Identifying brain targets for real-time fMRI neurofeedback in chronic pain: insights from functional neurosurgery.

BACKGROUND

The lack of clearly defined neuromodulation targets has contributed to the inconsistent results of real-time fMRI-based neurofeedback (rt-fMRI-NF) for the treatment of chronic pain. Functional neurosurgery (funcSurg) approaches have shown more consistent effects in reducing pain in patients with severe chronic pain.

OBJECTIVE

This study aims to redefine rt-fMRI-NF targets for chronic pain management informed by funcSurg studies.

METHODS

Based on independent systematic reviews, we identified the neuromodulation targets of the rt-fMRI-NF (in acute and chronic pain) and funcSurg (in chronic pain) studies. We then characterized the underlying functional networks using a subsample of the 7 T resting-state fMRI dataset from the Human Connectome Project. Principal component analyses (PCA) were used to identify dominant patterns (accounting for a cumulative explained variance >80%) within the obtained functional maps, and the overlap between these PCA maps and canonical intrinsic brain networks (default, salience, and sensorimotor) was calculated using a null map approach.

RESULTS

The anatomical targets used in rt-fMRI-NF and funcSurg approaches are largely distinct, with the middle cingulate cortex as a common target. Within the investigated canonical rs-fMRI networks, these approaches exhibit both divergent and overlapping functional connectivity patterns. Specifically, rt-fMRI-NF approaches primarily target the default mode network ( value range 0.001-0.002) and the salience network ( = 0.002), whereas funcSurg approaches predominantly target the salience network ( = 0.001) and the sensorimotor network ( value range 0.001-0.023).

CONCLUSION

Key hubs of the salience and sensorimotor networks may represent promising targets for the therapeutic application of rt-fMRI-NF in chronic pain.