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A small delegation of BRAINgineers recently attended the XVI National Congress of the Italian Association of Magnetic Resonance in Medicine (AIRMM), in Lecco from 24th to 26th March. The congress covered a wide range of MRI applications including sequence development, clinical trials, pre-clinical studies, synthetic data generation and various applications from body MRI to neuroimaging with a focus on brain tumours and connectomics.
I had the opportunity to present my latest research, “Exploring Brain Age Prediction Through MRI Molecular-Enriched Functional Connectivity”, which was selected as one of the top 4 oral presentations at the conference, an accomplishment I’m very pleased with.
Together with my colleagues M. Moretto and V. Sammassimo, under the supervision of Prof. M. Veronese, we explored whether integrating biological information from molecular templates into resting-state functional MRI data (a methodology known as Receptor-Enriched Analysis of Functional Connectivity by Target, or REACT) can predict brain age. This approach could potentially offer a novel biomarker for assessing neurological health and aging.
This research would not have been possible without the support of the DARE Foundation for Digital Lifelong Prevention, which is funding this project and my doctoral studies.
I also want to congratulate my colleague Simone Perra, whose presentation “Addressing Manufacturer Shift in Medical Image Segmentation: A Deep Learning Approach for Rectal Cancer and Mesorectum Segmentation” was also among the top 4 oral presentations.
Our group from the University of Padova also presented four additional works as poster presentations:
“Identifying Parkinson’s Disease Through Structure-Function Coupling: A Graph Signal Processing Approach” by M. Severino and colleagues, recognized as one of the best 4 posters
“Enhancing Brain Age Prediction in T1-weighted MRI: A Comparative Study of Kolmogorov-Arnold Networks and Convolutional Neural Networks” by D. de Crescenzo and colleagues, also among the top 4 posters
“Normative Modeling of Choroid Plexus Volume Across Adulthood: Applications in Depression and Multiple Sclerosis” by V. Visani and colleagues
“Time Encoded Arterial Spin Labelling Quantification with Physics-Informed Neural Network: A Simulation Study” by A. Giupponi and colleagues
It was great to catch up with other researchers and share ideas on the latest advances in neuroimaging. I’m thankful for the inspiring discussions and the recognition our team received!
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Una piccola delegazione di BRAINgineers ha recentemente partecipato al XVI Congresso Nazionale dell’Associazione Italiana di Risonanza Magnetica in Medicina (AIRMM), tenutosi a Lecco dal 24 al 26 marzo. Il congresso ha ospitato un’ampia gamma di applicazioni della risonanza magnetica, spaziando dallo sviluppo di sequenze agli studi clinici, dalla ricerca preclinica alla generazione di dati sintetici, e da applicazioni body al neuroimaging, con focus anche sui tumori e sulla connettività cerebrale.
Ho avuto l’opportunità di presentare il mio ultimo lavoro di ricerca dal titolo “Exploring Brain Age Prediction Through MRI Molecular-Enriched Functional Connectivity”, selezionato tra le migliori 4 presentazioni orali del congresso, un risultato che mi rende particolarmente soddisfatto.
Insieme alle mie colleghe M. Moretto e V. Sammassimo, sotto la supervisione del Prof. M. Veronese, abbiamo studiato se l’integrazione delle informazioni biologiche provenienti da template molecolari con i dati funzionali di risonanza magnetica resting-state (attraverso la metodologia nota come Receptor-Enriched Analysis of Functional Connectivity by Target, o REACT) possa predire l’età cerebrale. Questo approccio potrebbe offrire un nuovo biomarcatore utile per la valutazione della salute neurologica e dell’invecchiamento.
Questa ricerca non sarebbe stata possibile senza il sostegno della DARE Foundation for Digital Lifelong Prevention, che finanzia questo progetto e i miei studi di dottorato.
Desidero inoltre congratularmi con il mio collega Simone Perra, la cui presentazione dal titolo “Addressing Manufacturer Shift in Medical Image Segmentation: A Deep Learning Approach for Rectal Cancer and Mesorectum Segmentation” è stata anch’essa classificata tra le migliori 4 presentazioni orali.
Il nostro gruppo dell’Università di Padova ha inoltre presentato quattro ulteriori lavori sotto forma di poster:
- “Identifying Parkinson’s Disease Through Structure-Function Coupling: A Graph Signal Processing Approach” di M. Severino e colleghi, riconosciuto tra i migliori 4 poster
- “Enhancing Brain Age Prediction in T1-weighted MRI: A Comparative Study of Kolmogorov-Arnold Networks and Convolutional Neural Networks” di D. de Crescenzo e colleghi, anch’esso tra i migliori 4 poster
- “Normative Modeling of Choroid Plexus Volume Across Adulthood: Applications in Depression and Multiple Sclerosis” di V. Visani e colleghi
- “Time Encoded Arterial Spin Labelling Quantification with Physics-Informed Neural Network: A Simulation Study” di A. Giupponi e colleghi
È stato molto stimolante incontrare altri ricercatori e condividere idee sugli ultimi progressi nella neuroimmagine. Sono grato per le interessanti discussioni e per il riconoscimento ricevuto dal nostro team!
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