The different effect of adverse childhood experiences on Theory of Mind brain networks in schizophrenia and healthy controls by A. Pelucchi, F. Calesella, M. Bechi, R. Cavallaro, S. Poletti, B. Vai, F. Benedetti

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Inflammatory signature of post-COVID-19 depression by M. Palladini, M. G. Mazza, V. Aggio, S. Spadini, F. Calesella, S. Poletti, P. Rovere-Querini, F. Benedetti

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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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