2026-04-29T12:23:41Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/227222024-11-28T21:24:01Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16967

00925njm 22002777a 4500 dc Novaes Santana, Alex author Cifre, Ignacio author de Santana, Charles Novaes author Montoya, Pedro author 2019-12-17 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. 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. 10.3389/fnins.2019.01313 1662-453X Frontiers in Neuroscience http://hdl.handle.net/20.500.13003/12576 31920483 L630403938 2-s2.0-85077328865 https://hdl.handle.net/20.500.12105/22722 504990800001 Chronic pain machine learning Classification rs-fMRI Deep-learning DTW Using Deep Learning and Resting-State fMRI to Classify Chronic Pain Conditions