Back

EEG-based analysis of intrinsic brain network function in chronic pain: Insights from a comprehensive multi-data set study

Bott, F. S.; Zebhauser, P. T.; Hohn, V. D.; Turgut, O.; May, E. S.; Tiemann, L.; Gil Avila, C.; Heitmann, H.; Nickel, M. M.; Day, M. A.; Adhia, D. B.; Ashar, Y. K.; Wager, T. D.; Granovsky, Y.; Yarnitsky, D.; Jensen, M. P.; Gross, J.; Ploner, M.

2024-08-12 neuroscience
10.1101/2024.08.12.607584 bioRxiv
Show abstract

Chronic pain is associated with alterations in brain function. A better understanding of these alterations might help to develop new approaches for the diagnosis, prediction, and treatment of chronic pain. Here, we analyzed associations between chronic pain and alterations of intrinsic brain network function using resting-state electroencephalography. We included data from 537 people with chronic pain obtained from various research groups worldwide. We found strong evidence for associations between pain intensity and intrinsic brain network connectivity, but the replicability of these associations in independent data was mostly low. However, a mega-analysis revealed associations of chronic pain with salience-somatomotor network connectivity at theta frequencies and with more complex patterns of intrinsic brain network connectivity. These findings provide novel insights into brain network function in chronic pain. Moreover, they highlight the need for collaborative multi-center studies, which can be guided by the present approach to promote replicability and consistency of findings.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.