Abstract
In this study, we employed a clustering approach to analyze fMRI data from a publicly available dataset of patients with mild depression. We utilized the CONN toolbox, a widely recognized tool, to extract functional networks from the fMRI data. Subsequently, these networks were aligned using MULTIMAGNA++, a global multiple alignment software, to ensure consistency across individual datasets. The aligned data was then subjected to a clustering analysis to investigate the presence of distinct patterns. Our findings demonstrate that not only is it feasible to accurately cluster patients using this approach, but there is also potential to uncover previously unidentified subgroups among both control subjects and those affected by the disease. These results suggest new avenues for understanding the neurobiological underpinnings of mild depression and for developing targeted interventions.
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This work was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of ‘Innovation Ecosystems’, building ‘Territorial R &D Leaders’ (Directorial Decree n. 2021/3277) - project Tech4You - Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.
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Barillaro, L. et al. (2024). A Graph-Theory Based fMRI Analysis. In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2024. ICCS 2024. Lecture Notes in Computer Science, vol 14837. Springer, Cham. https://doi.org/10.1007/978-3-031-63778-0_6
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