Analysis of [11C]flumazenil PET studies
[11C]Flumazenil ([11C]FMZ, [11C]Ro15-1788) is a “benzodiazepine receptor” antagonist that binds reversibly to GABAA receptors.
[11C]Flumazenil has the properties of a good brain PET ligand:
- the metabolites formed in the liver are hydrophilic molecules that do not cross the blood-brain barrier (Shinotoh et al. 1986; Debruyene et al. 1991),
- it is not metabolized in the brain (Shinotoh et al. 1986),
- its binding in the brain is reversible with fast kinetics, and
- its non-specific binding in brain is low (Abadie & Baron 1991, Hansen et al. 1991).
After intravenous injection, [11C]flumazenil is initially distributed according to cerebral blood flow (Shinotoh et al. 1986).
However, it should be noted that the uptake of [11C]flumazenil is affected by the blood-brain barrier (BBB) efflux transporter P-glycoprotein, at least in rodents (Froklage et al., 2012). In humans, [11C]flumazenil is still useful for measuring the GABAA receptor density (Froklage et al., 2017).
fP and fND
In blood plasma, the fraction of [11C]FMZ bound to plasma proteins is about 0.4 (Klotz et al., 1984), i.e. free fraction fP is about 0.6. Lassen et al. (1995) reported an average fP=0.64 ± 0.03 from five subjects, and Price et al. (1993) estimated fP=0.50 ± 0.03 also in five subjects.
Fraction of free [11C]FMZ in the brain, fND, has been estimated (based on fP and K1/k2) to be 0.72 ± 0.03 (Price et al., 1993), or about 0.88 (Delforge et al., 1995).
For [11C]flumazenil, it seems that in the four-compartmental model (three tissue compartmental model)
In an inhibition study with five healthy subjects, Price et al. (1993) estimated that average grey matter K1/k2 is 0.68 ± 0.08 in a two-tissue compartmental model, which does not consider nonspecific binding separately; also for white matter K1/k2 was similar, 0.68 ± 0.17. Millet et al. (1995) estimated using a multi-injection protocol and five-parameter model that K1/k2 = 0.555 ± 0.056 in healthy control subjects.
Calculating K1/k2 using the ratio of the water contents of the brain and plasma (77.4/94) divided by (1+(1-fP)) gives an estimate 0.51. Lassen et al. (1995) estimated VF+VNS=0.79 using cold flumazenil infusion. If we assume that
and that fP and 1-fND are about 0.6 and 0.28, respectively, we can calculate that VF+VNS=0.8. This is close to the estimate by Lassen et al (1995). Magata et al. (2003) estimated with Logan plot that VT in brain stem is 0.84 ± 0.33, suggesting that specific binding is very low in white matter.
Two-compartmental model (one-tissue compartmental model)
Compartmental model analysis using arterial plasma input function (Koeppe et al., 1991; Holthoff et al., 1991) have been validated for the analysis of [11C]FMZ PET studies. The estimation of receptor density and dissociation constant requires measurements under different concentrations of bound ligand, which is usually obtained using at least two injections of the ligand with different specific radioactivity (Blomqvist et al., 1990) or using different multi-injection protocols (Millet et al., 1995).
A single tissue compartmental (2-compartmental model) has been found sufficient to describe the tissue dynamics of [11C]FMZ in several studies (Koeppe et al., 1991; Lassen et al., 1995; Millet et al., 1995; Millet et al., 2002). Altered ligand delivery does not affect distribution volume of [11C]flumazenil even with this simple model (Holthoff et al., 1991).
Later, however, Klumpers et al. (2005) showed that two-tissue model was preferable (based on AIC and SC), providing 6 ± 4% higher VT than one-tissue compartment model; however, this result was based on regional analysis, but for pixel-by-pixel analysis one-tissue model may still be preferable.
It must be noticed that iomazenil, which is widely used in SPET if labelled with 123I, has different kinetics, and Buck et al. (1996) have recommended three-compartment model for [11C]iomazenil and fitting simultaneously multiple brain regions with coupled k4 or k4 and K1/k’2.
Logan plot can be used to estimate VT without assumptions on compartmental model setting (Miederer et al., 2009). Richardson et al. 1997 and Koepp et al. 1997 have applied spectral analysis (SA) to produce VT images to be used in SPM, and since then SA has been widely used.
One analysis method used by Lassen et al. (1995) was to estimate distribution volumes using integrals of both the metabolite corrected plasma curves and the tissue curves. Both curves were extrapolated using a single exponential obtained from a fit over the interval from 40 to 120 min.
Arterial plasma input
Plasma protein binding is relatively low, about 40%, and blood-to-plasma ratio approaches unity (Klotz et al. 1984; Lassen et al. 1995), with somewhat higher concentration in plasma than in whole blood (Ishiwata et al., 1998). Red blood cells do not contain labelled metabolites.
Sanabria-Bohórquez et al. (2000) have suggested omitting the measurement of plasma metabolite fractions, but instead using a mathematical metabolite correction by fitting several regions simultaneously, assuming that [11C]flumazenil fraction in plasma can be modelled by a mono-exponential function plus a constant. The method was validated in a three-injection imaging protocol, but for single-injection protocol the validation was based on simulated data only (Sanabria-Bohórquez et al., 2000).
Okazawa et al (2004) proposed the use of exponential function with fixed parameters to replace individual metabolite analysis; using this method provided valid BPND estimates (calculated as distribution volume ratio - 1, where distribution volumes were estimated using Logan analysis). Using fixed metabolite correction will lead to bias in distribution volume, but since this bias probably is similar in all brain regions, the bias cancels out in calculation of distribution volume ratio (DVR) and BPND. Therefore, if fixed metabolite correction is used, only DVR or BPND should be reported, not VT; this should be kept in mind when interpreting VT results from for example Pascual et al (2012).
Image-derived input function
If appropriate partial volume correction method is used when PET images are reconstructed, then the input function can be derived noninvasively from carotid arteries in the PET image (Mourik et al., 2008).
Reference tissue input
Reference tissue input methods have been proposed to avoid the invasive arterial cannulation and error-prone plasma metabolite analysis also in [11C]FMZ studies.
Logan plot, SRTM and MRTM2 using pons as reference tissue have been shown to provide robust BPND estimates and parametric images (Klumpers et al., 2008; Miederer et al., 2009), although reproducibility and reliability are lower than when using arterial plasma input methods (Salmi et al., 2008). Normandin et al. (2012) proposed that the noise in reference region TAC should be included in calculation of weighting factors.
Pons as reference region
In [11C]FMZ studies, pons is most often used as reference region, because it provides reasonable binding estimates with acceptable coefficients of variation and is easier to define anatomically than hemispheric white matter (Abadie et al., 1991; Abadie et al., 1992).
In displacement studies the binding in pons was not changed but the binding in other regions was reduced to the level of pons (Persson et al., 1985). Yet, pons may contain a significant amount of benzodiazepine receptors (Braestrup et al. 1977; Alkire & Haier 2001; Millet et al. 2002). Hall et al. (1992) estimated that pons region has about 2% benzodiazepine receptors compared with frontal cortex. Delforge et al. (1995, 1997) estimated that B’max in pons is 5-7 % of cortical values, and the binding potential derived from their results is about 1.1. In vitro benzodiazepine binding densities in cortical regions are about 3.5-5 times higher than in pons (Alkire & Haier, 2001).
Neuroinflammation in rat brain does not prevent usage of pons as reference region (Parente et al., 2017).
Delforge et al. (1996, 1997) have corrected the pons curve for bound tracer using previously estimated (Delforge et al., 1995) model parameter values and average plasma curve. They estimated that the percent of bound ligand in pons is 52% with very small injected mass of flumazenil. However, because the receptor sites also affect the free concentration, the correction was simplified by subtracting the pons curve with the bound concentration estimated at 50 min after injection (Delforge et al., 1996).
White matter as reference region
As an alternative, Magata et al. (2003) have used white matter, when calculating regional binding potential from VT values derived from plasma-input Logan plots. Their estimate of VT in white matter (brain stem) was 0.84 ± 0.33.
Hammers et al. (2003) have found differences also in white matter VT in certain forms of epilepsy.
Klumpers et al. (2005) have used white matter and cerebellum as reference tissue with simplified reference tissue model (SRTM), but did not report the results from reference region comparison in that publication.
Benzodiazepine binding in white matter (corpus callosum) may be about 70% of pontial values (Alkire & Haier 2001).
Previously, Lassen et al. (1995) have considered white matter not suitable as a reference region, because it cannot a priori be assumed to have the same non-specific distribution volume (in this case VF + VNS) of the tracer as the grey matter. They estimated that BP in white matter is about 0.2 (Lassen et al. 1995).
Methods for producing parametric maps for SPM analysis were compared by Klumpers et al (2012).
Arterial input function
Figure 1. Simulations can be based on population average input data from the healthy subjects in test-retest study (Salmi et al., 2008).
- Compartmental models
- Reference region input compartmental models
- Bmax and KD
- Binding potential
- Tissue-to-reference ratio
- Partial saturation approach
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Created at: 2013-06-18
Updated at: 2018-05-18
Written by: Vesa Oikonen