• Open Access

A novel approach to probabilistic biomarker-based classification using functional near-infrared spectroscopy

Authors

  • Tim Hahn,

    Corresponding author
    1. Department of Cognitive Psychology II, Johann Wolfgang Goethe University Frankfurt/Main, Frankfurt am Main, Germany
    2. Department of Psychiatry, Psychosomatics and Psychotherapy, University of Wuerzburg, Fuechsleinstr. 15, Wuerzburg, Germany
    • Department of Cognitive Psychology II, Johann Wolfgang Goethe University Frankfurt/Main Mertonstraße 17, 60325 Frankfurt am Main, Germany
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    • Tim Hahn and Andre F. Marquand contributed equally to this work.

  • Andre F. Marquand,

    1. Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, KCL, London, United Kingdom
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    • Tim Hahn and Andre F. Marquand contributed equally to this work.

  • Michael M. Plichta,

    1. Department of Psychiatry, Division for Imaging in Psychiatry, Central Institute of Mental Health, Mannheim, Germany
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  • Ann-Christine Ehlis,

    1. Department of Psychiatry and Psychotherapy, University of Tuebingen, Calwerstr. 14, Tuebingen, Germany
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  • Martin W. Schecklmann,

    1. Department of Psychiatry, Psychosomatics and Psychotherapy, University of Regensburg, Universitaetsstraβe 84, Regensburg, Germany
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  • Thomas Dresler,

    1. Department of Psychiatry, Psychosomatics and Psychotherapy, University of Wuerzburg, Fuechsleinstr. 15, Wuerzburg, Germany
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  • Tomasz A. Jarczok,

    1. Department of Psychiatry, Psychosomatics and Psychotherapy, University of Wuerzburg, Fuechsleinstr. 15, Wuerzburg, Germany
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  • Elisa Eirich,

    1. Department of Psychiatry, Psychosomatics and Psychotherapy, University of Wuerzburg, Fuechsleinstr. 15, Wuerzburg, Germany
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  • Christine Leonhard,

    1. Department of Psychiatry, Psychosomatics and Psychotherapy, University of Wuerzburg, Fuechsleinstr. 15, Wuerzburg, Germany
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  • Andreas Reif,

    1. Department of Psychiatry, Psychosomatics and Psychotherapy, University of Wuerzburg, Fuechsleinstr. 15, Wuerzburg, Germany
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  • Klaus-Peter Lesch,

    1. Department of Psychiatry, Psychosomatics and Psychotherapy, University of Wuerzburg, Fuechsleinstr. 15, Wuerzburg, Germany
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  • Michael J. Brammer,

    1. Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, KCL, London, United Kingdom
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  • Janaina Mourao-Miranda,

    1. Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, KCL, London, United Kingdom
    2. Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College, London, United Kingdom
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  • Andreas J. Fallgatter

    1. Department of Psychiatry and Psychotherapy, University of Tuebingen, Calwerstr. 14, Tuebingen, Germany
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Abstract

Pattern recognition approaches to the analysis of neuroimaging data have brought new applications such as the classification of patients and healthy controls within reach. In our view, the reliance on expensive neuroimaging techniques which are not well tolerated by many patient groups and the inability of most current biomarker algorithms to accommodate information about prior class frequencies (such as a disorder's prevalence in the general population) are key factors limiting practical application. To overcome both limitations, we propose a probabilistic pattern recognition approach based on cheap and easy-to-use multi-channel near-infrared spectroscopy (fNIRS) measurements. We show the validity of our method by applying it to data from healthy controls (n = 14) enabling differentiation between the conditions of a visual checkerboard task. Second, we show that high-accuracy single subject classification of patients with schizophrenia (n = 40) and healthy controls (n = 40) is possible based on temporal patterns of fNIRS data measured during a working memory task. For classification, we integrate spatial and temporal information at each channel to estimate overall classification accuracy. This yields an overall accuracy of 76% which is comparable to the highest ever achieved in biomarker-based classification of patients with schizophrenia. In summary, the proposed algorithm in combination with fNIRS measurements enables the analysis of sub-second, multivariate temporal patterns of BOLD responses and high-accuracy predictions based on low-cost, easy-to-use fNIRS patterns. In addition, our approach can easily compensate for variable class priors, which is highly advantageous in making predictions in a wide range of clinical neuroimaging applications. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc.

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