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BibTeX
A novel approach to probabilistic biomarker-based classification using functional near-infrared spectroscopy
Human brain mapping, 2013-05, Vol.34 (5), p.1102-1114
Hahn, Tim
Marquand, Andre F.
Plichta, Michael M.
Ehlis, Ann-Christine
Schecklmann, Martin W.
Dresler, Thomas
Jarczok, Tomasz A.
Eirich, Elisa
Leonhard, Christine
Reif, Andreas
Lesch, Klaus-Peter
Brammer, Michael J.
Mourao-Miranda, Janaina
Fallgatter, Andreas J.
2013
Volltextzugriff (PDF)
Details
Autor(en) / Beteiligte
Hahn, Tim
Marquand, Andre F.
Plichta, Michael M.
Ehlis, Ann-Christine
Schecklmann, Martin W.
Dresler, Thomas
Jarczok, Tomasz A.
Eirich, Elisa
Leonhard, Christine
Reif, Andreas
Lesch, Klaus-Peter
Brammer, Michael J.
Mourao-Miranda, Janaina
Fallgatter, Andreas J.
Titel
A novel approach to probabilistic biomarker-based classification using functional near-infrared spectroscopy
Ist Teil von
Human brain mapping, 2013-05, Vol.34 (5), p.1102-1114
Ort / Verlag
Hoboken: Wiley Subscription Services, Inc., A Wiley Company
Erscheinungsjahr
2013
Quelle
Wiley-Blackwell Journals
Beschreibungen/Notizen
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.
Sprache
Englisch
Identifikatoren
ISSN: 1065-9471
eISSN: 1097-0193
DOI: 10.1002/hbm.21497
Titel-ID: cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_3763208
Format
–
Schlagworte
Adult
,
Biological and medical sciences
,
Brain Mapping
,
disease prevalence
,
Electrodiagnosis. Electric activity recording
,
Female
,
Functional Laterality
,
Hemoglobins - metabolism
,
Humans
,
Investigative techniques, diagnostic techniques (general aspects)
,
Male
,
Medical sciences
,
Memory, Short-Term
,
Middle Aged
,
Models, Psychological
,
Myoglobin - metabolism
,
Nervous system
,
Pattern Recognition, Visual - physiology
,
Photic Stimulation
,
Predictive Value of Tests
,
Probability
,
Radiodiagnosis. Nmr imagery. Nmr spectrometry
,
Reproducibility of Results
,
schizophrenia
,
Schizophrenia - classification
,
Schizophrenia - diagnosis
,
single subject classification
,
Spectroscopy, Near-Infrared
,
temporal classification
,
Visual Cortex - physiology
,
Vocabulary
,
Young Adult
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