Sfoglia per Autore CALESELLA, FEDERICO
Higher Interleukin 13 differentiates patients with a positive history of suicide attempts in major depressive disorder
2021-01-01 Vai, B.; Mazza, M. G.; Cazzetta, S.; Calesella, F.; Aggio, V.; Lorenzi, C.; Zanardi, R.; Poletti, S.; Colombo, C.; Benedetti, F.
A peripheral inflammatory signature discriminates bipolar from unipolar depression: A machine learning approach
2021-01-01 Poletti, Sara; Vai, Benedetta; Mazza, Mario Gennaro; Zanardi, Raffaella; Lorenzi, Cristina; Calesella, Federico; Cazzetta, Silvia; Branchi, Igor; Colombo, Cristina; Furlan, Roberto; Benedetti, Francesco
Reduced cortico-limbic habituation identifies bipolar depressed suicide attempers: a machine learning study
2021-01-01 Calesella, F; Vai, B; Caselani, E; Uya, Lf; Fiore, P; Lenti, C; 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
2021-01-01 Colombo, F; Calesella, F; Mazza, Mg; Melloni, Emt; Benedetti, F; Vai, B
Investigating predictive factors of dialectical behavior therapy skills training efficacy for alcohol and concurrent substance use disorders: A machine learning study
2021-01-01 Cavicchioli, M.; Calesella, F.; Cazzetta, S.; Mariagrazia, M.; Ogliari, A.; Maffei, C.; Vai, B.
Circulating inflammatory markers impact cognitive functions in bipolar depression
2021-01-01 Poletti, S.; Mazza, M. G.; Calesella, F.; Vai, B.; Lorenzi, C.; Manfredi, E.; Colombo, C.; Zanardi, R.; Benedetti, F.
Machine learning signature in differentiating bipolar and unipolar depression with multimodal structural neuroimaging data and neuropsychology
2022-01-01 Vai, B.; Calesella, F.; Colombo, F.; Bollettini, I.; Tassi, E.; Maggioni, E.; Zanardi, R.; Poletti, S.; Benedetti, F.
Immune-inflammation and structural neuroimaging differentiate bipolar and unipolar depression: a machine learning study
2022-01-01 Colombo, F.; Calesella, F.; Poletti, S.; Lorenzi, C.; Vai, B.; Benedetti, F.
A machine learning pipeline for efficient differentiation between depressed bipolar disorder and major depressive disorder patients based on structural neuroimaging
2022-01-01 Calesella, F.; Colombo, F.; Bravi, B.; Fortaner-Uyà, L.; Monopoli, C.; Tassi, E.; Maggioni, E.; Bollettini, I.; Poletti, S.; Vai, B.; Benedetti, F.
Data-driven stratification of depressed patients based on structural neuroimaging signatures: a stability-based relative clustering validation approach
2022-01-01 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.
Prediction of cognitive impairment in mood disorders using multimodal structural neuroimaging: a machine learning study
2022-01-01 Monopoli, C.; Fortaner-Uyà, L.; Calesella, F.; Colombo, F.; Bravi, B.; Maggioni, E.; Tassi, E.; Bollettini, I.; Poletti, S.; Vai, B.; Benedetti, F.
Identifying suicide attempters among bipolar depressed patients using structural neuroimaging: a machine learning study
2022-01-01 Fortaner-Uyà, L.; Monopoli, C.; Calesella, F.; Colombo, F.; Bravi, B.; Maggioni, E.; Tassi, E.; Poletti, S.; Bollettini, I.; Vai, B.; Benedetti, F.
Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: A systematic review and meta-analysis
2022-01-01 Colombo, F.; Calesella, F.; Mazza, M. G.; Melloni, E. M. T.; Morelli, M. J.; Scotti, G. M.; Benedetti, F.; Bollettini, I.; Vai, B.
One-year mental health outcomes in a cohort of COVID-19 survivors
2022-01-01 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.
Long-term effect of childhood trauma: Role of inflammation and white matter in mood disorders
2022-01-01 Poletti, Sara; Paolini, Marco; Ernst, Julia; Bollettini, Irene; Melloni, Elisa; Vai, Benedetta; Harrington, Yasmin; Bravi, Beatrice; Calesella, Federico; Lorenzi, Cristina; Zanardi, Raffaella; Benedetti, Francesco
Predicting cognitive impairment in depression: a machine learning approach on multimodal structural neuroimaging
2023-01-01 Monopoli, C.; Calesella, F.; Verri, A.; Fortaner-Uyà, L.; Colombo, F.; Bravi, B.; Bollettini, I.; Poletti, S.; Benedetti, F.; Vai, B.
Combining clinical data, genetics, and adverse childhood experiences for suicidality prediction in mood disorders: a machine learning approach
2023-01-01 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.
Unsupervised neurobiologically-driven stratification of clinical heterogeneity in treatment-resistant depression
2023-01-01 Colombo, F.; Calesella, F.; Bravi, B.; Fortaner-Uyà, L.; Monopoli, C.; Carminati, M.; Zanardi, R.; Bollettini, I.; Poletti, S.; Benedetti, F.; Vai, B.
Functional neuroimaging for the differentiation between healthy controls, depressed bipolar and major depressive patients: a machine learning study
2023-01-01 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.
Moving beyond clinical approaches: machine learning on neuroimaging and cognitive features for the differential diagnosis between unipolar and bipolar depression
2023-01-01 Serra, E.; Calesella, F.; Colombo, F.; Bravi, B.; Fortaner-Uyà, L.; Monopoli, C.; Poletti, S.; Bollentini, I.; Benedetti, F.; Vai, B.
| Titolo | Data di pubblicazione | Autore(i) | File |
|---|---|---|---|
| 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. | |
| 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 | |
| Reduced cortico-limbic habituation identifies bipolar depressed suicide attempers: a machine learning study | 1-gen-2021 | Calesella, F; Vai, B; Caselani, E; Uya, Lf; Fiore, P; Lenti, C; 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 | |
| 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. | |
| 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. | |
| 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. | |
| 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. | |
| 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. | |
| 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. | |
| Prediction of cognitive impairment in mood disorders using multimodal structural neuroimaging: a machine learning study | 1-gen-2022 | Monopoli, C.; Fortaner-Uyà, L.; Calesella, F.; Colombo, F.; Bravi, B.; Maggioni, E.; Tassi, E.; Bollettini, I.; Poletti, S.; Vai, B.; 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. | |
| 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. | |
| 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. | |
| 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 | |
| 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. | |
| 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. | |
| Unsupervised neurobiologically-driven stratification of clinical heterogeneity in treatment-resistant depression | 1-gen-2023 | Colombo, F.; Calesella, F.; Bravi, B.; Fortaner-Uyà, L.; Monopoli, C.; Carminati, M.; Zanardi, R.; Bollettini, I.; Poletti, S.; Benedetti, F.; Vai, B. | |
| 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. | |
| 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. |
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