1,923 research outputs found
Spectral Analysis of Dynamic PET Studies: A Review of 20 Years of Method Developments and Applications
Full text linkIn Positron Emission Tomography (PET), spectral analysis (SA) allows the quantification of dynamic data by relating the radioactivity measured by the scanner in time to the underlying physiological processes of the system under investigation. Among the different approaches for the quantification of PET data, SA is based on the linear solution of the Laplace transform inversion whereas the measured arterial and tissue time-activity curves of a radiotracer are used to calculate the input response function of the tissue. In the recent years SA has been used with a large number of PET tracers in brain and nonbrain applications, demonstrating that it is a very flexible and robust method for PET data analysis. Differently from the most common PET quantification approaches that adopt standard nonlinear estimation of compartmental models or some linear simplifications, SA can be applied without defining any specific model configuration and has demonstrated very good sensitivity to the underlying kinetics. This characteristic makes it useful as an investigative tool especially for the analysis of novel PET tracers. The purpose of this work is to offer an overview of SA, to discuss advantages and limitations of the methodology, and to inform about its applications in the PET field
TSPO: functions and applications of a mitochondrial stress response pathway
No full textThe mitochondrial outer membrane protein TSPO (translocator protein) lies in a privileged position at the interface between mitochondrion and cytosol. Since the initially discovery, nearly forty years ago, it has generated major interest among various disciplines of modern experimental and applied biomedicine. The focused meeting we have organized aimed at summarizing the state of the art knowledge on TSPO and the discipline-based segregated concepts that have made this an exciting and active field of science. The scientists who have generously contributed the event have agreed to generate a special issue here published—stemmed from the discussion of the vent. This consists in a series of contributions via which the know-how is shared aiming to inspire current and future endeavours to validate and accelerate the impact of TSPO science in human pathophysiology and clinical applications
Measuring specific receptor binding of a PET radioligand in human brain without pharmacological blockade: The genomic plot
Full text linkPET studies allow in vivo imaging of the density of brain receptor species. The PET signal, however, is the sum of the fraction of radioligand that is specifically bound to the target receptor and the non-displaceable fraction (i.e. the non-specifically bound radioligand plus the free ligand in tissue). Therefore, measuring the non-displaceable fraction, which is generally assumed to be constant across the brain, is a necessary step to obtain regional estimates of the specific fractions.
The nondisplaceable binding can be directly measured if a reference region, i.e. a region devoid of any specific binding, is available. Many receptors are however widely expressed across the brain, and a true reference region is rarely available. In these cases, the nonspecific binding can be obtained after competitive pharmacological blockade, which is often contraindicated in humans.
In this work we introduce the genomic plot for estimating the nondisplaceable fraction using baseline scans only. The genomic plot is a transformation of the Lassen graphical method in which the brain maps of mRNA transcripts of the target receptor obtained from the Allen brain atlas are used as a surrogate measure of the specific binding. Thus, the genomic plot allows the calculation of the specific and nondisplaceable components of radioligand uptake without the need of pharmacological blockade.
We first assessed the statistical properties of the method with computer simulations. Then we sought ground-truth validation using human PET datasets of seven different neuroreceptor radioligands, where nonspecific fractions were either obtained separately using drug displacement or available from a true reference region. The population nondisplaceable fractions estimated by the genomic plot were very close to those measured by actual human blocking studies (mean relative difference between 2% and 7%). However, these estimates were valid only when mRNA expressions were predictive of protein levels (i.e. there were no significant post-transcriptional changes). This condition can be readily established a priori by assessing the correlation between PET and mRNA expression
Milano consolato nell' elettione a questo arciuescouado, e promotione alla sagra porpora dell' eminentissimo Federico Visconti : colla sua solennissima entrata seguita a' 11. genaro 1682 e fontioni antecedenti /
No full textFrontispiece coat of arms of Milan, engraved by Federico Agnelli.Signatures: pi⁴ A-G⁴ H⁴(-H4).Mode of access: Internet.Binding: limp vellum. Author & title written on spine
MENGA: A New Comprehensive Tool for the Integration of Neuroimaging Data and the Allen Human Brain Transcriptome Atlas.
Full text linkIntroductionBrain-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.AimIn 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.ResultsWe 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
Kinetic modeling without accounting for the vascular component impairs the quantification of [11C]PBR28 brain PET data
No full textThe 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]-(R)-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)
sj-pdf-1-jcb-10.1177_0271678X231157958 - Supplemental material for The effects of acute Methylene Blue administration on cerebral blood flow and metabolism in humans and rats
No full textSupplemental material, sj-pdf-1-jcb-10.1177_0271678X231157958 for The effects of acute Methylene Blue administration on cerebral blood flow and metabolism in humans and rats by Nisha Singh, Eilidh MacNicol, Ottavia DiPasquale, Karen Randall, David Lythgoe, Ndabezinhle Mazibuko, Camilla Simmons, Pierluigi Selvaggi, Stephanie Stephenson, Federico E Turkheimer, Diana Cash, Fernando Zelaya and Alessandro Colasanti in Journal of Cerebral Blood Flow & Metabolism</p
Normalizing the Abnormal
No full textThe use of antipsychotic medication to manage psychosis, principally in those with a diagnosis of schizophrenia or bipolar disorder, is well established. Antipsychotics are effective in normalizing positive symptoms of psychosis in the short term (delusions, hallucinations and disordered thought). Their long-term use is, however, associated with side effects, including several types of movement (extrapyramidal syndrome, dyskinesia, akathisia), metabolic and cardiac disorders. Furthermore, higher lifetime antipsychotic dose-years may be associated with poorer cognitive performance and blunted affect, although the mechanisms driving the latter associations are not well understood. In this article, we propose a novel model of the long-term effects of antipsychotic administration focusing on the changes in brain metabolic homeostasis induced by the medication. We propose here that the brain metabolic normalization, that occurs in parallel to the normalization of psychotic symptoms following antipsychotic treatment, may not ultimately be sustainable by the cerebral tissue of some patients; these patients may be characterized by already reduced oxidative metabolic capacity and this may push the brain into an unsustainable metabolic envelope resulting in tissue remodeling. To support this perspective, we will review the existing data on the brain metabolic trajectories of patients with a diagnosis of schizophrenia as indexed using available neuroimaging tools before and after use of medication. We will also consider data from pre-clinical studies to provide mechanistic support for our model
Multi-scale hierarchical generation of PET parametric maps: Application and testing on [11C]DPN study
No full textIntroduction: We investigate a general approach to generate parametric maps that consists in a multi-stage hierarchical scheme whereas 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 pixel level. A-priori classes of voxels are generated either by anatomical segmentation or by functional segmentation using unsupervised clustering. Kinetic properties are then transmitted to the voxels in each class using Maximum a Posteriori (MAP) estimation approaches. We validate the algorithm on a test–retest data-sets of [11C]diprenorphine (DPN), which represents a challenge to estimation given its slow equilibration in tissue. We further offer internal validation by comparing resulting parametric maps generated from the anatomical and functional a-priori segmentation.Materialandmethods: We considered tracers that could be described by 1-tissue (1T) compartment model with 2 rate constants (K1 and k2). The parameters of the linearized 1T model have been obtained by using the MAP estimation approach in order to eliminate the bias introduced with the linearization of the model. Volume of distribution Vt was calculated as: Vt = K1/k2. The priors for the Bayesian estimation were derived from a weighted nonlinear least squares (WLQ) estimation done at region-of-interest (ROI) level. In order to investigate the impact of different ways to extract the priors, ROIs have been obtained A) using the anatomical atlas and B) using unsupervised clustering. Likelihood Estimation in Graphical Analysis (LEGA) [1] was also applied to quantify Vt at ROI level and its results have been compared to those obtained at pixel level. The 1T model was assumed to best describe [11C]DPN kinetics at pixel level, after comparison with more complex models (Akaike criterion). Five subjects (test and retest) underwent 95-min dynamic PET, following an injection with ∼185 MBq of [11C]DPN. Arterial plasma input functions corrected for radiolabelled metabolites were created. An individualized maximum probability atlas was created for each subject and used to derive 83 ROIs.Results: The regional distributions of Vt were consistent with opioid receptor distributions known from previous [11C]DPN studies [2]. When priors have been derived from the anatomical atlas (Fig. A), there was excellent agreement and strong correlation among pixel and LEGA ROI results (average R2 = 0.949) and excellent reliability test–retest for all subjects but the first (average R2 = 0.939).1T pixel level results didn't change when priors were defined from unsupervised clustering (Fig. B), i.e. the difference among the estimates varied between 0% and 2% among the subjects.Conclusion: The new method presented is fast (i.e. 15 min per subject) and robust. Applied to [11C]DPN data achieves accurate quantification of Vt as well as high quality Vt images. There is strong agreement between pixel level results and both LEGA ROI estimates and results from previous studies. Moreover, the way the priors are defined (i.e. using anatomical atlas or unsupervised clustering) doesn't affect the estimates
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