Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The connection machine
AFNI: software for analysis and visualization of functional magnetic resonance neuroimages
Computers and Biomedical Research
Learning in graphical models
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Protein Secondary-Structure Modeling with Probabilistic Networks
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
Learning class-discriminative dynamic Bayesian networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
The Journal of Machine Learning Research
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Hi-index | 0.00 |
Estimation of effective connectivity, a measure of the influence among brain regions, can potentially reveal valuable information about organization of brain networks. Effective connectivity is usually evaluated from the functional data of a single modality. In this paper we show why that may lead to incorrect conclusions about effective connectivity. In this paper we use Bayesian networks to estimate connectivity on two different modalities. We analyze structures of estimated effective connectivity networks using aggregate statistics from the field of complex networks. Our study is conducted on functional MRI and magnetoencephalography data collected from the same subjects under identical paradigms. Results showed some similarities but also revealed some striking differences in the conclusions one would make on the fMRI data compared with the MEG data and are strongly supportive of the use of multiple modalities in order to gain a more complete picture of how the brain is organized given the limited information one modality is able to provide.