Biology subjects

Matheson, G. J.

Publications and source records attributed to Matheson, G. J..

16 recordsLinked to original sources

bioRxiv2026

Building an open ecosystem for molecular neuroimaging: standards and tools from the OpenNeuroPET initiative

Molecular neuroimaging with positron emission tomography (PET) and single-photon emission computed tomography (SPECT) enables quantification of specific molecular targets in the living brain. Despite its scientific impact, molecular neuroimaging research has historically faced challenges due to high costs, small sample sizes, laboratory-specific analysis pipelines, and limited large-scale data sharing. These factors have hindered reproducibility and the broader reuse of valuable PET datasets. The OpenNeuroPET initiative was established to address these barriers by developing standards, infrastructure, and open-source tools currently focused on PET. Through collaborations across Europe and North America, OpenNeuroPET has supported the PET extension of the Brain Imaging Data Structure (PET-BIDS), providing a standardized framework for PET datasets and metadata. Building on PET-BIDS, tools such as PET2BIDS, ezBIDS, and BIDSCoin facilitate data conversion and curation. In parallel, OpenNeuro now hosts PET-BIDS datasets for open sharing, while complementary platforms such as PublicnEUro provide controlled-access pathways designed to support compliance with the European Union General Data Protection Regulation (GDPR). Emerging open-source workflows further support automated, reproducible PET analysis, promoting harmonization across centers. Together, these developments mark an important step toward an open molecular neuroimaging community in which datasets, software, and workflows can be transparently shared, reused, and scaled for collaborative research.

bioinformatics↗

bioRxiv2025

Building Multivariate Molecular Imaging Brain Atlases Using the NeuroMark PET Independent Component Analysis Framework

Molecular imaging analyses using positron emission tomography (PET) data often rely on macro-anatomical regions of interest (ROI), which may not align with chemo-architectural boundaries and obscure functional distinctions. While methods such as independent component analysis (ICA) have been useful to address this limitation, the fully data-driven nature can make it challenging to compare results across studies. Here, we introduce the NeuroMark PET approach, utilizing spatially constrained independent component analysis to define overlapping regions that may reflect the brains molecular architecture. We first generate an ICA template for the PET radiotracer florbetapir (FBP), targeting amyloid-{beta} (A{beta}) accumulation in the brain, using blind ICA on large datasets to identify replicable independent components. Only components that targeted A{beta} were included in this study, defined as A{beta} networks (A{beta}Ns), by omitting components targeting myelin or other non-A{beta} targets. Next, we use the A{beta}Ns as priors for spatially constrained ICA, resulting in a fully automated ICA pipeline called NeuroMark PET. This NeuroMark pipeline, including its A{beta}Ns, was validated against a standard neuroanatomical PET atlas, using data from the Alzheimers Disease Neuroimaging Initiative (ADNI). The study included 296 cognitively normal participants with FBP PET scans and 173 with florbetaben (FBB) PET scans, an analogue radiotracer also targeting A{beta} accumulation. Our results show that NeuroMark PET captures biologically meaningful, participant-specific features, such as subject specific loading values, consistent across individuals, and also shows higher sensitivity and power for detecting age-related changes compared to traditional atlas-based ROIs. Using this framework, we also highlight some of the advantages of using ICA analysis for PET data. In this study, an A{beta}N consists of weighted voxels and forms a pattern throughout the entire brain. For example, components may have weighted values at every voxel and can overlap with one another, enabling the separation of artifacts which may coincide with the A{beta}Ns of interest. In addition, this approach allows for the differentiation, separating white matter components, which may overlap in complex ways with the A{beta}Ns, mainly residing in the neighboring gray matter. Results also showed that the most age associated A{beta}N (representing the cognitive control network, CC1) exhibited a stronger association with age compared with macro-anatomical regions of interest. This may suggest that each NeuroMark FBP A{beta}N represents a spatial network following chemo-architectural uptake with greater biological relevance compared with anatomical ROIs. In summary, the proposed NeuroMark PET approach offers a fully automated framework, providing accurate and reproducible brain A{beta}Ns. This approach enhances our ability to investigate the molecular underpinnings of brain function and pathology, offering an alternative to traditional ROI-based analyses.

neuroscience↗

bioRxiv2024

A Reference Tissue Implementation of Simultaneous Multifactor Bayesian Analysis (SiMBA) of PET Time Activity Curve Data

PET analysis is conventionally performed as a two-stage process of quantification followed by analysis. We recently introduced SiMBA (Simultaneous Multifactor Bayesian Analysis), a hierarchical model that performs quantification and analysis for all brain regions of all individuals at once, and in so doing improves both the accuracy of parameter estimation as well as inferential efficiency. However until now, SiMBA has only been implemented for the two-tissue compartment model. We have now extended this general approach to also allow a non-invasive reference tissue implementation that includes both the full reference tissue model and the simplified reference tissue model. In simulated data, SiMBA improves quantitative parameter estimation accuracy, reducing error by, on average, 57% for binding potential (BPND). In considerations of statistical power, our simulation studies indicate that the efficiency of SiMBA modeling approximately corresponds to improvements that would require doubling the sample size if using conventional methods, with no increase in the false positive rate. We applied the model to PET data measured with [11C]AZ10419369, which binds selectively to the serotonin 1B receptor, in datasets collected at three different PET centres (n=139, n=44 and n=39). We show that SiMBA yields replicable inferences by comparing associations between PET parameters and age in the different datasets. Moreover, we show that time activity curve data from different centres can be combined in a single SiMBA model using covariates to control between-centre parameter differences, in order to harmonise data between centres. In summary, we present a novel approach for noninvasive quantification and analysis of PET time activity curve data which improves quantification and inferences, enables effective between-centre data harmonisation, and also yields replicable outcomes. This method has the potential to significantly expand the range of research questions which can be meaningfully tested using conventional sample sizes with PET imaging.

neuroscience↗

bioRxiv2024

PET Imaging of the Serotonin 1A Receptor in Major Depressive Disorder: Hierarchical Multivariate Analysis of 11CWAY100635 Overcomes Outcome Measure Discrepancies

The serotonin 1A receptor has been linked to both the pathophysiology of major depressive disorder (MDD) and the antidepressant action of serotonin reuptake inhibitors. Most PET studies of the serotonin 1A receptor in MDD used the receptor antagonist radioligand, [carbonyl-11C]WAY100635; however the interpretation of the combined results has been contentious owing to reports of higher or lower binding in MDD with different outcome measures. The reasons for these divergent results originate from several sources, including properties of the radiotracer itself, which complicate its quantification and interpretation; as well as from previously reported differences between MDD and healthy volunteers in both reference tissue binding and plasma free fraction, which are typically assumed not to differ. Recently, we have developed two novel hierarchical multivariate methods which we validated for the quantification and analysis of [11C]WAY100635, which show better accuracy and inferential efficiency compared to standard analysis approaches. Importantly, these new methods should theoretically be more resilient to many of the factors thought to have caused the discrepancies observed in previous studies. We sought to apply these methods in the largest [11C]WAY100635 sample to date, consisting of 160 individuals, including 103 MDD patients, of whom 50 were not-recently-medicated and 53 were antidepressant-exposed, as well as 57 healthy volunteers. While the outcome measure discrepancies were substantial using conventional univariate analysis, our multivariate analysis techniques instead yielded highly consistent results across PET outcome measures and across pharmacokinetic models, with all approaches showing higher serotonin 1A autoreceptor binding potential in the raphe nuclei of not-recently-medicated MDD patients relative to both healthy volunteers and antidepressant-exposed MDD patients. Moreover, with the additional precision of estimates afforded by this approach, we can show that while binding is also higher in projection areas in this group, these group differences are approximately half of those in the raphe nuclei, which are statistically distinguishable from one another. These results are consistent with the biological role of the serotonin 1A autoreceptor in the raphe nuclei in regulating serotonin neuron firing and release, and with preclinical and clinical evidence of deficient serotonin activity in MDD due to over expression of autoreceptors resulting from genetic and/or epigenetic effects. These results are also consistent with downregulation of autoreceptors as a mechanism of action of selective serotonin reuptake inhibitors. In summary, the results using multivariate analysis approaches therefore demonstrate both face and convergent validity, and may serve to provide a resolution and consensus interpretation for the disparate results of previous studies examining the serotonin 1A receptor in MDD.

neuroscience↗

bioRxiv2022

Multivariate Analysis of PET Pharmacokinetic Parameters

PurposeIn positron emission tomography (PET) 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. MethodsBy applying a hierarchical multifactor multivariate Bayesian approach, all estimated parameters from all regions can be analysed at once. We refer to this method as PuMBA (Parameters undergoing Multivariate Bayesian Analysis). We simulated patientcontrol 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. ResultsWe 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. ConclusionPuMBA 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.

neuroscience↗

bioRxiv2021

PET-BIDS, an extension to the brain imaging data structure for positron emission tomography

The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets. It serves 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 (PET-BIDS). We describe the PET-BIDS standard in detail and share several open-access datasets curated following PET-BIDS. Additionally, we highlight several tools which are already available for converting, validating and analyzing PET-BIDS datasets.

neuroscience↗

bioRxiv2020

Low convergent validity of FLB 457

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 centres. 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. 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 (Pearsons R: 0.30 - 0.56), in contrast to very strong correlations between repeated [11C]FLB 457 measurements (Pearsons R: 0.82 - 0.98). 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.

neuroscience↗

bioRxiv2020

Kinfitr - an open source tool for reproducible PET modelling: validation and evaluation of test-retest reliability

In positron emission tomography (PET) imaging, binding is typically estimated by fitting pharmacokinetic models to the series of measurements of radioactivity in the target tissue following intravenous injection of a radioligand. However, there are multiple different models to choose from and numerous analytical decisions which must be made when modelling PET data. Therefore, full communication of all the steps involved is often not feasible within the confines of a scientific publication. As such, there is a need to improve analytical transparency. Kinfitr, written in the open-source programming language R, is a tool developed for flexible and reproducible kinetic modelling of PET data, i.e. performing all steps using code which can be publicly shared in analysis notebooks. In this study, we compared outcomes obtained using kinfitr with those obtained using PMOD: a widely-used commercial tool. Using previously-collected test-retest data obtained with four different radioligands, a total of six different kinetic models were fitted to time-activity curves derived from different brain regions. We observed high agreement between the two kinetic modelling tools both for binding estimates and for microparameters. Likewise, no substantial differences were observed in the test-retest reliability estimates between the two tools. In summary, we showed excellent agreement between the open source R package kinfitr, and the widely-used commercial application PMOD. We therefore conclude that kinfitr is a valid and reliable tool for kinetic modelling of PET data.

neuroscience↗

bioRxiv2019

kinfitr: Reproducible PET Pharmacokinetic Modelling in R

Quantification of Positron Emission Tomography (PET) data is performed using pharmacokinetic models. There exist many models for describing this data, each of which may describe the data better or worse depending on the specific application, and there are both theoretical, practical and empirical reasons to select any one model over another. As such, effective PET modelling requires a high degree of flexibility, while effective communication of all steps taken through scientific publications is not always feasible. Reproducible research practices address these concerns, in that researchers share analysis code, and data if possible, such that all steps are recorded, allowing an independent researcher to reproduce the results and assess their veracity. In this article, I present kinfitr: a software package for performing kinetic modelling using the open-source R language, in a reproducible manner. The R community has a strong culture of reproducible research, and the language consists of numerous tools which allow both effective and easy sharing and communication of analysis code. The package is written in such a way as to allow the analyst the freedom to use and rapidly exchange between approaches, and to assess goodness of fit, with 14 different kinetic models currently implemented using a consistent syntax, as well as tools for working with the data. By providing open-source tools for kinetic modelling, including documentation and examples, it is hoped that this will extend access to methodology for research groups lacking software engineering expertise, as well as simplify and thereby encourage transparent and reproducible reporting.

bioinformatics↗

bioRxiv2018

Dopamine D1 receptor availability is not associated with delusional ideation measures of psychosis proneness

The dopamine D1 receptor (D1R) is thought to play a role in psychosis and schizophrenia, however the exact nature of this involvement is not clear. Positron emission tomography studies comparing D1R between patients and control subjects have produced inconsistent results. An important confounding factor in most clinical studies is previous exposure to antipsychotic treatment, which is thought to influence the density of D1R. To circumvent some of the limitations of clinical studies, an alternative approach for studying the relationship between D1R and psychosis is to examine individuals at increased risk for psychotic disorders, or variation in subclinical psychotic symptoms such as delusional ideation within the general population, referred to as psychosis proneness traits. In this study, we investigated whether D1R availability is associated with delusional ideation in healthy controls using data from 76 individuals measured with PET using [11C]SCH23390 and 217 individuals who completed delusional ideation questionnaires, belonging to three different study cohorts. We first performed exploratory, hypothesis-generating, analyses by creating and evaluating a new measure of delusional ideation (n=132 and n=27), which was then found to show a negative association with D1R availability (n=24). Next, we performed confirmatory analyses using Bayesian statistical modelling, in which we first attempted to replicate this result (n=20), and then evaluated the association of Peters Delusion Inventory scores with D1R availability in two independent cohorts (n=41 and 20). Collectively, we found strong evidence that there is little to no linear association between delusional ideation and D1R availability in healthy controls. If differences in D1R can be confirmed in drug-naive schizophrenia patients compared to controls, further studies are needed to ascertain whether these changes occur at the onset of psychotic symptoms or if they are associated with specific behavioural or genetic aspects of psychosis proneness other than delusional ideation.

neuroscience↗

bioRxiv2018

Test-retest reliability and convergent validity of (R)-PK11195 outcome measures without arterial input function

BackgroundThe positron emission tomography radioligand (R)-[11C]PK11195 can be used to quantify the expression of translocator protein (TSPO), which is considered a marker for activation of glial cells. TSPO is expressed throughout the brain, and for this reason no true reference region exists. When a radioligand does not have a reference region, an arterial input function (AIF) is usually required in order to quantify binding. However, obtaining an AIF can be difficult as well as uncomfortable for participants. Alternative methods have therefore been proposed with the aim of estimating (R)-[11C]PK11195 binding without arterial measurements, such as standardized uptake values (SUVs), supervised-cluster analysis (SVCA), or the use of a pseudo-reference region. The objective of this study was to evaluate the test-retest reliability and convergent validity of these techniques.\n\nMethodsData from a previously published (R)-[11C]PK11195 test-retest study in six healthy male subjects were reanalysed. Non-displaceable binding potential (BPND) was calculated for a set of cortical and subcortical brain regions using the simplified reference tissue model, with either cerebellum as reference region or a reference input derived using SVCA. SUVs were estimated for the time interval of 40-60 minutes. For comparison, total distribution volume (VT), specific distribution volume (VS) and BPND were estimated from the two-tissue-compartment model (2TCM) using AIF. Test-retest reliability was then assessed for all outcome measures. Convergent validity was examined by correlating all measures derived without an AIF to those derived using 2TCM.\n\nResultsTest-retest reliability for BPND estimates were poor (80% of all regional ICCs<0.5). SUVs showed, on average, moderate reliability. BPND estimates derived without an AIF were not correlated with VT, VS or BPND from the 2TCM (all R2<12%). SUVs were not correlated with any other outcome (all R2<9%).\n\nDiscussionBPND estimated using cerebellum or SVCA as reference input showed poor reliability and little to no convergent validity with outcomes derived using an AIF. SUVs showed moderate reliability but no convergent validity with any other outcome. Caution is warranted for interpreting patient-control comparisons employing (R)-[11C]PK11195 outcome measures obtained without an AIF.

neuroscience↗

bioRxiv2018

We need to talk about reliability: Making better use of test retest studies for study design and interpretation

Positron emission tomography (PET), along with many other fields of clinical research, is both timeconsuming 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.

neuroscience↗

bioRxiv2017

PET studies of the glial cell marker TSPO in psychosis patients - a meta-analysis using individual participant data

BackgroundAccumulating evidence suggests that the immune system may be an important target for new treatment approaches in schizophrenia. Positron emission tomography (PET) and radioligands binding to the translocator protein (TSPO), which is expressed in glial cells in brain including immune cells, represents a potential method for patient stratification and treatment monitoring. This study examined if patients with first episode psychosis and schizophrenia had altered TSPO levels as compared to healthy control subjects.\n\nMethodsPubMed was searched for studies comparing patients with psychosis to healthy controls using second-generation TSPO radioligands. The outcome measure was distribution volume (VT), an index of TSPO levels, in frontal cortex (FC), temporal cortex (TC) and hippocampus (HIP). Bayes factors (BF) were applied to examine the relative support for higher, lower or no-change of TSPO levels in patients as compared to healthy controls.\n\nResultsFive studies, with 75 patients with first-episode psychosis or schizophrenia and 77 healthy control subjects were included. BF showed strong support for lower patient VT relative to no-change (all BF>32) or relative to an increase (all BF>422) in all brain regions. From the posterior distributions, mean patient-control differences in standardized VT values were -0.48 for FC (95% credible interval (CredInt)=-0.88 to -0.09), -0.47 for TC (CredInt=-0.87 to -0.07) and -0.63 for HIP (CredInt=-1.00 to -0.25).\n\nDiscussionThe observed reduction of TPSO in compared to control subjects in patients may correspond to altered function or lower density of brain immune cells. Future studies should focus on investigating the underlying biological mechanisms and their relevance for treatment.

immunology↗

bioRxiv2017

Serotonin 5-HT1A receptor binding and self-transcendence in healthy control subjects - a replication study using Bayesian hypothesis testing

ObjectiveA 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.\n\nMethodsRegional 5-HT1A receptor binding potential (BPND) was examined in 50 healthy male subjects using PET with the radioligand [11C]WAY100635. 5-HT1A availability 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-HT1A BPND in frontal cortex, hippocampus and raphe nuclei were examined by calculation of default correlation Bayes factors (BFs) and replication BFs.\n\nResultsThere 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.\n\nConclusionWe 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.

neuroscience↗

bioRxiv2017

Is dopamine D1 receptor availability related to social behavior? A positron emission tomography replication study

BackgroundAssociations between dopamine receptor levels and pro- and antisocial behavior have previously been demonstrated in human subjects using positron emission tomography (PET) and self-rated measures of personality traits. So far, only one study has focused on the D1-dopamine receptor (D1-R), [fi]nding a positive correlation with the trait social desirability, which is characterized by low dominant and high af[fi]liative behavior, while physical aggression showed a negative correlation. The aim of the present study was to replicate these previous [fi]ndings using a new independent sample of subjects.\n\nMethodsTwenty-six healthy males were examined with the radioligand [11C]SCH-23390, and completed the Swedish universities Scales of Personality (SSP) which includes measures of social desirability and physical trait aggression. The simpli[fi]ed reference tissue model with cerebellum as reference region was used to calculate BPND values in the whole striatum and limbic striatum. The two regions were selected since they showed strong association between D1-R availability and personality scores in the previous study. Pearsons correlation coef[fi]cients and replication Bayes factors were then employed to assess the replicability and robustness of previous results.\n\nResultsThere were no signi[fi]cant correlations (all p values > 0.3) between regional BPND values and personality scale scores. Replication Bayes factors showed strong to moderate evidence in favor no relationship between D1-receptor availability and social desirability (striatum BF01 = 12.4; limbic striatum BF01 = 7.2) or physical aggression scale scores (limbic striatum BF01 = 3.3), compared to the original correlations.\n\nDiscussionWe could not replicate the previous [fi]ndings of associations between D1-R availability and either pro- or antisocial behavior as measured using the SSP. Rather, there was evidence in favor of failed replications of associations between BPND and scale scores. Potential reasons for these results are restrictive variance in both PET and personality outcomes due to high sample homogeneity, or that the previous [fi]ndings were false positives.

neuroscience↗

bioRxiv2017

The Readability Of Scientific Texts Is DecreasingOver Time

Clarity and accuracy of reporting are fundamental to the scientific process. The understandability of written language can be estimated using readability formulae. Here, in a corpus consisting of 707 452 scientific abstracts published between 1881 and 2015 from 122 influential biomedical journals, we show that the readability of science is steadily decreasing. Further, we demonstrate that this trend is indicative of a growing usage of general scientific jargon. These results are concerning for scientists and for the wider public, as they impact both the reproducibility and accessibility of research findings.

scientific communication and education↗