Dataset Information

MGluR₅ and GABA_A receptor-specific parametric PET atlas construction-PET/MR data processing pipeline, validation, and application.

ABSTRACT: The glutamate and γ-aminobutyric acid neuroreceptor subtypes mGluR₅ and GABA_A are hypothesized to be involved in the development of a variety of psychiatric diseases. However, detailed information relating to their in vivo distribution is generally unavailable. Maps of such distributions could potentially aid clinical studies by providing a reference for the normal distribution of neuroreceptors and may also be useful as covariates in advanced functional magnetic resonance imaging (MR) studies. In this study, we propose a comprehensive processing pipeline for the construction of standard space, in vivo distributions of non-displaceable binding potential (BP_ND ), and total distribution volume (V_T ) based on simultaneously acquired bolus-infusion positron emission tomography (PET) and MR data. The pipeline was applied to [¹¹ C]ABP688-PET/MR (13 healthy male non-smokers, 26.6 ± 7.0 years) and [¹¹ C]Flumazenil-PET/MR (10 healthy males, 25.8 ± 3.0 years) data. Activity concentration templates, as well as V_T and BP_ND atlases of mGluR₅ and GABA_A , were generated from these data. The maps were validated by assessing the percent error δ from warped space to native space in a selection of brain regions. We verified that the average δ_ABP = 3.0 ± 1.0% and δ_FMZ = 3.8 ± 1.4% were lower than the expected variabilities σ of the tracers (σ_ABP = 4.0%-16.0%, σ_FMZ = 3.9%-9.5%). An evaluation of PET-to-PET registrations based on the new maps showed higher registration accuracy compared to registrations based on the commonly used [¹⁵ O]H₂ O-template distributed with SPM12. Thus, we conclude that the resulting maps can be used for further research and the proposed pipeline is a viable tool for the construction of standardized PET data distributions.

SUBMITTER: Kaulen N

PROVIDER: S-EPMC8996359 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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mGluR5 and GABAA receptor-specific parametric PET atlas construction-PET/MR data processing pipeline, validation, and application.

Kaulen Nicolas N Rajkumar Ravichandran R Régio Brambilla Cláudia C Mauler Jörg J Ramkiran Shukti S Orth Linda L Sbaihat Hasan H Lang Markus M Wyss Christine C Rota Kops Elena E Scheins Jürgen J Neumaier Bernd B Ermert Johannes J Herzog Hans H Langen Karl-Joseph KJ Lerche Christoph C Shah N Jon NJ Veselinović Tanja T Neuner Irene I

Human brain mapping 20220125 7

The glutamate and γ-aminobutyric acid neuroreceptor subtypes mGluR5 and GABAA are hypothesized to be involved in the development of a variety of psychiatric diseases. However, detailed information relating to their in vivo distribution is generally unavailable. Maps of such distributions could potentially aid clinical studies by providing a reference for the normal distribution of neuroreceptors and may also be useful as covariates in advanced functional magnetic resonance ...[more]

PMID: 35076125

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Project description:Magnetic resonance imaging (MRI) is a key technology in multimodal animal studies of brain connectivity and disease pathology. In vivo MRI provides non-invasive, whole brain macroscopic images containing structural and functional information, thereby complementing invasive in vivo high-resolution microscopy and ex vivo molecular techniques. Brain mapping, the correlation of corresponding regions between multiple brains in a standard brain atlas system, is widely used in human MRI. For small animal MRI, however, there is no scientific consensus on pre-processing strategies and atlas-based neuroinformatics. Thus, it remains difficult to compare and validate results from different pre-clinical studies which were processed using custom-made code or individual adjustments of clinical MRI software and without a standard brain reference atlas. Here, we describe AIDAmri, a novel Atlas-based Imaging Data Analysis pipeline to process structural and functional mouse brain data including anatomical MRI, fiber tracking using diffusion tensor imaging (DTI) and functional connectivity analysis using resting-state functional MRI (rs-fMRI). The AIDAmri pipeline includes automated pre-processing steps, such as raw data conversion, skull-stripping and bias-field correction as well as image registration with the Allen Mouse Brain Reference Atlas (ARA). Following a modular structure developed in Python scripting language, the pipeline integrates established and newly developed algorithms. Each processing step was optimized for efficient data processing requiring minimal user-input and user programming skills. The raw data is analyzed and results transferred to the ARA coordinate system in order to allow an efficient and highly-accurate region-based analysis. AIDAmri is intended to fill the gap of a missing open-access and cross-platform toolbox for the most relevant mouse brain MRI sequences thereby facilitating data processing in large cohorts and multi-center studies.

Project description:PurposeTumor hypoxia and other microenvironmental factors are key determinants of treatment resistance. Hypoxia positron emission tomography (PET) and functional magnetic resonance imaging (MRI) are established prognostic imaging modalities to identify radiation resistance in head-and-neck cancer (HNC). The aim of this preclinical study was to develop a multi-parametric imaging parameter specifically for focal radiotherapy (RT) dose escalation using HNC xenografts of different radiation sensitivities.MethodsA total of eight human HNC xenograft models were implanted into 68 immunodeficient mice. Combined PET/MRI using dynamic [18F]-fluoromisonidazole (FMISO) hypoxia PET, diffusion-weighted (DW), and dynamic contrast-enhanced MRI was carried out before and after fractionated RT (10 × 2 Gy). Imaging data were analyzed on voxel-basis using principal component (PC) analysis for dynamic data and apparent diffusion coefficients (ADCs) for DW-MRI. A data- and hypothesis-driven machine learning model was trained to identify clusters of high-risk subvolumes (HRSs) from multi-dimensional (1-5D) pre-clinical imaging data before and after RT. The stratification potential of each 1D to 5D model with respect to radiation sensitivity was evaluated using Cohen's d-score and compared to classical features such as mean/peak/maximum standardized uptake values (SUVmean/peak/max) and tumor-to-muscle-ratios (TMRpeak/max) as well as minimum/valley/maximum/mean ADC.ResultsComplete 5D imaging data were available for 42 animals. The final preclinical model for HRS identification at baseline yielding the highest stratification potential was defined in 3D imaging space based on ADC and two FMISO PCs ([Formula: see text]). In 1D imaging space, only clusters of ADC revealed significant stratification potential ([Formula: see text]). Among all classical features, only ADCvalley showed significant correlation to radiation resistance ([Formula: see text]). After 2 weeks of RT, FMISO_c1 showed significant correlation to radiation resistance ([Formula: see text]).ConclusionA quantitative imaging metric was described in a preclinical study indicating that radiation-resistant subvolumes in HNC may be detected by clusters of ADC and FMISO using combined PET/MRI which are potential targets for future functional image-guided RT dose-painting approaches and require clinical validation.

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MGluR₅ and GABA_A receptor-specific parametric PET atlas construction-PET/MR data processing pipeline, validation, and application.

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mGluR<sub>5</sub> and GABA<sub>A</sub> receptor-specific parametric PET atlas construction-PET/MR data processing pipeline, validation, and application.

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Dataset Information

MGluR5 and GABAA receptor-specific parametric PET atlas construction-PET/MR data processing pipeline, validation, and application.

Publications

mGluR<sub>5</sub> and GABA<sub>A</sub> receptor-specific parametric PET atlas construction-PET/MR data processing pipeline, validation, and application.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

MGluR₅ and GABA_A receptor-specific parametric PET atlas construction-PET/MR data processing pipeline, validation, and application.