Brazilian Journal of Probability and Statistics

Studying the effective brain connectivity using multiregression dynamic models

Lilia Costa, Thomas Nichols, and Jim Q. Smith

Full-text: Open access

Abstract

The Multiregression Dynamic Model (MDM) is a multivariate graphical model for a multidimensional time series that allows the estimation of time-varying effective connectivity. An MDM is a state space model where connection weights reflect the contemporaneous interactions between brain regions. Because the marginal likelihood has a closed form, model selection across a large number of potential connectivity networks is easy to perform. With application of the Integer Programming Algorithm, we can quickly find optimal models that satisfy acyclic graph constraints and, due to a factorisation of the marginal likelihood, the search over all possible directed (acyclic or cyclic) graphical structures is even faster. These methods are illustrated using recent resting-state and steady-state task fMRI data.

Article information

Source
Braz. J. Probab. Stat., Volume 31, Number 4 (2017), 765-800.

Dates
Received: September 2016
Accepted: August 2017
First available in Project Euclid: 15 December 2017

Permanent link to this document
https://projecteuclid.org/euclid.bjps/1513328767

Digital Object Identifier
doi:10.1214/17-BJPS375

Mathematical Reviews number (MathSciNet)
MR3738178

Zentralblatt MATH identifier
1385.92013

Keywords
Multiregression dynamic model Bayesian network effective connectivity functional magnetic resonance imaging integer programming algorithm

Citation

Costa, Lilia; Nichols, Thomas; Smith, Jim Q. Studying the effective brain connectivity using multiregression dynamic models. Braz. J. Probab. Stat. 31 (2017), no. 4, 765--800. doi:10.1214/17-BJPS375. https://projecteuclid.org/euclid.bjps/1513328767


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