Nicola Toschi

Responsabile di Sezione

Macro-area: Fisica Applicata, didattica e storia della Fisica
SSD: FIS/07 - SC: 02/D1

+39 06 72596008


Nicola Toschi è Professore Ordinario di Fisica Medica presso l’Università di Roma “Tor Vergata” e membro del personale di ricerca e della facoltà presso il Centro Athinoula A. Martinos per l’Imaging Biomedico (Harvard Medical School).

In precedenza ha lavorato come consulente strategico presso McKinsey & Company, come coordinatore per la Convenzione delle Nazioni Unite sui cambiamenti climatici, con la RAI e come coordinatore di progetti con AMREF. La sua ricerca è interdisciplinare, con particolare attenzione alle soluzioni scientifiche e tecnologiche per l’impiego di tecniche fisiche e matematiche avanzate al fine di estrarre informazioni quantitative di valore investigativo, diagnostico e prognostico in un contesto clinico.

È membro senior della società IEEE, membro attivo dell’ISMRM e dell’OHBM, membro fondatore dell’Alzheimer Precision Medicine Initiative (AMPI) e membro del Technical Committee on Cardiopulmonary Systems.


  • B.Sc. Physics (Imperial College, London)
  • M.Sc. Applied Mathematics (ST. Catherine’s College, Oxford, UK),
  • MSc. Physics (University of Rome Tor Vergata)
  • PhD Natural Sciences (Ludwig Maximilian University of Munich, max Planck Institute of Psychiatry)
  • Specialization School in Medical Physics (University of Rome Tor Vergata).


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Ultime 5 pubblicazioni (PubMed)

  • Enabling uncertainty estimation in neural networks through weight perturbation for improved Alzheimer's disease classification
    on 21 Febbraio 2024

    CONCLUSION: We believe that being able to estimate the uncertainty of a prediction, along with tools that can modulate the behavior of the network to a degree of confidence that the user is informed about (and comfortable with), can represent a crucial step in the direction of user compliance and easier integration of deep learning tools into everyday tasks currently performed by human operators.

  • Identifying cortical structure markers of resilience to adversity in young people using surface-based morphometry
    on 30 Gennaio 2024

    Previous research on the neurobiological bases of resilience in youth has largely used categorical definitions of resilience and voxel-based morphometry methods that assess gray matter volume. However, it is important to consider brain structure more broadly as different cortical properties have distinct developmental trajectories. To address these limitations, we used surface-based morphometry and data-driven, continuous resilience scores to examine associations between resilience and cortical...

  • A translational MRI approach to validate acute axonal damage detection as an early event in multiple sclerosis
    on 9 Gennaio 2024

    Axonal degeneration is a central pathological feature of multiple sclerosis and is closely associated with irreversible clinical disability. Current noninvasive methods to detect axonal damage in vivo are limited in their specificity and clinical applicability, and by the lack of proper validation. We aimed to validate an MRI framework based on multicompartment modeling of the diffusion signal (AxCaliber) in rats in the presence of axonal pathology, achieved through injection of a neurotoxin...

  • The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI-Dynamic Contrast-Enhanced challenge
    on 20 Dicembre 2023

    CONCLUSIONS: This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within K trans $$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with...

  • Beyond multilayer perceptrons: Investigating complex topologies in neural networks
    on 14 Dicembre 2023

    This study delves into the crucial aspect of network topology in artificial neural networks (NNs) and its impact on model performance. Addressing the need to comprehend how network structures influence learning capabilities, the research contrasts traditional multilayer perceptrons (MLPs) with models built on various complex topologies using novel network generation techniques. Drawing insights from synthetic datasets, the study reveals the remarkable accuracy of complex NNs, particularly in...


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