Neuroimaging-based pain detector using artificial intelligence approaches

Chronic pain is a complex, multifactorial experience that varies significantly across time, sex, and individual physiology. This thesis presents the development of a deep learning-based sys- tem for classifying pain-related brain activity using functional magnetic resonance imaging (fMRI) from a rodent model of a comorbid pain condition (masseter muscle inflammation fol- lowed by stress) that induces chronic visceral pain hypersensitivity (CPH). The proposed sys- tem evaluates the potential ofconvolutional neural networks (CNNs) to detect pain-associated neural patterns under different experimental conditions.Three variations of the VGG16 architecture were implemented and tested: a modified 2D VGG16 adapted to 3D volumes, a multiview 2D ensemble (M2D) fed with axial, sagittal, and coronal slices, and a fully 3D VGG16 model. After an initial benchmarking phase using data from rest sessions, the 3D VGG16 model was selected for subsequent experiments due to its consistent performance and the ability to learn from full volumetric input.Classification tasks involved multiple comparison scenarios, including sex differences, longitudinal progression of pain (from baseline to weeks 1 and week 7 after the CPH pro- cedure), and the impact of data selection strategies (full rest sessions vs. distension-specific volume extraction). Grad-CAM was used to provide anatomical interpretation of model at- tention, revealing consistent activation of pain-related brain regions such as the insular cortex, somatosensory cortex, thalamic nuclei, and prelimbic area, with marked differences observed between male and female subjects.The results demonstrate the feasibility of using deep neural networks, combined with explainable AI techniques, to decode and interpret pain-related patterns in fMRI data. Fur- thermore, the performance trends observed in classification tasks align with behavioral find- ings reported in the literature, supporting the potential of AI-driven neuroimaging analysis to uncover meaningful biological signatures of chronic pain.This study builds directly upon the work conducted by Da Silva et. al. [1], who previ- ously processed the same dataset to generate VMR representations and statistical t-maps from fMRI data. His analysis focused on identifying regions with significant activation differences between conditions using traditional statistical parametric mapping. Expanding on this foun- dation, the present research integrates deep learning methods, specifically 3D convolutional neural networks (CNNs), to classify experimental conditions directly from the fMRI volumes. Moreover, it incorporates explainable AI techniques (Grad-CAM) to reveal the spatial patterns most influential to classification. This approach offers a shift from region-centric hypothesis testing toward a data-driven, whole-brain interpretability framework, enabling the detection of distributed neural patterns that might not reach statistical significance individually but are collectively informative.