Using Deep Learning and Resting-State fMRI to Classify Chronic Pain Conditions

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.

Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment

“…As a result, several techniques and tools have been specifically developed or incorporated for measuring all of these multidimensional aspects of pain. Thus, for instance, surveys and self-report questionnaires [ 8 , 9 ], Quantitative Sensory Tests (QSTs) [ 10 , 11 , 12 , 13 ], genetic factors [ 14 , 15 , 16 ], physical activity patterns [ 17 , 18 , 19 , 20 ], Electroencephalography (EEG) [ 21 ], neuroimaging [ 22 , 23 , 24 ], and, more recently, functional near-infrared spectroscopy (fNIRS) [ 25 ] have been incorporated into studies of the emotional and cognitive factors that modulate pain. Eventually, these approaches are used to classify or differentiate groups, comparing patients with one chronic pain syndrome against pain-free controls or other chronic pain syndromes.…”

“…Finally, it seems clear that complex and multidimensional classification problems could take advantage of machine learning techniques applied to clinical data for supporting clinical decisions [ 47 , 48 , 49 , 50 , 51 ]. In the context of pain and pain chronification, machine learning approaches have recently been applied to several pain syndromes [ 24 , 52 , 53 , 54 ], including fibromyalgia [ 55 , 56 , 57 ] and chronic lower back pain [ 58 , 59 , 60 , 61 ]. While traditional statistical analyses commonly make some a priori assumptions about the data model (e.g., normality) and about the relationships among variables (e.g., linearity), machine learning prioritizes a “distribution-free” context.…”

Machine Learning in Pain Medicine: An Up-To-Date Systematic Review

“…Bayes [17,19,20,22] Estimates the probability of data patterns belonging to a specific class Boosting: functional data boosting (FDboost) [13]; gradient boosting (GB) [24,28]; extreme gradient boosting regression (XGBoost) [27,31] Merges weak classifiers into strong ones Deep learning neural network (DLNN) [10,11,14,16,18,34,35] Similarly to multiple linear regression it contains layers of interconnected nodes. A subclass of NN is the convolutional neural network (CNN)…”

“…k-means clustering [14] Divides a number of data points into a number of clusters based on the nearest mean k-nearest neighbors (kNN) [10,22,24,27,29,32] Assigns data patterns to a class on the basis of the distance to the training patterns of a certain class Multilayer perceptron (MLP) [9,22] Trains on a set of input data patterns to predict/classify the output class Random forest (RF) [14,15,28,29,31,32] Builds and merges multiple decision trees to provide a more accurate prediction Regression: kernel ridge regression (KRR), [30]; elastic net (EN) [23,28]; generalized linear mixed-models (GLMMs) based on repeated data points, Lasso [15,24]; least square (LS) [28]; linear regression (LiR) [27,33]; logistic regression (LoR) [10,15,29,31,32]; ridge regression (RR) [28] Predicts the probability of agreement using continuous data points Support vector machine (SVM) [9, 12, 15, 21, 22, 25-27, 29, 32, 34] Creates a hyperplane to separate two classes. The hyperplane is found by optimizing a cost function Multi-subject dictionary learning (MSDL) [16] It is a feature learning method where a training example is represented as a linear combination of basic functions, and is assumed to be a sparse matrix medical hypotheses, etc. ); (3) publications where ML method was not used for pain assessment; (4) publications presented trials with fewer than ten patients per treatment arm;…”

“…Table 2 presents the characteristics of the studies, including the ML method that was applied and the respective results. ML techniques for classifying the intensity of pain were found to be effective in patients with low back pain (LBP) [9,12,13,16], osteoarthritis [21], ankylosing spondylitis [30], spinal cord injury [26], thoracic pain [29], sickle cell disease (SCD) [32], evoked heat pain [25,27], and other types of pain [15]. In their case-controlled study, Abdollahi et al classified pain based on quantitative kinematic data.…”