Graph-theoretical approaches have become a popular way to model brain data collected using magnetic resonance imaging (MRI), both from the structural and the functional perspectives. In structural networks, tract-based mapping allows to model different aspects of brain structures by means of the specific characteristics of the different MRI modalities. However, there has been little effort to join the information carried by each modality and to understand what level of common variance is shown in these data. In this paper, we proposed a combined approach based on graph theory and factor analysis to model magnetization transfer and microstructural properties in 18 relapsing remitting multiple sclerosis (RRMS) patients and 17 healthy controls. After defining the common factors and outlining their relationships with MRI data, we evaluated between-group differences using global and local graph measures. The results showed that one common factor describes brain structures in terms of myelin and global integrity, and such factor is able to highlight specific between-group differences.
|Titolo:||Estimating multimodal brain connectivity in multiple sclerosis: An exploratory factor analysis|
|Data di pubblicazione:||2016|
|Citazione:||Mancini, M., Giulietti, G., Spanò, B., Bozzali, M., Cercignani, M., & Conforto, S. (2016). Estimating multimodal brain connectivity in multiple sclerosis: An exploratory factor analysis. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp.1131-1134). Institute of Electrical and Electronics Engineers Inc..|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|