Schizophrenia

BrainAGE score indicates accelerated brain aging in schizophrenia, but not bipolar disorder.

BrainAGE (brain age gap estimation) is a novel morphometric parameter providing a univariate score derived from multivariate voxel-wise analyses. It uses a machine learning approach and can be used to analyse deviation from physiological developmental or aging-related trajectories. Using structural MRI data and BrainAGE quantification of acceleration or deceleration of in individual aging, we anal...

Auditory prediction errors as individual biomarkers of schizophrenia.

Schizophrenia is a complex psychiatric disorder, typically diagnosed through symptomatic evidence collected through patient interview. We aim to develop an objective biologically-based computational tool which aids diagnosis and relies on accessible imaging technologies such as electroencephalography (EEG). To achieve this, we used machine learning techniques and a combination of paradigms designe...

Using short-range and long-range functional connectivity to identify schizophrenia with a family-based case-control design.

Abnormal short-range and long-range functional connectivities (FCs) have been implicated in the neurophysiology of schizophrenia. This study was condu...

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 sc...

Multi-center machine learning in imaging psychiatry: A meta-model approach.

One of the biggest problems in automated diagnosis of psychiatric disorders from medical images is the lack of sufficiently large samples for training...

Mimicking Neurotransmitter Release in Chemical Synapses via Hysteresis Engineering in MoS Transistors.

Neurotransmitter release in chemical synapses is fundamental to diverse brain functions such as motor action, learning, cognition, emotion, perception...

Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach.

OBJECTIVE: Suicide is a major concern for those afflicted by schizophrenia. Identifying patients at the highest risk for future suicide attempts remai...

A symptom network structure of the psychosis spectrum.

Current diagnostic systems mainly focus on symptoms needed to classify patients with a specific mental disorder and do not take into account the varia...

Deep dreaming, aberrant salience and psychosis: Connecting the dots by artificial neural networks.

Why some individuals, when presented with unstructured sensory inputs, develop altered perceptions not based in reality, is not well understood. Machi...

Effectiveness of Electroconvulsive Therapy Augmentation on Clozapine-Resistant Schizophrenia.

OBJECTIVE: This retrospective case series study of the effectiveness of electroconvulsive therapy (ECT) augmentation on clozapine-resistant schizophre...

Cortico-Striatal-Thalamic Loop Circuits of the Salience Network: A Central Pathway in Psychiatric Disease and Treatment.

The salience network (SN) plays a central role in cognitive control by integrating sensory input to guide attention, attend to motivationally salient ...

A Novel Sensitive Method to Measure Catechol-O-Methyltransferase Activity Unravels the Presence of This Activity in Extracellular Vesicles Released by Rat Hepatocytes.

There is a clear need for drug treatments to be selected according to the characteristics of an individual patient, in order to improve efficacy and r...

Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia.

Neuroimaging-based models contribute to increasing our understanding of schizophrenia pathophysiology and can reveal the underlying characteristics of...

Using clinical information to make individualized prognostic predictions in people at ultra high risk for psychosis.

Recent studies have reported an association between psychopathology and subsequent clinical and functional outcomes in people at ultra-high risk (UHR)...

Precuneus functioning differentiates first-episode psychosis patients during the fantasy movie Alice in Wonderland.

BACKGROUND: While group-level functional alterations have been identified in many brain regions of psychotic patients, multivariate machine-learning m...

Individual prediction of long-term outcome in adolescents at ultra-high risk for psychosis: Applying machine learning techniques to brain imaging data.

An important focus of studies of individuals at ultra-high risk (UHR) for psychosis has been to identify biomarkers to predict which individuals will ...

Thought-Controlled Nanoscale Robots in a Living Host.

We report a new type of brain-machine interface enabling a human operator to control nanometer-size robots inside a living animal by brain activity. R...

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 wit...

Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features.

Recently, an increasing number of researchers have endeavored to develop practical tools for diagnosing patients with schizophrenia using machine lear...

Dynamic cerebral reorganization in the pathophysiology of schizophrenia: a MRI-derived cortical thickness study.

BACKGROUND: A structural neuroanatomical change indicating a reduction in brain tissue is a notable feature of schizophrenia. Several pathophysiologic...