Turkish Journal of Electrical Engineering and Computer Sciences




Millions of people throughout the world suffer from the complicated and crippling condition of chronic pain. It can be brought on by several underlying disorders or injuries and is defined by chronic pain that lasts for a period exceeding three months. To better understand the brain processes behind pain and create prediction models for pain-related outcomes, machine learning is a potent technology that may be applied in Functional magnetic resonance imaging (fMRI) chronic pain research. Data (fMRI and T1-weighted images) from 76 participants has been included (30 chronic pain and 46 healthy controls). The raw data were preprocessed using fMRIprep and then parcellated using five various atlases such as MSDL, Yeo?17, Harvard, Schaefer, and Pauli. Then the functional connectivity between the parcellated Region of Interests (ROIs) has been taken as features for the machine learning classifier models using the Blood Oxygenation Level Dependent (BOLD) signals. To distinguish between those with chronic pain and healthy controls, this study used Support Vector Machines (SVM), Boosting, Bagging, convolutional neural network (CNN), XGboost, and Stochastic Gradient Descent (SDG) classifiers. The classification models use stratified shuffle split sampling to fragment the training and testing dataset during various iterations. Hyperparameter tuning was used to get the best classifier model across several combinations of parameters. The best parameters for the classifier were measured by the accuracy, sensitivity, and specificity of the model. Finally, to identify the top ROIs involved in chronic pain was unveiled by the probability-based feature importance method. The result shows that Pauli (subcortical atlas) and MSDL (cortical atlas) worked well for this chronic pain fMRI data. Boosting algorithm classified chronic pain and healthy controls with 94.35% accuracy on the data parcellated with the Pauli atlas. The top four regions contributing to this classifier model were the extended Amygdala, the Subthalamic nucleus, the Hypothalamus, and the Caudate Nucleus. Also, the fMRI data parcellated using a cortical MSDL atlas was classified using the XGboost model with an accuracy of 87.5%. Left Frontal Pole, Medial Default mode Network, right pars opercularis, dorsal anterior cingulate cortex (dACC), and Front Default mode network are the top five regions that contributed to classify the participants. These findings demonstrate that patterns of brain activity in areas associated with pain processing can be used to categorize individuals as chronic pain patients or healthy controls reliably. These discoveries may help with the identification and management of chronic pain and may pave the way for the creation of more potent tailored medicines for those who suffer from it.


Biomarkers, chronic pain, machine learning, fMRI, neuroimaging, classification

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