Here’s a video abstract of this paper: 
(1/9) One of the key features of clinical pain is its sustained nature (as in the definition of chronic pain). Our current work started from the simple question: Can we experimentally manipulate the "sustained pain" component to identify its underlying neural representation?
(2/9) To answer the question, we experimentally induced long-lasting orofacial pain in healthy participants by applying capsaicin-containing hot sauce on their tongue (Study 1, n = 19), and developed a functional connectivity-based marker predictive of tonic pain intensity.
(3/9) Our model, termed as the Tonic Pain Signature (ToPS), showed a high level of sensitivity and specificity across multiple datasets, including training, validation, and independent test datasets (Studies 1-3, n = 109).
(4/9) Next, we wanted to test our marker on clinical pain datasets to know whether the ToPS generalized to clinical pain. We used publicly available fMRI datasets of subacute and chronic back pain as test datasets (Studies 4-5, n = 95 and 97; from http://openpain.org/ ).
(5/9) and we found that the ToPS explained the overall pain severity of the patients and also discriminated patients from healthy controls with (not high, but) reasonable accuracies.
(6/9) Then, we wanted to know what made the ToPS predictive of clinical pain. By comparing it with additionally trained models for experimental brief (phasic) pain and clinical back pain, with similar methods to develop the ToPS, we found that ...
(7/9) The ToPS was more similar to clinical pain than experimental phasic pain (in terms of their model weights), and the important brain networks for this similarity included the somatomotor, dorsal attention, and frontoparietal networks.
(8/9) Somatomotor is understandable, but why dorsal attention and frontoparietal? Our speculation is that in the sustained pain, compared to the phasic pain, the top-down coping/regulatory brain responses may play more important roles in shaping the overall pain experience.
(9/9) We share our data and codes at github repository ( https://github.com/cocoanlab/tops ), and our model upon request. Great thanks to all co-authors again, and happy new year!!!
You can follow @_LeeJaeJoong_.
Tip: mention @twtextapp on a Twitter thread with the keyword “unroll” to get a link to it.

Latest Threads Unrolled:

By continuing to use the site, you are consenting to the use of cookies as explained in our Cookie Policy to improve your experience.