Psychiatry

Schizophrenia

Latest AI and machine learning research in schizophrenia for healthcare professionals.

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Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness.

Neuropsychiatric disorders such as schizophrenia are very heterogeneous in nature and typically diag...

Schizophrenia Detection in Adolescents from EEG Signals using Symmetrically weighted Local Binary Patterns.

Schizophrenia is one of the most complex of all mental diseases. In this paper, we propose a symmetr...

Obesity as a Risk Factor for Accelerated Brain Ageing in First-Episode Psychosis-A Longitudinal Study.

BACKGROUND: Obesity is highly prevalent in schizophrenia, with implications for psychiatric prognosi...

A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images.

Computer Assisted Diagnosis (CAD) based on brain Magnetic Resonance Imaging (MRI) is a popular resea...

Robot-induced hallucinations in Parkinson's disease depend on altered sensorimotor processing in fronto-temporal network.

Hallucinations in Parkinson's disease (PD) are disturbing and frequent non-motor symptoms and consti...

Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk.

BACKGROUND: Using novel data mining methods such as natural language processing (NLP) on electronic ...

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression.

IMPORTANCE: Diverse models have been developed to predict psychosis in patients with clinical high-r...

Computational framework for detection of subtypes of neuropsychiatric disorders based on DTI-derived anatomical connectivity.

Many brain disorders - such as Alzheimer's disease, Parkinson's disease, schizophrenia and autism - ...

Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker.

The aging process is accompanied by changes in the brain's cortex at many levels. There is growing i...

Predicting Early Stage Drug Induced Parkinsonism using Unsupervised and Supervised Machine Learning.

Drug Induced Parkinsonism (DIP) is the most common, debilitating movement disorder induced by antips...

MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide.

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ...

Increasing the Clinical Psychiatric Knowledge Base About Pathogenic Copy Number Variation.

Specific copy number variants (CNVs) have been robustly associated with intellectual disability, aut...

Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning.

Neurobiological heterogeneity in schizophrenia is poorly understood and confounds current analyses. ...

Identifying Schizo-Obsessive Comorbidity by Tract-Based Spatial Statistics and Probabilistic Tractography.

A phenomenon in schizophrenia patients that deserves attention is the high comorbidity rate with obs...

Functional, Anatomical, and Morphological Networks Highlight the Role of Basal Ganglia-Thalamus-Cortex Circuits in Schizophrenia.

Evidence from electrophysiological, functional, and structural research suggests that abnormal brain...

Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence.

Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect ps...

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 ...

Hierarchical Structured Sparse Learning for Schizophrenia Identification.

Fractional amplitude of low-frequency fluctuation (fALFF) has been widely used for resting-state fun...

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

Machine learning technique has long been utilized to assist disease diagnosis, increasing clinical p...

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

BACKGROUND: Schizophrenia can be interpreted as a pathology involving the neocortex whose cognitive ...

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