scholarly journals Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Walid Yassin ◽  
Hironori Nakatani ◽  
Yinghan Zhu ◽  
Masaki Kojima ◽  
Keiho Owada ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
First Episode Psychosis ◽  
First Episode ◽  
Machine Learning Classification ◽  
Episode Psychosis ◽  
Neuroimaging Data ◽  
Ultra High Risk

scholarly journals S150. EMOTIONAL BEHAVIOUR IN HIGH-RISK AND FIRST-EPISODE PSYCHOSIS

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S93-S93
Author(s):  
Irina Falkenberg ◽  
Huai-Hsuan Tseng ◽  
Gemma Modinos ◽  
Barbara Wild ◽  
Philip McGuire ◽  
...  
Keyword(s):  
High Risk ◽  
Emotion Recognition ◽  
Psychotic Disorders ◽  
First Episode Psychosis ◽  
First Episode ◽  
Healthy Controls ◽  
Emotional Faces ◽  
Facial Movements ◽  
Episode Psychosis ◽  
Ultra High Risk

Abstract Background Studies indicate that people with schizophrenia and first-episode psychosis experience deficits in their ability to accurately detect and display emotions through facial expressions, and that functioning and symptoms are associated with these deficits. This study aims to examine how emotion recognition and facial emotion expression are related to functioning and symptoms in a sample of individuals at ultra-high risk, first-episode psychosis and healthy controls. Methods During fMRI, we combined the presentation of emotional faces with the instruction to react with facial movements predetermined and assigned. 18 patients with first-episode psychosis (FEP), 18 individuals at ultra high risk of psychosis (UHR) and 22 healthy controls (HCs) were examined while viewing happy, sad, or neutral faces and were instructed to simultaneously move the corners of their mouths either (a). upwards or (b). downwards, or (c). to refrain from movement. The subjects’ facial movements were recorded with an MR-compatible video camera. Results Neurofunctional and behavioral response to emotional faces were measured. Analyses have only recently commenced and are ongoing. Full results of the clinical and functional impact of behavioral and neuroimaging results will be presented at the meeting. Discussion Increased knowledge about abnormalities in emotion recognition and behaviour as well as their neural correlates and their impact on clinical measures and functional outcome can inform the development of novel treatment approaches to improve social skills early in the course of schizophrenia and psychotic disorders.

scholarly journals Aripiprazole for Drug-Naive or Antipsychotic-Short-Exposure Subjects With Ultra-High Risk State and First-Episode Psychosis

2013 ◽  
Vol 33 (1) ◽  
pp. 18-23 ◽  
Author(s):  
Chen-Chung Liu ◽  
Yi-Ling Chien ◽  
Ming H. Hsieh ◽  
Tzung-Jeng Hwang ◽  
Hai-Gwo Hwu ◽  
...  
Keyword(s):  
High Risk ◽  
First Episode Psychosis ◽  
Short Exposure ◽  
First Episode ◽  
Episode Psychosis ◽  
Ultra High Risk ◽  
Drug Naïve

scholarly journals Using genetic, cognitive and multi-modal neuroimaging data to identify ultra-high-risk and first-episode psychosis at the individual level

2013 ◽  
Vol 43 (12) ◽  
pp. 2547-2562 ◽  
Author(s):  
W. Pettersson-Yeo ◽  
S. Benetti ◽  
A. F. Marquand ◽  
F. Dell‘Acqua ◽  
S. C. R. Williams ◽  
...  
Keyword(s):  
High Risk ◽  
First Episode Psychosis ◽  
First Episode ◽  
Support Vector ◽  
Single Subject ◽  
Individual Level ◽  
Episode Psychosis ◽  
Ultra High Risk ◽  
The Individual ◽  
Cognitive Data

BackgroundGroup-level results suggest that relative to healthy controls (HCs), ultra-high-risk (UHR) and first-episode psychosis (FEP) subjects show alterations in neuroanatomy, neurofunction and cognition that may be mediated genetically. It is unclear, however, whether these groups can be differentiated at single-subject level, for instance using the machine learning analysis support vector machine (SVM). Here, we used a multimodal approach to examine the ability of structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor neuroimaging (DTI), genetic and cognitive data to differentiate between UHR, FEP and HC subjects at the single-subject level using SVM.MethodThree age- and gender-matched SVM paired comparison groups were created comprising 19, 19 and 15 subject pairs for FEPversusHC, UHRversusHC and FEPversusUHR, respectively. Genetic, sMRI, DTI, fMRI and cognitive data were obtained for each participant and the ability of each to discriminate subjects at the individual level in conjunction with SVM was tested.ResultsSuccessful classification accuracies (p < 0.05) comprised FEPversusHC (genotype, 67.86%; DTI, 65.79%; fMRI, 65.79% and 68.42%; cognitive data, 73.69%), UHRversusHC (sMRI, 68.42%; DTI, 65.79%), and FEPversusUHR (sMRI, 76.67%; fMRI, 73.33%; cognitive data, 66.67%).ConclusionsThe results suggest that FEP subjects are identifiable at the individual level using a range of biological and cognitive measures. Comparatively, only sMRI and DTI allowed discrimination of UHR from HC subjects. For the first time FEP and UHR subjects have been shown to be directly differentiable at the single-subject level using cognitive, sMRI and fMRI data. Preliminarily, the results support clinical development of SVM to help inform identification of FEP and UHR subjects, though future work is needed to provide enhanced levels of accuracy.

Memory Impairments Identified in People at Ultra-High Risk for Psychosis Who Later Develop First-Episode Psychosis

2005 ◽  
Vol 162 (1) ◽  
pp. 71-78 ◽  
Author(s):  
Warrick J. Brewer ◽  
Shona M. Francey ◽  
Stephen J. Wood ◽  
Henry J. Jackson ◽  
Christos Pantelis ◽  
...  
Keyword(s):  
High Risk ◽  
First Episode Psychosis ◽  
First Episode ◽  
Memory Impairments ◽  
Episode Psychosis ◽  
Ultra High Risk

Toxoplasma gondii, Herpesviridae and long-term risk of transition to first-episode psychosis in an ultra high-risk sample

2021 ◽  
Vol 233 ◽  
pp. 24-30
Author(s):  
E. Burkhardt ◽  
M. Berger ◽  
R.H. Yolken ◽  
A. Lin ◽  
H.P. Yuen ◽  
...  
Keyword(s):  
High Risk ◽  
Toxoplasma Gondii ◽  
First Episode Psychosis ◽  
First Episode ◽  
Episode Psychosis ◽  
Ultra High Risk

scholarly journals Formal thought disorder in people at ultra-high risk of psychosis

BJPsych Open ◽  
2017 ◽  
Vol 3 (4) ◽  
pp. 165-170 ◽  
Author(s):  
Arsime Demjaha ◽  
Sara Weinstein ◽  
Daniel Stahl ◽  
Fern Day ◽  
Lucia Valmaggia ◽  
...  
Keyword(s):  
High Risk ◽  
First Episode Psychosis ◽  
High Risk Group ◽  
Thought Disorder ◽  
Psychotic Symptoms ◽  
First Episode ◽  
Control Group ◽  
Formal Thought Disorder ◽  
Episode Psychosis ◽  
Ultra High Risk

BackgroundFormal thought disorder is a cardinal feature of psychosis. However, the extent to which formal thought disorder is evident in ultra-high-risk individuals and whether it is linked to the progression to psychosis remains unclear.AimsExamine the severity of formal thought disorder in ultra-high-risk participants and its association with future psychosis.MethodThe Thought and Language Index (TLI) was used to assess 24 ultra-high-risk participants, 16 people with first-episode psychosis and 13 healthy controls. Ultra-high-risk individuals were followed up for a mean duration of 7 years (s.d.=1.5) to determine the relationship between formal thought disorder at baseline and transition to psychosis.ResultsTLI scores were significantly greater in the ultra-high-risk group compared with the healthy control group (effect size (ES)=1.2), but lower than in people with first-episode psychosis (ES=0.8). Total and negative TLI scores were higher in ultra-high-risk individuals who developed psychosis, but this was not significant. Combining negative TLI scores with attenuated psychotic symptoms and basic symptoms predicted transition to psychosis (P=0.04; ES=1.04).ConclusionsTLI is beneficial in evaluating formal thought disorder in ultra-high-risk participants, and complements existing instruments for the evaluation of psychopathology in this group.

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