Clinical correlates of early onset antipsychotic treatment resistance.

Background: There is evidence of heterogeneity within treatment-resistant schizophrenia (TRS), with some people not responding to antipsychotic treatment from illness onset and others becoming treatment-resistant after an initial response period. These groups may have different aetiologies.

Aim: This study investigates sociodemographic and clinical correlates of early onset of TRS.

A predictor model of treatment resistance in schizophrenia using data from electronic health records.

Objectives: To develop a prognostic tool of treatment resistant schizophrenia (TRS) in a large and diverse clinical cohort, with comprehensive coverage of patients using mental health services in four London boroughs.

Methods: We used the Least Absolute Shrinkage and Selection Operator (LASSO) for time-to-event data, to develop a risk prediction model from the first antipsychotic prescription to the development of TRS, using data from electronic health records.

Results: We reviewed the clinical records of 1,515 patients with a schizophrenia spectrum disorder and observed that 253 (17%) developed TRS.

Using a statistical learning approach to identify sociodemographic and clinical predictors of response to clozapine.

Background: A proportion of people with treatment-resistant schizophrenia fail to show improvement on clozapine treatment. Knowledge of the sociodemographic and clinical factors predicting clozapine response may be useful in developing personalised approaches to treatment.

Methods: This retrospective cohort study used data from the electronic health records of the South London and Maudsley (SLaM) hospital between 2007 and 2011.

NeuroPharm study: EEG wakefulness regulation as a biomarker in MDD.

While several electroencephalogram (EEG)-based biomarkers have been proposed as diagnostic or predictive tools in major depressive disorder (MDD), there is a clear lack of replication studies in this field. Markers that link clinical features such as disturbed wakefulness regulation in MDD with neurophysiological patterns are particularly promising candidates for e.g.

Pretreatment qEEG biomarkers for predicting pharmacological treatment outcome in major depressive disorder: Independent validation from the NeuroPharm study.

Several electroencephalogram (EEG) biomarkers for prediction of drug response in major depressive disorder (MDD) have been proposed, but validations in larger independent datasets are missing. In the current study, we investigated the prognostic value of previously suggested EEG biomarkers. We gathered data that matched prior studies in terms of EEG methodology, clinical criteria for MDD, and statistical approach as closely as possible.

A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data.

The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias and overfitting by invoking simulated data in the design process and analysis in two independent machine-learning approaches, one based on a single algorithm and the other incorporating an ensemble of algorithms. We aimed to (1) classify patients from controls to establish the framework, (2) predict short- and long-term treatment response, and (3) validate the methodological framework.

Effects of Vortioxetine and Escitalopram on Electroencephalographic Recordings - A Randomized, Crossover Trial in Healthy Males.

The antidepressant drug vortioxetine has a multimodal action modulating neurotransmission through inhibition of the serotonin transporter and modulation of serotonin receptors. Vortioxetine has also been shown to alleviate cognitive symptoms in preclinical studies and in patients with depression. However, it is largely unclear how vortioxetine affects the brain processing in humans.

Pre-intervention test-retest reliability of EEG and ERP over four recording intervals.

In this study we present the test-retest reliability of pre-intervention EEG/ERP (electroencephalogram/event-related potentials) data across four recording intervals separated by a washout period (18-22 days). POz-recording-reference EEG/ERP (28 sites, average reference) were recorded from thirty-two healthy male participants. Participants were randomly allocated into different intervention sequences, each with four intervention regimens: 10 mg vortioxetine, 20 mg vortioxetine, 15 mg escitalopram and Placebo.

Sleep stability and transitions in patients with idiopathic REM sleep behavior disorder and patients with Parkinson's disease.

Objective: Patients with idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) are at high risk of developing Parkinson's disease (PD). As wake/sleep-regulation is thought to involve neurons located in the brainstem and hypothalamic areas, we hypothesize that the neurodegeneration in iRBD/PD is likely to affect wake/sleep and REM/non-REM (NREM) sleep transitions.

Methods: We determined the frequency of wake/sleep and REM/NREM sleep transitions and the stability of wake (W), REM and NREM sleep as measured by polysomnography (PSG) in 27 patients with PD, 23 patients with iRBD, 25 patients with periodic leg movement disorder (PLMD) and 23 controls.

Data-driven modeling of sleep EEG and EOG reveals characteristics indicative of pre-Parkinson's and Parkinson's disease.

Background: Manual scoring of sleep relies on identifying certain characteristics in polysomnograph (PSG) signals. However, these characteristics are disrupted in patients with neurodegenerative diseases.

New Method: This study evaluates sleep using a topic modeling and unsupervised learning approach to identify sleep topics directly from electroencephalography (EEG) and electrooculography (EOG).

Automatic sleep classification using a data-driven topic model reveals latent sleep states.

Background: The golden standard for sleep classification uses manual scoring of polysomnography despite points of criticism such as oversimplification, low inter-rater reliability and the standard being designed on young and healthy subjects.

New Method: To meet the criticism and reveal the latent sleep states, this study developed a general and automatic sleep classifier using a data-driven approach. Spectral EEG and EOG measures and eye correlation in 1s windows were calculated and each sleep epoch was expressed as a mixture of probabilities of latent sleep states by using the topic model Latent Dirichlet Allocation.

Discovery and development of 11C-Lu AE92686 as a radioligand for PET imaging of phosphodiesterase10A in the brain.

Unlabelled: Phosphodiesterase 10A (PDE10A) plays a key role in the regulation of brain striatal signaling, and several pharmaceutical companies currently investigate PDE10A inhibitors in clinical trials for various central nervous system diseases. A PDE10A PET ligand may provide evidence that a clinical drug candidate reaches and binds to the target. Here we describe the successful discovery and initial validation of the novel radiolabeled PDE10A ligand 5,8-dimethyl-2-[2-((1-(11)C-methyl)-4-phenyl-1H-imidazol-2-yl)-ethyl]-[1,2,4]triazolo[1,5-a]pyridine ((11)C-Lu AE92686) and its tritiated analog (3)H-Lu AE92686.

Decreased sleep spindle density in patients with idiopathic REM sleep behavior disorder and patients with Parkinson's disease.

Objective: To determine whether sleep spindles (SS) are potentially a biomarker for Parkinson's disease (PD).

Methods: Fifteen PD patients with REM sleep behavior disorder (PD+RBD), 15 PD patients without RBD (PD-RBD), 15 idiopathic RBD (iRBD) patients and 15 age-matched controls underwent polysomnography (PSG). SS were scored in an extract of data from control subjects.

Classification of iRBD and Parkinson's patients using a general data-driven sleep staging model built on EEG.

Sleep analysis is an important diagnostic tool for sleep disorders. However, the current manual sleep scoring is time-consuming as it is a crude discretization in time and stages. This study changes Esbroeck and Westover's [1] latent sleep staging model into a global model.

Classification of iRBD and Parkinson's disease patients based on eye movements during sleep.

Patients suffering from the sleep disorder idiopathic rapid-eye-movement sleep behavior disorder (iRBD) have been observed to be in high risk of developing Parkinson's disease (PD). This makes it essential to analyze them in the search for PD biomarkers. This study aims at classifying patients suffering from iRBD or PD based on features reflecting eye movements (EMs) during sleep.

Can pharmaco-electroencephalography help improve survival of central nervous system drugs in early clinical development?

Pharmaco-electroencephalography has significant yet unrealised promise as a translatable intermediate biomarker of central pharmacodynamic activity that could help reduce Phase 2 attrition in the development of central nervous system drugs. In an effort to understand its true potential, a framework for decision-making was proposed and the utility of pharmaco-electroencephalography was assessed through several case studies. A key finding was that lack of standardisation reduces the value of data pooling and meta-analyses and renders assessment of translatability difficult, limiting utility in all but simple cases.

Separation of Parkinson's patients in early and mature stages from control subjects using one EOG channel.

In this study, polysomnographic left side EOG signals from ten control subjects, ten iRBD patients and ten Parkinson's patients were decomposed in time and frequency using wavelet transformation. A total of 28 features were computed as the means and standard deviations in energy measures from different reconstructed detail subbands across all sleep epochs during a whole night of sleep. A subset of features was chosen based on a cross validated Shrunken Centroids Regularized Discriminant Analysis, where the controls were treated as one group and the patients as another.

Broadband criticality of human brain network synchronization.

Self-organized criticality is an attractive model for human brain dynamics, but there has been little direct evidence for its existence in large-scale systems measured by neuroimaging. In general, critical systems are associated with fractal or power law scaling, long-range correlations in space and time, and rapid reconfiguration in response to external inputs. Here, we consider two measures of phase synchronization: the phase-lock interval, or duration of coupling between a pair of (neurophysiological) processes, and the lability of global synchronization of a (brain functional) network.

Local and global effects of I(h) distribution in dendrites of mammalian neurons.

The hyperpolarization-activated cation current I(h) exhibits a steep gradient of channel density in dendrites of pyramidal neurons, which is associated with location independence of temporal summation of EPSPs at the soma. In striking contrast, here we show by using dendritic patch-clamp recordings that in cerebellar Purkinje cells, the principal neurons of the cerebellar cortex, I(h) exhibits a uniform dendritic density, while location independence of EPSP summation is observed. Using compartmental modeling in realistic and simplified dendritic geometries, we demonstrate that the dendritic distribution of I(h) only weakly affects the degree of temporal summation at the soma, while having an impact at the dendritic input location.

Membrane potential bistability is controlled by the hyperpolarization-activated current I(H) in rat cerebellar Purkinje neurons in vitro.

We investigated the role of the hyperpolarization-activated mixed cation current, I(H), in the control of spontaneous action potential firing of rat cerebellar Purkinje neurons in brain slices. Extracellular recordings revealed that the continual action potential firing of Purkinje neurons was disrupted by the pharmacological blockade of I(H). Blockade of I(H) revealed spontaneous transitions between periods of tonic action potential firing and quiescence, without effects on the frequency or variance of action potential generation.