Machine learning classifications of first-episode psychosis (FEP) using neuroimaging have predominantly analyzed brain volumes. Some studies examined cortical thickness, but most of them have used parcellation approaches with data from single sites, which limits claims of generalizability. To address these limitations, we conducted a large-scale, multi-site analysis of cortical thickness comparing parcellations and vertex-wise approaches. By leveraging the multi-site nature of the study, we further investigated how different demographical and site-dependent variables affected predictions. Finally, we assessed relationships between predictions and clinical variables. 428 subjects (147 females, mean age 27.14) with FEP and 448 (230 females, mean age 27.06) healthy controls were enrolled in 8 centers by the ClassiFEP group. All subjects underwent a structural MRI and were clinically assessed. Cortical thickness parcellation (68 areas) and full cortical maps (20,484 vertices) were extracted. Linear Support Vector Machine was used for classification within a repeated nested cross-validation framework. Vertex-wise thickness maps outperformed parcellation-based methods with a balanced accuracy of 66.2% and an Area Under the Curve of 72%. By stratifying our sample for MRI scanner, we increased generalizability across sites. Temporal brain areas resulted as the most influential in the classification. The predictive decision scores significantly correlated with age at onset, duration of treatment, and positive symptoms. In conclusion, although far from the threshold of clinical relevance, temporal cortical thickness proved to classify between FEP subjects and healthy individuals. The assessment of site-dependent variables permitted an increase in the across-site generalizability, thus attempting to address an important machine learning limitation.
- MeSH
- dospělí MeSH
- lidé MeSH
- magnetická rezonanční tomografie metody MeSH
- mozek MeSH
- neurozobrazování MeSH
- psychotické poruchy * diagnostické zobrazování MeSH
- support vector machine MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- multicentrická studie MeSH
- práce podpořená grantem MeSH
- Publikační typ
- abstrakt z konference MeSH
BACKGROUND: Cognitive disturbances are widely pronounced in schizophrenia and schizophrenia spectrum disorders. Whilst cognitive deficits are well established in the prodromal phase and are known to deteriorate at the onset of schizophrenia, there is a certain discrepancy of findings regarding the cognitive alterations over the course of the illness. METHODS: We bring together the results of the longitudinal studies identified through PubMed which have covered more than 3 years follow-up and to reflect on the potential factors, such as sample characteristics and stage of the illness which may contribute to the various trajectories of cognitive changes. RESULTS: A summary of recent findings comprising the changes of the cognitive functioning in schizophrenia patients along the longitudinal course of the illness is provided. The potential approaches for addressing cognition in the course of schizophrenia are discussed. CONCLUSIONS: Given the existing controversies on the course of cognitive changes in schizophrenia, differentiated approaches specifically focusing on the peculiarities of the clinical features and changes in specific cognitive domains could shed light on the trajectories of cognitive deficits in schizophrenia and spectrum disorders.
- MeSH
- dospělí MeSH
- kognice * MeSH
- kognitivní poruchy * diagnóza etiologie MeSH
- lidé MeSH
- longitudinální studie MeSH
- schizofrenie (psychologie) MeSH
- schizofrenie komplikace MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
- Publikační typ
- abstrakt z konference MeSH
