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. 2023 Jun 2:14:1195923.
doi: 10.3389/fneur.2023.1195923. eCollection 2023.

Detection of brain regions responsible for chronic pain in osteoarthritis: an fMRI-based neuroimaging study using deep learning

Indranath Chatterjee  1   2 , Lea Baumgartner  1   3 , Migyung Cho  4
Affiliations

Detection of brain regions responsible for chronic pain in osteoarthritis: an fMRI-based neuroimaging study using deep learning

Indranath Chatterjee et al. Front Neurol. .

Abstract

Introduction: Chronic pain is a multifaceted condition that has yet to be fully comprehended. It is frequently linked with a range of disorders, particularly osteoarthritis (OA), which arises from the progressive deterioration of the protective cartilage that cushions the bone endings over time.

Methods: In this paper, we examine the impact of chronic pain on the brain using advanced deep learning (DL) algorithms that leverage resting-state functional magnetic resonance imaging (fMRI) data from both OA pain patients and healthy controls. Our study encompasses fMRI data from 51 pain patients and 20 healthy subjects. To differentiate chronic pain-affected OA patients from healthy controls, we introduce a DL-based computer-aided diagnosis framework that incorporates Multi-Layer Perceptron and Convolutional Neural Networks (CNN), separately.

Results: Among the examined algorithms, we discovered that CNN outperformed the others and achieved a notable accuracy rate of nearly 85%. In addition, our investigation scrutinized the brain regions affected by chronic pain and successfully identified several regions that have not been mentioned in previous literature, including the occipital lobe, the superior frontal gyrus, the cuneus, the middle occipital gyrus, and the culmen.

Discussion: This pioneering study explores the applicability of DL algorithms in pinpointing the differentiating brain regions in OA patients who experience chronic pain. The outcomes of our research could make a significant contribution to medical research on OA pain patients and facilitate fMRI-based pain recognition, ultimately leading to enhanced clinical intervention for chronic pain patients.

Keywords: chronic pain; classification; deep learning; functional magnetic resonance imaging (fMRI); medical imaging; osteoarthritis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Overview of the workflow diagram showing the steps performed in this study.
Figure 2
Figure 2
(A,B) Metric chart for accuracy, AUC, precision, recall, and F1-score values for (A) MLP results; (B) CNN results.
Figure 3
Figure 3
Performance visualization of MLP and CNN using ROC and AUC. (A) ROC and AUC performance of MLP. (B) ROC and AUC performance of CNN.
Figure 4
Figure 4
(A) The brain regions showing the significant brain activities on the lobe level. (B) The brain regions showing the significant brain activities on the gyrus level.
Figure 5
Figure 5
Visualization of the identified brain areas potentially responsible for chronic pain in osteoarthritis patients.
See this image and copyright information in PMC

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