Dr. Forloni has served as a member of several committees of the European Community for the examination of the projects in the Neuroscience field. He is President of the Italian Association on Brain Aging Research and member of the European Academy of Sciences. He has been in charge of an elective course on genetic of neurodegenerative disorders at the Medical School of the University of Milan, he was invited for lectures and seminars in numerous Universities and Research Centers. Dr. Forloni is the author of more than 325 peer-review scientific (H index Google = 68) articles and about 30 reviews or book chapters.
EDUCATION
1976 Diploma in Industrial Chemistry, Rho (Mi)
1985 Degree in Biological Sciences, University of Milan
RESEARCH INTERESTS
Biological and genetic bases of Alzheimer’s disease (AD), Prion-related encephalopathies (PRE) and Parkinson's disease (PD). Studies on pathogenesis and possible therapeutic approaches based on the role of protein aggregation in the neurodegenerative diseases and mechanisms of neuronal death using in vitro and in vivo models
Correction: A gut–brain axis on-a-chip platform for drug testing challenged with donepezil Francesca Fanizza, Simone Perottoni, Lucia Boeri, Francesca Donnaloja, Francesca Negro, et al. Lab on A Chip, 2026 Correction for ‘A gut–brain axis on-a-chip platform for drug testing challenged with donepezil’ by Francesca Fanizza et al. , Lab Chip , 2025, 25 , 1854–1874, https://doi.org/10.1039/D4LC00273C.
A Comprehensive Framework for Uncertainty Quantification of Voxel-Wise Supervised Deep Learning Models in IVIM MRI Nicola Casali, Alessandro Brusaferri, Giuseppe Baselli, Stefano Fumagalli, Edoardo Micotti, et al. NMR in Biomedicine, 2026 Accurate estimation of intravoxel incoherent motion (IVIM) parameters from diffusion‐weighted MRI remains challenging due to the ill‐posed nature of the inverse problem and high sensitivity to noise, particularly in the perfusion compartment. In this work, we propose a probabilistic deep learning framework based on deep ensembles (DEs) of mixture density networks (MDNs), enabling estimation of total predictive uncertainty and decomposition into aleatoric (AU) and epistemic (EU) components. The method was benchmarked against nonprobabilistic neural networks, a Bayesian fitting approach, and a probabilistic network with single Gaussian parametrization. Supervised training was performed on synthetic data, and evaluation was conducted on both simulated and in vivo brain mouse dataset. The reliability of the quantified uncertainties was assessed using calibration curves, output distribution sharpness, and the continuous ranked probability score (CRPS). MDNs produced more calibrated and sharper predictive distributions for the diffusion coefficient () and the perfusion fraction () parameters, although slight overconfidence was observed in the pseudodiffusion coefficient (). The robust coefficient of variation (RCV) indicated smoother in vivo estimates for with MDNs compared with Gaussian model. Despite the training data covering the expected physiological range, elevated EU in vivo suggests a mismatch with real acquisition conditions, highlighting the importance of incorporating EU, which was allowed by DE. Overall, we present a comprehensive framework for IVIM fitting with uncertainty quantification, which enables the identification and interpretation of unreliable estimates. The proposed approach can also be adopted for fitting other physical models through appropriate architectural and simulation adjustments.
Deep Learning and Atlas-Based MRI Segmentation Enable Longitudinal Characterization of Healthy Mouse Brain Edoardo Micotti, Liviu Soltuzu, Elisa Bianchi, Sebastiano La Ferla, Lorenzo Carnevale, et al. Journal of Imaging, 2025 We compared the results of brain magnetic resonance image (MRI) segmentation across a longitudinal dataset spanning mouse adulthood using an atlas-based approach and deep learning. Our results demonstrate that deep learning performs similarly yet faster than more established segmentation methods, even when computational resources are limited. Both methods enabled the large-scale analysis of a cohort of C57Bl6/J healthy mice, revealing sex-dependent morphological differences in the aging brain. These findings highlight the potential use of deep learning for high-throughput, longitudinal neuroimaging studies and underscore the importance of considering sex as a biological variable in preclinical brain research.
