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COLOMBO, FEDERICA

COLOMBO, FEDERICA  

Facoltà di Psicologia  

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Titolo Data di pubblicazione Autore(i) File
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.
Brain correlates of subjective cognitive complaints in COVID-19 survivors: A multimodal magnetic resonance imaging study 1-gen-2022 Paolini, Marco; Palladini, Mariagrazia; Mazza, Mario Gennaro; Colombo, Federica; Vai, Benedetta; Rovere-Querini, Patrizia; Falini, Andrea; Poletti, Sara; Benedetti, Francesco
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 remediation therapy for post-acute persistent cognitive deficits in COVID-19 survivors: A proof-of-concept study 1-gen-2022 Palladini, Mariagrazia; Bravi, Beatrice; Colombo, Federica; Caselani, Elisa; Di Pasquasio, Camilla; D'Orsi, Greta; Rovere-Querini, Patrizia; Poletti, Sara; Benedetti, Francesco; Mazza, Mario Gennaro
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.
Correction: The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium (Molecular Psychiatry, (2023), 10.1038/s41380-023-02077-0) 1-gen-2023 Bruin, W. B.; Abe, Y.; Alonso, P.; Anticevic, A.; Backhausen, L. L.; Balachander, S.; Bargallo, N.; Batistuzzo, M. C.; Benedetti, F.; Bertolin Triquell, S.; Brem, S.; Calesella, F.; Couto, B.; Denys, D. A. J. P.; Echevarria, M. A. N.; Eng, G. K.; Ferreira, S.; Feusner, J. D.; Grazioplene, R. G.; Gruner, P.; Guo, J. Y.; Hagen, K.; Hansen, B.; Hirano, Y.; Hoexter, M. Q.; Jahanshad, N.; Jaspers-Fayer, F.; Kasprzak, S.; Kim, M.; Koch, K.; Bin Kwak, Y.; Kwon, J. S.; Lazaro, L.; Li, C. -S. R.; Lochner, C.; Marsh, R.; Martinez-Zalacain, I.; Menchon, J. M.; Moreira, P. S.; Morgado, P.; Nakagawa, A.; Nakao, T.; Narayanaswamy, J. C.; Nurmi, E. L.; Zorrilla, J. C. P.; Piacentini, J.; Pico-Perez, M.; Piras, F.; Piras, F.; Pittenger, C.; Reddy, J. Y. C.; Rodriguez-Manrique, D.; Sakai, Y.; Shimizu, E.; Shivakumar, V.; Simpson, B. H.; Soriano-Mas, C.; Sousa, N.; Spalletta, G.; Stern, E. R.; Evelyn Stewart, S.; Szeszko, P. R.; Tang, J.; Thomopoulos, S. I.; Thorsen, A. L.; Yoshida, T.; Tomiyama, H.; Vai, B.; Veer, I. M.; Venkatasubramanian, G.; Vetter, N. C.; Vriend, C.; Walitza, S.; Waller, L.; Wang, Z.; Watanabe, A.; Wolff, N.; Yun, J. -Y.; Zhao, Q.; van Leeuwen, W. A.; van Marle, H. J. F.; van de Mortel, L. A.; van der Straten, A.; van der Werf, Y. D.; Arai, H.; Bollettini, I.; Escalona, R. C.; Coelho, A.; Colombo, F.; Darwich, L.; Fontaine, M.; Ikuta, T.; Ipser, J. C.; Juaneda-Segui, A.; Kitagawa, H.; Kvale, G.; Machado-Sousa, M.; Morer, A.; Nakamae, T.; Narumoto, J.; O'Neill, J.; Okawa, S.; Real, E.; Roessner, V.; Sato, J. R.; Segalas, C.; Shavitt, R. G.; Veltman, D. J.; Yamada, K.; van Leeuwen, W. A.; van Marle, H. J. F.; van de Mortel, L. A.; van der Straten, A.; van der Werf, Y. D.; van den Heuvel, O. A.; van Wingen, G. A.; Thompson, P. M.; Stein, D. J.; van den Heuvel, O. A.; van Wingen, G. A.
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.
Hippocampal and parahippocampal volume and function predict antidepressant response in patients with major depression: A multimodal neuroimaging study 1-gen-2023 Paolini, Marco; Harrington, Yasmin; Colombo, Federica; Bettonagli, Valentina; Poletti, Sara; Carminati, Matteo; Colombo, Cristina; Benedetti, Francesco; Zanardi, Raffaella
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.
Inflammation and neuroimaging differentiate bipolar and unipolar depression: a multiple kernel learning approach 1-gen-2020 Parenti, L; Colombo, F; Bollettini, I; Vai, B; Poletti, S; Benedetti, F
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 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 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.
Multimodal brain-derived subtypes of Major depressive disorder differentiate patients for anergic symptoms, immune-inflammatory markers, history of childhood trauma and treatment-resistance 1-gen-2024 Colombo, F.; Calesella, F.; Bravi, B.; Fortaner-Uya, L.; Monopoli, C.; Tassi, E.; Carminati, M.; Zanardi, R.; Bollettini, I.; Poletti, S.; Lorenzi, C.; Spadini, S.; Brambilla, P.; Serretti, A.; Maggioni, E.; Fabbri, C.; Benedetti, F.; Vai, B.
One-year mental health outcomes in a cohort of COVID-19 survivors 1-gen-2022 Mazza, M. G.; Palladini, M.; De Lorenzo, R.; Bravi, B.; Poletti, S.; Furlan, R.; Ciceri, F.; Vai, B.; Bollettini, I.; Melloni, E. M. T.; Mazza, E. B.; Aggio, V.; Calesella, F.; Paolini, M.; Caselani, E.; Colombo, F.; D'Orsi, G.; Di Pasquasio, C.; Fiore, P.; Calvisi, S.; Canti, V.; Castellani, J.; Cilla, M.; Cinel, E.; Damanti, S.; Ferrante, M.; Martinenghi, S.; Santini, C.; Vitali, G.; Rovere-Querini, P.; Benedetti, F.
Predicting cognitive impairment in depression: a machine learning approach on multimodal structural neuroimaging 1-gen-2023 Monopoli, C.; Calesella, F.; Verri, A.; Fortaner-Uyà, L.; Colombo, F.; Bravi, B.; Bollettini, I.; Poletti, S.; Benedetti, F.; Vai, B.
Predicting Suicide Attempts among Major Depressive Disorder Patients with Structural Neuroimaging: A Machine Learning Approach 1-gen-2023 Fortaner-Uyà, L.; Monopoli, C.; Calesella, F.; Colombo, F.; Bravi, B.; Maggioni, E.; Tassi, E.; Poletti, S.; Bollettini, I.; Benedetti, F.; Vai, B.