Author name cluster

David McGonigle

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

YNIMG Journal 2016 Journal Article

Transcranial electric stimulation (tES) and NeuroImaging: the state-of-the-art, new insights and prospects in basic and clinical neuroscience

Surjo R. Soekadar
Jim Don Herring
David McGonigle

Transcranial electric stimulation (tES) of the brain has attracted an increased interest in recent years. Yet, despite remarkable research efforts to date, the underlying neurobiological mechanisms of tES’ effects are still incompletely understood. This Special Issue aims to provide a comprehensive and up-to-date overview of the state-of-the-art in studies combining tES and neuroimaging, while introducing most recent insights and outlining future prospects related to this new and rapidly growing field. The findings reported here combine methodological advancements with insights into the underlying mechanisms of tES itself. At the same time, they also point to the many caveats and specific challenges associated with such studies, which can arise from both technical and biological sources. Besides promising to advance basic neuroscience, combined tES and neuroimaging studies may also substantially change previous conceptions about the methods of action of electric or magnetic stimulation on the brain.

Details DOI

NeurIPS Conference 2006 Conference Paper

Learning Structural Equation Models for fMRI

Enrico Simonotto
Heather Whalley
Stephen Lawrie
Lawrence Murray
David McGonigle
Amos Storkey

David McGonigle Centre for Functional Imaging Studies University of Edinburgh Structural equation models can be seen as an extension of Gaussian belief net- works to cyclic graphs, and we show they can be understood generatively as the model for the joint distribution of long term average equilibrium activity of Gaus- sian dynamic belief networks. Most use of structural equation models in fMRI involves postulating a particular structure and comparing learnt parameters across different groups. In this paper it is argued that there are situations where priors about structure are not ﬁrm or exhaustive, and given sufﬁcient data, it is worth investigating learning network structure as part of the approach to connectivity analysis. First we demonstrate structure learning on a toy problem. We then show that for particular fMRI data the simple models usually assumed are not supported. We show that is is possible to learn sensible structural equation models that can provide modelling beneﬁts, but that are not necessarily going to be the same as a true causal model, and suggest the combination of prior models and learning or the use of temporal information from dynamic models may provide more beneﬁts than learning structural equations alone.

PDF Details