Speaker
Description
Characterizing directed connections between brain regions is fundamental for understanding the brain’s network organization and how it supports cognitive processes. However, a reliable methodology allowing assessment of effective connectivity (EC) at the whole-brain level and across high-resolution parcellations has remained elusive. Here, using fMRI data from the Human Connectome Project S1200 release, we show that the multivariate Ornstein-Uhlenbeck (mOU) approach for EC estimation can be adapted to yield EC estimates that are in broad agreement with those of an advanced dynamic causal model (DCM), at least for healthy participants for which inter-regional differences in hemodynamic responses remain small. In particular, we analytically decompose the Jacobian into symmetric and antisymmetric components, showing that the time scale τ explicitly controls their relative contribution and thus regulates the emergence of directionality in EC estimates. We explore a wide range of τ values, and identify regimes in which the optimization converges and yields stable EC matrices, remarkably consistent with DCM. Furthermore, thanks to its low computational cost, mOU can be easily scaled up to large neuroimaging cohorts and high-resolution parcellations. We show that MOU-based EC is reliable for high-resolution parcellations and outperforms functional connectivity (FC) in single-subject connectome fingerprinting, with accuracy increasing with resolution level, pinpointing the method’s potential in identifying subject-specific functional features. Finally, mOU-based whole-brain EC can be used to uncover directed community structure and functional hierarchies in large-scale brain organization, features that are inaccessible using undirected measures, such as FC. Overall, our methodology provides a reliable and scalable EC estimation pipeline of wide applicability to cognitive and network neuroscience
| Preferred Presentation | Poster Presentation |
|---|