Evaluation of [13N]ammonia Positron Emission Tomography for Quantifying Glutamine Synthetase Activity in the Human Brain.

Purpose The conversion of synaptic glutamate to glutamine in astrocytes by glutamine synthetase (GS) is critical to maintaining healthy brain activity and may be disrupted in several brain disorders. As the GS catalysed conversion of glutamate to glutamine requires ammonia, we evaluated whether [ 13 N]ammonia positron emission tomography (PET) could reliability quantify GS activity in humans. Methods In this test-retest study, eight healthy volunteers each received two dynamic [ 13 N]ammonia PET scans on the morning and afternoon of the same day. Each [ 13 N]ammonia scan was preceded by a [ 15 O]water PET scan to account for effects of cerebral blood ow (CBF). Results Concentrations of radioactive metabolites in arterial blood were available for both sessions in ve of the eight subjects. Our results demonstrated that kinetic modelling was unable to reliably distinguish estimates of the kinetic rate constant k 3 (related to GS activity) from K 1 (related to [ 13 N]ammonia brain uptake), and indicated a non-negligible back-ux of [ 13 N]ammonia to blood (k 2 ). Model selection favoured a reversible one tissue compartmental model, and [ 13 N]ammonia K 1 correlated reliably (r 2 = 0.72–0.92) with [ 15 O]water CBF. Conclusion The [ 13 N]ammonia PET method was unable to reliably estimate GS activity in the human brain but may provide an alternative index of


Introduction
The metabolism of glutamate to glutamine by the enzyme glutamine synthetase (GS) is a key process for maintaining healthy synaptic function. GS (encoded by the gene glutamate-ammonia ligase, Glul) is predominantly expressed in astrocytes [1] and converts glutamate released into the synapse during neurotransmission to glutamine, for recycling to neuronal glutamate and gamma-amino butyric acid (GABA). GS is therefore critical to the homeostasis of excitatory and inhibitory neurotransmission and normal brain activity [2,3]. This process may be compromised in several brain disorders [3], and neuroimaging techniques to assess GS activity in vivo could have wide-ranging research or clinical impact.
Abnormalities in GS have been most clearly linked to epileptogenesis [4]. Very rare inherited de cits in GS are associated with neonatal seizures [5,6]. Pharmacological inhibition of GS [2] or genetic GS de ciency [7] can be used as animal models of epilepsy, and there are marked reductions in GS in areas of hippocampal tissue resected from patients with mesial temporal lobe epilepsy [8,9]. Furthermore, regional differences in the level of GS protein, mRNA expression or activity have been detected in postmortem brain tissue across many psychiatric and neurological disorders. The results of these studies, summarised in Supplementary Table 1, suggest that in addition to applications in epilepsy, GS imaging could be important in understanding or predicting schizophrenia, depression or suicidal behaviour, amongst other disorders.
GS is also the main pathway for metabolism of brain ammonia, which is required for the conversion of glutamate of glutamine [10]. This raises the possibility that radiolabelled ammonia in combination with positron emission tomography (PET) may be utilised to measure brain GS activity. [ 13 N]Ammonia PET is used clinically to assess myocardial perfusion ("blood ow") and has been applied in research studies examining abnormalities in brain ammonia uptake associated with liver disease [11][12][13][14][15][16][17][18][19][20] and in the diagnosis of brain tumours [21].
The aim of this study was to evaluate [ 13 N]ammonia as a PET tracer for quanti cation of brain GS activity. This evaluation requires kinetic modelling of the dynamic concentrations of 13 N-derived radioactivity in the brain and arterial blood following radiotracer injection, in an attempt to reliably extract rate constants indexing GS activity, as the rate of conversion of [ 13 N]ammonia to [ 13 N]glutamine, from the signal relating to [ 13 N]ammonia brain uptake and clearance (see Supplement Fig. 1). To do this we sought to acquire two [ 13 N]ammonia scans (test and re-test) in eight healthy volunteers. In order to account for effects of cerebral blood ow (CBF), we additionally acquired test and re-test [ 15  At the start of the PET scan visit, a cannula was inserted in a vein in the arm for radiotracer injection. After application of local anaesthetic, an arterial line was inserted into the radial artery and ushed every 20 minutes with heparinised saline (20 IU/mL of heparin in sterile 0.9% w/v sodium chloride) until removal at the end of PET scanning. Just before the start of each scanning session, 6 mL of arterial blood was taken to measure baseline blood ammonia levels.
Participants were positioned in the PET-CT scanner, with head movement minimised via a moulded headrest and head strap. The arterial line was connected to an automated blood sampling system (Allogg ABSS, www.allogg.se, Sweden). CT scout (0.015 mSV) and CT attenuation correction (0.05 mSv) scans were acquired. 15 O-water (target dose at time of administration: 960 MBq, 1.10 mSv) was injected through the venous cannula over 10 seconds. PET image acquisition started 10 seconds before the start of [ 15 O]water injection and continued for a total of 5 minutes. Arterial blood collection via the uid analyser commenced 70 seconds before [ 15 O]water injection and 60 seconds before the start of scan acquisition and continued for the 5 minute scan duration, to a total of 25 mL. Additionally, a single 2 mL arterial blood sample was manually drawn at 3 minutes into the scan. Arterial blood collection via the uid analyser commenced 70 seconds before [ 13 N]ammonia injection and 60 seconds before the start of scan acquisition and continued for 15 minutes, to a total of 75 mL. In addition, 6 manual arterial blood samples of 10 mL each were drawn at 5-minute intervals during the

Ammonia and metabolite analysis
Levels of non-radioactive ammonia in arterial blood were determined from samples collected before radiotracer collection. These samples were collected in K-EDTA tubes (pre-tested and con rmed as ammonia-free) and transported on ice within 20 minutes of collection to the hospital laboratory for standard analysis.
Unless stated otherwise, all water used in these metabolite analyses was passed through ion exchange resin and 0.22 µm membrane ltered to produce water with a speci c resistance of 18.2 micro-ohms using a Milli-Q Ultrapure water puri cation system manufactured by Millipore Corporation.
Plasma was separated from whole blood by centrifuging at 3000 x g for 3 minutes at room temperature (RT). Levels of radioactive metabolites in plasma were estimated through solid phase extraction, based on the methods of Keiding et al. [17] In preparation for solid phase extraction, one cartridge was lled with 0.6 mL Dowex 1 × 8-50 anion exchange resin and pre-treated with 6 mL 0.75 M sodium acetate solution. A second cartridge, connected in series via an Agilent Bond Elut adapter, was lled with 0.35 mL AG50W-X8 cation exchange resin and pretreated with 3.5 mL 0.8 M Tris-acetate solution. The third cartridge which connected to second cartridge in same way via adapter was lled with 0.35 mL AG50W-X8 cation exchange resin and pretreated with 3.5 mL Milipore water.
For extraction, 0.5 mL of the supernatant protein-free plasma was loaded onto the rst cartridge followed by washing with 3 mL of Milipore water through the cartridge stack and ushed with 10 mL of air. The eluent from the rst cartridge passed through second cartridge and third cartridge, which were subsequently washed with 7 mL of Milipore water followed by 10 mL of air. The third cartridge was washed with 7 ml Milipore water and followed by 10 mL of air. All eluate were collected with a 25 mL pot. With this method, the radioactivity measured on the rst cartridge corresponded to [13N A 10-detector gamma-counter (Wizard2 2470, Perkin-Elmer) cross-calibrated to the PET scanner was used to measure radioactivity concentrations in whole blood (0.5 mL per sample), plasma (0.5 mL per sample) and metabolite fractions (3 mL for urea and full cartridge contents for other fractions). All samples were counted for 3 minutes on a xed energy window (358-664 keV) with software cross-talk correction and in-house volumetric geometry correction. The samples and cartridges were corrected for weight to calculate the total radioactivity of blood sample analysed. Image processing [ 15 O]water PET list mode data was unlisted to 26 frames (1 × 10 sec, 10 x sec, 6 × 10 sec and 9 × 20 sec).
Frame-by-frame motion correction was performed on dynamic PET data using the NAC image to derive the rigid-body motion parameters. Regions of interest (ROI) were de ned by the "Hammers_mith Atlas" [24,25] (83 regions) in MNI stereotaxic space. Non-linear warps from MNI to subject space were de ned using the uni ed segmentation algorithm [26] in SPM8 (www. l.ucl.ac.uk/spm) on each subject's T1 MRI.
Resliced atlases for each subject were then co-registered to a summed PET image for each PET scan via the MRI.

Kinetic analysis
For both the [ 15 O]water and [ 13 N]ammonia scans, time activity curves (TACs) were extracted from the coregistered Hammers_mith atlas [24,25]. Using each subject's co-registered probabilistic grey matter mask from the segmented MRI, TAC's were extracted using the mean voxel value within the region, or a weighted mean for cortical regions. This resulted in values from 77 individual ROI's. A whole-brain grey matter weighted mean TAC was also de ned Ventricular and white matter regions were ignored.
For the [ 13 N]ammonia scans, arterial whole blood input functions were created from decay-corrected continuous blood samples with manual samples used for cross-calibration to scanner and interpolation to scan end. Plasma-over-blood ratio was calculated as the mean of the manual sample ratios for each subject. Parent fraction data (ratio of [ 13 N]ammonia to total 13 N activity) was tted to a biexponential curve for each subject. [11] A population parent fraction function was created by tting a biexponential curve to all subject data. Parent plasma input functions (i.e. [ 13 N]ammonia in plasma only) for the kinetic modelling were created by multiplying the whole-blood input function by plasma-over-blood ratios and the biexponential curve tted to the parent fractions. Whole blood and parent plasma input functions were delay corrected by visually matching the blood rise with the grey matter TAC, with appropriate decay correction.
Regional cerebral blood ow (CBF) was calculated from the [ 15 O]water TACs using a 5-parameter free diffusion model [27]. In brief, a nonlinear least squares t method was used to estimate the 5 parameters of this 1-tissue compartment model: K 1 (CBF), k 2 (wash-out), blood fraction, and delay and dispersion of the blood curve between the brain and sampling point.
Ammonia is a freely diffusible tracer and as such has been used to quantify perfusion in myocardium [28] and brain. [29] Though ammonia is rapidly trapped in tissue, in order to index GS activity, the kinetic parameters describing the uptake of [ 13 N]ammonia by GS must be distinguishable from those re ecting CBF. The model chosen for primary analysis of [ 13 N]ammonia scans was an irreversible two tissue compartment model (2TCM) as used in Keiding et al., 2006 [11]. To con rm the model choice a nonlinear spectral analysis approach was used to identify the most appropriate tissue uptake model [30]. In brief, the data was tted to a number of candidate PET compartmental models with increasing numbers of parameters. In this case, a reversible 2TCM [Supplement Fig. 1] was the most complex model considered, with increasingly simpler models de ned by setting k 4 , k 3 , k 2 to zero (i.e. 4 candidate models). The blood fraction contributing to the TAC for each region was also included as a free parameter.
Each compartmental model was tted using a weighted least squares method with residual weights for each frame determined by frame duration and radioactive decay: , where is the decay rate constant, and and are the frame duration and frame mid-point time respectively for frame . Model t was assessed using the Akaike Information Criterion. [31] Additional macroparameters from the 15 O and 13 N scans were calculated to compare with the results of Keiding et al (2006) [11]. PS BBB ( ow independent permeability-surface area product of the blood brain barrier to [ [32]. PS met ( ow-independent permeability-surface area product of conversion of ammonia to intracellular glutamine) was calculated as Finally, the cerebral metabolic rate of ammonia, CMRA, was calculated as where A is the measured concentration of endogenous ammonia in the blood.
Kinetic parameter repeatability between the test-retest scans was assessed using mean fractional difference (VAR), absolute fractional difference (AbsVAR), and intraclass correlation coe cient (ICC) using a two-way random model for consistency [33]. For 8 subjects, the threshold for a signi cantly positive ICC is 0.58 at the at the p < 0.05 level. VAR and AbsVAR were calculated for N subjects as a percentage: Image registration, TAC extraction, blood data processing, kinetic modeling and statistical analyses were performed in Matlab (www.mathworks.com). Data are presented as mean ± s.d. unless otherwise stated.

Participants
Eight volunteers (3 female) underwent all PET-CT and MRI scans. Age at scan was 25.0 ± 2.5 years.

Scan parameters
Ammonia blood levels immediately prior to scan session one were 24.5 ± 5.7 µmol/L and prior to session two 23.9 ± 4.8 µmol/L. No signi cant difference was found between scan session within-subject (0.6 ± 7.9 µmol/L; range − 12.0-11.0    (Fig. 1). Data from these 5 subjects were used to determine the [ 13 N]ammonia kinetic model parameter ts and repeatability.
[ 13 N]ammonia PET images showed regional variation in signal intensity, with highest uptake in the occipital lobes, posterior cingulate gyri, putamina, thalami and cerebella Table 1 Table 2). This is likely to be due to the even larger variation in k 2 values across subjects rather than re ecting strong test-retest repeatability. Test-retest variation in the trapping rate constant k 3 was extremely poor (Table 2). Based on estimated parameter errors from the kinetic model tting, k 3 was not signi cantly greater than zero in 728/780 regions (compared to 11/780 and 337/780 for K 1 and k 2 respectively). The values obtained for Kmet, PSmet and CMRA showed quite high fractional differences across test and retest scans (Supplement Table 2).
Model selection: Considering the ten scans in subjects 1-5, grey matter TAC ts were good for all models with k 2 > 0 (Fig. 2). In contrast to the trapping models, a reversible one tissue compartment model (1TCM, k 3 & k 4 = 0) was the favoured model (majority vote) for grey matter (8/10 scans) and across 74/77 atlas regions (Supplement Fig. 3). Two subjects each had one scan mostly favouring models with k 3 > 0, though these were not replicable in the individuals' other scan. Of all 780 TACs (ten scans x 77 ROI and grey matter) 649 (83%) favoured the 1TCM. The repeatability metrics for K 1 and k 2 calculated using the 1TCM (Table 3) were similar to those calculated using the 2TCM (Table 2). K 1 continued to show low or negative ICC values and fractional differences of approximately 10%; k 2 showed higher ICC but larger inter-subject variability and test-retest differences. estimates from 1TCM were most highly correlated with CBF in all 10 scan pairs, with r 2 values ranging from 0.72 to 0.92 (Fig. 3). In each of the 10 scan pairs, r 2 values were second highest with the 2TCM K 1 (r 2 between 0.56 to 0.85) and poorest and most variable with K met (r 2 between 0.2 to 0.85). However, these strong within-subject correlations were not replicated across subjects within region: no region revealed a signi cant positive correlation between CBF and K 1 or Kmet.

Discussion
This study evaluated [ 13 N]ammonia PET as an in vivo method to estimate the rate of conversion of glutamate to glutamine by the enzyme glutamine synthetase (GS) in the human brain. We were able to acquire full datasets comprising two [ 13 N]ammonia (test and retest) scans, two [ 15 O]water scans and corresponding arterial input functions in ve subjects, each on a single day. Kinetic modelling in these subjects was unable to reliably estimate the rate constant relating to GS activity (k 3 ) from that related to [ 13 N]ammonia brain uptake (K 1 ) and indicated non-negligible back-ux of [ 13 N]ammonia from the brain to the blood. In addition, comparison of K1 estimates with [ 15 O]water CBF across brain regions and withinsubjects found that these measures were highly correlated and of comparable reliability. Together these results indicate that the applied [ 13 N]ammonia PET method is unable to quantify GS activity in the human brain, and instead may principally index CBF.
Studies in experimental animals have indicated [ 13 N]ammonia PET might be able to index GS activity, as irreversible blockade of GS with methionine sulfoximine (MSO) decreases the brain [ 13 N]ammonia signal.
[ 34] In kinetic modelling of dynamic [ 13 N]ammonia PET images of the human brain, GS activity would be captured by the rate constant k 3 in an irreversible two tissue compartment model. Using this model, our analysis returned values for k 3 that were highly variable within subjects, as well as between subjects and across grey matter regions. In most instances, k 3 values were also too low to be estimated compared to the estimated error. A previous [ 13 N]ammonia study in subjects with cirrhosis and healthy volunteers using similar methodology was also unable to provide estimates of k 3 [20]. While volume of distribution (K 1 /k 2 ) may potentially have provided a surrogate index of GS activity from a reversible model, we were also unable to reliably estimate k 2 . Overall, this indicates that [ 13 N]ammonia PET is unlikely to be a suitable method for measuring the rate of metabolism of glutamate to glutamine by GS in the human brain.
Although the question as to whether ammonia in the brain can diffuse into the blood has previously been debated [35], back-ux of [ 13 N]ammonia from brain to blood has now been demonstrated in healthy volunteers as well as subjects with cirrhosis [20,11,19]. Similar to these studies, our nding of small but positive values for [ 13 N]ammonia k 2 also indicate non-zero back-ux of unmetabolized [ 13 N]ammonia to blood. Consistent with this, the simplest irreversible model with a single tissue compartment and one rate constant, K 1 , showed the poorest t in nearly every dataset. The presence of non-negligible wash-out was also consistent with the plateau of the decay-corrected brain time-activity curves in conjunction with approximately 10% of parent tracer compound remaining in the arterial plasma at the end of the scan.
Patlak plots were nonlinear at late times, also indicating the presence of reversibility of the tracer. Our data as well as that of Goldbecker et al. [20]  (calculated using Patlak analysis) and CBF. The correlations between K 1 and CBF were qualitatively tighter for K 1 calculated from the one compared to two-tissue compartmental model, as would be expected given there are fewer parameters. Nonetheless the slope of the best t was not identical between subjects and scans (Fig. 3). Correlating CBF and K 1 within region, across subjects and scans, did not yield signi cant correlation. The source of this variance between subjects and scans is unclear. In rhesus monkeys, Phelps et al. [37] found non-linear relationships between K 1 (or speci cally extraction fraction, E) and CBF, over a wide range of CBF values. Our data indicate that a linear relationship between K 1 and CBF exists when CBF lies within the normal range investigated here.
Compared to the previous study of Keiding and colleagues [11] the values for [ 13 N]ammonia K 1 obtained in our study are approximately 40% lower. The estimates for permeability-surface area product values and CMRA were similar, while PS met estimates were also slightly lower in our study, which is consistent with lower K met found from the possibly unsuitable graphical method. As in Keiding et al., [11] we applied the Patlak method [32] to calculating K met which avoided replicating the poor identi cation of k 2 and k 3 values with an explicit calculation. Nonetheless the fractional differences for K met , PS met and CMRA between test and retest scans were high.
One limitation of our study was that full data for plasma time activity curves was unavailable in three of the eight subjects, who were therefore excluded from the main analysis. However, results remained similar when population parent plasma input functions were substituted for these individuals.
Repeatability may also have been limited by participant fatigue during the second scanning session, due to the technical complexity of the study and as subjects remained in the PET centre for an average of 3.5 hours between the start of the rst scan and end of the last. Although our CBF and K 1 ([ 13 N]ammonia) values were lower than in Keiding et al [11], inter-subject variances were comparable and the absolute values of CBF were in keeping with the variances seen between centres for quantitative PET studies (e.g. Fan et al [38]). The strong correlations observed between K 1 and CBF yielded variable slopes between subjects and scan pairs. It is unclear how much is attributable to a true representation of physiology or to unforeseen errors from the challenges of complex timing with short half-life tracers. GE Discovery 710 PET-CT scanners have a longer scanner bore than the ECAT EXACT HR PET used by Keiding [11]  Availability of data and material: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.