Background Several biological abnormalities in major depressive disorder (MDD) persist during episode remission, including altered serotonin neurotransmission, and may reflect underlying pathophysiology. We previously described elevated brain serotonin 1A (5-HT1A) receptor binding in antidepressant-naïve subjects with MDD within a major depressive episode (MDE) compared to healthy controls using positron emission tomography (PET). In the current study, we measured 5-HT1A receptor binding in unmedicated subjects with MDD during sustained remission, hypothesizing higher binding compared with healthy controls, and binding comparable to currently depressed antidepressant-naïve subjects, indicative of a biologic trait. Methods We compared 5-HT1A binding potential (BPF) assessed through PET scanning with [11C]WAY-100635 in 15 subjects with recurrent MDD in remission for ≥12 months and off antidepressant medication for ≥ six months, 51 healthy controls, and 13 antidepressant-naïve MDD subjects in a current MDE. Metabolite-corrected arterial input functions were acquired for estimation of BPF. Results Remitted depressed subjects had higher 5-HT1A BPF than healthy controls; this group difference did not vary significantly in magnitude across brain regions. 5-HT1A BPF was comparable in remitted and currently depressed subjects. Conclusions Elevated 5-HT1A BPF among subjects with remitted MDD appears to be a trait abnormality in MDD, which may underlie recurrent major depressive episodes. Future studies should evaluate the role of genetic and environmental factors in producing elevated 5-HT1A BPF and MDD, and examine whether 5-HT1A BPF is a vulnerability factor to MDEs that could have a role in screening high-risk populations for MDD.

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.

Previous proposals in data dependent wavelet threshold selection have used only the magnitudes of the wavelet coefficients in choosing a threshold for each level. Since a jump (or other unusual feature) in the underlying function results in several non-zero coefficients which are adjacent to each other, it is possible to use change-point approaches to take advantage of the information contained in the relative position of the coefficients as well as their magnitudes. The method introduced here represents an initial step in wavelet thresholding when coefficients are kept in the original order.

11 C]PBR28 is a positron emission tomography radioligand used to estimate the expression of 18kDa translocator protein (TSPO). TSPO is expressed on glial cells and can function as a marker for immune activation. Since TSPO is expressed throughout the brain, no true reference region exists. For this reason, an arterial input function is required for accurate quantification of [ 11 C]PBR28 binding and the most common outcome measure is the total distribution volume (V T ). Notably, V T reflects both specific binding and non-displaceable binding (V ND ). Therefore, estimates of specific binding, such as binding potentials (e.g., BP ND ) and specific distribution volume (V S ) should theoretically be more sensitive to underlying differences in TSPO expression. It is unknown, however, if unbiased and accurate estimates of these measures are obtainable for [ 11 C]PBR28.The Simultaneous Estimation (SIME) method uses time-activity-curves from multiple brain regions with the aim to obtain a brain-wide estimate of V ND , which can subsequently be used to improve the estimation of BP ND and V S . In this study we evaluated the accuracy of SIMEderived V ND , and the reliability of resulting estimates of specific binding for [ 11 C]PBR28, using a combination of simulation experiments and in vivo studies in healthy humans.The simulation experiments showed that V ND values estimated using SIME were both precise and accurate. Data from a pharmacological competition challenge showed that SIME provided V ND values that were on average 19% lower than those obtained using the Lassen plot, but similar to values obtained using the Likelihood-Estimation of Occupancy technique. Test-retest data showed that SIME-derived V S values exhibited good reliability and precision, while larger variability was observed in SIME-derived BP ND values.The results support the use of SIME for quantifying specific binding of [ 11 C]PB28, and suggest that V S can be used in preference to, or as a complement to the conventional outcome measure V T . Additional studies in patient cohorts are warranted.

No abstract

Summary Treatment response heterogeneity poses serious challenges for selecting treatment for many diseases. To better understand this heterogeneity and to help in determining the best patient-specific treatments for a given disease, many clinical trials are collecting large amounts of patient-level data prior to administering treatment in the hope that some of these data can be used to identify moderators of treatment effect. These data can range from simple scalar values to complex functional data such as curves or images. Combining these various types of baseline data to discover “biosignatures” of treatment response is crucial for advancing precision medicine. Motivated by the problem of selecting optimal treatment for subjects with depression based on clinical and neuroimaging data, we present an approach that both (1) identifies covariates associated with differential treatment effect and (2) estimates a treatment decision rule based on these covariates. We focus on settings where there is a potentially large collection of candidate biomarkers consisting of both scalar and functional data. The validity of the proposed approach is justified via extensive simulation experiments and illustrated using data from a placebo-controlled clinical trial investigating antidepressant treatment response in subjects with depression.

Background Major depressive disorder (MDD) and anxiety disorders are highly co-morbid. Research has shown conflicting evidence for white matter alteration and amygdala volume reduction in mood and anxiety disorders. To date, no studies have examined differences in structural connectivity between anxious depressed and non-anxious depressed individuals. This study compared fractional anisotropy (FA) and density of selected white matter tracts and amygdala volume between anxious depressed and non-anxious depressed individuals. Methods 64- direction DTI and T1 scans were collected from 110 unmedicated subjects with MDD, 39 of whom had a co-morbid anxiety disorder diagnosis. Region of interest (ROI) and tractography methods were performed to calculate amygdala volume and FA in the uncinate fasciculus, respectively. Diffusion connectometry was performed to identify whole brain group differences in white matter health. Correlations were computed between biological and clinical measures. Results Tractography and ROI analyses showed no significant differences between bilateral FA values or bilateral amygdala volumes when comparing the anxious depressed and non-anxious depressed groups. The diffusion connectometry analysis showed no significant differences in anisotropy between the groups. Furthermore, there were no significant relationships between MRI-based and clinical measures. Conclusion The lack of group differences could indicate that structural connectivity and amygdalae volumes of those with anxious-depression are not significantly altered by a co-morbid anxiety disorder. Improving understanding of anxiety co-morbid with MDD would facilitate development of treatments that more accurately target the underlying networks.

A maximum likelihood point estimate and a Bayesian-based interval estimator are derived for a change-point, TE(O, T), in a Poisson process. The procedures are evaluated through simulation studies and application to the British coal-mining disaster data. The procedures are freely available for use over the World Wide Web at