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

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

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

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Beyond the Global Brain Differences: Intraindividual Variability Differences in 1q21.1 Distal and 15q11.2 BP1-BP2 Deletion Carriers by Boen, Rune, Kaufmann, Tobias, van der Meer, Dennis, Frei, Oleksandr, Agartz, Ingrid, Ames, David, Andersson, Micael, Armstrong, Nicola J, Artiges, Eric, Atkins, Joshua R, Bauer, Jochen, Benedetti, Francesco, Boomsma, Dorret I, Brodaty, Henry, Brosch, Katharina, Buckner, Randy L, Cairns, Murray J, Calhoun, Vince, Caspers, Svenja, Cichon, Sven, Corvin, Aiden P, Crespo-Facorro, Benedicto, Dannlowski, Udo, David, Friederike S, de Geus, Eco J C, de Zubicaray, Greig I, Desrivières, Sylvane, Doherty, Joanne L, Donohoe, Gary, Ehrlich, Stefan, Eising, Else, Espeseth, Thomas, Fisher, Simon E, Forstner, Andreas J, Fortaner-Uyà, Lidia, Frouin, Vincent, Fukunaga, Masaki, Ge, Tian, Glahn, David C, Goltermann, Janik, Grabe, Hans J, Green, Melissa J, Groenewold, Nynke A, Grotegerd, Dominik, Grøntvedt, Gøril Rolfseng, Hahn, Tim, Hashimoto, Ryota, Hehir-Kwa, Jayne Y, Henskens, Frans A, Holmes, Avram J, Håberg, Asta K, Haavik, Jan, Jacquemont, Sebastien, Jansen, Andreas, Jockwitz, Christiane, Jönsson, Erik G, Kikuchi, Masataka, Kircher, Tilo, Kumar, Kuldeep, Le Hellard, Stephanie, Leu, Costin, Linden, David E, Liu, Jingyu, Loughnan, Robert, Mather, Karen A, McMahon, Katie L, McRae, Allan F, Medland, Sarah E, Meinert, Susanne, Moreau, Clara A, Morris, Derek W, Mowry, Bryan J, Mühleisen, Thomas W, Nenadić, Igor, Nöthen, Markus M, Nyberg, Lars, Ophoff, Roel A, Owen, Michael J, Pantelis, Christos, Paolini, Marco, Paus, Tomas, Pausova, Zdenka, Persson, Karin, Quidé, Yann, Marques, Tiago Reis, Sachdev, Perminder S, Sando, Sigrid B, Schall, Ulrich, Scott, Rodney J, Selbæk, Geir, Shumskaya, Elena, Silva, Ana I, Sisodiya, Sanjay M, Stein, Frederike, Stein, Dan J, Straube, Benjamin, Streit, Fabian, Strike, Lachlan T, Teumer, Alexander, Teutenberg, Lea

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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 Monopoli, C, taner-Uyà, L, Calesella, F, Colombo, F, Bravi, B, Maggioni, E, Tassi, E, Bollettini, I, Poletti, S, Benedetti, F, Vai, B

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P.0689 Reduced cortico-limbic habituation identifies bipolar depressed suicide attempers: a machine learning study by Calesella, F., Vai, B., Caselani, E., Uyà, L. Fortaner, Fiore, P., Lenti, C., Poletti, S., Benedetti, F.

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Predicting cognitive impairment in depression: a machine learning approach on multimodal structural neuroimaging by Monopoli, C., Calesella, F., Verri, A., Fortaner-Uyà, L., Colombo, F., Bravi, B., Bollettini, I., Poletti, S., Benedetti, F., Vai, B.

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Cognitive distortions and structural neuroimaging data predict depression severity in unipolar and bipolar depression: a machine learning study by Perziani, S., Colombo, F., Calesella, F., Fortaner-Uyà, L., Monopoli, C., Bravi, B., Poletti, S., Bollettini, I., Benedetti, F., Vai, B.

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Functional neuroimaging for the differentiation between healthy controls, depressed bipolar and major depressive patients: a machine learning study by 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.

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Moving beyond clinical approaches: machine learning on neuroimaging and cognitive features for the differential diagnosis between unipolar and bipolar depression by 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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Unsupervised neurobiologically-driven stratification of clinical heterogeneity in treatment-resistant depression by Colombo, F., Calesella, F., Bravi, B., Fortaner-Uyà, L., Monopoli, C., Carminati, M., Zanardi, R., Bollettini, I., Poletti, S., Benedetti, F., Vai, B.

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Combining clinical data, genetics, and adverse childhood experiences for suicidality prediction in mood disorders: a machine learning approach by 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.

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Identifying suicide attempters among bipolar depressed patients using structural neuroimaging: a machine learning study by Fortaner-Uyà, L., Monopoli, C., Calesella, F., Colombo, F., Bravi, B., Maggioni, E., Tassi, E., Poletti, S., Bollettini, I., Vai, B., Benedetti, F.

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Data-driven stratification of depressed patients based on structural neuroimaging signatures: a stability-based relative clustering validation approach by 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.

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A machine learning pipeline for efficient differentiation between depressed bipolar disorder and major depressive disorder patients based on structural neuroimaging by Calesella, F., Colombo, F., Bravi, B., Fortaner-Uyà, L., Monopoli, C., Tassi, E., Maggioni, E., Bollettini, I., Poletti, S., Vai, B., Benedetti, F.

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Prediction of cognitive impairment in mood disorders using multimodal structural neuroimaging: a machine learning study by Monopoli, C., Fortaner-Uyà, L., Calesella, F., Colombo, F., Bravi, B., Maggioni, E., Tassi, E., Bollettini, I., Poletti, S., Vai, B., Benedetti, F.

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Beyond the Global Brain Differences: Intra-individual Variability Differences in 1q21.1 Distal and 15q11.2 BP1-BP2 Deletion Carriers by Boen, Rune, Kaufmann, Tobias, van der Meer, Dennis, Frei, Oleksandr, Agartz, Ingrid, Ames, David, Andersson, Micael, Armstrong, Nicola J., Artiges, Eric, Atkins, Joshua R., Bauer, Jochen, Benedetti, Francesco, Boomsma, Dorret I., Brodaty, Henry, Brosch, Katharina, Buckner, Randy L., Cairns, Murray J., Calhoun, Vince, Caspers, Svenja, Cichon, Sven, Corvin, Aiden P., Facorro, Benedicto Crespo, Dannlowski, Udo, David, Friederike S., de Geus, Eco J.C., de Zubicaray, Greig I., Desrivières, Sylvane, Doherty, Joanne L., Donohoe, Gary, Ehrlich, Stefan, Eising, Else, Espeseth, Thomas, Fisher, Simon E., Forstner, Andreas J., Uyà, Lidia Fortaner, Frouin, Vincent, Fukunaga, Masaki, Ge, Tian, Glahn, David C., Goltermann, Janik, Grabe, Hans J., Green, Melissa J., Groenewold, Nynke A., Grotegerd, Dominik, Hahn, Tim, Hashimoto, Ryota, Hehir-Kwa, Jayne Y., Henskens, Frans A., Holmes, Avram J., Håberg, Asta K., Haavik, Jan, Jacquemont, Sebastien, Jansen, Andreas, Jockwitz, Christiane, Jönsson, Erik G., Kikuchi, Masataka, Kircher, Tilo, Kumar, Kuldeep, Le Hellard, Stephanie, Leu, Costin, Linden, David E., Liu, Jingyu, Loughnan, Robert, Mather, Karen A., McMahon, Katie L., McRae, Allan F., Medland, Sarah E., Meinert, Susanne, Moreau, Clara A., Morris, Derek W., Mowry, Bryan J., Mühleisen, Thomas W., Nenadić, Igor, Nöthen, Markus M., Nyberg, Lars, Owen, Michael J., Paolini, Marco, Paus, Tomas, Pausova, Zdenka, Persson, Karin, Quidé, Yann, Marques, Tiago Reis, Sachdev, Perminder S., Sando, Sigrid B., Schall, Ulrich, Scott, Rodney J., Selbæk, Geir, Shumskaya, Elena, Silva, Ana I., Sisodiya, Sanjay M., Stein, Frederike, Stein, Dan J., Straube, Benjamin, Streit, Fabian, Strike, Lachlan T., Teumer, Alexander, Teutenberg, Lea, Thalamuthu, Anbupalam, Tooney, Paul A, Tordesillas-Gutierrez, Diana

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