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Koutsouleris N, Meisenzahl EM, Davatzikos C, Bottlender R, Frodl T, Scheuerecker J, Schmitt G, Zetzsche T, Decker P, Reiser M, Möller HJ, Gaser C. Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Arch Gen Psychiatry . 2009 Jul 1 ; 66(7):700-12. PubMed Abstract

Comments on Paper and Primary News
Comment by:  Thomas McGlashan
Submitted 17 July 2009 Posted 17 July 2009

The paper by Koutsouleris et al. offers a neuroimaging pattern classification system to identify subjects at risk for developing psychosis. Overall, I found that around the dependent variables, i.e., the images, the paper offered more than I would ever want to know, but that around the independent variables, i.e., the individuals meeting various at-risk clinical definitions, the paper offered far less than I wanted to know. It is a complicated sample with inclusion criteria covering more than half a page in the Archives. Nevertheless, the definitions are operationalized and clear. What is missing is any proof that these definitions were used reliably, including the classification of transition from prodromal to psychotic. Reference is made on page 701 for a paper containing details of the recruitment protocol, but I still could find nothing on reliability. This is a oversight which I find uncharacteristic of the Archives.

View all comments by Thomas McGlashan

Comment by:  Henry Holcomb
Submitted 23 July 2009 Posted 23 July 2009

Koutsouleris and colleagues’ recent article in Archives of General Psychiatry provides a robust and enlightening challenge to those who would look for the etiology of schizophrenia in isolated networks or circumscribed brain regions. Through the application of supervised learning algorithms (Noble, 2006; Vapnik, 1999) and multivariate classification strategies, the authors convincingly demonstrate extensive gray matter distribution differences among various categories of participants at risk for psychosis. They also demonstrate significant morphological pattern differences between those who transitioned to psychosis and those who did not. This study extends and expands findings previously published by Davatzikos and associates (Davatzikos et al., 2005). It also uses tools similar to those applied by the Philadelphia group.

Because the central nervous system is inherently multidimensional, and because many diseases affect the nervous...  Read more

View all comments by Henry Holcomb

Comment by:  Neeltje E.M. van Haren
Submitted 15 September 2009 Posted 15 September 2009

It was with great interest that we read the paper by Koutsouleris et al. in the July 2009 issue of the Archives in which they report on a method to identify among individuals at high risk of developing psychosis those that go on the reach full-blown psychosis. Psychiatric research in general and schizophrenia research in particular have been set up as a comparison between groups of affected and unaffected individuals. Although this has improved our understanding in the etiology and pathophysiology of the illness, results so far have failed to be of diagnostic or prognostic significance. For many researchers, the ultimate goal would be to find a disease-specific (set of) biological marker(s) to support the diagnostic process or predict the onset or course of illness.

In this well-written and elegant study, Koutsouleris et al. propose a statistical method, specifically, a multivariate machine learning algorithm (support vector machine) that is able to estimate group membership from effective integration of, in this case, neuroanatomical information. Two important issues can...  Read more

View all comments by Neeltje E.M. van Haren
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