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SINAPSE experts from around Scotland have developed ten online modules designed to explain medical imaging. They are freely available and are intended for non-specialists.


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Online Short Courses

Prediction of illness severity in patients with major depression using structural MR brain scans

Author(s): B. Mwangi, K. Matthews, J. D. Steele

Abstract:
PURPOSE: To develop a model for the prediction of Major Depressive Disorder (MDD) illness severity ratings from individual structural MRI brain scans. MATERIALS AND METHODS: Structural T1-weighted MRI scans were obtained from 30 patients with MDD recruited from two different scanning centers. Self-rated (Beck Depression Inventory; BDI), and clinician-rated (Hamilton Rating Scale for Depression, HRSD), syndrome-specific illness severity ratings were obtained just before scanning. Relevance vector regression (RVR) was used to predict the scores (BDI, HRSD) from T1-weighted MRI scans. RESULTS: It was possible to predict the BDI score (correlation between actual score and RVR predicted scores r = 0.694; P < 0.0001), but not the HRSD scores (r = 0.34; P = 0.068) from individual subjects. BDI scores from the most ill patients were predicted more accurately than those from patients who were least ill (standard deviation of difference between predicted and actual scores 2.5 versus 7.4, respectively). CONCLUSION: These data suggest that T1-weighted MRI scans contain sufficient information about neurobiological change in patients with MDD to permit accurate predictions about illness severity, on an individual subject basis, particularly for the most ill patients.

Full version: Available here

Click the link to go to an external website with the full version of the paper


ISBN: 1522-2586 (Electronic) 1053-1807 (Linking)
Publication Year: 2011
Periodical: J Magn Reson Imaging
Periodical Number: 1
Volume: 35
Pages: 64-71
Author Address: Centre for Neuroscience, Division of Medical Sciences, Ninewells Hospital and Medical School, University of Dundee, Dundee, United Kingdom. b.m.irungu@dundee.ac.uk