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The acquisition of dynamic positron emission tomography (PET) data and reconstruction into time frames allows for metabolic brain connectivity analysis at the single-subject level. We describe a method to acquire [18F]FDG dynamic PET data of the rat brain and obtain a connectivity matrix through the extraction of time-activity curves of volumes of interest.
To this day, metabolic brain connectivity is mostly studied on a group level through the acquisition of static positron emission tomography (PET) data of multiple subjects. Our research groups are currently studying changes in metabolic connectivity across multiple time points following an intracerebral hemorrhage on an intrasubject level in rats. To investigate intrasubject metabolic brain connectivity, temporal information of the tracer uptake in different brain regions is required, which can be achieved through dynamic PET. In this publication, we give a detailed description of our data acquisition and analysis protocol.
Dynamic PET data of the rat brain are acquired on a dedicated preclinical PET system using 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) as tracer. The tracer is injected intravenously as a bolus at the start of the PET scan. During the 60 min acquisition, animals are sedated with medetomidine.
After acquisition, the PET data are reconstructed into thirty 2 min time frames using an iterative reconstruction algorithm (Maximum-Likelihood Expectation-Maximization). A parcellated atlas consisting of multiple volumes of interest (VOIs) is used to extract time-activity curves of each VOI, which are then used to calculate the Pearson correlation coefficient between each pair of VOIs.
This dynamic PET protocol enables the assessment of metabolic connectivity differences between two single scans, rather than between groups of scans. This approach allows for the study of changes in metabolic connectivity within a single subject across different time points, or for the comparison of an individual's metabolic connectivity to a normal database. Such comparisons could be useful for tracking disease progression or aiding in the diagnosis of neurological disorders characterized by disrupted communication between brain regions, such as epilepsy or dementia.
Positron emission tomography (PET) is a molecular imaging technique commonly used in research as well as in clinical settings. Due to the development of various PET tracers, PET can be used to study disease pathophysiology and monitor disease progression and response to treatments1. One of the most widely used radiotracers is 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG), which allows for the imaging of glucose metabolism, indicative of cellular activation. It is utilized in oncology for diagnosis, staging, and prognosis; in neurology, commonly in the context of neurodegenerative diseases such as dementia; and in cardiology, to diagnose conditions like sarcoidosis, to name just a few examples1,2,3.
Assessment of metabolic brain connectivity, obtained from [18F]FDG PET data, refers to the functional relationships between tracer uptake in different brain regions. This approach enables the computation of a "connectivity matrix" by selecting a set of brain regions, which can provide insights into how different parts of the brain interact and function together. This type of analysis is particularly useful for studying brain function in health and disease, including conditions such as dementia, epilepsy, and other neurological disorders4,5.
The first study assessing metabolic brain connectivity already dates back to the 1980s6, but researchers mainly explored structural brain connectivity, also known as the "connectome"7, by means of diffusion-weighted magnetic resonance imaging (DW-MRI). Furthermore, functional connectivity using techniques such as functional MRI (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) has been widely investigated for multiple decades8,9.
Recently, there is a regained interest in studying metabolic brain connectivity using [18F]FDG PET, not only on its own, but also in combination with other forms of brain connectivity10. However, due to the inherent "static" nature of PET images (in contrast to, e.g., functional MRI), the vast majority of brain network PET-based results are based on group-level analysis, where correlations between brain regions are calculated at the intersubject level. This limitation makes a within-subject analysis of PET images impossible, which is essential for longitudinal studies that can track changes over time within the same individual4. Therefore, the development of methods that allow single-subject analysis, such as dynamic PET-based molecular connectivity, is an important research direction in brain research investigating network disorders, since it opens the door to the use of molecular network analysis in clinical practice. Hence, dynamic PET data were used in our preclinical study.
Our research groups are currently conducting a study that examines changes in metabolic connectivity following an intracerebral hemorrhage on an intrasubject level across multiple time points using the rat collagenase model11. To investigate intrasubject metabolic brain connectivity, temporal information of the tracer uptake in different brain regions is required, which can be obtained through dynamic PET. In the following sections, we give a detailed description of the data acquisition and analysis protocol.
All procedures are in accordance with the European guidelines (directive 2010/63/EU), and the protocol was approved by the local Animal Ethical Committee of Ghent University (ECD 23/33). Twelve Sprague Dawley rats (six female, six male) were included in the study. Their PET scans were obtained using the following protocol at multiple time points ranging from 2 weeks before to 18 weeks after an induced intracerebral hemorrhage. At the time of the first scan, all animals were 18 weeks of age and the females weighed 244.8 Β± 10.1 g (mean Β± SD) while males weighed 363.6 Β± 13.3 g.
Ensure that radioactive materials are only worked with and handled by trained personnel. Keep the dose to staff and animals as low as reasonably achievable (ALARA).
1. Data acquisition
NOTE: See the Table of Materials for details about the preclinical PET imager and software used for data acquisition. The imager is a 45 detector (arranged in 5-rings) PET scanner covering an axial field of view (FOV) of 13 cm and a transaxial FOV of 7.6 cm, using LYSO crystals and SiPM detectors. The system shows a spatial resolution of 850 Β΅m, a sensitivity of 12%, and an energy resolution of 12.6%12. The following steps were written with this in mind.
2. Data reconstruction and quality check
NOTE: Using the hard- and software available in our study, all PET data were corrected for radionuclide decay, and acquired sinograms were reconstructed with the ordered subset expectation maximization 3-dimensional (OSEM-3D) algorithm using a 30% window around the 511 keV photopeak. The OSEM reconstruction software used its default settings of 10 subsets with 20 million events per subset. Images were reconstructed into a 192 x 192 x 384 transverse matrix with cubic image voxels of 0.4 mm. No attenuation correction was performed.
3. Data analysis
NOTE: The following steps 3.1 and 3.2 are performed in a biomedical software environment dedicated to quantification of PET data, using the Image Registration and Fusion Tool (PFUS) and the General Kinetic Modeling Tool (PKIN).
Once the scan is completed, the TAC of the detected rate during the acquisition can be investigated to check for a correct tracer injection and uptake. Figure 1 displays a TAC resulting from the whole FOV of the scanner after a successful tracer injection and acquisition (panel A), and a TAC resulting after a partially paravenous tracer injection (panel B). In the successful case, the count rate rises rapidly after tracer injection and reaches its peak within the first 4 min...
The protocol provided here guides users through the process of acquiring 1 h dynamic PET data using [18F]FDG as a tracer in rats. In the end, a correlation matrix of VOIs is obtained, which can be used to assess metabolic connectivity on a single-subject level. Experienced researchers may adjust the protocol to fit their specific needs at various points, for example, by using a different radiotracer, acquisition time, or time frame widths for image reconstructions, and selecting relevant VOIs in the data analy...
The authors have no conflicts of interest to disclose.
This work was supported by a research grant fromΒ the Flemish Research Foundation [G0A7422N].
Name | Company | Catalog Number | Comments |
Antisedan | Orion Pharma | Atipamezole hydrochloride 5 mg/mL | |
BD Micro-Fine+ insulin syringe 1 mL | BD | 324827 | 0.33 mm (29G) x 12.7 mm |
BD Microlance 3 Needles 30 G x 1/2" | BD | 304000 | 30 G x 1/2"; 0,3 x 13 mm |
BD Plastipak syringe 1 mL | BD | 303172 | for infusion pump |
BTPE-10 Polyethylene tubing | Instech | 0.11x.024in (.28x60mm) | |
Domitor | Orion Pharma | 1070499 | Medetomidine hydrochloride 1 mg/mL |
Fusion 100 infusion pump | Chemyx Inc. | 07100 | Newer model available: Fusion 100X |
Isoflutek 1000 mg/g | Alivira | Isoflurane | |
MOLECUBES Ξ²-CUBE with CUBEFLOW software | MOLECUBES NV | Preclinical PET scanner | |
PMOD Software version 4.4Β | Bruker Corporation | http://www.pmod.com; quantification of PET data | |
Saline | B. Braun | 394496 | NaCl 0.9% |
Vidisic eye gel | Vidisic | Carbomerum 980 2 mg/g |
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