pre-fixed sample time and the injected dose normalised to some anthropometric characteristic of the subject (generally body weight or body surface area). An alternative to the SUV is the tissue-to-plasma ratio (ratio). As its name indicates, this index is computed as the ratio between the tracer activity measured in the tissue and in the plasma pool within a pre-fixed time window. Moving from static to more informative dynamic PET acquisition, three model classes represent the most frequently used approaches: compartmental models, the spectral analysis modelling approach, and graphical methods. These approaches differ in terms of application assumptions (e.g. reversibility of tracer uptake, model structure, etc.) and computational complexity. They also produce different information about the system under study: from a macro-description of tracer uptake to a full quantitative characterisation of the physiological processes in which the tracer is involved. The application of these approaches to clinical routine is restricted by the need for invasive blood sampling. In order to avoid arterial cannulation and blood sample management, different alternative approaches have been developed for quantification of PET kinetics, including reference tissue methods. Although these approaches are appealing, the results obtained with several tracers are questionable. This review provides a complete overview of the semi-quantitative and quantitative methods used in PET analysis. The pros and cons of each method are evaluated and discussed.