Improved estimation of the risk of manic relapse by combining clinical and brain scan data.

Introduction: Estimating the risk of manic relapse could help the psychiatrist individually adjust the treatment to the risk. Some authors have attempted to estimate this risk from baseline clinical data. Still, no studies have assessed whether the estimation could improve by adding structural magnetic resonance imaging (MRI) data.

Emerging cycloid psychosis episodes during COVID-19 pandemic: a case series.

Aims: Cycloid psychosis (CP) is a clinical entity characterized by sudden onset of psychotic polymorphic symptomatology and fluctuant course. It has a reported rate of psychosocial precipitating factors ranging 30-65%. The aim of the study was to describe all cases of CP, admitted in our Psychiatry ward, during the first two months of the COVID-19 pandemic.

Differential failure to deactivate the default mode network in unipolar and bipolar depression.

Objectives: Neuroimaging studies have revealed evidence of brain functional abnormalities in bipolar depressive disorder (BDD) and major depressive disorder (MDD). However, few studies to date have compared these two mood disorders directly.

Methods: Matched groups of 26 BDD type I patients, 26 MDD patients and 26 healthy controls underwent functional magnetic resonance imaging (fMRI) while performing the n-back working memory task.

Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis.

A relatively large number of studies have investigated the power of structural magnetic resonance imaging (sMRI) data to discriminate patients with schizophrenia from healthy controls. However, very few of them have also included patients with bipolar disorder, allowing the clinically relevant discrimination between both psychotic diagnostics. To assess the efficacy of sMRI data for diagnostic prediction in psychosis we objectively evaluated the discriminative power of a wide range of commonly used machine learning algorithms (ridge, lasso, elastic net and L0 norm regularized logistic regressions, a support vector classifier, regularized discriminant analysis, random forests and a Gaussian process classifier) on main sMRI features including grey and white matter voxel-based morphometry (VBM), vertex-based cortical thickness and volume, region of interest volumetric measures and wavelet-based morphometry (WBM) maps.

Structural and Functional Brain Correlates of Cognitive Impairment in Euthymic Patients with Bipolar Disorder.

Introduction: Cognitive impairment in the euthymic phase is a well-established finding in bipolar disorder. However, its brain structural and/or functional correlates are uncertain.

Methods: Thirty-three euthymic bipolar patients with preserved memory and executive function and 28 euthymic bipolar patients with significant memory and/or executive impairment, as defined using two test batteries, the Rivermead Behavioural Memory Test (RBMT) and the Behavioural Assessment of the Dysexecutive Syndrome (BADS), plus 28 healthy controls underwent structural MRI using voxel-based morphometry (VBM).