A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data.

Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is impor...

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Autores principales:	Daniel Callan, Lloyd Mills, Connie Nott, Robert England, Shaun England
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/130dde09c1e54461832c7e34dbe6dc5c
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id	oai:doaj.org-article:130dde09c1e54461832c7e34dbe6dc5c
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spelling	oai:doaj.org-article:130dde09c1e54461832c7e34dbe6dc5c2021-11-18T08:16:41ZA tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data.1932-620310.1371/journal.pone.0098007https://doaj.org/article/130dde09c1e54461832c7e34dbe6dc5c2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24905072/?tool=EBIhttps://doaj.org/toc/1932-6203Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups.Daniel CallanLloyd MillsConnie NottRobert EnglandShaun EnglandPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 6, p e98007 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Daniel Callan Lloyd Mills Connie Nott Robert England Shaun England A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data.
description	Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups.
format	article
author	Daniel Callan Lloyd Mills Connie Nott Robert England Shaun England
author_facet	Daniel Callan Lloyd Mills Connie Nott Robert England Shaun England
author_sort	Daniel Callan
title	A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data.
title_short	A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data.
title_full	A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data.
title_fullStr	A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data.
title_full_unstemmed	A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data.
title_sort	tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/130dde09c1e54461832c7e34dbe6dc5c
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A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data.

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