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Decoding the perception of pain from fMRI using multivariate pattern analysis.

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Decoding the perception of pain from fMRI using multivariate pattern analysis. / Brodersen, Kay H; Wiech, Katja; Lomakina, Ekaterina I; Lin, Chia-Shu; Buhmann, Joachim M; Bingel, Ulrike; Ploner, Markus; Stephan, Klaas Enno; Tracey, Irene.

In: NEUROIMAGE, Vol. 63, No. 3, 3, 2012, p. 1162-1170.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Brodersen, KH, Wiech, K, Lomakina, EI, Lin, C-S, Buhmann, JM, Bingel, U, Ploner, M, Stephan, KE & Tracey, I 2012, 'Decoding the perception of pain from fMRI using multivariate pattern analysis.', NEUROIMAGE, vol. 63, no. 3, 3, pp. 1162-1170. <http://www.ncbi.nlm.nih.gov/pubmed/22922369?dopt=Citation>

APA

Brodersen, K. H., Wiech, K., Lomakina, E. I., Lin, C-S., Buhmann, J. M., Bingel, U., Ploner, M., Stephan, K. E., & Tracey, I. (2012). Decoding the perception of pain from fMRI using multivariate pattern analysis. NEUROIMAGE, 63(3), 1162-1170. [3]. http://www.ncbi.nlm.nih.gov/pubmed/22922369?dopt=Citation

Vancouver

Brodersen KH, Wiech K, Lomakina EI, Lin C-S, Buhmann JM, Bingel U et al. Decoding the perception of pain from fMRI using multivariate pattern analysis. NEUROIMAGE. 2012;63(3):1162-1170. 3.

Bibtex

@article{4e152d8b28804576aef3405bbf2998d4,
title = "Decoding the perception of pain from fMRI using multivariate pattern analysis.",
abstract = "Pain is known to comprise sensory, cognitive, and affective aspects. Despite numerous previous fMRI studies, however, it remains open which spatial distribution of activity is sufficient to encode whether a stimulus is perceived as painful or not. In this study, we analyzed fMRI data from a perceptual decision-making task in which participants were exposed to near-threshold laser pulses. Using multivariate analyses on different spatial scales, we investigated the predictive capacity of fMRI data for decoding whether a stimulus had been perceived as painful. Our analysis yielded a rank order of brain regions: during pain anticipation, activity in the periaqueductal gray (PAG) and orbitofrontal cortex (OFC) afforded the most accurate trial-by-trial discrimination between painful and non-painful experiences; whereas during the actual stimulation, primary and secondary somatosensory cortex, anterior insula, dorsolateral and ventrolateral prefrontal cortex, and OFC were most discriminative. The most accurate prediction of pain perception from the stimulation period, however, was enabled by the combined activity in pain regions commonly referred to as the 'pain matrix'. Our results demonstrate that the neural representation of (near-threshold) pain is spatially distributed and can be best described at an intermediate spatial scale. In addition to its utility in establishing structure-function mappings, our approach affords trial-by-trial predictions and thus represents a step towards the goal of establishing an objective neuronal marker of pain perception.",
keywords = "Adult, Humans, Male, Female, Young Adult, Magnetic Resonance Imaging, Brain/*physiology, Image Interpretation, Computer-Assisted/*methods, Brain Mapping/*methods, Pain Perception/*physiology, Adult, Humans, Male, Female, Young Adult, Magnetic Resonance Imaging, Brain/*physiology, Image Interpretation, Computer-Assisted/*methods, Brain Mapping/*methods, Pain Perception/*physiology",
author = "Brodersen, {Kay H} and Katja Wiech and Lomakina, {Ekaterina I} and Chia-Shu Lin and Buhmann, {Joachim M} and Ulrike Bingel and Markus Ploner and Stephan, {Klaas Enno} and Irene Tracey",
year = "2012",
language = "English",
volume = "63",
pages = "1162--1170",
journal = "NEUROIMAGE",
issn = "1053-8119",
publisher = "Academic Press",
number = "3",

}

RIS

TY - JOUR

T1 - Decoding the perception of pain from fMRI using multivariate pattern analysis.

AU - Brodersen, Kay H

AU - Wiech, Katja

AU - Lomakina, Ekaterina I

AU - Lin, Chia-Shu

AU - Buhmann, Joachim M

AU - Bingel, Ulrike

AU - Ploner, Markus

AU - Stephan, Klaas Enno

AU - Tracey, Irene

PY - 2012

Y1 - 2012

N2 - Pain is known to comprise sensory, cognitive, and affective aspects. Despite numerous previous fMRI studies, however, it remains open which spatial distribution of activity is sufficient to encode whether a stimulus is perceived as painful or not. In this study, we analyzed fMRI data from a perceptual decision-making task in which participants were exposed to near-threshold laser pulses. Using multivariate analyses on different spatial scales, we investigated the predictive capacity of fMRI data for decoding whether a stimulus had been perceived as painful. Our analysis yielded a rank order of brain regions: during pain anticipation, activity in the periaqueductal gray (PAG) and orbitofrontal cortex (OFC) afforded the most accurate trial-by-trial discrimination between painful and non-painful experiences; whereas during the actual stimulation, primary and secondary somatosensory cortex, anterior insula, dorsolateral and ventrolateral prefrontal cortex, and OFC were most discriminative. The most accurate prediction of pain perception from the stimulation period, however, was enabled by the combined activity in pain regions commonly referred to as the 'pain matrix'. Our results demonstrate that the neural representation of (near-threshold) pain is spatially distributed and can be best described at an intermediate spatial scale. In addition to its utility in establishing structure-function mappings, our approach affords trial-by-trial predictions and thus represents a step towards the goal of establishing an objective neuronal marker of pain perception.

AB - Pain is known to comprise sensory, cognitive, and affective aspects. Despite numerous previous fMRI studies, however, it remains open which spatial distribution of activity is sufficient to encode whether a stimulus is perceived as painful or not. In this study, we analyzed fMRI data from a perceptual decision-making task in which participants were exposed to near-threshold laser pulses. Using multivariate analyses on different spatial scales, we investigated the predictive capacity of fMRI data for decoding whether a stimulus had been perceived as painful. Our analysis yielded a rank order of brain regions: during pain anticipation, activity in the periaqueductal gray (PAG) and orbitofrontal cortex (OFC) afforded the most accurate trial-by-trial discrimination between painful and non-painful experiences; whereas during the actual stimulation, primary and secondary somatosensory cortex, anterior insula, dorsolateral and ventrolateral prefrontal cortex, and OFC were most discriminative. The most accurate prediction of pain perception from the stimulation period, however, was enabled by the combined activity in pain regions commonly referred to as the 'pain matrix'. Our results demonstrate that the neural representation of (near-threshold) pain is spatially distributed and can be best described at an intermediate spatial scale. In addition to its utility in establishing structure-function mappings, our approach affords trial-by-trial predictions and thus represents a step towards the goal of establishing an objective neuronal marker of pain perception.

KW - Adult

KW - Humans

KW - Male

KW - Female

KW - Young Adult

KW - Magnetic Resonance Imaging

KW - Brain/physiology

KW - Image Interpretation, Computer-Assisted/methods

KW - Brain Mapping/methods

KW - Pain Perception/physiology

KW - Adult

KW - Humans

KW - Male

KW - Female

KW - Young Adult

KW - Magnetic Resonance Imaging

KW - Brain/physiology

KW - Image Interpretation, Computer-Assisted/methods

KW - Brain Mapping/methods

KW - Pain Perception/physiology

M3 - SCORING: Journal article

VL - 63

SP - 1162

EP - 1170

JO - NEUROIMAGE

JF - NEUROIMAGE

SN - 1053-8119

IS - 3

M1 - 3

ER -