AIMC Topic: Schizophrenia
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The Early Psychosis Screener (EPS): Quantitative validation against the SIPS using machine learning.
Schizophrenia research
Jan 19, 2018
Machine learning techniques were used to identify highly informative early psychosis self-report items and to validate an early psychosis screener (EPS) against the Structured Interview for Psychosis-risk Syndromes (SIPS). The Prodromal Questionnaire...
Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity.
Human brain mapping
Jan 16, 2018
Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory events remains difficult and time-consuming. This article first proposes a machine-learning algorithm to automatically identify resting-state fMRI pe...
Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study.
Schizophrenia research
Dec 21, 2017
Machine learning is a powerful tool that has previously been used to classify schizophrenia (SZ) patients from healthy controls (HC) using magnetic resonance images. Each study, however, uses different datasets, classification algorithms, and validat...
Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods.
Schizophrenia research
Dec 21, 2017
UNLABELLED: Schizophrenia is associated with heterogeneous clinical symptoms and neuroanatomical alterations. In this work, we aim to disentangle the patterns of neuroanatomical alterations underlying a heterogeneous population of patients using a se...
Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM.
BMC medical informatics and decision making
Dec 20, 2017
BACKGROUND: Schizophrenia is a kind of serious mental illness. Due to the lack of an objective physiological data supporting and a unified data analysis method, doctors can only rely on the subjective experience of the data to distinguish normal peop...
Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI.
Schizophrenia research
Dec 6, 2017
Although regional brain deficits have been demonstrated in schizophrenia patients by structural MRI studies, one important question that remains largely unanswered is whether the complex and subtle deficits revealed by MRI could be used as objective ...
A novel fuzzy rough selection of non-linearly extracted features for schizophrenia diagnosis using fMRI.
Computer methods and programs in biomedicine
Dec 6, 2017
BACKGROUND AND OBJECTIVES: Schizophrenia is a severe brain disorder primarily diagnosed through externally observed behavioural symptoms due to the dearth of established clinical tests. Functional magnetic resonance imaging (fMRI) can capture the dis...
Does this robot have a mind? Schizophrenia patients' mind perception toward humanoid robots.
Schizophrenia research
Dec 2, 2017
Influence of facial feedback during a cooperative human-robot task in schizophrenia.
Scientific reports
Nov 3, 2017
Rapid progress in the area of humanoid robots offers tremendous possibilities for investigating and improving social competences in people with social deficits, but remains yet unexplored in schizophrenia. In this study, we examined the influence of ...
Neurocognitive Graphs of First-Episode Schizophrenia and Major Depression Based on Cognitive Features.
Neuroscience bulletin
Nov 2, 2017
Neurocognitive deficits are frequently observed in patients with schizophrenia and major depressive disorder (MDD). The relations between cognitive features may be represented by neurocognitive graphs based on cognitive features, modeled as Gaussian ...
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