Maria Giulia Preti

Maria Giulia Preti received her Ph.D. in Bioengineering at Politecnico di Milano (Milan, Italy) in 2013, after her M. Sc. (2009) and B. Sc. (2007) in Biomedical Engineering, as well at Politecnico di Milano. During her Ph.D., mentored by Prof. Giuseppe Baselli, she focused on advanced techniques of brain magnetic resonance imaging, in particular she developed a method of groupwise fMRI-guided tractography, that revealed to be useful in the in-vivo investigation of the pathophysiological changes across the evolution of Alzheimers disease. For this project, she had been collaborating full-time with the hospital Fondazione Don Gnocchi in Milan (Magnetic Resonance Laboratory). In 2011, she was awarded a Progetto Rocca fellowship from MIT-Italy and spent a visiting research period at the MIT and Harvard Medical School (Boston, USA), under the supervision of Prof. Nikos Makris, where she could focus on the anatomical study of specific neruonal bundles.

She has joined Prof. Van De Ville group at EPFL as a post-doc in 2013. Her current research aims at understanding the connections between brain functionality and brain microscopic anatomy by using advanced techniques of Magnetic Resonance Imaging. In particular, she is working on functional MRI, functional connectivity, diffusion tensor imaging and tractography, integration of MRI with other techniques (e.g. EEG), and the application of these methods to several clinical contexts, e.g., epilepsy, Alzheimer's disease and mild cognitive impairment, multiple sclerosis, attention deficit hyperactivity disorder.

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related units (2)

Related units

Courses taught by this person

Related research domains (15)

Related research domains

Related publications (32)

Related publications

People doing similar research (90)

People doing similar research

Brain

A brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. It is located in the head, usually close to the sensory organs for senses such as

Functional magnetic resonance imaging

Functional magnetic resonance imaging or functional MRI (fMRI) measures brain activity by detecting changes associated with blood flow. This technique relies on the fact that cerebral blood flow and n

Patient

A patient is any recipient of health care services that are performed by healthcare professionals. The patient is most often ill or injured and in need of treatment by a physician, nurse, optometris

, , ,

Rivier et al. report that global measures of brain connectivity estimated by introducing virtual lesions in healthy structural connectomes significantly increase the accuracy of motor recovery prediction in stroke patients when added to focal and clinical measures used until now. Following a stroke in regions of the brain responsible for motor activity, patients can lose their ability to control parts of their body. Over time, some patients recover almost completely, while others barely recover at all. It is known that lesion volume, initial motor impairment and cortico-spinal tract asymmetry significantly impact motor changes over time. Recent work suggested that disabilities arise not only from focal structural changes but also from widespread alterations in inter-regional connectivity. Models that consider damage to the entire network instead of only local structural alterations lead to a more accurate prediction of patients' recovery. However, assessing white matter connections in stroke patients is challenging and time-consuming. Here, we evaluated in a data set of 37 patients whether we could predict upper extremity motor recovery from brain connectivity measures obtained by using the patient's lesion mask to introduce virtual lesions in 60 healthy streamline tractography connectomes. This indirect estimation of the stroke impact on the whole brain connectome is more readily available than direct measures of structural connectivity obtained with magnetic resonance imaging. We added these measures to benchmark structural features, and we used a ridge regression regularization to predict motor recovery at 3 months post-injury. As hypothesized, accuracy in prediction significantly increased (R-2 = 0.68) as compared to benchmark features (R-2 = 0.38). This improved prediction of recovery could be beneficial to clinical care and might allow for a better choice of intervention.

OXFORD UNIV PRESS2023

The arrow-of-time in neuroimaging time series identifies causal triggers of brain function

Enrico Amico, Thomas William Arthur Bolton, Raphaël Pierre Liégeois, Maria Giulia Preti, Dimitri Nestor Alice Van De Ville

Moving from association to causal analysis of neuroimaging data is crucial to advance our understanding of brain function. The arrow-of-time (AoT), that is, the known asymmetric nature of the passage of time, is the bedrock of causal structures shaping physical phenomena. However, almost all current time series metrics do not exploit this asymmetry, probably due to the difficulty to account for it in modeling frameworks. Here, we introduce an AoT-sensitive metric that captures the intensity of causal effects in multivariate time series, and apply it to high-resolution functional neuroimaging data. We find that causal effects underlying brain function are more distinctively localized in space and time than functional activity or connectivity, thereby allowing us to trace neural pathways recruited in different conditions. Overall, we provide a mapping of the causal brain that challenges the association paradigm of brain function.

WILEY2023

Dynamic segregation and integration during interictal epileptic discharges revealed by connectome spectrum analyses

Patric Hagmann, Maria Giulia Preti, Dimitri Nestor Alice Van De Ville

WILEY2022