Brain Activation Associated With Response to Psychotherapies for Late-Life Depression: A Task-Based fMRI Study.

Background: The course of late-life depression is associated with functioning of multiple brain networks. Understanding the brain mechanisms associated with response to psychotherapy can inform treatment development and a personalized treatment approach. This study examined how activation of key regions of the salience network, default mode network and reward systems is associated with response to psychotherapies for late-life depression.

Wisdom, Resilience, and Well-Being in Later Life.

As the numbers of older adults continue to increase globally, the need for facilitating healthy aging has become critical. While a physically healthy lifestyle, including exercise and diet, is important, recent research has highlighted a major impact of psychosocial determinants of health, such as resilience, wisdom, positive social connections, and mental well-being, on whole health. This article focuses on keeping the mind and brain healthy with psychosocially active aging.

Longitudinal trajectories of symptom change during antidepressant treatment among managed care patients with depression and anxiety.

Despite the high correlation between anxiety and depression, little remains known about the course of each condition when presenting concurrently. This study aimed to identify longitudinal patterns during antidepressant treatment in patients with depression and anxiety, and evaluate related factors associated with these patterns. By analyzing longitudinal self-report Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7) scores that tracked courses of depression and anxiety over a three-month window among the 577 adult participants, six depression and six anxiety trajectory subgroups were computationally derived using group-based trajectory modeling.

Increasing Completion of Daily Patient-Reported Outcomes in Psychotherapies for Late-Life Depression through User-Centered Design.

Background:  Treatment of depressive symptoms in older adults is a growing public health concern. Collecting patient-reported outcomes (PROs) may facilitate efficiently scaling psychotherapy for older adults but user-specific tailoring is needed to improve completion.

Objectives:  This study investigates (1) the effect of updating PRO collection tools for middle-aged and older adults with depressive symptoms through a user-centered design process on user completion of PRO questions, (2) what sociodemographic factors correspond with participant completion, and (3) how completion of PRO questions change during the course of a psychotherapy intervention.

Predictors of attrition during acute pharmacotherapy of psychotic depression in a clinical trial.

Little is known about factors that contribute to attrition in clinical trials of the pharmacotherapy of psychotic depression. The purpose of this study was to identify factors associated with attrition during acute pharmacotherapy in the Study of the Pharmacotherapy of Psychotic Depression II (STOP-PD II) clinical trial. Sociodemographic and clinical variables were assessed at baseline in 269 men and women, aged 18-85 years, who were treated with up to 12 weeks of open-label sertraline plus olanzapine.

Effect of olanzapine exposure on relapse and brain structure in patients with major depressive disorder with psychotic features.

Some data suggest that antipsychotics may adversely affect brain structure. We examined the relationship among olanzapine exposure, relapse, and changes in brain structure in patients with major depressive disorder with psychotic features. We analyzed data from the Study of the Pharmacotherapy of Psychotic Depression II trial (STOP-PD II), a randomized, placebo-controlled trial in patients with psychotic depression who attained remission on sertraline and olanzapine and were randomized to continue sertraline plus olanzapine or placebo for 36 weeks.

Using Machine Learning to Predict Antidepressant Treatment Outcome From Electronic Health Records.

Objective: To evaluate if a machine learning approach can accurately predict antidepressant treatment outcome using electronic health records (EHRs) from patients with depression.

Method: This study examined 808 patients with depression at a New York City-based outpatient mental health clinic between June 13, 2016 and June 22, 2020. Antidepressant treatment outcome was defined based on trend in depression symptom severity over time and was categorized as either "Recovering" or "Worsening" (i.

Traumatic pneumocephalus as a possible early sign of acute spinal cord injury: Case report.

Background: Traumatic pneumocephalus (TPC) following craniofacial injuries is common, but isolated TPC secondary to pneumorrhachis (PR) is the rare result of upward gas migration from the spinal canal. In the absence of craniofacial and grossly unstable spinal fractures, the etiology of TPC in polytrauma can be elusive and an underlying diagnosis of acute spinal cord injury (SCI) can be easily missed. We report the first polytrauma case where TPC was the most reliable early sign of SCI.

Brain metabolite levels in remitted psychotic depression with consideration of effects of antipsychotic medication.

Background: The neurobiology of psychotic depression is not well understood and can be confounded by antipsychotics. Magnetic resonance spectroscopy (MRS) is an ideal tool to measure brain metabolites non-invasively. We cross-sectionally assessed brain metabolites in patients with remitted psychotic depression and controls.

Trajectories of remitted psychotic depression: identification of predictors of worsening by machine learning.

Background: Remitted psychotic depression (MDDPsy) has heterogeneity of outcome. The study's aims were to identify subgroups of persons with remitted MDDPsy with distinct trajectories of depression severity during continuation treatment and to detect predictors of membership to the worsening trajectory.

Method: One hundred and twenty-six persons aged 18-85 years participated in a 36-week randomized placebo-controlled trial (RCT) that examined the clinical effects of continuing olanzapine once an episode of MDDPsy had remitted with sertraline plus olanzapine.

Longitudinal Trajectories of Symptom Change During Antidepressant Treatment Among Managed Care Patients with Co-Occurring Depression and Anxiety.

Depression and anxiety are highly correlated, yet little is known about the course of each condition when presenting concurrently. This study aimed to identify longitudinal patterns and changes in depression and anxiety symptoms during antidepressant treatment, and evaluate clinical factors associated with each response pattern. Self-report Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7) scores were used to track the courses of depression and anxiety respectively over a three-month window, and group-based trajectory modeling was used to derive subgroups of patients who have similar response patterns.

Prognostics of Systemic Malignancy ICD-O Topography and Morphology Types on Brain Metastases: An NCDB Time-to-event Cohort.

Background: The primary site and histology of systemic malignancy are known predictors of progression to brain metastases (BM). We investigated the combinational interactions of International Classification of Diseases for Oncology (ICD-O) primary topography and morphology types on the survival of BM after adjusting for relevant clinical and demographic prognostic factors.

Methods: The cohort included all adult patients with BM at diagnosis of an invasive malignancy in the National Cancer Database (2010 to 2018).

Risk stratification models for predicting preventable hospitalization in commercially insured late middle-aged adults with depression.

Background: A significant number of late middle-aged adults with depression have a high illness burden resulting from chronic conditions which put them at high risk of hospitalization. Many late middle-aged adults are covered by commercial health insurance, but such insurance claims have not been used to identify the risk of hospitalization in individuals with depression. In the present study, we developed and validated a non-proprietary model to identify late middle-aged adults with depression at risk for hospitalization, using machine learning methods.