Classification of first-episode psychosis using cortical thickness: A large multicenter MRI study
Jazyk angličtina Země Nizozemsko Médium print-electronic
Typ dokumentu časopisecké články, multicentrická studie, práce podpořená grantem
Grantová podpora
Department of Health - United Kingdom
PubMed
33957410
DOI
10.1016/j.euroneuro.2021.04.002
PII: S0924-977X(21)00189-9
Knihovny.cz E-zdroje
- Klíčová slova
- First episode psychosis, Machine learning, Multivariate pattern analysis, Psychosis, Support vector machine, Thickness,
- 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
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.
Department of Clinical and Behavioural Neurology IRCCS Santa Lucia Foundation Rome Italy
Department of Pathophysiology and Transplantation University of Milan Milan Italy
Department of Psychiatry and Psychotherapy Jena University Hospital Jena Germany
Department of Psychiatry and Psychotherapy Ludwig Maximilian University Munich Germany
Department of Psychiatry University of Basel Basel Switzerland
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