Abstract

MethodAim of this study was to automatically select a suitable pseudo-reference brain region for the accurate, non-invasive quantification of neuroinflammation in a rat brain using dynamic [18F]DPA-714 PET imaging.ProceduresA supervised clustering analysis approach considering three kinetic classes (SVCA3) was used to select an appropriate pseudo-reference brain region. This pseudo-reference region was determined by selecting only brain regions with low specific tracer uptake (SVCA3low) or by taking into account all brain regions and weighting each brain region with the corresponding fraction of low specific binding (SVCA3wlow). Both SVCA3 approaches were evaluated in an animal model of neuro-inflammation induced by lipopolysaccharide injection in the right striatum of female Wistar rats. For this study setup, a population of 25 female Wistar rats received a dynamic PET scan after injection of ~ 60 MBq [18F]DPA-714. Animals were scanned at baseline (n = 3) and at different time points after inducing neuroinflammation: 1 day (n = 3), 3 days (n = 12), 7 days (n = 4), and 30 days (n = 3). Binding potential (BP) values using a simplified reference tissue model (SRTM) and the contralateral striatum as pseudo-reference region were considered as a reference method (BPL STR) and compared with SRTM BP values using a pseudo-reference region obtained by either the SVCA3low or SVCA3wlow approach for both a 90- and 120-min acquisition time interval.ResultsFor the right striatum, SRTM BP values using a SVCA3low- or SVCA3wlow-based pseudo-reference region demonstrated a strong and highly significant correlation with SRTM BPL STR values (Spearman r ≥ 0.89, p < 0.001). For the SVCA3low approach, Friedman tests revealed no significant difference with SRTM BPL STR values for a 120-min acquisition time while small but signification differences were found for a 90-min acquisition time (p < 0.05). For the SVCA3wlow approach, highly signification differences (p < 0.001) were found with SRTM BPL STR values for both a 90- and 120-min acquisition time interval.ConclusionsA SVCA3 approach using three kinetic classes allowed the automatic selection of pseudo-reference brain regions with low specific tracer binding for accurate and non-invasive quantification of rat brain PET imaging using [18F]DPA-714. A shorter acquisition time interval of 90 min can be considered with only limited impact on the SVCA3-based selection of the pseudo-reference brain regions.

Highlights

  • Positron emission tomography (PET) imaging of neuroinflammation has proven to be a valuable tool for studying and quantifying microglial activation in brain tissue associated with both healthy aging and neurodegenerative disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington’s disease (HD) [1,2,3]

  • For the SVCA3low approach, Friedman tests revealed no significant difference with simplified reference tissue model (SRTM) BPL Right striatum (STR) values for a 120-min acquisition time while small but signification differences were found for a 90-min acquisition time (p < 0.05)

  • A Supervised clustering analysis using three kinetic classes (SVCA3) approach using three kinetic classes allowed the automatic selection of pseudo-reference brain regions with low specific tracer binding for accurate and non-invasive quantification of rat brain PET imaging using [18F]DPA-714

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Summary

Introduction

PET imaging of neuroinflammation has proven to be a valuable tool for studying and quantifying microglial activation in brain tissue associated with both healthy aging and neurodegenerative disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington’s disease (HD) [1,2,3]. Instead of applying appropriate weighting and including all PET voxels, this approach used a minimal threshold for the SVCA weights representing the fraction of non-displaceable tracer binding and took into account only PET voxels with low specific uptake As such, it showed a high correlation with the validated two-tissue compartment plasma input model while being more robust and more accurate than a reference tissue model using only the cerebellar gray matter as reference tissue. In terms of preclinical SVCA for dynamic [18F]DPA-714 PET imaging of neuroinflammation in the rat brain, Sridharan et al [15] evaluated a SVCA implementation with three kinetic classes representing “activated tissue,” “normal tissue,” and “tissue with intermediate binding,” respectively

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