Faculty Bibliography

Although disrupted function of frontal and limbic areas of cerebral cortex are closely associated with major depressive disorder (MDD) and schizophrenia (SZ), cellular pathology has also been found in other brain areas, including primary sensory areas. Auditory cortex is of particular interest, given the prominence of auditory hallucinations in SZ, and sensory deficits in MDD. We used stereological sampling methods in auditory cortex to look for cellular differences between MDD, SZ and non-psychiatric subjects. Additionally, as all of our MDD subjects died of suicide, we evaluated the association of suicide with our measurements by selecting a SZ sample that was divided between suicide and non-suicide subjects. Measurements were done in primary auditory cortex (area A1) and auditory association cortex (area Tpt), two areas with distinct roles in sensory processing and obvious differences in neuron density and size. In MDD, densities of GABAergic interneurons immunolabeled for calretinin (CR) and calbindin (CB) were 23-29% lower than non-psychiatric controls in both areas. Parvalbumin (PV) interneurons (counted only in area Tpt) showed a nominally smaller (16%) reduction that was not statistically significant. Total neuron and glia densities measured in Nissl stained sections did not show corresponding reductions. Analysis of suicide in the SZ sample indicated that reduced CR cell density was associated with suicide, whereas the densities of CB and other cells were not. Our results are consistent with previous studies in MDD that found altered GABA-associated markers throughout the cerebral cortex including primary sensory areas.

The risk of Alzheimer's disease can be predicted by volumetric analyses of MRI data in the medial temporal lobe. The present study compared a volumetric measurement of the hippocampus with a novel measure of hippocampal integrity (HI) derived from the ratio of parenchyma volume over total volume. Participants were cognitively intact and aged 60 years or older at baseline, and were tested twice, roughly 3 years apart. Participants had been recruited for a study on late-life major depression (LLMD) and were evenly split between depressed patients and controls. Linear regression models were applied to the data with a cognitive composite score as the outcome, and HI and volume, together or separately, as predictors. Subsequent cognitive performance was predicted well by models that included an interaction between HI and LLMD status, such that lower HI scores predicted more cognitive decline in depressed patients. More research is needed, but tentative results from this study appear to suggest that the newly introduced measure HI is an effective tool for the purpose of predicting future changes in general cognitive ability, and especially so in individuals with LLMD.

Understanding heterogeneity in phenotypical characteristics, symptoms manifestations and response to treatment of subjects with psychiatric illnesses is a continuing challenge in mental health research. A long-standing goal of medical studies is to identify groups of subjects characterized with a particular trait or quality and to distinguish them from other subjects in a clinically relevant way. This paper develops and illustrates a novel approach to this problem based on a method of optimal-partitioning (clustering) of functional data. The proposed method allows for the simultaneous clustering of different populations (e.g., symptoms of drug and placebo treated patients) in order to identify prototypical outcome profiles that are distinct from one or the other treatment and outcome profiles common to the different treatments. The clustering results are used to discover potential treatment effect modifiers (i.e., moderators), in particular, moderators of specific drug effects and placebo response. A depression clinical trial is used to illustrate the method.

UNLABELLED:Remission rates for Major Depressive Disorder (MDD) are low and unpredictable for any given antidepressant. No biological or clinical marker has demonstrated sufficient ability to match individuals to efficacious treatment. Biosignatures developed from the systematic exploration of multiple biological markers, which optimize treatment selection for individuals (moderators) and provide early indication of ultimate treatment response (mediators) are needed. The rationale and design of a multi-site, placebo-controlled randomized clinical trial of sertraline examining moderators and mediators of treatment response is described. The target sample is 300 participants with early onset (≤30 years) recurrent MDD. Non-responders to an 8-week trial are switched double blind to either bupropion (for sertraline non-responders) or sertraline (for placebo non-responders) for an additional 8 weeks. Clinical moderators include anxious depression, early trauma, gender, melancholic and atypical depression, anger attacks, Axis II disorder, hypersomnia/fatigue, and chronicity of depression. Biological moderator and mediators include cerebral cortical thickness, task-based fMRI (reward and emotion conflict), resting connectivity, diffusion tensor imaging (DTI), arterial spin labeling (ASL), electroencephalograpy (EEG), cortical evoked potentials, and behavioral/cognitive tasks evaluated at baseline and week 1, except DTI, assessed only at baseline. The study is designed to standardize assessment of biomarkers across multiple sites as well as institute replicable quality control methods, and to use advanced data analytic methods to integrate these markers. A Differential Depression Treatment Response Index (DTRI) will be developed. The data, including biological samples (DNA, RNA, and plasma collected before and during treatment), will become available in a public scientific repository. CLINICAL TRIAL REGISTRATION:Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC). Identifier: NCT01407094. URL: http://clinicaltrials.gov/show/NCT01407094.

Epidemiological findings suggest that diabetic individuals are at a greater risk for developing Alzheimer's disease (AD). To examine the mechanisms by which diabetes mellitus (DM) may contribute to AD pathology in humans, we examined brain tissue from streptozotocin-treated type 1 diabetic adult male vervet monkeys receiving twice-daily exogenous insulin injections for 8-20 weeks. We found greater inhibitory phosphorylation of insulin receptor substrate 1 in each brain region examined of the diabetic monkeys when compared with controls, consistent with a pattern of brain insulin resistance that is similar to that reported in the human AD brain. Additionally, a widespread increase in phosphorylated tau was seen, including brain areas vulnerable in AD, as well as relatively spared structures, such as the cerebellum. An increase in active ERK1/2 was also detected, consistent with DM leading to changes in tau-kinase activity broadly within the brain. In contrast to these widespread changes, we found an increase in soluble amyloid-beta (Abeta) levels that was restricted to the temporal lobe, with the greatest increase seen in the hippocampus. Consistent with this localized Abeta increase, a hippocampus-restricted decrease in the protein and mRNA for the Abeta-degrading enzyme neprilysin (NEP) was found, whereas various Abeta-clearing and -degrading proteins were unchanged. Thus, we document multiple biochemical changes in the insulin-controlled DM monkey brain that can link DM with the risk of developing AD, including dysregulation of the insulin-signaling pathway, changes in tau phosphorylation, and a decrease in NEP expression in the hippocampus that is coupled with a localized increase in Abeta. SIGNIFICANCE STATEMENT: Given that diabetes mellitus (DM) appears to increase the risk of developing Alzheimer's disease (AD), understanding the mechanisms by which DM promotes AD is important. We report that DM in a nonhuman primate brain leads to changes in the levels or posttranslational processing of proteins central to AD pathobiology, including tau, amyloid-beta (Abeta), and the Abeta-degrading protease neprilysin. Additional evidence from this model suggests that alterations in brain insulin signaling occurred that are reminiscent of insulin signaling pathway changes seen in human AD. Thus, in anin vivomodel highly relevant to humans, we show multiple alterations in the brain resulting from DM that are mechanistically linked to AD risk.

OBJECTIVE: Patient treatment preference may moderate treatment effect in major depressive disorder (MDD) studies. Little research has addressed preference in posttraumatic stress disorder (PTSD); almost none has assessed actual patients' PTSD psychotherapy preferences. From a 14-week trial of chronic PTSD comparing prolonged exposure, relaxation therapy, and interpersonal psychotherapy, we report treatment preferences of the 110 randomized patients, explore preference correlates, and assess effects on treatment outcome. METHOD: Patients recruited between 2008 and 2013 with chronic DSM-IV PTSD (Clinician-Administered PTSD Scale [CAPS] score >/= 50) received balanced, scripted psychotherapy descriptions prerandomization and indicated their preferences. Analyses assessed relationships of treatment attitudes to demographic and clinical factors. We hypothesized that patients randomized to preferred treatments would have better outcomes, and to unwanted treatment worse outcomes. RESULTS: Eighty-seven patients (79%) voiced treatment preferences or disinclinations: 29 (26%) preferred prolonged exposure, 29 (26%) preferred relaxation therapy, and 56 (50%) preferred interpersonal psychotherapy (Cochran Q = 18.46, P < .001), whereas 29 (26%) were disinclined to prolonged exposure, 18 (16%) to relaxation therapy, and 3 (3%) to interpersonal psychotherapy (Cochran Q = 22.71, P < .001). Several baseline clinical variables correlated with treatment preferences. Overall, treatment preference/disinclination did not predict change in CAPS score, treatment response, or dropout. Comorbidly depressed patients receiving unwanted treatment had worse final CAPS scores. CONCLUSIONS: These exploratory findings are the first relating patients' PTSD psychotherapy preferences to outcome. Despite explanations emphasizing prolonged exposure's greater empirical support, patients significantly preferred interpersonal psychotherapy. Preference subtly affected psychotherapy outcome; depression appeared an important moderator of the effect of unwanted treatment on outcome. Potential biases to avoid in future research are discussed. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00739765.

BACKGROUND: Post-traumatic stress disorder (PTSD) psychotherapy research has failed to identify patient characteristics that consistently predict differential outcome. AIMS: To identify patient characteristics associated with differential outcome via a statistically generated composite moderator among women with childhood abuse-related PTSD in a randomised controlled trial comparing exposure therapy, skills training and their combination. METHOD: Six baseline patient characteristics were combined in a composite moderator of treatment effects for PTSD symptoms across the three treatment conditions through a 6-month follow-up. RESULTS: The optimal moderator was the combined burden of all symptoms and emotion regulation strength. Those with high moderator scores, reflecting high symptom load relative to emotion regulation, did least well in exposure, moderately well in skills and best in the combination. CONCLUSIONS: A clinically meaningful moderator, which combines patient symptom burden and strengths, was identified. Assessment at follow-up may provide a more accurate indicator of variability in outcome than that obtained immediately post-treatment

A major focus of personalized medicine is on the development of individualized treatment rules. Good decision rules have the potential to significantly advance patient care and reduce the burden of a host of diseases. Statistical methods for developing such rules are progressing rapidly, but few methods have considered the use of pre-treatment functional data to guide in decision-making. Furthermore, those methods that do allow for the incorporation of functional pre-treatment covariates typically make strong assumptions about the relationships between the functional covariates and the response of interest. We propose two approaches for using functional data to select an optimal treatment that address some of the shortcomings of previously developed methods. Specifically, we combine the flexibility of functional additive regression models with Q-learning or A-learning in order to obtain treatment decision rules. Properties of the corresponding estimators are discussed. Our approaches are evaluated in several realistic settings using synthetic data and are applied to real data arising from a clinical trial comparing two treatments for major depressive disorder in which baseline imaging data are available for subjects who are subsequently treated.