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Directed acyclic graphs (DAGs) and associated probability models are widely used to model neural connectivity and communication channels. In many experiments, data are collected from multiple subjects whose connectivities may differ but are likely to share many features. In such circumstances, it is natural to leverage similarity among subjects to improve statistical efficiency. The first exact algorithm for estimation of multiple related DAGs was recently proposed by Oates, Smith, Mukherjee, and Cussens ( 2014 ). In this letter we present examples and discuss implications of the methodology as applied to the analysis of fMRI data from a multisubject experiment. Elicitation of tuning parameters requires care, and we illustrate how this may proceed retrospectively based on technical replicate data. In addition to joint learning of subject-specific connectivity, we allow for heterogeneous collections of subjects and simultaneously estimate relationships between the subjects themselves. This letter aims to highlight the potential for exact estimation in the multisubject setting.

Original publication

DOI

10.1162/NECO_a_00690

Type

Journal article

Journal

Neural Comput

Publication Date

01/2015

Volume

27

Pages

151 - 170

Keywords

Algorithms, Animals, Brain, Computer Graphics, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Neurological, Nerve Net, Neurons, Oxygen, Probability, Time Factors