Tania Filaferro
Spatiotemporal Deep Learning for early detection and differentiation in idiopathic REM Sleep Behavior Disorder and Parkinson's Disease using functional MRI data.
Rel. Filippo Molinari, Massimo Salvi, Massimo Filippi, Federica Agosta, Silvia Basaia. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2024
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Abstract
Isolated REM sleep behavior disorder (iRBD) has garnered significant attention as a potential early indicator of neurodegenerative diseases, particularly Parkinson's disease (PD). The growing acknowledgment of iRBD as a potential prodromal phase of PD has intensified efforts to identify brain biomarkers that may facilitate early diagnosis and intervention. There is an urgent need for these biomarkers to facilitate neuroprotection and improve patient outcomes. In this context, functional magnetic resonance imaging (fMRI) has emerged as a powerful tool for exploring the neural correlates of iRBD and PD. By providing insights into the dynamic brain activity patterns associated with these disorders, fMRI holds promise for enhancing our understanding of the underlying mechanisms and enabling timely therapeutic strategies.
This study aimed to develop a spatiotemporal deep neural network (stDNN) using fMRI data to identify brain biomarkers linked to iRBD
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