Nicola Toschi

Full Professor and Principal Investigator

PHYS-06/A - Fisica per le scienze della vita, l'ambiente e i beni culturali

toschi@med.uniroma2.it

+39 06 72596008

Biography

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.

Academic Qualifications

  • 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).

Profiles

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Last 5 articles (Scopus)

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Activation of endogenous retroviruses characterizes the maternal-fetal interface in the BTBR mouse model of autism spectrum disorder; Scientific Reports; December 2025; DOI: 10.1038/s41598-025-91541-8
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Transforming Multimodal Models into Action Models for Radiotherapy; Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 2025; DOI: 10.1007/978-3-031-82007-6_5
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Neural activation during processing of emotional faces as a function of resilience in adolescents; European Child and Adolescent Psychiatry; 2025; DOI: 10.1007/s00787-025-02703-y
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Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging; Computer Methods and Programs in Biomedicine; November 2024; DOI: 10.1016/j.cmpb.2024.108375
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Cortical structure and subcortical volumes in conduct disorder: a coordinated analysis of 15 international cohorts from the ENIGMA-Antisocial Behavior Working Group; The Lancet Psychiatry; August 2024; DOI: 10.1016/S2215-0366(24)00187-1
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Last 5 articles (PubMed)

  • Neural activation during processing of emotional faces as a function of resilience in adolescents
    on 10 April 2025

    Neuroimaging studies suggest that resilience to adversity is linked to reduced emotional reactivity or enhanced emotion regulation. However, such studies are scarce and mainly use adult samples and categorical definitions of resilience. Using a novel, data-driven approach to define resilience dimensionally, based on cumulative adversity exposure across childhood and psychopathology, we investigated associations between resilience and brain activation during facial emotion processing in youth. We...

  • Activation of endogenous retroviruses characterizes the maternal-fetal interface in the BTBR mouse model of autism spectrum disorder
    on 11 March 2025

    Endogenous retroviruses (ERVs) are genetic elements derived from a process of germline infection by exogenous retroviruses. Some ERVs have been co-opted for physiological functions, and their activation has been associated with complex diseases, including Autism Spectrum Disorder (ASD). We have already demonstrated an abnormal expression of ERVs in the BTBR T + tf/J (BTBR) mouse model of ASD during intrauterine life till adulthood. Thus, starting from the assumptions that ERVs may contribute to...

  • Age-Dependent Spatial Patterns of Brain Noise in fMRI Series
    on 5 March 2025

    Functional Magnetic Resonance Imaging (fMRI) serves as a unique non-invasive tool for investigating brain function by analyzing blood oxygenation level-dependent (BOLD) series. These signals result from the complex interplay between deterministic and stochastic components underpinning biological brain activity. In this context, the quantification of the stochastic component, here defined as brain noise, is challenging without making assumptions on the deterministic dynamics. Leveraging on...

  • Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging
    on 24 August 2024

    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 brain representations from electroencephalography through knowledge distillation and latent diffusion models
    on 20 June 2024

    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...