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CALESELLA, FEDERICO

CALESELLA, FEDERICO  

Facoltà di Psicologia  

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A machine learning pipeline for efficient differentiation between depressed bipolar disorder and major depressive disorder patients based on structural neuroimaging 1-gen-2022 Calesella, F.; Colombo, F.; Bravi, B.; Fortaner-Uyà, L.; Monopoli, C.; Tassi, E.; Maggioni, E.; Bollettini, I.; Poletti, S.; Vai, B.; Benedetti, F.
A peripheral inflammatory signature discriminates bipolar from unipolar depression: A machine learning approach 1-gen-2021 Poletti, Sara; Vai, Benedetta; Mazza, Mario Gennaro; Zanardi, Raffaella; Lorenzi, Cristina; Calesella, Federico; Cazzetta, Silvia; Branchi, Igor; Colombo, Cristina; Furlan, Roberto; Benedetti, Francesco
Choroid plexus volume is increased in mood disorders and associates with circulating inflammatory cytokines 1-gen-2024 Bravi, B; Melloni, Emt; Paolini, M; Palladini, M; Calesella, F; Servidio, L; Agnoletto, E; Poletti, S; Lorenzi, C; Colombo, C; Benedetti, F
Circulating inflammatory markers impact cognitive functions in bipolar depression 1-gen-2021 Poletti, S.; Mazza, M. G.; Calesella, F.; Vai, B.; Lorenzi, C.; Manfredi, E.; Colombo, C.; Zanardi, R.; Benedetti, F.
Cognitive distortions and structural neuroimaging data predict depression severity in unipolar and bipolar depression: a machine learning study 1-gen-2023 Perziani, S.; Colombo, F.; Calesella, F.; Fortaner-Uyà, L.; Monopoli, C.; Bravi, B.; Poletti, S.; Bollettini, I.; Benedetti, F.; Vai, B.
COGNITIVE IMPAIRMENT IN MOOD DISORDERS: NEUROPSYCHOLOGY, MULTIMODAL BRAIN IMAGING, AND THE EFFECT OF NEUROINFLAMMATION 11-gen-2024 Calesella, Federico
Combining clinical data, genetics, and adverse childhood experiences for suicidality prediction in mood disorders: a machine learning approach 1-gen-2023 Fortaner-Uyà, L.; Mazzilli, F.; Monopoli, C.; Calesella, F.; Colombo, F.; Bravi, B.; Fabbri, C.; Serretti, A.; Lorenzi, C.; Spadini, S.; Mascia, E.; Poletti, S.; Bollettini, I.; Benedetti, F.; Vai, B.
Data-driven stratification of depressed patients based on structural neuroimaging signatures: a stability-based relative clustering validation approach 1-gen-2022 Colombo, F.; Calesella, F.; Bravi, B.; Fortaner-Uyà, L.; Monopoli, C.; Maggioni, E.; Tassi, E.; Zanardi, R.; Attanasio, F.; Bollettini, I.; Poletti, S.; Vai, B.; Benedetti, F.
Functional neuroimaging for the differentiation between healthy controls, depressed bipolar and major depressive patients: a machine learning study 1-gen-2023 Calesella, F.; Serra, E.; Colombo, F.; Fortaner-Uyà, L.; Monopoli, C.; Bravi, B.; Tassi, E.; Bollettini, I.; Brambilla, P.; Poletti, S.; Maggioni, E.; Benedetti, F.; Vai, B.
Higher Interleukin 13 differentiates patients with a positive history of suicide attempts in major depressive disorder 1-gen-2021 Vai, B.; Mazza, M. G.; Cazzetta, S.; Calesella, F.; Aggio, V.; Lorenzi, C.; Zanardi, R.; Poletti, S.; Colombo, C.; Benedetti, F.
Identifying suicide attempters among bipolar depressed patients using structural neuroimaging: a machine learning study 1-gen-2022 Fortaner-Uyà, L.; Monopoli, C.; Calesella, F.; Colombo, F.; Bravi, B.; Maggioni, E.; Tassi, E.; Poletti, S.; Bollettini, I.; Vai, B.; Benedetti, F.
Immune-inflammation and structural neuroimaging differentiate bipolar and unipolar depression: a machine learning study 1-gen-2022 Colombo, F.; Calesella, F.; Poletti, S.; Lorenzi, C.; Vai, B.; Benedetti, F.
Inflammatory Markers Predict Blood Neurofilament Light Chain Levels in Acute COVID-19 Patients 1-gen-2024 De Lorenzo, R.; Loré, N. I.; Finardi, A.; Mandelli, A.; Calesella, F.; Palladini, M.; Cirillo, D. M.; Tresoldi, C.; Ciceri, F.; Rovere-Querini, P.; Manfredi, A. A.; Mazza, M. G.; Benedetti, F.; Furlan, R.
Insulin resistance disrupts white matter microstructure and amplitude of functional spontaneous activity in Bipolar Disorder 1-gen-2022 Mazza, Elena; Calesella, Federico; Paolini, Marco; di Pasquasio, Camilla; Poletti, Sara; Lorenzi, Cristina; Falini, Andrea; Zanardi, Raffaella; Colombo, Cristina; Benedetti, Francesco
Investigating predictive factors of dialectical behavior therapy skills training efficacy for alcohol and concurrent substance use disorders: A machine learning study 1-gen-2021 Cavicchioli, M.; Calesella, F.; Cazzetta, S.; Mariagrazia, M.; Ogliari, A.; Maffei, C.; Vai, B.
Long-term effect of childhood trauma: Role of inflammation and white matter in mood disorders 1-gen-2022 Poletti, Sara; Paolini, Marco; Ernst, Julia; Bollettini, Irene; Melloni, Elisa; Vai, Benedetta; Harrington, Yasmin; Bravi, Beatrice; Calesella, Federico; Lorenzi, Cristina; Zanardi, Raffaella; Benedetti, Francesco
Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: A systematic review and meta-analysis 1-gen-2022 Colombo, F.; Calesella, F.; Mazza, M. G.; Melloni, E. M. T.; Morelli, M. J.; Scotti, G. M.; Benedetti, F.; Bollettini, I.; Vai, B.
Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: a systematic review and meta-analysis 1-gen-2021 Colombo, F; Calesella, F; Mazza, Mg; Melloni, Emt; Benedetti, F; Vai, B
Machine learning signature in differentiating bipolar and unipolar depression with multimodal structural neuroimaging data and neuropsychology 1-gen-2022 Vai, B.; Calesella, F.; Colombo, F.; Bollettini, I.; Tassi, E.; Maggioni, E.; Zanardi, R.; Poletti, S.; Benedetti, F.
Moving beyond clinical approaches: machine learning on neuroimaging and cognitive features for the differential diagnosis between unipolar and bipolar depression 1-gen-2023 Serra, E.; Calesella, F.; Colombo, F.; Bravi, B.; Fortaner-Uyà, L.; Monopoli, C.; Poletti, S.; Bollentini, I.; Benedetti, F.; Vai, B.