<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-04-29T12:23:02Z</responseDate><request verb="GetRecord" identifier="oai:repisalud.isciii.es:20.500.12105/22722" metadataPrefix="mets">https://repisalud.isciii.es/rest/oai/request</request><GetRecord><record><header><identifier>oai:repisalud.isciii.es:20.500.12105/22722</identifier><datestamp>2024-11-28T21:24:01Z</datestamp><setSpec>com_20.500.12105_15322</setSpec><setSpec>com_20.500.12105_2051</setSpec><setSpec>col_20.500.12105_16967</setSpec></header><metadata><mets xmlns="http://www.loc.gov/METS/" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" ID="&#xa;&#x9;&#x9;&#x9;&#x9;DSpace_ITEM_20.500.12105-22722" TYPE="DSpace ITEM" PROFILE="DSpace METS SIP Profile 1.0" xsi:schemaLocation="http://www.loc.gov/METS/ http://www.loc.gov/standards/mets/mets.xsd" OBJID="&#xa;&#x9;&#x9;&#x9;&#x9;hdl:20.500.12105/22722">
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                  <mods:namePart>Novaes Santana, Alex</mods:namePart>
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                  <mods:namePart>Cifre, Ignacio</mods:namePart>
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                  <mods:namePart>de Santana, Charles Novaes</mods:namePart>
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               <mods:name>
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                  <mods:namePart>Montoya, Pedro</mods:namePart>
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                  <mods:dateAccessioned encoding="iso8601">2024-09-10T13:08:58Z</mods:dateAccessioned>
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                  <mods:dateIssued encoding="iso8601">2019-12-17</mods:dateIssued>
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               <mods:identifier type="citation">Novaes Santana A, Cifre I, Novaes de Santana C, Montoya P. Using Deep Learning and Resting-State fMRI to Classify Chronic Pain Conditions. Front Neurosci. 2019 Dec 17;13:1313.</mods:identifier>
               <mods:identifier type="doi">10.3389/fnins.2019.01313</mods:identifier>
               <mods:identifier type="e-issn">1662-453X</mods:identifier>
               <mods:identifier type="journal">Frontiers in Neuroscience</mods:identifier>
               <mods:identifier type="other">http://hdl.handle.net/20.500.13003/12576</mods:identifier>
               <mods:identifier type="pubmedID">31920483</mods:identifier>
               <mods:identifier type="pui">L630403938</mods:identifier>
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               <mods:identifier type="uri">https://hdl.handle.net/20.500.12105/22722</mods:identifier>
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               <mods:abstract>Chronic pain is known as a complex disease due to its comorbidities with other symptoms and the lack of effective treatments. As a consequence, chronic pain seems to be under-diagnosed in more than 75% of patients. At the same time, the advance in brain imaging, the popularization of machine learning techniques and the development of new diagnostic tools based on these technologies have shown that these tools could be an option in supporting decision-making of healthcare professionals. In this study, we computed functional brain connectivity using resting-state fMRI data from one hundred and fifty participants to assess the performance of different machine learning models, including deep learning (DL) neural networks in classifying chronic pain patients and pain-free controls. The best result was obtained by training a convolutional neural network fed with data preprocessed using the MSDL probabilistic atlas and using the dynamic time warping (DTW) as connectivity measure. DL models had a better performance compared to other less costly models such as support vector machine (SVM) and RFC, with balanced accuracy ranged from 69 to 86%, while the area under the curve (ROC) ranged from 0.84 to 0.93. Also, DTW overperformed correlation as connectivity measure. These findings support the notion that resting-state fMRI data could be used as a potential biomarker of chronic pain conditions.</mods:abstract>
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                  <mods:languageTerm authority="rfc3066">eng</mods:languageTerm>
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               <mods:subject>
                  <mods:topic>Chronic pain</mods:topic>
               </mods:subject>
               <mods:subject>
                  <mods:topic>machine learning</mods:topic>
               </mods:subject>
               <mods:subject>
                  <mods:topic>Classification</mods:topic>
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               <mods:subject>
                  <mods:topic>rs-fMRI</mods:topic>
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               <mods:subject>
                  <mods:topic>Deep-learning</mods:topic>
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               <mods:subject>
                  <mods:topic>DTW</mods:topic>
               </mods:subject>
               <mods:titleInfo>
                  <mods:title>Using Deep Learning and Resting-State fMRI to Classify Chronic Pain Conditions</mods:title>
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               <mods:genre>research article</mods:genre>
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