| A systematic review on the potential use of machine learning to classify major depressive disorder from healthy controls using resting state fMRI measures E Bondi, E Maggioni, P Brambilla, G Delvecchio Neuroscience & Biobehavioral Reviews 144, 104972, 2023 | 29 | 2023 |
| Machine learning methods to predict outcomes of pharmacological treatment in psychosis L Del Fabro, E Bondi, F Serio, E Maggioni, A D’Agostino, P Brambilla Translational Psychiatry 13 (1), 75, 2023 | 24 | 2023 |
| Integrating virtual reality, electroencephalography, and transcranial magnetic stimulation to study the neural origin of the sublime: The SUBRAIN protocol E Bondi, F Carbone, M Pizzolante, G Schiena, A Ferro, M Mazzocut-Mis, ... medRxiv, 2024.04. 14.24305786, 2024 | | 2024 |
| A TMS-EEG Pre-processing Parameters Tuning Study E Bondi, V Pescuma, Y Massalha, M Pizzolante, A Chirico, G Schiena, ... Mediterranean Conference on Medical and Biological Engineering and Computing …, 2023 | | 2023 |
| Multimodal Integration in Psychiatry: Clinical Potential and Challenges E Maggioni, MC Piani, E Bondi, AM Bianchi, P Brambilla Computational Neuroscience, 235-256, 2023 | | 2023 |
| Brain complexity EEG analysis of the sublime experience induced in virtual reality: a future antidepressant treatment? F Carbone, E Bondi, Y Massalha, A Anastasi, V Pescuma, A Ferro, ... Neuroscience Applied 2, 102673, 2023 | | 2023 |
| Electroencephalogram activity underlying virtual reality-based sublime experiences in major depressive disorder Y Massalha, E Bondi, V Pescuma, M Pizzolante, A Chirico, G Schiena, ... Neuroscience Applied 2, 102457, 2023 | | 2023 |
| The neurovascular coupling of inhibitory control in elderly depression: an EEG-fMRI integrated study E Bondi Politecnico di Milano, 2019 | | 2019 |
