Collaboration in the Mental Health and Psychiatric Research Areas

Core Activities:

The Laboratory of Neuroinformatics at the IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli specialises in developing classical and generative Artificial Intelligence (AI) and Machine Learning (ML) pipelines, with particular expertise in analysing multimodal data from clinical contexts.

Research activities aim to accurately characterise brain psychiatric disorders and neurodegenerative diseases, and include the design of interactive web platforms, such as: NeuGRID (https://www.neugrid2.eu/), NewPsy4U (https://www.newpsy4u.eu/), and ALL-EMBRACED (https://www.allembraced.eu). The laboratory of Neuroinformatics has experience developing smartphone apps and tools for post-processing clinical, neuropsychological, and neuroimaging data (CT, MRI, PET).

The research group is composed of specialists in bioinformatics, biomedical and electronic engineering, physics, and robotics, with professionals having decades of consolidated experience in the field.

Looking for: We welcome collaboration with project consortia or organizations interested in mental health and psychiatric disorders.

Research areas: Brain cognitive decline, Mental Health and Psychiatry

Publications in the field:

e-Infrastructure development:

• Redolfi A, Archetti D, De Francesco S, et al. Italian, European, and international neuroinformatics efforts: An overview. Eur J Neurosci. 2023;57(12):2017-2039. doi:10.1111/ejn.15854

• Redolfi A, De Francesco S, Palesi F, et al. Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts. Front Neurol. 2020;11:1021. Published 2020 Sep 23. doi:10.3389/fneur.2020.01021

Classical Machine Learning tools for MRI harmonization and processing:

• Archetti D, Venkatraghavan V, Weiss B, et al. A Machine Learning Model to Harmonize Volumetric Brain MRI Data for Quantitative Neuroradiologic Assessment of Alzheimer Disease. Radiol Artif Intell. 2025;7(1):e240030. doi:10.1148/ryai.240030

• De Francesco S, Galluzzi S, Vanacore N, et al. Norms for Automatic Estimation of Hippocampal Atrophy and a Step Forward for Applicability to the Italian Population. Front Neurosci. 2021;15:656808. Published 2021 Jun 28. doi:10.3389/fnins.2021.656808

• Archetti D, Young AL, Oxtoby NP, et al. Inter-Cohort Validation of SuStaIn Model for Alzheimer's Disease. Front Big Data. 2021;4:661110. Published 2021 May 20. doi:10.3389/fdata.2021.661110

• Archetti D, Ingala S, Venkatraghavan V, et al. Multi-study validation of data-driven disease progression models to characterize evolution of biomarkers in Alzheimer's disease. Neuroimage Clin. 2019;24:101954. doi:10.1016/j.nicl.2019.101954

• Ten Kate M, Redolfi A, Peira E, et al. MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study. Alzheimers Res Ther. 2018;10(1):100. Published 2018 Sep 27. doi:10.1186/s13195-018-0428-1

• Redolfi A, Manset D, Barkhof F, et al. Head-to-head comparison of two popular cortical thickness extraction algorithms: a cross-sectional and longitudinal study. PLoS One. 2015;10(3):e0117692. Published 2015 Mar 17. doi:10.1371/journal.pone.0117692

Explainable AI:

• De Francesco S, Crema C, Archetti D, et al. Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA. Sci Rep. 2023;13(1):17355. Published 2023 Oct 13. doi:10.1038/s41598-023-43706-6

NLP tools:

• Crema C, Verde F, Tiraboschi P, et al. Medical Information Extraction With NLP-Powered QABots: A Real-World Scenario. IEEE J Biomed Health Inform. 2024;28(11):6906-6917. doi:10.1109/JBHI.2024.3450118

• Crema C, Buonocore TM, Fostinelli S, et al. Advancing Italian biomedical information extraction with transformers-based models: Methodological insights and multicenter practical application. J Biomed Inform. 2023;148:104557. doi:10.1016/j.jbi.2023.104557

• Crema C, Attardi G, Sartiano D, Redolfi A. Natural language processing in clinical neuroscience and psychiatry: A review. Front Psychiatry. 2022;13:946387. Published 2022 Sep 14. doi:10.3389/fpsyt.2022.946387