Arrow Research search

Author name cluster

Stephen Lawrie

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.

5 papers
1 author row

Possible papers

5

YNIMG Journal 2006 Journal Article

A visual joke fMRI investigation into Theory of Mind and enhanced risk of schizophrenia

  • Dominic Marjoram
  • Dominic E. Job
  • Heather C. Whalley
  • Viktoria-Eleni Gountouna
  • Andrew M. McIntosh
  • Enrico Simonotto
  • David Cunningham-Owens
  • Eve C. Johnstone

Theory of Mind (ToM) or mentalizing is the ability of individuals to determine the intentions and behavior of others. This ability is known to be compromised in schizophrenia and has been shown to fluctuate with symptom severity. Neuropsychological investigations into relatives of individuals with schizophrenia have shown that some relatives also show a deficit in this area of social cognition. In order to address this state and trait issue, we investigated the performance of high-risk relatives of individuals with schizophrenia to those of a matched control group (n = 13) on a blocked design visual joke fMRI paradigm. The task involved looking at two sets of cartoon jokes, one set which required mentalizing abilities to understand the jokes and another set that did not require such abilities. Relatives were divided into two groups based on the presence (HR+, n = 12) or absence (HR−, n = 12) of positive symptoms. The task provided robust activations across the groups in areas previously associated with mentalizing abilities, such as the PFC, precuneus, and temporal lobes. Significant between-group activations were observed in the PFC (primarily BA6, 8, and 9) with the HR− activating significantly greater than the HR+ in these regions. Both a secondary state-specific analysis and a third post hoc analysis further investigating state effects showed significant PFC between-group differences. This study is the first time relatives of individuals with schizophrenia have been imaged using a ToM paradigm, and the results provide evidence of both a state and state-mediated trait effect.

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
v2026.09.13