May 15, 2022 · We introduce a fully data-driven approach based on graph learning to extract meaningful repeating network patterns from regionally-averaged timecourses.
We introduce a fully data-driven approach based on graph learning to extract meaningful repeating network patterns from regionally-averaged timecourses.
May 26, 2021 · Here, we introduce a fully data-driven approach based on graph learning to extract meaningful repeating network patterns from regionally- ...
Oct 22, 2024 · The GLMM allows to learn graphs entirely from the functional activity that, in practice, turn out to reveal high degrees of similarity to the ...
May 23, 2021 · Moreover, we find that these networks correspond better to structure compared to traditional methods. Keywords: dynamic functional connectivity, ...
Here, we introduce a fully data-driven approach based on graph learning to extract meaningful repeating network patterns from regionally-averaged time-courses.
Dynamics of functional network organization through graph mixture learning ; Journal: NeuroImage, 2022, p. 119037 ; Publisher: Elsevier BV ; Authors: Ilaria Ricchi ...
Network science and graph theory applications can help in understanding how human cognitive functions are linked to neuronal network structure. •. The present ...
Missing: mixture | Show results with:mixture
The brain is increasingly seen as functioning via the dynamic interaction between networks at multiple spatial and temporal scales.
Feb 25, 2022 · The GLMM allows to learn graphs entirely from the functional activity that, in practice, turn out to reveal high degrees of similarity to the ...