Classification of first-episode psychosis: a multi-modal multi-feature approach integrating structural and diffusion imaging

Autor: Katia De Santi, Gianluca Rambaldelli, Sarah Tosato, Veronica Marinelli, Paolo Brambilla, Umberto Castellani, Antonio Lasalvia, Marcella Bellani, Denis Peruzzo, Cinzia Perlini, Vittorio Murino, Mirella Ruggeri
Jazyk: angličtina
Rok vydání: 2015
Předmět:
Adult
Male
Support Vector Machine
Brain imaging
Multimodal Imaging
Developmental psychology
White matter
Brain imaging
Machine learning
Multiple kernel learning (MKL)
Support vector machine (SVM)
Magnetic resonance imaging (MRI)
DTI
Schizophrenia
Neuroimaging
Multiple kernel learning (MKL)
Image Interpretation
Computer-Assisted
Machine learning
medicine
Cingulum (brain)
Humans
Magnetic resonance imaging (MRI)
Biological Psychiatry
Multiple kernel learning
business.industry
Brain
Pattern recognition
Magnetic Resonance Imaging
White Matter
Support vector machine
Psychiatry and Mental health
medicine.anatomical_structure
Diffusion Tensor Imaging
Neurology
Superior frontal gyrus
Psychotic Disorders
ROC Curve
DTI
Area Under Curve
Multivariate Analysis
Schizophrenia
Female
Neurology (clinical)
Artificial intelligence
Support vector machine (SVM)
Psychology
business
Parahippocampal gyrus
Diffusion MRI
Popis: Currently, most of the classification studies of psychosis focused on chronic patients and employed single machine learning approaches. To overcome these limitations, we here compare, to our best knowledge for the first time, different classification methods of first-episode psychosis (FEP) using multi-modal imaging data exploited on several cortical and subcortical structures and white matter fiber bundles. 23 FEP patients and 23 age-, gender-, and race-matched healthy participants were included in the study. An innovative multivariate approach based on multiple kernel learning (MKL) methods was implemented on structural MRI and diffusion tensor imaging. MKL provides the best classification performances in comparison with the more widely used support vector machine, enabling the definition of a reliable automatic decisional system based on the integration of multi-modal imaging information. Our results show a discrimination accuracy greater than 90 % between healthy subjects and patients with FEP. Regions with an accuracy greater than 70 % on different imaging sources and measures were middle and superior frontal gyrus, parahippocampal gyrus, uncinate fascicles, and cingulum. This study shows that multivariate machine learning approaches integrating multi-modal and multisource imaging data can classify FEP patients with high accuracy. Interestingly, specific grey matter structures and white matter bundles reach high classification reliability when using different imaging modalities and indices, potentially outlining a prefronto-limbic network impaired in FEP with particular regard to the right hemisphere.
Databáze: OpenAIRE
Externí odkaz: