Tim Hahn and Andre F. Marquand contributed equally to this work.
A novel approach to probabilistic biomarker-based classification using functional near-infrared spectroscopy
Article first published online: 16 JAN 2012
Copyright © 2012 Wiley Periodicals, Inc.
Human Brain Mapping
Volume 34, Issue 5, pages 1102–1114, May 2013
How to Cite
Hahn, T., Marquand, A. F., Plichta, M. M., Ehlis, A.-C., Schecklmann, M. W., Dresler, T., Jarczok, T. A., Eirich, E., Leonhard, C., Reif, A., Lesch, K.-P., Brammer, M. J., Mourao-Miranda, J. and Fallgatter, A. J. (2013), A novel approach to probabilistic biomarker-based classification using functional near-infrared spectroscopy. Hum. Brain Mapp., 34: 1102–1114. doi: 10.1002/hbm.21497
- Issue published online: 6 APR 2013
- Article first published online: 16 JAN 2012
- Manuscript Accepted: 23 SEP 2011
- Manuscript Revised: 8 SEP 2011
- Manuscript Received: 6 JUN 2011
- Wellcome Trust Career Development Fellowship (JMM). Grant Number: WT086565/Z/08/Z
- Wellcome Trust and EPSRC (AM). Grant Number: WT088641/Z/09/Z
- disease prevalence;
- single subject classification;
- temporal classification
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.