TY - JOUR
T1 - Classification of first-episode psychosis in a large cohort of patients using support vector machine and multiple kernel learning techniques
AU - Squarcina, Letizia
AU - Castellani, Umberto
AU - Bellani, Marcella
AU - Perlini, Cinzia
AU - Lasalvia, Antonio
AU - Dusi, Nicola
AU - Bonetto, Chiara
AU - Cristofalo, Doriana
AU - Tosato, Sarah
AU - Rambaldelli, Gianluca
AU - Alessandrini, Franco
AU - Zoccatelli, Giada
AU - Pozzi-Mucelli, Roberto
AU - Lamonaca, Dario
AU - Ceccato, Enrico
AU - Pileggi, Francesca
AU - Mazzi, Fausto
AU - Santonastaso, Paolo
AU - Ruggeri, Mirella
AU - Brambilla, Paolo
PY - 2015/8/14
Y1 - 2015/8/14
N2 - First episode psychosis (FEP) patients are of particular interest for neuroimaging investigations because of the absence of confounding effects due to medications and chronicity. Nonetheless, imaging data are prone to heterogeneity because for example of age, gender or parameter setting differences. With this work, we wanted to take into account possible nuisance effects of age and gender differences across dataset, not correcting the data as a pre-processing step, but including the effect of nuisance covariates in the classification phase. To this aim, we developed a method which, based on multiple kernel learning (MKL), exploits the effect of these confounding variables with a subject-depending kernel weighting procedure. We applied this method to a dataset of cortical thickness obtained from structural magnetic resonance images (MRI) of 127 FEP patients and 127 healthy controls, who underwent either a 3. Tesla (T) or a 1.5. T MRI acquisition. We obtained good accuracies, notably better than those obtained with standard SVM or MKL methods, up to more than 80% for frontal and temporal areas. To our best knowledge, this is the largest classification study in FEP population, showing that fronto-temporal cortical thickness can be used as a potential marker to classify patients with psychosis.
AB - First episode psychosis (FEP) patients are of particular interest for neuroimaging investigations because of the absence of confounding effects due to medications and chronicity. Nonetheless, imaging data are prone to heterogeneity because for example of age, gender or parameter setting differences. With this work, we wanted to take into account possible nuisance effects of age and gender differences across dataset, not correcting the data as a pre-processing step, but including the effect of nuisance covariates in the classification phase. To this aim, we developed a method which, based on multiple kernel learning (MKL), exploits the effect of these confounding variables with a subject-depending kernel weighting procedure. We applied this method to a dataset of cortical thickness obtained from structural magnetic resonance images (MRI) of 127 FEP patients and 127 healthy controls, who underwent either a 3. Tesla (T) or a 1.5. T MRI acquisition. We obtained good accuracies, notably better than those obtained with standard SVM or MKL methods, up to more than 80% for frontal and temporal areas. To our best knowledge, this is the largest classification study in FEP population, showing that fronto-temporal cortical thickness can be used as a potential marker to classify patients with psychosis.
KW - Affective psychosis
KW - Cortical thickness
KW - Frontal
KW - MRI
KW - Schizophrenia
KW - Temporal cortex
UR - http://www.scopus.com/inward/record.url?scp=84953432531&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84953432531&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2015.12.007
DO - 10.1016/j.neuroimage.2015.12.007
M3 - Article
AN - SCOPUS:84953432531
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
ER -