Name
Title | ||
|---|---|---|
Product Hidden Markov Models For Subject-Based Dynamic Functional Connectivity Analysis Of The Resting State Brain |
Abstract | ||
|---|---|---|
The study of time-varying connectivity networks is a young but growing field of research in functional MRI, where dynamic Bayesian networks (DBNs) should play an important role for many reasons. In this paper, Product Hidden Markov Models (PHMMs), an instance of DBNs, are introduced to capture the dynamic functional connectivity (DFC) of spontaneous co-activation maps (SAMs), including resting state networks (RSNs), at the subject level. The abilities of PHMMs to learn dependencies between interacting processes are illustrated to compare and analyze inter-sessions DFCs of healthy subjects taking medication (methylphenidate) vs a placebo. PHMMs are presented as a novel methodology for characterization of and knowledge extraction from the DFC. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ISBI.2016.7493503 | 2016 IEEE 13TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI) |
Keywords | Field | DocType |
Dynamic functional connectivity, Dynamic Bayesian network, Resting state fMRI, Product HMM | Variable-order Bayesian network,Computer science,Resting state fMRI,Taking medication,Knowledge extraction,Artificial intelligence,Hidden Markov model,Dynamic functional connectivity,Machine learning,Dynamic Bayesian network | Conference |
ISSN | Citations | PageRank |
1945-7928 | 1 | 0.37 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marion Sourty | 1 | 7 | 0.84 |
| Laurent Thoraval | 2 | 42 | 7.00 |
| Jean-Paul Armspach | 3 | 221 | 26.60 |
| j foucher | 4 | 8 | 1.25 |