[Long-term courses of bipolar disorders].

Background: Bipolar disorder (short: BD) is a severe illness with very heterogeneous trajectories. While some of the patients show no or hardly any long-term impairments, other affected individuals show substantial neurocognitive deficits with a clear decline in psychosocial functioning. Which factors influence the course of the disease is the subject of current research efforts.

Larger putamen in individuals at risk and with manifest bipolar disorder.

Background: Individuals at risk for bipolar disorder (BD) have a wide range of genetic and non-genetic risk factors, like a positive family history of BD or (sub)threshold affective symptoms. Yet, it is unclear whether these individuals at risk and those diagnosed with BD share similar gray matter brain alterations.

Methods: In 410 male and female participants aged 17-35 years, we compared gray matter volume (3T MRI) between individuals at risk for BD (as assessed using the EPI scale; = 208), patients with a DSM-IV-TR diagnosis of BD ( = 87), and healthy controls ( = 115) using voxel-based morphometry in SPM12/CAT12.

Young people at risk for developing bipolar disorder: Two-year findings from the multicenter prospective, naturalistic Early-BipoLife study.

Early identification and intervention of individuals with an increased risk for bipolar disorder (BD) may improve the course of illness and prevent long‑term consequences. Early-BipoLife, a multicenter, prospective, naturalistic study, examined risk factors of BD beyond family history in participants aged 15-35 years. At baseline, positively screened help-seeking participants (screenBD at-risk) were recruited at Early Detection Centers and in- and outpatient depression and attention-deficit/hyperactivity disorder (ADHD) settings, references (Ref) drawn from a representative cohort.

Prediction of estimated risk for bipolar disorder using machine learning and structural MRI features.

Background: Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features.

Training a machine learning classifier to identify ADHD based on real-world clinical data from medical records.

The diagnostic process of attention deficit hyperactivity disorder (ADHD) is complex and relies on criteria sensitive to subjective biases. This may cause significant delays in appropriate treatment initiation. An automated analysis relying on subjective and objective measures might not only simplify the diagnostic process and reduce the time to diagnosis, but also improve reproducibility.

[Practical aspects of ketamine treatment-Safety, combination treatment and comorbidities].

Background: Although multiple studies and meta-analyses have documented the rapid antidepressive efficacy of ketamine, there are numerous questions regarding the practical use in the clinical routine that are still unanswered.

Objective: Based on personal clinical experience, by comparison and supplementation of the current data situation, answers are given to questions regarding the practical use of ketamine for depression that have not yet been satisfactorily clarified.

Material And Methods: The clinical experiences with antidepressive treatment using ketamine over more than 5 years were evaluated with respect to the questions at hand.

Individuals at increased risk for development of bipolar disorder display structural alterations similar to people with manifest disease.

In psychiatry, there has been a growing focus on identifying at-risk populations. For schizophrenia, these efforts have led to the development of early recognition and intervention measures. Despite a similar disease burden, the populations at risk of bipolar disorder have not been sufficiently characterized.

Effects of early life adversity and FKBP5 genotype on hippocampal subfields volume in major depression.

Background: Smaller hippocampus volume represents a consistent finding in major depression (MDD). Hippocampal neuroplasticity due to chronic stress might have differential effect on hippocampal subfields. We investigated the effects of the rs1360780 polymorphism of the hypothalamic-pituitary-axis related gene FKBP5 in combination with early life stress (ELA) on the structure of hippocampal subfields in MDD.

Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy.

Background: Early diagnosis of schizophrenia could improve the outcome of the illness. Unlike classical between-group comparisons, machine learning can identify subtle disease patterns on a single subject level, which could help realize the potential of MRI in establishing a psychiatric diagnosis. Machine learning has previously been predominantly tested on gray-matter structural or functional MRI data.

Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: a machine-learning study.

Background: Early diagnosis of schizophrenia could improve the outcomes and limit the negative effects of untreated illness. Although participants with schizophrenia show aberrant functional connectivity in brain networks, these between-group differences have a limited diagnostic utility. Novel methods of magnetic resonance imaging (MRI) analyses, such as machine learning (ML), may help bring neuroimaging from the bench to the bedside.

Altered Neural Correlate of the Self-Agency Experience in First-Episode Schizophrenia-Spectrum Patients: An fMRI Study.

Background: The phenomenology of the clinical symptoms indicates that disturbance of the sense of self be a core marker of schizophrenia.

Aims: To compare neural activity related to the self/other-agency judgment in patients with first-episode schizophrenia-spectrum disorders (FES, n = 35) and healthy controls (HC, n = 35).

Method: A functional magnetic resonance imaging (fMRI) using motor task with temporal distortion of the visual feedback was employed.

Sad mood induction has an opposite effect on amygdala response to emotional stimuli in euthymic patients with bipolar disorder and healthy controls.

Background: Aberrant amygdala reactivity to affective stimuli represents a candidate factor predisposing patients with bipolar disorder (BD) to relapse, but it is unclear to what extent amygdala reactivity is state-dependent. We evaluated the modulatory influence of mood on amygdala reactivity and functional connectivity in patients with remitted BD and healthy controls.

Methods: Amygdala response to sad versus neutral faces was investigated using fMRI during periods of normal and sad mood induced by autobiographical scripts.

White matter changes in first episode psychosis and their relation to the size of sample studied: a DTI study.

Background: White matter abnormality has been recently proposed as a pathophysiological feature of schizophrenia (SZ). However, most of the data available has been gathered from chronic patients, and was therefore possibly confounded by factors such as duration of the disease, and treatment received. The extent and localization of these changes is also not clear.

A virtual reality task based on animal research - spatial learning and memory in patients after the first episode of schizophrenia.

Objectives: Cognitive deficit is considered to be a characteristic feature of schizophrenia disorder. A similar cognitive dysfunction was demonstrated in animal models of schizophrenia. However, the poor comparability of methods used to assess cognition in animals and humans could be responsible for low predictive validity of current animal models.

GEI-8, a homologue of vertebrate nuclear receptor corepressor NCoR/SMRT, regulates gonad development and neuronal functions in Caenorhabditis elegans.

NCoR and SMRT are two paralogous vertebrate proteins that function as corepressors with unliganded nuclear receptors. Although C. elegans has a large number of nuclear receptors, orthologues of the corepressors NCoR and SMRT have not unambiguously been identified in Drosophila or C.

Analysis of fMRI time-series by entropy measures.

Entropy is a measure of information content or complexity. Information-theoretic modeling has been successfully used in various biological data analyses including functional magnetic resonance (fMRI). Several studies have tested and evaluated entropy measures on simulated datasets and real fMRI data.