Unifying treatments for Depression: An application of the Free Energy Principle

Major Depressive Disorder is a debilitating and increasingly prevalent psychiatric condition(1,2). At present, its primary treatments are antidepressant medications and psychotherapy. Curiously, although the pharmacological effects of antidepressants manifest within hours, remission of clinical symptoms takes a number of weeks – if at all. Independently, support has grown for an idea – proposed as early as Helmholtz (3) – that the brain is a prediction machine, holding generative models for the purpose of inferring causes of sensory information (4–8). If the brain does indeed represent a collection of beliefs about the causal structure of the world, then the depressed phenotype may emerge from a collection of depressive beliefs. These beliefs are modified gradually through successive combinations of expectations with observations. As a result, phenotypic remission ought to take some time as the brain’s relevant statistical structures become less pessimistic

PTSD and the War of Words

Trauma-related symptoms among veterans of military engagement have been documented at least since the time of the ancient Greeks. 1 Since the third edition of the Diagnostic and Statistical Manual in 1980, this condition has been known as posttraumatic stress disorder, but the name has changed repeatedly over the past century, including shell shock, war neurosis, and soldier’s heart. Using over 14 million articles in the digital archives of the New York Times , Associated Press , and Reuters , we quantify historical changes in trauma-related terminology over the past century. These data suggest that posttraumatic stress disorder has historically peaked in public awareness after the end of US military engagements, but denoted by a different name each time—a phenomenon that could impede clinical and scientific progress

Altered functional brain connectivity in children and young people with paediatric opsoclonus-myoclonus syndrome (OMS)

Aim: Opsoclonus-Myoclonus syndrome (OMS) is a rare, poorly understood condition that can result in long-term cognitive, behavioural and motor sequelae. Several studies have investigated structural brain changes associated with this condition, but little is known about changes in function. This study aimed to investigate changes in brain functional connectivity in patients with OMS. Method: Seven patients with OMS and 10 age-matched control participants underwent 3T magnetic resonance imaging (MRI) to acquire resting state functional MRI data (whole-brain echo-planar images; 2mm isotropic voxels; multiband factor x 2) for a cross-sectional study. A seed-based analysis identified brain regions in which signal changes over time correlated with the cerebellum. Model-free analysis was employed to determine brain networks showing altered connectivity. Results: In OMS patients the motor cortex showed significantly reduced connectivity, and the occipito-parietal region significantly increased connectivity with the cerebellum relative to the control group. A model-free analysis also showed extensive connectivity within a visual network, including the cerebellum and basal ganglia, not present in controls. No other networks showed any differences between groups. Interpretation: OMS patients showed reduced connectivity between the cerebellum and motor cortex, but increased connectivity with occipitoparietal regions. This pattern of change supports widespread brain involvement in OMS.</p

Altered functional brain connectivity in children and young people with paediatric opsoclonus-myoclonus syndrome (OMS)

Aim: Opsoclonus-Myoclonus syndrome (OMS) is a rare, poorly understood condition that can result in long-term cognitive, behavioural and motor sequelae. Several studies have investigated structural brain changes associated with this condition, but little is known about changes in function. This study aimed to investigate changes in brain functional connectivity in patients with OMS. Method: Seven patients with OMS and 10 age-matched control participants underwent 3T magnetic resonance imaging (MRI) to acquire resting state functional MRI data (whole-brain echo-planar images; 2mm isotropic voxels; multiband factor x 2) for a cross-sectional study. A seed-based analysis identified brain regions in which signal changes over time correlated with the cerebellum. Model-free analysis was employed to determine brain networks showing altered connectivity. Results: In OMS patients the motor cortex showed significantly reduced connectivity, and the occipito-parietal region significantly increased connectivity with the cerebellum relative to the control group. A model-free analysis also showed extensive connectivity within a visual network, including the cerebellum and basal ganglia, not present in controls. No other networks showed any differences between groups. Interpretation: OMS patients showed reduced connectivity between the cerebellum and motor cortex, but increased connectivity with occipitoparietal regions. This pattern of change supports widespread brain involvement in OMS.</p

Parsing the antidepressant effects of non-invasive brain stimulation and pharmacotherapy: A symptom clustering approach on ELECT-TDCS

Background: Transcranial direct current stimulation (tDCS) presents small antidepressant efficacy at group level and considerable inter-individual variability of response. Its heterogeneous effects bring the need to investigate whether specific groups of patients submitted to tDCS could present comparable or larger improvement compared to pharmacotherapy. Aggregate measurements might be insufficient to address its effects. Objective: /Hypothesis: To determine the efficacy of tDCS, compared to pharmacotherapy and placebo, in depressive symptom clusters. Methods: Data from ELECT-TDCS (Escitalopram versus Electrical Direct-Current Therapy for Treating Depression Clinical Study, ClinicalTrials.gov, NCT01894815), in which antidepressant-free, depressed patients were randomized to receive 22 bifrontal tDCS (2 mA, 30 min) sessions (n = 94), escitalopram 20 mg/day (n = 91), or placebo (n = 60) over 10 weeks. Agglomerative hierarchical clustering identified sleep/insomnia, core depressive, guilt/anxiety, and atypical clusters that were the dependent measure. Trajectories were estimated using linear mixed regression models. Effect sizes are expressed in raw HAM-D units. P-values were adjusted for multiple comparisons. Results: For core depressive symptoms, escitalopram was superior to tDCS (ES =-0.56; CI95% =-0.94 to-0.17, p = .009), which was superior to placebo (ES = 0.49; CI95% = 0.06 to 0.92, p = .042). TDCS but not escitalopram was superior to placebo in sleep/insomnia symptoms (ES = 0.87; CI95% = 0.22 to 1.52,p = .015). Escitalopram but not tDCS was superior to placebo in guilt/anxiety symptoms (ES = 1.66; CI95% = 0.58 to 2.75, p = .006). No active intervention was superior to placebo for atypical symptoms. Conclusions: Pharmacotherapy and non-invasive brain stimulation produce distinct effects in depressive symptoms. TDCS or escitalopram could be chosen according to specific clusters of symptoms for a bigger response. (C) 2021 The Authors. Published by Elsevier Inc

Personalized symptom clusters that predict depression treatment outcomes: A replication of machine learning methods

Objectives: The purpose of this study is to use independent datasets to externally validate the three symptom clusters of unipolar depression identified by Chekroud, to evaluate personalized treatment trajectories and outcomes based on these symptom clusters, and to verify predictors. Methods: The Quick Inventory of Depressive Symptomatology-Self Report (QIDS-SR16)11 QIDS-SR16: Quick Inventory of Depressive Symptomatology-Self Report. and Hamilton Rating Scale for Depression (Ham-D)22 Ham-D: Hamilton Rating Scale for Depression. data from two placebo controlled, double-blind clinical trials (Dual Therapy and Duloxetine) were used for external validation. Machine learning methods were applied to replicate the three symptom clusters and to produce treatment trajectories. Penalized logistic regressions were conducted to identify top baseline variables that best predicted treatment outcomes. Results: The variables Chekroud identified as comprising sleep, atypical and core emotional clusters are replicated. Treatment trajectories demonstrate that dual treatment (escitalopram and bupropion) performed best across all symptom clusters but did not outperform escitalopram monotherapy over time. For each symptom cluster, there were differences in treatment efficacy among antidepressants. Conclusion: By using different treatment trajectories based on a patient's symptom cluster profile, clinicians could potentially select best fit antidepressants to achieve the biggest benefit. Our results showed that total baseline QIDS, Ham-D score, anxiety disorder diagnosis and course of depressive illness were the best baseline predictors. Results could enhance personalized depression treatment plans and help to improve outcomes. Clinical trials registration: NCT00519428, NCT00360724

Differences in words used to describe racial and gender groups in Medical Student Performance Evaluations.

The transition from medical school to residency is a critical step in the careers of physicians. Because of the standardized application process-wherein schools submit summative Medical Student Performance Evaluations (MSPE's)-it also represents a unique opportunity to assess the possible prevalence of racial and gender disparities, as shown elsewhere in medicine.The authors conducted textual analysis of MSPE's from 6,000 US students applying to 16 residency programs at a single institution in 2014-15. They used custom software to extract demographic data and keyword frequency from each MSPE. The main outcome measure was the proportion of applicants described using 24 pre-determined words from four thematic categories ("standout traits", "ability", "grindstone habits", and "compassion").The data showed significant differences based on race and gender. White applicants were more likely to be described using "standout" or "ability" keywords (including "exceptional", "best", and "outstanding") while Black applicants were more likely to be described as "competent". These differences remained significant after controlling for United States Medical Licensing Examination Step 1 scores. Female applicants were more frequently described as "caring", "compassionate", and "empathic" or "empathetic". Women were also more frequently described as "bright" and "organized".While the MSPE is intended to reflect an objective, summative assessment of students' qualifications, these data demonstrate for the first time systematic differences in how candidates are described based on racial/ethnic and gender group membership. Recognizing possible implicit biases and their potential impact is important for faculty who strive to create a more egalitarian medical community