AIMC Topic: Schizophrenia
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Identification and evaluation of cognitive deficits in schizophrenia using "Machine learning".
Psychiatria Danubina
Sep 1, 2019
BACKGROUND: Schizophrenia can be interpreted as a pathology involving the neocortex whose cognitive dysfunctions represent a central and persistent characteristic of the disease, as well as one of the more important symptoms in relation to the impair...
EEG Signals Classification Using Machine Learning for The Identification and Diagnosis of Schizophrenia.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
This paper presents the design of a machine learning-based classifier for the differentiation between Schizophrenia patients and healthy controls using features extracted from electroencephalograph(EEG) signals based on event related potential(ERP). ...
Principi di farmacodinamica e farmacocinetica nello switch tra antipsicotici: focus su cariprazina.
Rivista di psichiatria
Jan 1, 2019
Cariprazina {RGH-188; trans-N- [4- [2- [4- (2,3-diclorofenil) piperazin-1-il] etil] cicloesil] -N_, N_-dimetilurea cloridrato} รจ un antipsicotico atipico di nuova generazione, con un originale profilo farmacodinamico e farmacocinetico. Cariprazina ha...
Brain Age in Early Stages of Bipolar Disorders or Schizophrenia.
Schizophrenia bulletin
Jan 1, 2019
BACKGROUND: The greater presence of neurodevelopmental antecedants may differentiate schizophrenia from bipolar disorders (BD). Machine learning/pattern recognition allows us to estimate the biological age of the brain from structural magnetic resona...
Will Machine Learning Enable Us to Finally Cut the Gordian Knot of Schizophrenia.
Schizophrenia bulletin
Aug 20, 2018
Multivariate Pattern Analysis of Genotype-Phenotype Relationships in Schizophrenia.
Schizophrenia bulletin
Aug 20, 2018
Genetic risk variants for schizophrenia have been linked to many related clinical and biological phenotypes with the hopes of delineating how individual variation across thousands of variants corresponds to the clinical and etiologic heterogeneity wi...
Brain Subtyping Enhances The Neuroanatomical Discrimination of Schizophrenia.
Schizophrenia bulletin
Aug 20, 2018
Identifying distinctive subtypes of schizophrenia could ultimately enhance diagnostic and prognostic accuracy. We aimed to uncover neuroanatomical subtypes of chronic schizophrenia patients to test whether stratification can enhance computer-aided di...
Disease Definition for Schizophrenia by Functional Connectivity Using Radiomics Strategy.
Schizophrenia bulletin
Aug 20, 2018
Specific biomarker reflecting neurobiological substrates of schizophrenia (SZ) is required for its diagnosis and treatment selection of SZ. Evidence from neuroimaging has implicated disrupted functional connectivity in the pathophysiology. We aimed t...
Multisite Machine Learning Analysis Provides a Robust Structural Imaging Signature of Schizophrenia Detectable Across Diverse Patient Populations and Within Individuals.
Schizophrenia bulletin
Aug 20, 2018
Past work on relatively small, single-site studies using regional volumetry, and more recently machine learning methods, has shown that widespread structural brain abnormalities are prominent in schizophrenia. However, to be clinically useful, struct...
Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis.
Schizophrenia bulletin
Aug 20, 2018
BACKGROUND: The variability of responses to plasticity-inducing repetitive transcranial magnetic stimulation (rTMS) challenges its successful application in psychiatric care. No objective means currently exists to individually predict the patients' r...
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