MENGA applications in representative imaging modalities with matched genomic data.

<p>The figure reports the genomic-imaging cross-correlation values along with genomic auto-correlation. In the results matrix, each element represents the correlation between couples of donors (for the auto-correlation) or between one donor’s mRNA levels and the image sampled in the same donor’s space (for the cross-correlation). A) Serotonin system: [¹¹C]CUMI and [¹¹C]WAY100635 PET imaging vs. 5HT_1A receptor (HTR1A) mRNA expression; B) Myelin system: myelin water content MR imaging vs. myelin-associated oligodendrocyte basic protein (MOBP) mRNA expression C) Dopamine system: [¹¹C]Raclopride PET imaging vs. dopamine D₂ receptor (DRD2) mRNA expression; [¹²³I]FP-CIT SPET imaging vs dopamine transporter (DAT) mRNA expression; [¹⁸F]FDOPA PET imaging vs. dopa decarboxylase (DDC) mRNA expression.</p

MENGA applications in representative imaging modalities with mismatched genomic data.

<p>The figure reports the genomic-imaging cross-correlation values along with genomic auto-correlation. In the results matrix, each element represents the correlation between couples of donors (for the auto-correlation) or between one donor’s mRNA levels and the image sampled in the same donor’s space (for the cross-correlation) in three different cases of images vs. mismatched genomic data integration. A) [¹¹C]CUMI PET imaging vs. D₂ receptor (DRD2) mRNA expression; B) [¹²³I]FP-CIT SPET imaging vs 5HT_1A receptor (HTR1A) mRNA expression; C) myelin water content MR imaging vs. dopa decarboxylase (DDC) mRNA expression.</p

Summary statistics of MENGA applications in representative imaging modalities with matched and mismatched genomic data.

<p>Cross-correlation results as A) univariate Pearson’s R², B) multivariate correlation coefficient and C) chance-likelihood associated to the R-squared are reported for all the sets of image vs. genomic applications, both for matched (green bars) and mismatched (red bars) cases. Univariate correlations are presented as mean and variability across donors.</p

MENGA software.

<p>A) Matlab-based graphic user interface. B) Overview of MENGA’s outputs. Left: Printed result report on Matlab workspace. Right: summary statistics and scatter analysis plot for gene vs. image cross-correlation and image auto-correlation.</p

Tested neuroimaging modalities.

<p>A) Serotonin system—[¹¹C]CUMI and [¹¹C]WAY100635 PET imaging are used as 5HT_1A receptor markers (agonist and antagonist respectively). The protein density maps are images of non-displaceable specific binding, <i>BP</i>_<i>ND</i> [unitless] and volume of distribution, <i>V</i>_<i>T</i> [ml/cm³], respectively. B) Myelin system–MR-based absolute myelin water content modality (, [unitless]) is used as myelin density marker. C) Dopamine system–[¹¹C]Raclopride PET imaging is used as D₂/D₃ receptor maker; [¹²³I]FP-CIT SPET imaging is used as dopamine transporter (DAT) marker; [¹⁸F]FDOPA PET imaging refers to dopamine synthesis capacity (DDC, dopa decarboxylase enzymatic rate). The parametric maps are images of non-displaceable specific binding, <i>BP</i>_<i>ND</i> [unitless], index of uptake <i>IU</i> [unitless] and rate of net uptake, <i>K</i>_<i>i</i> [ml/cm³], respectively.</p

Cumulative probability distribution of rCPS.

<p>The ordinate is the fraction of the total brain voxels at each of the rCPS levels shown on the abscissa. Data are from 1.9x10⁶ voxels in the brain of a healthy 23 year old male studied awake and under propofol sedation. Values of rCPS were estimated on an interval of 30 min (red), 45 min (yellow), 60 min (green), 75 min (magenta), and 90 min (blue). rCPS was computed for each voxel in the brain from the rate constants of the homogeneous tissue kinetic model estimated with the Basis Function Method.</p

rCPS estimated over the interval beginning at the time of injection and ending 30, 45, 60, 75, or 90 min later.

<p>The Basis Function Method was used to estimate the rate constants of the homogeneous tissue kinetic model, rCPS was computed for each voxel in the brain, and ROI values were obtained by averaging values over all voxels in the ROI. The following regions were evaluated: whole brain (WB), cerebellum (Ce), frontal cortex (FCx), parietal cortex (PCx), thalamus (Thal), caudate (Cau), putamen (Pu), amygdala (Amyg), and hippocampus (Hi). Points represent means ± SD for 12 healthy control subjects studied awake, 12 healthy control subjects studied sedated, and 15 FrX subjects studied sedated. Estimates of each parameter were analyzed for statistically significant effects by means of RM ANOVA with region and scan duration as within subjects variables. <b>Awake healthy control group</b>: Interaction between region and scan duration was statistically significant (F_7.9,87.2 = 3.82, P = 0.001). <i>Post-hoc</i> paired comparisons between the estimates at a 90 min scan duration with all other scan durations for each region are indicated on the figure. *, P ≤ 0.05; **, P ≤ 0.01. <b>Propofol-sedated healthy control group</b>: Interaction between region and scan duration was statistically significant (F_6.3,69.0 = 2.37, P = 0.036). <i>Post-hoc</i> paired comparisons between the estimates at a 90 min scan duration with all other scan durations for each region are indicated on the figure. *, P ≤ 0.05; **, P ≤ 0.01. <b>Propofol-sedated subjects with FXS</b>: Interaction between region and scan duration was statistically significant (F_9.3,130.6 = 4.78, P<0.001). <i>Post-hoc</i> paired comparisons between the estimates at a 90 min scan duration with all other scan durations for each region are indicated on the figure. *, P ≤ 0.05; **, P ≤ 0.01.</p

Effects of shortened scanning intervals on calculated regional rates of cerebral protein synthesis determined with the L-[1-¹¹C]leucine PET method

<div><p>To examine effects of scan duration on estimates of regional rates of cerebral protein synthesis (rCPS), we reanalyzed data from thirty-nine previously reported L-[1-¹¹C]leucine PET studies. Subjects consisted of 12 healthy volunteers studied twice, awake and under propofol sedation, and 15 subjects with fragile X syndrome (FXS) studied once under propofol sedation. All scans were acquired on a high resolution scanner. We used a basis function method for voxelwise estimation of parameters of the kinetic model of L-[1-¹¹C]leucine and rCPS over the interval beginning at the time of tracer injection and ending 30, 45, 60, 75 or 90 min later. For each study and scan interval, regional estimates in nine regions and whole brain were obtained by averaging voxelwise estimates over all voxels in the region. In all three groups rCPS was only slightly affected by scan interval length and was very stable between 60 and 90 min. Furthermore, statistical comparisons of rCPS between awake and sedated healthy volunteers provided almost identical results when they were based on 60 min scan data as when they were based on data from the full 90 min interval. Statistical comparisons between sedated healthy volunteers and sedated subjects with FXS also yielded almost identical results when based on 60 and 90 min scan intervals. We conclude that, under the conditions of our studies, scan duration can be shortened to 60 min without loss of precision.</p></div

The relationship between striatal dopamine and anterior cingulate glutamate in first episode psychosis changes with antipsychotic treatment

The neuromodulator dopamine and excitatory neurotransmitter glutamate have both been implicated in the pathogenesis of psychosis, and dopamine antagonists remain the predominant treatment for psychotic disorders. To date no study has measured the effect of antipsychotics on both of these indices together, in the same population of people with psychosis. Striatal dopamine synthesis capacity (Kicer) and anterior cingulate glutamate were measured using 18F-DOPA positron emission tomography and proton magnetic resonance spectroscopy respectively, before and after at least 5 weeks’ naturalistic antipsychotic treatment in people with first episode psychosis (n = 18) and matched healthy controls (n = 20). The relationship between both measures at baseline and follow-up, and the change in this relationship was analyzed using a mixed linear model. Neither anterior cingulate glutamate concentrations (p = 0.75) nor striatal Kicer (p = 0.79) showed significant change following antipsychotic treatment. The change in relationship between whole striatal Kicer and anterior cingulate glutamate, however, was statistically significant (p = 0.017). This was reflected in a significant difference in relationship between both measures for patients and controls at baseline (t = 2.1, p = 0.04), that was not present at follow-up (t = 0.06, p = 0.96). Although we did not find any effect of antipsychotic treatment on absolute measures of dopamine synthesis capacity and anterior cingulate glutamate, the relationship between anterior cingluate glutamate and striatal dopamine synthesis capacity did change, suggesting that antipsychotic treatment affects the relationship between glutamate and dopamine