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Abstract #3556

Multi-modal pattern recognition: an application to schizophrenia.

Orla M Doyle 1 , Brandon Whitcher 2,3 , Steven C.R. Williams 1 , Mitul A Mehta 1 , and Stephen M Lawrie 4

1 Dept of Neuroimaging, IoPPN, King's College London, London, United Kingdom, 2 Clinical & Translational Imaging, Pfizer, Cambridge, MA, United States, 3 Dept of Mathematics, Imperial College London, London, United Kingdom, 4 Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom

This is the first time that structural brain data, rCBF and MRS data have been jointly assessed for discriminating schizophrenia from controls. Twenty-four patients diagnosed with schizophrenia and 24 age- and gender-matched controls were included. An increase in discriminative power was not observed on combining modalities. rCBF was the most highly weighted modality. Predictive probabilities (the probability of belonging to the SCZ group) were not correlated with the level of antipsychotic medication. These results imply that perfusion imaging is a highly sensitive marker for schizophrenia. Future work should assess the specificity via differential diagnosis of psychiatric disorders.

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