BACKGROUND: Reliable prognostic biomarkers are needed for the early recognition of psychosis. Recently, multivariate machine learning methods have demonstrated the feasibility to predict illness onset in clinically defined at-risk individuals using structural magnetic resonance imaging (MRI) data. However, it remains unclear whether these findings could be replicated in independent populations. METHODS: We evaluated the performance of an MRI-based classification system in predicting disease conversion in at-risk individuals recruited within the prospective FePsy (Früherkennung von Psychosen) study at the University of Basel, Switzerland. Pairwise and multigroup biomarkers were constructed using the MRI data of 22 healthy volunteers, 16/21 at-risk subjects with/without a subsequent disease conversion. Diagnostic performance was measured in unseen test cases using repeated nested cross-validation. RESULTS: The classification accuracies in the "healthy controls (HCs) vs converters," "HCs vs nonconverters," and "converters vs nonconverters" analyses were 92.3%, 66.9%, and 84.2%, respectively. A positive likelihood ratio of 6.5 in the converters vs nonconverters analysis indicated a 40% increase in diagnostic certainty by applying the biomarker to an at-risk population with a transition rate of 43%. The neuroanatomical decision functions underlying these results particularly involved the prefrontal perisylvian and subcortical brain structures. CONCLUSIONS: Our findings suggest that the early prediction of psychosis may be reliably enhanced using neuroanatomical pattern recognition operating at the single-subject level. These MRI-based biomarkers may have the potential to identify individuals at the highest risk of developing psychosis, and thus may promote informed clinical strategies aiming at preventing the full manifestation of the disease.
BACKGROUND: Reliable prognostic biomarkers are needed for the early recognition of psychosis. Recently, multivariate machine learning methods have demonstrated the feasibility to predict illness onset in clinically defined at-risk individuals using structural magnetic resonance imaging (MRI) data. However, it remains unclear whether these findings could be replicated in independent populations. METHODS: We evaluated the performance of an MRI-based classification system in predicting disease conversion in at-risk individuals recruited within the prospective FePsy (Früherkennung von Psychosen) study at the University of Basel, Switzerland. Pairwise and multigroup biomarkers were constructed using the MRI data of 22 healthy volunteers, 16/21 at-risk subjects with/without a subsequent disease conversion. Diagnostic performance was measured in unseen test cases using repeated nested cross-validation. RESULTS: The classification accuracies in the "healthy controls (HCs) vs converters," "HCs vs nonconverters," and "converters vs nonconverters" analyses were 92.3%, 66.9%, and 84.2%, respectively. A positive likelihood ratio of 6.5 in the converters vs nonconverters analysis indicated a 40% increase in diagnostic certainty by applying the biomarker to an at-risk population with a transition rate of 43%. The neuroanatomical decision functions underlying these results particularly involved the prefrontal perisylvian and subcortical brain structures. CONCLUSIONS: Our findings suggest that the early prediction of psychosis may be reliably enhanced using neuroanatomical pattern recognition operating at the single-subject level. These MRI-based biomarkers may have the potential to identify individuals at the highest risk of developing psychosis, and thus may promote informed clinical strategies aiming at preventing the full manifestation of the disease.
Authors: R Smieskova; P Fusar-Poli; P Allen; K Bendfeldt; R D Stieglitz; J Drewe; E W Radue; P K McGuire; A Riecher-Rössler; S J Borgwardt Journal: Neurosci Biobehav Rev Date: 2010-02-06 Impact factor: 8.989
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Authors: James M Stone; Sagnik Bhattacharyya; Gareth J Barker; Philip K McGuire Journal: Eur Neuropsychopharmacol Date: 2011-07-08 Impact factor: 4.600
Authors: Fumiko Hoeft; Bruce D McCandliss; Jessica M Black; Alexander Gantman; Nahal Zakerani; Charles Hulme; Heikki Lyytinen; Susan Whitfield-Gabrieli; Gary H Glover; Allan L Reiss; John D E Gabrieli Journal: Proc Natl Acad Sci U S A Date: 2010-12-20 Impact factor: 11.205
Authors: G Paul Amminger; Miriam R Schäfer; Konstantinos Papageorgiou; Claudia M Klier; Sue M Cotton; Susan M Harrigan; Andrew Mackinnon; Patrick D McGorry; Gregor E Berger Journal: Arch Gen Psychiatry Date: 2010-02
Authors: Nikolaos Koutsouleris; Christos Davatzikos; Stefan Borgwardt; Christian Gaser; Ronald Bottlender; Thomas Frodl; Peter Falkai; Anita Riecher-Rössler; Hans-Jürgen Möller; Maximilian Reiser; Christos Pantelis; Eva Meisenzahl Journal: Schizophr Bull Date: 2013-10-13 Impact factor: 9.306
Authors: Sanne de Wit; Tim B Ziermans; M Nieuwenhuis; Patricia F Schothorst; Herman van Engeland; René S Kahn; Sarah Durston; Hugo G Schnack Journal: Hum Brain Mapp Date: 2016-10-04 Impact factor: 5.038