AIMC Topic: Schizophrenia
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Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.
NeuroImage
May 15, 2015
Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, ...
Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM.
Neural networks : the official journal of the International Neural Network Society
Apr 23, 2015
Resting state functional Magnetic Resonance Imaging (rs-fMRI) is increasingly used for the identification of image biomarkers of brain diseases or psychiatric conditions such as schizophrenia. This paper deals with the application of ensembles of Ext...
Abstract computation in schizophrenia detection through artificial neural network based systems.
TheScientificWorldJournal
Mar 5, 2015
Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fr...
Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI.
International journal of neural systems
Jan 19, 2015
Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and even a small percentage of healthy individuals, may also experience AH. Elucidating the neural mecha...
A machine learning approach using auditory odd-ball responses to investigate the effect of Clozapine therapy.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Aug 27, 2014
OBJECTIVE: To develop a machine learning (ML) methodology based on features extracted from odd-ball auditory evoked potentials to identify neurophysiologic changes induced by Clozapine (CLZ) treatment in responding schizophrenic (SCZ) subjects. This ...
Predictors of schizophrenia spectrum disorders in early-onset first episodes of psychosis: a support vector machine model.
European child & adolescent psychiatry
Aug 11, 2014
Identifying early-onset schizophrenia spectrum disorders (SSD) at a very early stage remains challenging. To assess the diagnostic predictive value of multiple types of data at the emergence of early-onset first-episode psychosis (FEP), various suppo...
RIPTOSO: The development of a screening tool for adverse events during forensic-psychiatric inpatient treatments of offenders with schizophrenia spectrum disorders.
Psychiatry research
Aug 1, 2025
Adverse events such as compulsory measures, absconding, illicit substance use, self-harm, aggressive behavior, and prolonged hospitalization pose significant challenges in forensic psychiatric inpatient care. This study introduces a machine learning-...
Modeling the Determinants of Subjective Well-Being in Schizophrenia.
Schizophrenia bulletin
Jul 7, 2025
BACKGROUND: The ultimate goal of successful schizophrenia treatment is not just to alleviate psychotic symptoms, but also to reduce distress and achieve subjective well-being (SWB). We aimed to identify the determinants of SWB and their interrelation...
Distinct processing stages of cross-modal conflict in schizophrenia: The role of auditory cortex underactivation.
Schizophrenia research
Jul 1, 2025
BACKGROUND: The cross-modal conflict deficit is a key feature of schizophrenia. However, it remains largely unknown whether cross-modal conflict in schizophrenia diverges at distinct processing stages and its potential association with the auditory c...
Machine learning approaches for fine-grained symptom estimation in schizophrenia: A comprehensive review.
Artificial intelligence in medicine
Jul 1, 2025
Schizophrenia is a severe yet treatable mental disorder, and it is diagnosed using a multitude of primary and secondary symptoms. Diagnosis and treatment for each individual depends on the severity of the symptoms. Therefore, there is a need for accu...
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