Predicting Suicide Attempts among Major Depressive Disorder Patients with Structural Neuroimaging: A Machine Learning Approach by L. Fortaner-Uyà, C. Monopoli, F. Calesella, F. Colombo, B. Bravi, E. Maggioni, E. Tassi, S. Poletti, I. Bollettini, F. Benedetti, B. Vai

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Structural neuroimaging differentiates between depressed bipolar disorder and major depressive disorder patients: a machine learning study by F. Calesella, F. Colombo, B. Bravi, L. Fortaner-Uyà, C. Monopoli, E. Tassi, E. Maggioni, I. Bollettini, S. Poletti, F. Benedetti, B. Vai

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Identifying a predictive model of cognitive impairment in bipolar disorder patients: a machine learning study by C. Monopoli, L. Fortaner-Uyà, F. Calesella, F. Colombo, B. Bravi, E. Maggioni, E. Tassi, I. Bollettini, S. Poletti, F. Benedetti, B. Vai

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Unsupervised neurobiology-driven stratification of clinical heterogeneity in depression by F. Colombo, F. Calesella, B. Bravi, L. Fortaner-Uyà, C. Monopoli, E. Maggioni, E. Tassi, R. Zanardi, F. Attanasio, I. Bollettini, S. Poletti, F. Benedetti, B. Vai

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Predicting unipolar and bipolar depression using inflammatory markers, neuroimaging and neuropsychological data: a machine learning study by L. Raffaelli, F. Colombo, F. Calesella, L. Fortaner-Uya, I. Bollettini, C. Lorenzi, E. Maggioni, E. Tassi, S. Poletti, R. Zanardi, F. Attanasio, F. Benedetti, B. Vai

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