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Craig Morgan

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YNICL Journal 2017 Journal Article

Meta-analysis of associations between childhood adversity and hippocampus and amygdala volume in non-clinical and general population samples

  • Maria Calem
  • Konstantinos Bromis
  • Philip McGuire
  • Craig Morgan
  • Matthew J Kempton

BACKGROUND: Studies of psychiatric populations have reported associations between childhood adversity and volumes of stress-related brain structures. This meta-analysis investigated these associations in non-clinical samples and therefore independent of the effects of severe mental health difficulties and their treatment. METHODS: The MEDLINE database was searched for magnetic resonance imaging studies measuring brain structure in adults with and without childhood adversity. Fifteen eligible papers (1781 participants) reporting hippocampal volumes and/or amygdala volumes were pooled using a random effects meta-analysis. RESULTS: = 0.124). This association differed depending on whether studies included participants with some psychopathology, though this may be due to differences in the type of adversity these studies examined. There was no strong evidence of any differences in amygdala volume. DISCUSSION: Childhood adversity may have only a modest impact on stress-related brain structures in those without significant mental health difficulties.

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YNIMG Journal 2017 Journal Article

Multi-center MRI prediction models: Predicting sex and illness course in first episode psychosis patients

  • Mireille Nieuwenhuis
  • Hugo G. Schnack
  • Neeltje E. van Haren
  • Julia Lappin
  • Craig Morgan
  • Antje A. Reinders
  • Diana Gutierrez-Tordesillas
  • Roberto Roiz-Santiañez

Structural Magnetic Resonance Imaging (MRI) studies have attempted to use brain measures obtained at the first-episode of psychosis to predict subsequent outcome, with inconsistent results. Thus, there is a real need to validate the utility of brain measures in the prediction of outcome using large datasets, from independent samples, obtained with different protocols and from different MRI scanners. This study had three main aims: 1) to investigate whether structural MRI data from multiple centers can be combined to create a machine-learning model able to predict a strong biological variable like sex; 2) to replicate our previous finding that an MRI scan obtained at first episode significantly predicts subsequent illness course in other independent datasets; and finally, 3) to test whether these datasets can be combined to generate multicenter models with better accuracy in the prediction of illness course. The multi-center sample included brain structural MRI scans from 256 males and 133 females patients with first episode psychosis, acquired in five centers: University Medical Center Utrecht (The Netherlands) (n=67); Institute of Psychiatry, Psychology and Neuroscience, London (United Kingdom) (n=97); University of São Paulo (Brazil) (n=64); University of Cantabria, Santander (Spain) (n=107); and University of Melbourne (Australia) (n=54). All images were acquired on 1. 5-Tesla scanners and all centers provided information on illness course during a follow-up period ranging 3 to 7years. We only included in the analyses of outcome prediction patients for whom illness course was categorized as either “continuous” (n=94) or “remitting” (n=118). Using structural brain scans from all centers, sex was predicted with significant accuracy (89%; p<0. 001). In the single- or multi-center models, illness course could not be predicted with significant accuracy. However, when reducing heterogeneity by restricting the analyses to male patients only, classification accuracy improved in some samples. This study provides proof of concept that combining multi-center MRI data to create a well performing classification model is possible. However, to create complex multi-center models that perform accurately, each center should contribute a sample either large or homogeneous enough to first allow accurate classification within the single-center.

Details DOI
v2026.09.13