Neuroimaging Analysis Tools in Pain Research: A Comparative Review Across Modalities

AbstractPain is a multidimensional experience processed by complex neural networks, including the anterior cingulate cortex, insula, thalamus, and somatosensory cortex. Neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), magnetoencephalography (MEG), and stereo-electroencephalography (sEEG), have advanced our understanding of the neural mechanisms underlying pain perception and modulation. This review provides a modality-wise comparative analysis of neuroimaging tools used in pain research, highlighting the key pipelines, brain region specificity, and clinical relevance. We summarize real-time applications such as neurofeedback using fMRI and EEG, as well as emerging machine learning models applied to fNIRS and multimodal datasets. The review also evaluates software frameworks, including SPM, FSL, CONN, DISCOVER-EEG, and custom deep learning approaches for pain classification and prediction. While fMRI offers superior spatial resolution, EEG and MEG provide millisecond-level temporal precision, and fNIRS is a promising option for bedside monitoring. By synthesizing evidence from 32 peer-reviewed studies, we emphasize the growing role of data integration, automated analysis, and real-time feedback paradigms in shaping the future of pain research and clinical intervention.