Schizophrenia

Systematic Review of Digital Phenotyping and Machine Learning in Psychosis Spectrum Illnesses.

BACKGROUND: Digital phenotyping is the use of data from smartphones and wearables collected in situ for capturing a digital expression of human behaviors. Digital phenotyping techniques can be used to analyze both passively (e.g., sensor) and actively (e.g., survey) collected data. Machine learning offers a possible predictive bridge between digital phenotyping and future clinical state. This revi...

Hierarchical Structured Sparse Learning for Schizophrenia Identification.

Fractional amplitude of low-frequency fluctuation (fALFF) has been widely used for resting-state functional magnetic resonance imaging (rs-fMRI) based schizophrenia (SZ) diagnosis. However, previous studies usually measure the fALFF within low-frequency fluctuation (from 0.01 to 0.08Hz), which cannot fully cover the complex neural activity pattern in the resting-state brain. In addition, existing ...

Machine learning technique reveals intrinsic characteristics of schizophrenia: an alternative method.

Machine learning technique has long been utilized to assist disease diagnosis, increasing clinical physicians' confidence in their decision and expedi...

Identification and evaluation of cognitive deficits in schizophrenia using "Machine learning".

BACKGROUND: Schizophrenia can be interpreted as a pathology involving the neocortex whose cognitive dysfunctions represent a central and persistent ch...

Annotating Temporal Relations to Determine the Onset of Psychosis Symptoms.

For patients with a diagnosis of schizophrenia, determining symptom onset is crucial for timely and successful intervention. In mental health records,...

EEG Signals Classification Using Machine Learning for The Identification and Diagnosis of Schizophrenia.

This paper presents the design of a machine learning-based classifier for the differentiation between Schizophrenia patients and healthy controls usin...

A Social Media Study on the Effects of Psychiatric Medication Use.

Understanding the effects of psychiatric medications during mental health treatment constitutes an active area of inquiry. While clinical trials help ...

Recurrent Neural Networks in Mobile Sampling and Intervention.

The rapid rise and now widespread distribution of handheld and wearable devices, such as smartphones, fitness trackers, or smartwatches, has opened a ...

Brain Age in Early Stages of Bipolar Disorders or Schizophrenia.

BACKGROUND: The greater presence of neurodevelopmental antecedants may differentiate schizophrenia from bipolar disorders (BD). Machine learning/patte...

Use of Machine Learning to Determine Deviance in Neuroanatomical Maturity Associated With Future Psychosis in Youths at Clinically High Risk.

IMPORTANCE: Altered neurodevelopmental trajectories are thought to reflect heterogeneity in the pathophysiologic characteristics of schizophrenia, but...

Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis.

BACKGROUND: The variability of responses to plasticity-inducing repetitive transcranial magnetic stimulation (rTMS) challenges its successful applicat...

Multisite Machine Learning Analysis Provides a Robust Structural Imaging Signature of Schizophrenia Detectable Across Diverse Patient Populations and Within Individuals.

Past work on relatively small, single-site studies using regional volumetry, and more recently machine learning methods, has shown that widespread str...

Disease Definition for Schizophrenia by Functional Connectivity Using Radiomics Strategy.

Specific biomarker reflecting neurobiological substrates of schizophrenia (SZ) is required for its diagnosis and treatment selection of SZ. Evidence f...

Brain Subtyping Enhances The Neuroanatomical Discrimination of Schizophrenia.

Identifying distinctive subtypes of schizophrenia could ultimately enhance diagnostic and prognostic accuracy. We aimed to uncover neuroanatomical sub...

Multivariate Pattern Analysis of Genotype-Phenotype Relationships in Schizophrenia.

Genetic risk variants for schizophrenia have been linked to many related clinical and biological phenotypes with the hopes of delineating how individu...

Will Machine Learning Enable Us to Finally Cut the Gordian Knot of Schizophrenia.

Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI.

Schizophrenia has been proposed to result from impairment of functional connectivity. We aimed to use machine learning to distinguish schizophrenic su...

A New Machine Learning Framework for Understanding the Link Between Cannabis Use and First-Episode Psychosis.

Lately, several studies started to investigate the existence of links between cannabis use and psychotic disorders. This work proposes a refined Machi...

A pet-type robot Artificial Intelligence Robot-assisted therapy for a patient with schizophrenia.

Threshold of Dopamine D2/3 Receptor Occupancy for Hyperprolactinemia in Older Patients With Schizophrenia.

OBJECTIVE: Although hyperprolactinemia carries a long-term risk of morbidity, the threshold of dopamine D2/3 receptor (D2/3R) occupancy for hyperprola...