Biology subjects

Tiger, M.

Publications and source records attributed to Tiger, M..

3 recordsLinked to original sources

bioRxiv2026

A Comparison and Validation of BIDS Apps for Preprocessing of UCB-J PET data

Background Preprocessing of Positron Emission Tomography (PET) data can be performed in many different ways and often involves manual steps, resulting in reduced comparability across analyses and pipelines. Apps working on PET data organized according to the Brain Imaging Data Structure (BIDS) seek to automate preprocessing, enabling the application of standardized pipelines that increase comparability and reproducibility. In this study, two of these apps were validated against in-house software. Methods Data were acquired in-house from 14 subjects who received placebo in a randomized controlled clinical trial and underwent two PET measurements using the radioligand [11C]UCB-J. Preprocessing was performed with two BIDS Apps, PETPrep and PICNIC, as well as an in-house software. Arterial blood data were processed using Bloodstream, and [11C]UCB-J total distribution volume (VT) values were calculated using a 1-tissue compartment model using kinfitr. Results VT values for all 9 regions of interest were highly consistent across tools with estimates of Intraclass Correlation Coefficients for consistency ranging between 0.94 and >0.99, although bias between tools measured as signed Percentage Difference was present, ranging from 0.1% - 10.2% depending on the region. Tools performed similarly on test-retest metrics. We observed a negative test-retest bias in [11C]UCB-J VT of unknown origin. Conclusion BIDS Apps produce reliable and consistent results when compared both with each other and with an in-house software optimized for processing and analysis of our in-house data. Region-dependant bias between tools warrants caution for direct comparison of VT between tools. The usage of these apps could enable better comparability across publications

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↗

bioRxiv2019

Validity and reliability of extrastriatal raclopride binding quantification in the living human brain

[11C]raclopride is a well established PET tracer for the quantification of dopamine 2/3 receptors (D2/3R) in the striatum. Outside of the striatum the receptor density is up to two orders of magnitude lower. In contrast to striatal binding, the characteristics of extrastriatal [11C]raclopride binding quantification has not been thoroughly described. Still, binding data for e.g., neocortex is frequently reported in the scientific literature. Here we evaluate the validity and reliability of extrastriatal [11C]raclopride binding quantification. Two sets of healthy control subjects were examined with HRRT and [11C]raclopride: i) To assess the validity of extrastriatal [11C]raclopride binding estimates, eleven subjects were examined at baseline and after dosing with quetiapine, a D2/3R antagonist. ii) To assess test-retest repeatability, nine subjects were examined twice. Non displaceable binding potential (BPND) was quantified using the simplified reference tissue model. Quetiapine dosing was associated with decrease in [11C]raclopride BPND in temporal cortex (18{+/-}17% occupancy) and thalamus (20{+/-}17%), but not in frontal cortex. Extrastriatal occupancy was lower than in putamen (51{+/-}4%). The mean absolute variation was 4-7% in the striatal regions, 17% in thalamus, and 13-59% in cortical regions. Our data indicate that [11C]raclopride PET is not a suitable tool for D2/3R binding quantification in extrastriatal regions.

neuroscience↗