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Classifying individuals at high-risk for psychosis based on functional brain activity during working memory processing

Overview of attention for article published in NeuroImage: Clinical, September 2015
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Title
Classifying individuals at high-risk for psychosis based on functional brain activity during working memory processing
Published in
NeuroImage: Clinical, September 2015
DOI 10.1016/j.nicl.2015.09.015
Pubmed ID
Authors

Kerstin Bendfeldt, Renata Smieskova, Nikolaos Koutsouleris, Stefan Klöppel, André Schmidt, Anna Walter, Fabienne Harrisberger, Johannes Wrege, Andor Simon, Bernd Taschler, Thomas Nichols, Anita Riecher-Rössler, Undine E. Lang, Ernst-Wilhelm Radue, Stefan Borgwardt

Abstract

The psychosis high-risk state is accompanied by alterations in functional brain activity during working memory processing. We used binary automatic pattern-classification to discriminate between the at-risk mental state (ARMS), first episode psychosis (FEP) and healthy controls (HCs) based on n-back WM-induced brain activity. Linear support vector machines and leave-one-out-cross-validation were applied to fMRI data of matched ARMS, FEP and HC (19 subjects/group). The HC and ARMS were correctly classified, with an accuracy of 76.2% (sensitivity 89.5%, specificity 63.2%, p = 0.01) using a verbal working memory network mask. Only 50% and 47.4% of individuals were classified correctly for HC vs. FEP (p = 0.46) or ARMS vs. FEP (p = 0.62), respectively. Without mask, accuracy was 65.8% for HC vs. ARMS (p = 0.03) and 65.8% for HC vs. FEP (p = 0.0047), and 57.9% for ARMS vs. FEP (p = 0.18). Regions in the medial frontal, paracingulate, cingulate, inferior frontal and superior frontal gyri, inferior and superior parietal lobules, and precuneus were particularly important for group separation. These results suggest that FEP and HC or FEP and ARMS cannot be accurately separated in small samples under these conditions. However, ARMS can be identified with very high sensitivity in comparison to HC. This might aid classification and help to predict transition in the ARMS.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Unknown 116 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 17%
Student > Master 16 14%
Student > Bachelor 15 13%
Student > Ph. D. Student 15 13%
Other 7 6%
Other 17 15%
Unknown 27 23%
Readers by discipline Count As %
Psychology 27 23%
Medicine and Dentistry 22 19%
Neuroscience 17 15%
Nursing and Health Professions 4 3%
Agricultural and Biological Sciences 3 3%
Other 11 9%
Unknown 33 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 October 2015.
All research outputs
#20,688,303
of 25,411,814 outputs
Outputs from NeuroImage: Clinical
#2,348
of 2,803 outputs
Outputs of similar age
#209,746
of 286,401 outputs
Outputs of similar age from NeuroImage: Clinical
#49
of 62 outputs
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