Faculty Bibliography

Summary. Permutation tests based on distances among multivariate observations have found many applications in the biological sciences. Two major testing frameworks of this kind are multiresponse permutation procedures and pseudo-F tests arising from a distance-based extension of multivariate analysis of variance. In this article, we derive conditions under which these two frameworks are equivalent. The methods and equivalence results are illustrated by reanalyzing an ecological data set and by a novel application to functional magnetic resonance imaging data

OBJECTIVE: Posttraumatic stress disorder (PTSD) related to childhood abuse is associated with features of affect regulation and interpersonal disturbances that substantially contribute to impairment. Existing treatments do not address these problems or the difficulties they may pose in the exploration of trauma memories, an efficacious and frequently recommended approach to resolving PTSD. The authors evaluated the benefits and risks of a treatment combining an initial preparatory phase of skills training in affect and interpersonal regulation (STAIR) followed by exposure by comparing it against two control conditions: Supportive Counseling followed by Exposure (Support/Exposure) and skills training followed by Supportive Counseling (STAIR/Support). METHOD: Participants were women with PTSD related to childhood abuse (N=104) who were randomly assigned to the STAIR/Exposure condition, Support/Exposure condition (exposure comparator), or STAIR/Support condition (skills comparator) and assessed at posttreatment, 3 months, and 6 months. RESULTS: The STAIR/Exposure group was more likely to achieve sustained and full PTSD remission relative to the exposure comparator, while the skills comparator condition fell in the middle (27% versus 13% versus 0%). STAIR/Exposure produced greater improvements in emotion regulation than the exposure comparator and greater improvements in interpersonal problems than both conditions. The STAIR/Exposure dropout rate was lower than the rate for the exposure comparator and similar to the rate for the skills comparator. There were significantly lower session-to-session PTSD symptoms during the exposure phase in the STAIR/Exposure condition than in the Support/Exposure condition. STAIR/Exposure was associated with fewer cases of PTSD worsening relative to both of the other two conditions. CONCLUSIONS: For a PTSD population with chronic and early-life trauma, a phase-based skills-to-exposure treatment was associated with greater benefits and fewer adverse effects than treatments that excluded either skills training or exposure

To provide effective treatments for childhood posttraumatic stress disorder (PTSD) children with PTSD must first be identified. The authors implemented a 'screen and treat' program following a widely witnessed school suicide. Three months after the suicide, exposed students received the Child Trauma Symptom Questionnaire at school. Parents received the questionnaire to rate their children's PTSD symptoms. Children with scores > or =5 received follow-up interviews and those diagnosed with PTSD were referred for treatment. Ninety-six percent of exposed students were screened, 14% screened positive, and 6% had PTSD. Child and parent agreement was generally poor. All children with PTSD were successfully referred to treatment. Screen and treat programs using existing clinical instruments are efficient and acceptable for use in school settings following trauma

OBJECTIVE: The goal of our study was to investigate the impact of dysthymic disorder (DD), a form of chronic depression, on naturalistic outcome in individuals with personality disorders (PDs). METHOD: The Collaborative Longitudinal Personality Disorders Study is a cohort initially including 573 subjects with 4 targeted PDs (borderline, avoidant, schizotypal, and obsessive-compulsive) and 95 subjects with major depression but no PD. At baseline, 115 subjects were diagnosed with coexisting DD, of whom 109 (94.8%) were PD subjects. Regression analyses were performed to predict 3 classes of broad clinical outcome after 2 years of prospective follow-up. We hypothesized that DD diagnosis at baseline would be associated with worse outcome on (1) persistence of a PD diagnosis, (2) impairment in psychosocial functioning (as measured by the Longitudinal Interval Follow-up Evaluation), and (3) crisis-related treatment utilization. RESULTS: Baseline DD diagnosis was associated with persistence of PD diagnosis at 2 years, particularly for borderline and avoidant PDs. It was associated with worse outcome on global social adjustment, life satisfaction, recreation, and friendships, but not employment or relationship with spouse. Contrary to expectation, DD did not increase suicide attempts, emergency room visits, or psychiatric hospitalizations. CONCLUSIONS: Comorbidity of DD is associated with persistence of PD diagnosis and with worse outcome on many, but not all, measures of psychosocial functioning

Finite mixture models have come to play a very prominent role in modelling data. The finite mixture model is predicated on the assumption that distinct latent groups exist in the population. The finite mixture model therefore is based on a categorical latent variable that distinguishes the different groups. Often in practice distinct sub-populations do not actually exist. For example, disease severity (e.g. depression) may vary continuously and therefore, a distinction of diseased and not-diseased may not be based on the existence of distinct sub-populations. Thus, what is needed is a generalization of the finite mixture's discrete latent predictor to a continuous latent predictor. We cast the finite mixture model as a regression model with a latent Bernoulli predictor. A latent regression model is proposed by replacing the discrete Bernoulli predictor by a continuous latent predictor with a beta distribution. Motivation for the latent regression model arises from applications where distinct latent classes do not exist, but instead individuals vary according to a continuous latent variable. The shapes of the beta density are very flexible and can approximate the discrete Bernoulli distribution. Examples and a simulation are provided to illustrate the latent regression model. In particular, the latent regression model is used to model placebo effect among drug treated subjects in a depression study

Principal points are cluster means for theoretical distributions. A discriminant methodology based on principal points is introduced. The principal point classification method is useful in clinical trials where the goal is to distinguish and differentiate between different treatment effects. Particularly, in psychiatric studies where placebo response rates can be very high, the principal point classification is illustrated to distinguish specific drug responders from non-specific placebo responders

Non-specific responses to treatment (commonly known as placebo response) are pervasive when treating mental illness. Subjects treated with an active drug may respond in part due to non-specific aspects of the treatment, i.e, those not related to the chemical effect of the drug. To determine the extent a subject responds due to the chemical effect of a drug, one must disentangle the specific drug effect from the non-specific placebo effect. This paper presents a unique statistical model that allows for the separate prediction of a specific effect and non-specific effects in drug treated subjects. Data from a clinical trial comparing fluoxetine to a placebo for treating depression is used to illustrate this methodology.

Recent years have witnessed an upsurge in the usage of resting-state functional magnetic resonance imaging (fMRI) to examine functional connectivity (fcMRI), both in normal and pathological populations. Despite this increasing popularity, concerns about the psychologically unconstrained nature of the 'resting-state' remain. Across studies, the patterns of functional connectivity detected are remarkably consistent. However, the test-retest reliability for measures of resting state fcMRI measures has not been determined. Here, we quantify the test-retest reliability, using resting scans from 26 participants at 3 different time points. Specifically, we assessed intersession (>5 months apart), intrasession (<1 h apart), and multiscan (across all 3 scans) reliability and consistency for both region-of-interest and voxel-wise analyses. For both approaches, we observed modest to high reliability across connections, dependent upon 3 predictive factors: 1) correlation significance (significantly nonzero > nonsignificant), 2) correlation valence (positive > negative), and 3) network membership (default mode > task positive network). Short- and long-term measures of the consistency of global connectivity patterns were highly robust. Finally, hierarchical clustering solutions were highly reproducible, both across participants and sessions. Our findings provide a solid foundation for continued examination of resting state fcMRI in typical and atypical populations