Por favor, use este identificador para citar o enlazar este ítem:
https://www.arca.fiocruz.br/handle/icict/45854
Tipo
ArtículoDerechos de autor
Acceso restringido
Colecciones
Metadatos
Mostrar el registro completo del ítem
SUBTYPING SCHIZOPHRENIA BASED ON SYMPTOMATOLOGY AND COGNITION USING A DATA DRIVEN APPROACH
Positive and negative symptoms
Clustering
Data-driven subgrouping
Afiliación
Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil / University of Melbourne. Austin Health. Studley Road, Department of Psychiatry. Heidelberg, Victoria, Australia.
Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil / London School of Hygiene and Tropical Medicine. King's College London. Centre for Global Mental Health. David Goldberg Centre, De Crespigny Park. London United Kingdom.
Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil / Federal University of Bahia. Institute of Collective Health. Salvador, BA, Brazil.
University of Melbourne. Austin Health. Studley Road, Department of Psychiatry. Heidelberg, Victoria, Australia.
Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil / London School of Hygiene and Tropical Medicine. King's College London. Centre for Global Mental Health. David Goldberg Centre, De Crespigny Park. London United Kingdom.
Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil / Federal University of Bahia. Institute of Collective Health. Salvador, BA, Brazil.
University of Melbourne. Austin Health. Studley Road, Department of Psychiatry. Heidelberg, Victoria, Australia.
Resumen en ingles
Schizophrenia is a highly heterogeneous disorder, not only in its phenomenology but in its clinical course. This
limits the usefulness of the diagnosis as a basis for both research and clinical management. Methods of reducing
this heterogeneity may inform the diagnostic classification. With this in mind, we performed k-means clustering
with symptom and cognitive measures to generate groups in a machine-driven way. We found that our data was
best organised in three clusters: high cognitive performance, high positive symptomatology, low positive
symptomatology. We hypothesized that these clusters represented biological categories, which we tested by
comparing these groups in terms of brain volumetric information. We included all the groups in an ANCOVA
analysis with post hoc tests, where brain volume areas were modelled as dependent variables, controlling for age
and estimated intracranial volume. We found six brain volumes significantly differed between the clusters: left
caudate, left cuneus, left lateral occipital, left inferior temporal, right lateral, and right pars opercularis. The kmeans
clustering provides a way of subtyping schizophrenia which appears to have a biological basis, though
one that requires both replication and confirmation of its clinical significance.
Palabras clave en ingles
SchizophreniaPositive and negative symptoms
Clustering
Data-driven subgrouping
Compartir