Neural Decoding of Ultrasound-Modulated Pain States via Deep Learning: Insights from Functional Magnetic Resonance Imaging Data

Abstract

Focused ultrasound (FUS) is a noninvasive neuromodulation technique capable of modulating deep-brain circuits with millimeter precision. When combined with functional magnetic resonance imaging (fMRI), it enables causal decoding of brain-state dynamics in vivo. In this study, the authors applied deep learning to fMRI data acquired from nonhuman primates during thermal pain stimulation with and without transcranial FUS targeting the ventral posterolateral (VPL) nucleus of the thalamus, a key somatosensory relay. A 3D convolutional neural network was trained to classify three experimental conditions: Heat (pain only), Heat + FUS, and FUS-only. The model achieved an average accuracy of 92.2% (binary) and 91.2% (multiclass), with receiver operating characteristic-area under the curve values exceeding 0.90. Saliency mapping revealed significant cortical suppression in the secondary somatosensory cortex (S2, p = 0.036) and insula (p = 0.024), accompanied by preserved thalamic relevance in the VPL. In FUS-only trials, saliency shifted toward dorsolateral prefrontal regions, suggesting cognitive engagement even in the absence of nociceptive input. These findings demonstrate that FUS attenuates thalamocortical pain transmission while maintaining subcortical relay integrity. This work highlights the translational potential of FUS as a targeted, reversible neuromodulation strategy and underscores the value of artificial intelligence-driven neuroimaging in decoding dynamic pain states.