Schizophrenia

Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia.

Retrotransposons can cause somatic genome variation in the human nervous system, which is hypothesized to have relevance to brain development and neuropsychiatric disease. However, the detection of individual somatic mobile element insertions presents a difficult signal-to-noise problem. Using a machine-learning method (RetroSom) and deep whole-genome sequencing, we analyzed L1 and Alu retrotransp...

Hard for humans, hard for machines: predicting readmission after psychiatric hospitalization using narrative notes.

Machine learning has been suggested as a means of identifying individuals at greatest risk for hospital readmission, including psychiatric readmission. We sought to compare the performance of predictive models that use interpretable representations derived via topic modeling to the performance of human experts and nonexperts. We examined all 5076 admissions to a general psychiatry inpatient unit b...

High quality and fast compressed sensing MRI reconstruction via edge-enhanced dual discriminator generative adversarial network.

Generative adversarial networks (GAN) are widely used for fast compressed sensing magnetic resonance imaging (CSMRI) reconstruction. However, most exi...

Identification of Children at Risk of Schizophrenia via Deep Learning and EEG Responses.

The prospective identification of children likely to develop schizophrenia is a vital tool to support early interventions that can mitigate the risk o...

The machine learning algorithm for the diagnosis of schizophrenia on the basis of gene expression in peripheral blood.

BACKGROUND: Schizophrenia (SCZ) is a highly heritable mental disorder with a substantial disease burden. Machine learning (ML) method can be used to i...

Digital Gaming Interventions in Psychiatry: Evidence, Applications and Challenges.

Human evolution has regularly intersected with technology. Digitalization of various services has brought a paradigm shift in consumerism. Treading th...

Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites.

Structural brain alterations have been repeatedly reported in schizophrenia; however, the pathophysiology of its alterations remains unclear. Multivar...

Moving targets in drug discovery.

Drug Discovery is a lengthy and costly process and has faced a period of declining productivity within the last two decades resulting in increasing im...

Comparing machine and deep learning-based algorithms for prediction of clinical improvement in psychosis with functional magnetic resonance imaging.

Previous work using logistic regression suggests that cognitive control-related frontoparietal activation in early psychosis can predict symptomatic i...

Characterization of specific and distinct patient types in clinical trials of acute schizophrenia using an uncorrelated PANSS score matrix transform (UPSM).

Understanding the specificity of symptom change in schizophrenia can facilitate the evaluation antipsychotic efficacy for different symptom domains. P...

Construction of embedded fMRI resting-state functional connectivity networks using manifold learning.

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired...

Identifying and validating subtypes within major psychiatric disorders based on frontal-posterior functional imbalance via deep learning.

Converging evidence increasingly implicates shared etiologic and pathophysiological characteristics among major psychiatric disorders (MPDs), such as ...

Inner speech.

Inner speech travels under many aliases: the inner voice, verbal thought, thinking in words, internal verbalization, "talking in your head," the "litt...

Multi-dimensional predictions of psychotic symptoms via machine learning.

The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of ...

Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks.

BACKGROUND: Schizophrenia spectrum disorders (SSDs) are chronic conditions, but the severity of symptomatic experiences and functional impairments vac...

Reconfiguration of αmplitude driven dominant coupling modes (DoCM) mediated by α-band in adolescents with schizophrenia spectrum disorders.

Electroencephalography (EEG) based biomarkers have been shown to correlate with the presence of psychotic disorders. Increased delta and decreased alp...

Identifying influential factors distinguishing recidivists among offender patients with a diagnosis of schizophrenia via machine learning algorithms.

PURPOSE: There is a lack of research on predictors of criminal recidivism of offender patients diagnosed with schizophrenia.

A deep learning model for detecting mental illness from user content on social media.

Users of social media often share their feelings or emotional states through their posts. In this study, we developed a deep learning model to identif...

Automated design and optimization of multitarget schizophrenia drug candidates by deep learning.

Complex neuropsychiatric diseases such as schizophrenia require drugs that can target multiple G protein-coupled receptors (GPCRs) to modulate complex...

A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI.

Segmentation of the hippocampus (HC) in magnetic resonance imaging (MRI) is an essential step for diagnosis and monitoring of several clinical situati...