Dataset Information

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

ABSTRACT:

Background

Group-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.

Method

Three age- and gender-matched SVM paired comparison groups were created comprising 19, 19 and 15 subject pairs for FEP versus HC, UHR versus HC and FEP versus UHR, 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.

Results

Successful classification accuracies (p < 0.05) comprised FEP versus HC (genotype, 67.86%; DTI, 65.79%; fMRI, 65.79% and 68.42%; cognitive data, 73.69%), UHR versus HC (sMRI, 68.42%; DTI, 65.79%), and FEP versus UHR (sMRI, 76.67%; fMRI, 73.33%; cognitive data, 66.67%).

Conclusions

The 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.

SUBMITTER: Pettersson-Yeo W

PROVIDER: S-EPMC3821374 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Publications

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

Pettersson-Yeo W W Benetti S S Marquand A F AF Dell'acqua F F Williams S C R SC Allen P P Prata D D McGuire P P Mechelli A A

Psychological medicine 20130314 12

<h4>Background</h4>Group-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 magneti ...[more]

PMID: 23507081

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Project description:Cognitive deficits have an important role in the neurodevelopment of schizophrenia and other psychotic disorders. However, there is a continuing debate as to whether cognitive impairments in the psychosis prodrome are stable predictors of eventual psychosis or undergo a decline due to the onset of psychosis. In the present study, to determine how cognition changes as illness emerges, we examined baseline neurocognitive performance in a large sample of helping-seeking youth ranging in clinical state from low-risk for psychosis through individuals at clinical high-risk (CHR) for illness to early first-episode patients (EFEP). At baseline, the MATRICS Cognitive Consensus battery was administered to 322 individuals (205 CHRs, 28 EFEPs, and 89 help-seeking controls, HSC) that were part of the larger Early Detection, Intervention and Prevention of Psychosis Program study. CHR individuals were further divided into those who did (CHR-T; n = 12, 6.8%) and did not (CHR-NT, n = 163) convert to psychosis over follow-up (Mean = 99.20 weeks, SD = 21.54). ANCOVAs revealed that there were significant overall group differences (CHR, EFEP, HSC) in processing speed, verbal learning, and overall neurocognition, relative to healthy controls (CNTL). In addition, the CHR-NTs performed similarly to the HSC group, with mild to moderate cognitive deficits relative to the CTRL group. The CHR-Ts mirrored the EFEP group, with large deficits in processing speed, working memory, attention/vigilance, and verbal learning (>1 SD below CNTLs). Interestingly, only verbal learning impairments predicted transition to psychosis, when adjusting for age, education, symptoms, antipsychotic medication, and neurocognitive performance in the other domains. Our findings suggest that large neurocognitive deficits are present prior to illness onset and represent vulnerability markers for psychosis. The results of this study further reinforce that verbal learning should be specifically targeted for preventive intervention for psychosis.

Dataset Information

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

Background

Method

Results

Conclusions

Publications

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

Similar Datasets

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