Background Serotonin 1A receptors (5-HT1A) are implicated in major depressive disorder (MDD). We previously reported higher 5-HT1A binding potential (BPF=Bavail/KD) in antidepressant naïve MDD subjects compared to controls while other studies report lower BPND(BPND=fNDBavail/KD). Discrepancies can be related to differences in study population or methodology. We sought to replicate our findings in a novel cohort and determine whether choice of reference region and outcome measure could explain discrepancies. Methods Nine new controls and 22 new not recently medicated (4 years, NRM) MDD subjects underwent positron emission tomography with [11C]WAY100635. BPF and BPND were determined using a metabolite and free fraction corrected arterial input function. BPND was also determined using cerebellar gray matter (CGM) and cerebellar white matter (CWM) reference regions as input functions. Results BPF is higher in the new NRM cohort (p=0.037) compared to new controls, which is comparable to the effect in the previous cohort (p=0.04). We combined the cohorts to examine the effects of the reference region and outcome measure. In the combined cohort, BPF is higher in the NRM compared to controls (p=0.0001). Neither BPND using CWM (p=0.86) nor VT (p=0.374) differs between groups. When CGM is used, the NRM group has lower 5HT1A BPND compared with controls (p=0.03). CGM VT is higher in NRM compared to controls (p=0.007). Conclusions Choice of reference region and outcome measure can produce different 5-HT1A findings in MDD. Higher 5-HT1A BPF in MDD was found with the method with fewest assumptions about nonspecific binding and a reference region without receptors.

A clinically important task in diabetes management is the prevention of hypo/hyperglycemic events. In this proof-of-concept paper, we assess the feasibility of approaching the problem with continuous glucose monitoring (CGM) devices. In particular, we study the possibility to predict ahead in time glucose levels by exploiting their recent history monitored every 3 min by a minimally invasive CGM system, the Glucoday, in 28 type 1 diabetic volunteers for 48 h. Simple prediction strategies, based on the description of past glucose data by either a first-order polynomial or a first-order autoregressive (AR) model, both with time-varying parameters determined by weighted least squares, are considered. Results demonstrate that, even by using these simple methods, glucose can be predicted ahead in time, e.g., with a prediction horizon of 30 min crossing of the hypoglycemic threshold can be predicted 20-25 min ahead in time, a sufficient margin to mitigate the event by sugar ingestion.

The serotonin (5-hydroxytryptamine, or 5-HT) type 1A receptor (5-HT1AR) is implicated in the pathophysiology of numerous neuropsychiatric disorders. We have published the initial evaluation and reproducibility in vivo of [O-methyl-11C]2-(4-(4-(2-methoxyphenyl)piperazin-1-yl)butyl)-4-methyl-1,2,4-triazine-3,5 (2H,4H)dione (11C-CUMI-101), a novel 5-HT1A agonist radiotracer, in Papio anubis. Here, we report the optimal modeling parameters of 11C-CUMI-101 for human PET studies. Methods PET scans were obtained for 7 adult human volunteers. 11C-CUMI-101 was injected as an intravenous bolus, and emission data were collected for 120 min in 3-dimensional mode. We evaluated 10 different models using metabolite-corrected arterial input functions or reference region approaches and several outcome measures. Results When using binding potential (BPF = Bavail/KD [total available receptor concentration divided by the equilibrium dissociation constant]) as the outcome measure, the likelihood estimation in the graphical analysis (LEGA) model performed slightly better than the other methods evaluated at full scan duration. The average test–retest percentage difference was 9.90% ± 5.60%. When using BPND (BPND = fnd × Bavail/KD; BPND equals the product of BPF and fnd [free fraction in the nondisplaceable compartment]), the simplified reference tissue method (SRTM) achieved the lowest percentage difference and smallest bias when compared with nondisplaceable binding potential obtained from LEGA using the metabolite-corrected plasma input function (r2 = 0.99; slope = 0.92). The time–stability analysis indicates that a 120-min scan is sufficient for the stable estimation of outcome measures. Voxel results were comparable to region-of-interest–based analysis, with higher spatial resolution. Conclusion On the basis of its measurable and stable free fraction, high affinity and selectivity, good blood–brain barrier permeability, and plasma and brain kinetics, 11C-CUMI-101 is suitable for the imaging of high-affinity 5-HT1A binding in humans.

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Background We previously reported higher serotonin 1A receptor (5-HT1A) binding in subjects with major depressive disorder (MDD) during a major depressive episode using positron emission tomography imaging with [11C]WAY-100635. 5-HT1A receptor binding is also associated with treatment outcome after nonstandardized antidepressant treatment. We examined whether pretreatment 5-HT1A binding is associated with treatment outcome following standardized escitalopram treatment in MDD. We also compared 5-HT1A binding between all MDD subjects in this cohort and a sample of healthy control subjects. Methods Twenty-four MDD subjects in a current major depressive episode and 51 previously studied healthy control subjects underwent positron emission tomography scanning with [11C]WAY-100635, acquiring a metabolite-corrected arterial input function and free-fraction measurement to estimate 5-HT1A binding potential (BPF = Bmax/KD, where Bmax = available receptors and KD = dissociation constant). Major depressive disorder subjects then received 8 weeks of treatment with escitalopram; remission was defined as a posttreatment 24-item Hamilton Depression Rating Scale <10 and ≥50% reduction in Hamilton Depression Rating Scale. Results Remitters to escitalopram had 33% higher baseline 5-HT1A binding in the raphe nuclei than nonremitters (p = .047). Across 12 cortical and subcortical regions, 5-HT1A binding did not differ between remitters and nonremitters (p = .86). Serotonin 1A receptor binding was higher in MDD than control subjects across all regions (p = .0003). Remitters did not differ from nonremitters in several relevant clinical measures. Conclusions Elevated 5-HT1A binding in raphe nuclei is associated with subsequent remission with the selective serotonin reuptake inhibitor escitalopram; this is consistent with data from a separate cohort receiving naturalistic antidepressant treatment. We confirmed our previous findings of higher 5-HT1A binding in current MDD compared with control subjects.

In neuroreceptor mapping, methods for the estimation of distribution volume require determination of a metabolite-corrected arterial input function. In application, this may be accomplished by collecting arterial blood samples during scanning, adjusting these measurements according to a separate metabolite analysis, and then modeling the resulting concentration data. Although many groups do this routinely, it is invasive and requires considerable effort. Furthermore, both the plasma and the metabolite data are noisy, and thus estimation of kinetic parameters can be affected by this variability. One promising alternative to full-input function modeling is the simultaneous estimation (SIME) approach, in which kinetic parameters and common input function parameters are estimated using results obtained from several regions at once. We investigate the performance of this approach on data from four different radioligands, using various kinetic models, comparing the results with those obtained by estimation using full-input function modeling. Results indicate that SIME provides a promising alternative for all the radioligands considered.

It is a growing concern that outcomes of neuroimaging studies often cannot be replicated. To counteract this, the magnetic resonance (MR) neuroimaging community has promoted acquisition standards and created data sharing platforms, based on a consensus on how to organize and share MR neuroimaging data. Here, we take a similar approach to positron emission tomography (PET) data. To facilitate comparison of findings across studies, we first recommend publication standards for tracer characteristics, image acquisition, image preprocessing, and outcome estimation for PET neuroimaging data. The co-authors of this paper, representing more than 25 PET centers worldwide, voted to classify information as mandatory, recommended, or optional. Second, we describe a framework to facilitate data archiving and data sharing within and across centers. Because of the high cost of PET neuroimaging studies, sample sizes tend to be small and relatively few sites worldwide have the required multidisciplinary expertise to properly conduct and analyze PET studies. Data sharing will make it easier to combine datasets from different centers to achieve larger sample sizes and stronger statistical power to test hypotheses. The combining of datasets from different centers may be enhanced by adoption of a common set of best practices in data acquisition and analysis.

An accurate characterization of tissue residue function R(t) in bolus-tracking magnetic resonance imaging is of crucial importance to quantify cerebral hemodynamics. R(t) estimation requires to solve a deconvolution problem. The most popular deconvolution method is singular value decomposition (SVD). However, SVD is known to bear some limitations, e.g., R(t) profiles exhibit nonphysiological oscillations and take on negative values. In addition, SVD estimates are biased in presence of bolus delay and dispersion. Recently, other deconvolution methods have been proposed, in particular block-circulant SVD (cSVD) and Tikhonov regularization (TIKH). Here we propose a new method based on nonlinear stochastic regularization (NSR). NSR is tested on simulated data and compared with SVD, cSVD, and TIKH in presence and absence of bolus dispersion. A clinical case in one patient has also been considered. NSR is shown to perform better than SVD, cSVD, and TIKH in reconstructing both the peak and the residue function, in particular when bolus dispersion is considered. In addition, differently from SVD, cSVD, and TIKH, NSR always provides positive and smooth R(t).