Nicola Toschi is a Full Professor in Medical Physics at the University of Rome “Tor Vergata” and Research Staff and Faculty at the Athinoula A. Martinos Center for Biomedical Imaging (Harvard Medical School).
He has previously worked as a strategy consultant at McKinsey & Company, as a facilitator for the United Nations convention on Climate Change, with the Italian National Television (RAI) and as a project coordinator with AMREF.
His research is interdisciplinary, with a focus on scientific and technological solutions for the deployment of advanced physical and mathematical techniques in order to extract quantitative information of investigative, diagnostic and prognostic value in a clinical context.
He is a senior member of the IEEE society, an active member of ISMRM and OHBM, a founding member of the Alzheimer’s Precision Medicine Initiative (AMPI) a member of the Technical Committee on Cardiopulmonary Systems.
CONCLUSIONS: These results not only validate our method's accuracy and reliability but also establish a foundation for a streamlined, non-invasive approach to dynamic PET data quantification. By offering a precise and less invasive alternative to traditional quantification methods, our technique holds significant promise for expanding the applicability of PET imaging across a wider range of tracers, thereby enhancing its utility in both clinical research and diagnostic settings.
Decoding visual representations from human brain activity has emerged as a thriving research domain, particularly in the context of brain-computer interfaces. Our study presents an innovative method that employs knowledge distillation to train an EEG classifier and reconstruct images from the ImageNet and THINGS-EEG 2 datasets using only electroencephalography (EEG) data from participants who have viewed the images themselves (i.e. "brain decoding"). We analyzed EEG recordings from 6...
CONCLUSIONS: The promotion of a Mediterranean diet during pregnancy has a significant effect on maternal brain structure.
Objective.Brain decoding is a field of computational neuroscience that aims to infer mental states or internal representations of perceptual inputs from measurable brain activity. This study proposes a novel approach to brain decoding that relies on semantic and contextual similarity.Approach.We use several functional magnetic resonance imaging (fMRI) datasets of natural images as stimuli and create a deep learning decoding pipeline inspired by the bottom-up and top-down processes in human...
Sex differences affect Parkinson's disease (PD) development and manifestation. Yet, current PD identification and treatments underuse these distinctions. Sex-focused PD literature often prioritizes prevalence rates over feature importance analysis. However, underlying aspects could make a feature significant for predicting PD, despite its score. Interactions between features require consideration, as do distinctions between scoring disparities and actual feature importance. For instance, a...
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