{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T07:18:11Z","timestamp":1777533491629,"version":"3.51.4"},"reference-count":48,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2012,1,16]],"date-time":"2012-01-16T00:00:00Z","timestamp":1326672000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Human Brain Mapping"],"published-print":{"date-parts":[[2013,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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\u2010to\u2010use multi\u2010channel near\u2010infrared spectroscopy (fNIRS) measurements. We show the validity of our method by applying it to data from healthy controls (<jats:italic>n<\/jats:italic> = 14) enabling differentiation between the conditions of a visual checkerboard task. Second, we show that high\u2010accuracy single subject classification of patients with schizophrenia (<jats:italic>n<\/jats:italic> = 40) and healthy controls (<jats:italic>n<\/jats:italic> = 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\u2010based classification of patients with schizophrenia. In summary, the proposed algorithm in combination with fNIRS measurements enables the analysis of sub\u2010second, multivariate temporal patterns of BOLD responses and high\u2010accuracy predictions based on low\u2010cost, easy\u2010to\u2010use 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. \u00a9 2012 Wiley Periodicals, Inc.<\/jats:p>","DOI":"10.1002\/hbm.21497","type":"journal-article","created":{"date-parts":[[2012,1,16]],"date-time":"2012-01-16T13:33:52Z","timestamp":1326720832000},"page":"1102-1114","source":"Crossref","is-referenced-by-count":26,"title":["A novel approach to probabilistic biomarker\u2010based classification using functional near\u2010infrared spectroscopy"],"prefix":"10.1002","volume":"34","author":[{"given":"Tim","family":"Hahn","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andre F.","family":"Marquand","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael M.","family":"Plichta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ann\u2010Christine","family":"Ehlis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin W.","family":"Schecklmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Dresler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomasz A.","family":"Jarczok","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elisa","family":"Eirich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christine","family":"Leonhard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Reif","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Klaus\u2010Peter","family":"Lesch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael J.","family":"Brammer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Janaina","family":"Mourao\u2010Miranda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas J.","family":"Fallgatter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2012,1,16]]},"reference":[{"key":"e_1_2_6_2_1","volume-title":"Das AMDP\u2010System. 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