A Multi-Subject, Dynamic Bayesian Networks (DBNS) Framework for Brain Effective Connectivity

As dynamic connectivity is shown essential for normal brain function and is disrupted in disease, it is critical to develop models for inferring brain effective connectivity from non-invasive (e.g., fMRI) data. Increasingly, (dynamic) Bayesian network (BNs) have been suggested for this purpose due to their exibility and suitability. However, ultimately extrapolating BN results from one subject to an entire population rst requires methods meaningfully addressing inter-subject, within-group variability. Here we explore two group analysis approaches in fMRI using DBNs: one is to construct a group network based on a common structure assumption across individuals, and the other is to identify signi cant structure features by examining DBNs individually-trained. By investigating real fMRI data from Parkinsons Disease (PD) and normal subjects performing a motor task at three progressive levels of dif culty, we noted that both methods detected statistically signi cant, biologically plausible ...

• Type: Conference
• Year: 2007
• Where: ICASSP
• Authors: Junning Li, Z. Jane Wang, Martin J. McKeown
• Keywords: Brain Effective Connectivity, ICASSP 2007, Normal Brain Function, Normal Subjects, Signal Processing