Smaller cornu ammonis (CA3) as a potential risk factor for suicidal behavior in mood disorders.

Mood disorders and suicidal behavior have moderate heritability and familial transmission, and are associated with smaller hippocampal volumes. However, it is unclear whether hippocampal alterations reflect heritable risk or epigenetic effects of childhood adversity, compensatory mechanisms, illness-related changes, or treatment effects. We sought to separate the relationships of hippocampal substructure volumes to mood disorder, suicidal behavior, and risk and resilience to both by examining high familial risk individuals (HR) who have passed the age of greatest risk for psychopathology onset. Structural brain imaging and hippocampal substructure segmentation quantified Cornu Ammonis (CA1-4), dentate gyrus, and subiculum gray matter volumes in healthy volunteers (HV, N = 25) and three groups with one or more relatives reporting early-onset mood disorder and suicide attempt: 1. Unaffected HR (N = 20); 2. HR with lifetime mood disorder and no suicide attempt (HR-MOOD, N = 25); and 3. HR with lifetime mood disorder and a previous suicide attempt (HR-MOOD + SA, N = 18). Findings were tested in an independent cohort not selected for family history (HV, N = 47; MOOD, N = 44; and MOOD + SA, N = 21). Lower CA3 volume was found in HR (vs. HV), consistent with the direction of previously published findings in MOOD+SA (vs. HV and MOOD), suggesting the finding reflects a familial biological risk marker, not illness or treatment-related sequelae, of suicidal behavior and mood disorder. Familial suicide risk may be mediated in part by smaller CA3 volume. The structure may serve as a risk indicator and therapeutic target for suicide prevention strategies in high-risk families.

Guidelines for the content and format of PET brain data in publications and archives: A consensus paper.

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.

Constructing treatment decision rules based on scalar and functional predictors when moderators of treatment effect are unknown.

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.

Cortical thickness is not associated with current depression in a clinical treatment study.

BACKGROUND: Reduced cortical thickness is a candidate biological marker of depression, although findings are inconsistent. This could reflect analytic heterogeneity, such as use of region-wise cortical thickness based on the Freesurfer Desikan-Killiany (DK) atlas or surface-based morphometry (SBM). The Freesurfer Destrieux (DS) atlas (more, smaller regions) has not been utilized in depression studies. This could also reflect differential gender and age effects. METHODS: Cortical thickness was collected from 170 currently depressed adults and 52 never-depressed adults. Visually inspected and approved Freesurfer-generated surfaces were used to extract cortical thickness estimates according to the DK atlas (68 regions) and DS atlas (148 regions) for region-wise analysis (216 total regions) and for SBM. RESULTS: Overall, except for small effects in a few regions, the two region-wise approaches generally failed to discriminate depressed adults from nondepressed adults or current episode severity. Differential effects by age and gender were also rare and small in magnitude. Using SBM, depressed adults showed a significantly thicker cluster in the left supramarginal gyrus than nondepressed adults (P = 0.047) but there were no associations with current episode severity. CONCLUSIONS: Three analytic approaches (i.e., DK atlas, DS atlas, and SBM) converge on the notion that cortical thickness is a relatively weak discriminator of current depression status. Differential age and gender effects do not appear to represent key moderators. Robust associations with demographic factors will likely hinder translation of cortical thickness into a clinically useful biomarker. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc. Hum Brain Mapp 38:4370-4385, 2017. © 2017 Wiley Periodicals, Inc.

A comparison of structural connectivity in anxious depression versus non-anxious 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.

Variable Selection in Function-on-Scalar Regression.

For regression models with functional responses and scalar predictors, it is common for the number of predictors to be large. Despite this, few methods for variable selection exist for function-on-scalar models, and none account for the inherent correlation of residual curves in such models. By expanding the coefficient functions using a B-spline basis, we pose the function-on-scalar model as a multivariate regression problem. Spline coefficients are grouped within coefficient function, and group-minimax concave penalty (MCP) is used for variable selection. We adapt techniques from generalized least squares to account for residual covariance by "pre-whitening" using an estimate of the covariance matrix, and establish theoretical properties for the resulting estimator. We further develop an iterative algorithm that alternately updates the spline coefficients and covariance; simulation results indicate that this iterative algorithm often performs as well as pre-whitening using the true covariance, and substantially outperforms methods that neglect the covariance structure. We apply our method to two-dimensional planar reaching motions in a study of the effects of stroke severity on motor control, and find that our method provides lower prediction errors than competing methods.

Elevated serotonin 1A binding in remitted major depressive disorder: evidence for a trait biological abnormality.

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-HT(1A)) receptor binding in antidepressant-naive (AN) subjects with MDD within a major depressive episode (MDE) compared with that in healthy controls using positron emission tomography (PET). In this study, we measured 5-HT(1A) receptor binding in unmedicated subjects with MDD during sustained remission, hypothesizing higher binding compared with that in healthy controls, and binding comparable with currently depressed AN subjects, indicative of a biological trait. We compared 5-HT(1A) binding potential (BP(F)) assessed through PET scanning with [(11)C]WAY-100635 in 15 subjects with recurrent MDD in remission for >or=12 months and off antidepressant medication for >or=6 months, 51 healthy controls, and 13 AN MDD subjects in a current MDE. Metabolite-corrected arterial input functions were acquired for the estimation of BP(F). Remitted depressed subjects had higher 5-HT(1A) BP(F) compared with healthy controls; this group difference did not vary significantly in magnitude across brain regions. 5-HT(1A) BP(F) was comparable in remitted and currently depressed subjects. Elevated 5-HT(1A) BP(F) level among subjects with remitted MDD appears to be a trait abnormality in MDD, which may underlie recurrent MDEs. Future studies should evaluate the role of genetic and environmental factors in producing elevated 5-HT(1A) BP(F) and MDD, and should examine whether 5-HT(1A) BP(F) is a vulnerability factor to MDEs that could have a role in screening high-risk populations for MDD.