PNRR grants

This 4-year PhD research project aims to push the boundaries of current neuroimaging capabilities by developing robust and quantitative brain diffusion MRI methods. It is expected that this will contribute to providing clinicians and researchers with a valuable tool for early diagnosis and treatment monitoring of brain diseases. The potential impact of this work extends beyond the academic realm, offering tangible benefits in terms of improved patient outcomes and a deeper understanding of the underlying mechanisms of neurological diseases.

More specifically, the goals of the project are:
•    Optimization of Diffusion MRI Sequences: Develop and optimize diffusion MRI sequences that are highly sensitive to cell swelling and dendritic beading, ensuring superior temporal resolution. This optimization process will involve taking the correlation tensor imaging protocol demonstrated in our first human feasibility study [Novello et al., 2022] and further investigate strategies for shortening its acquisition. Considerations will include a systematic study of the reduction in gradient encoding directions, with an assessment of how this affects the metrics extracted (micro kurtosis, isotropic and anisotropic kurtosis) also as a function of brain regions.
•    Microstructural Modeling: Employ advanced microstructural modeling techniques to interpret the diffusion signal changes in terms of cellular alterations. While these methods were initially developed and tested in pre-clinical MRI systems, a collaboration with the developers (Gonzalo A Alvarez and Analia Zwick, Instituto Balseiro, Argentina) enabled the implementation of tortuosis imaging contrast in a clinical 3T system using phantoms [Saidman et al 2023] and human data [Zwick et al., 2024]. Our findings reveled restriction-sizes consistent with human histological findings, and evidence that the predominant signals originate from extra-axonal spaces, supporting microscopic tortuosity effects. Further efforts are required to optimize acquisition times and extend the evaluation to a larger sample and clinical groups. In addition, based on the novel denoising strategies recently proposed [Henriques et al., in press], the acquisition protocols will be adapted as needed to include the data needed for efficient signal denoising, with evaluations of the improvements in the model estimations.
•    Evaluation in healthy subjects: The optimized protocols will be tested in a group of healthy young, middle age and elderly volunteers to characterize the variability of the metrics of interest across subjects and across brain regions. This will also create normative datasets against which patient populations can be compared.
•    Preliminary clinical evaluation: Investigate the clinical applicability of the developed methods by conducting studies on cohorts of patients at different stages of neurological diseases, like dementia (healthy elderly controls, mild cognitive impairment, Alzheimer’s disease). The goal is to establish these novel diffusion MRI markers as reliable indicators for disease presence and progression stage. Comparative analyses with existing imaging modalities (diffusion tensor and kurtosis models) will be conducted to establish the superiority of the proposed frameworks, CTI and tortuosity imaging. We will evaluate the association between the microstructure markers and clinical variables. 

Expected Results: Development of new diffusion MRI markers for early diagnosis and treatment monitoring of neurological disorders, which can undergo preliminary testing in a clinical scanner. The MRI sequence and analysis pipeline should be made shareable across Siemens' infrastructure used for this purpose.

Supervisor: Jorge Jovicich

The project intends to study the impact of physical activity on slowing down cognitive decline related to aging. The project activity will be centered on achieving two specific objectives.

First, to investigate the role of physical activity on cognitive fitness, systematically quantifying its effect on different neural and cognitive functions. To this end, cognitive neuroscience methods, profiling and e-health technology will be used to better explore the impact of individual differences in determining healthy aging trajectories. Second, to identify the best physical exercise practices to enhance learning in older individuals while performing cognitive tasks, leveraging the knowledge identified from the phase described above.

Supervisor: Veronica Mazza