Sparse models for predicting psychosocial impairments in patients with PTSD: An empirical Bayes approach.

OBJECTIVE: Posttraumatic stress disorder (PTSD) is associated with psychosocial impairments, which represent a relevant focus for therapy. Previous results on the clinical predictors of these psychosocial impairments were inconsistent. The data analyzed in these contexts often suffer from a high number of correlated predictors and small sample sizes, entailing the risk of model overfitting. In Bayesian regression, the problem of overfitting can be mitigated by usage of specific zero-centered (regularizing) prior distributions. In this study, we used the 2 most common Bayesian regression models, the Bayesian Ridge and the Bayesian Lasso, to predict psychosocial impairments in 192 patients of a day clinic for the treatment of PTSD. METHOD: Predictions were based on specific dimensions of PTSD symptoms previously revealed by factor analyses, as well as posttraumatic cognitions, depressive symptoms, comorbid disorders, and demographics. The variance of the prior distribution was estimated through empirical Bayes (maximum marginal likelihood) and an approximation to the posterior distribution was obtained with stochastic variational inference and with a local approximation (Laplace approximation). RESULTS: Severe psychosocial impairments were mainly related to depressive symptoms and symptoms from the amnesia and numbing dimension of PTSD, while gender, posttraumatic cognitions, and reexperience and avoidance symptoms had no impact. As expected, the model coefficients were shrunken to zero when regularizing prior distributions were used, particularly for the Bayesian Lasso. CONCLUSION: Depressive and numbing symptoms are the main clinical correlates of psychosocial impairments in patients with PTSD. Usage of Bayesian and regularized regression can contribute to the generalizability and interpretability of research results. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Neuropsychological Testing and Machine Learning Distinguish Alzheimer's Disease from Other Causes for Cognitive Impairment.

With promising results in recent treatment trials for Alzheimer's disease (AD), it becomes increasingly important to distinguish AD at early stages from other causes for cognitive impairment. However, existing diagnostic methods are either invasive (lumbar punctures, PET) or inaccurate Magnetic Resonance Imaging (MRI). This study investigates the potential of neuropsychological testing (NPT) to specifically identify those patients with possible AD among a sample of 158 patients with Mild Cognitive Impairment (MCI) or dementia for various causes. Patients were divided into an early stage and a late stage group according to their Mini Mental State Examination (MMSE) score and labeled as AD or non-AD patients based on a post-mortem validated threshold of the ratio between total tau and beta amyloid in the cerebrospinal fluid (CSF; Total tau/Aß(1-42) ratio, TB ratio). All patients completed the established Consortium to Establish a Registry for Alzheimer's Disease-Neuropsychological Assessment Battery (CERAD-NAB) test battery and two additional newly-developed neuropsychological tests (recollection and verbal comprehension) that aimed at carving out specific Alzheimer-typical deficits. Based on these test results, an underlying AD (pathologically increased TB ratio) was predicted with a machine learning algorithm. To this end, the algorithm was trained in each case on all patients except the one to predict (leave-one-out validation). In the total group, 82% of the patients could be correctly identified as AD or non-AD. In the early group with small general cognitive impairment, classification accuracy was increased to 89%. NPT thus seems to be capable of discriminating between AD patients and patients with cognitive impairment due to other neurodegenerative or vascular causes with a high accuracy, and may be used for screening in clinical routine and drug studies, especially in the early course of this disease.

Correction: Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning.

[This corrects the article DOI: 10.1371/journal.pcbi.1005328.].

Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning.

Theoretical accounts suggest that an alteration in the brain's learning mechanisms might lead to overhasty inferences, resulting in psychotic symptoms. Here, we sought to elucidate the suggested link between maladaptive learning and psychosis. Ninety-eight healthy individuals with varying degrees of delusional ideation and hallucinatory experiences performed a probabilistic reasoning task that allowed us to quantify overhasty inferences. Replicating previous results, we found a relationship between psychotic experiences and overhasty inferences during probabilistic reasoning. Computational modelling revealed that the behavioral data was best explained by a novel computational learning model that formalizes the adaptiveness of learning by a non-linear distortion of prediction error processing, where an increased non-linearity implies a growing resilience against learning from surprising and thus unreliable information (large prediction errors). Most importantly, a decreased adaptiveness of learning predicted delusional ideation and hallucinatory experiences. Our current findings provide a formal description of the computational mechanisms underlying overhasty inferences, thereby empirically substantiating theories that link psychosis to maladaptive learning.