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Lee, J., Mawlaa, I., Kim, J., Loggiaa, M. L., Ortiza, A., Jung, C., Chan, S., Gerbera, J., Schmithorst, V. J., Edwards, R. R., Wasane, A. D., Berna, C., Kong, J., Kaptchuk, T. J., Golluba, R. L., Rosen, B. R., & Napadowa, V. (2019) Machine learning–based prediction of clinical pain using multimodal neuroimaging and autonomic metrics. Pain. 160:550–560.
Lee, J., Mawlaa, I., Kim, J., Loggiaa, M. L., Ortiza, A., Jung, C., Chan, S., Gerbera, J., Schmithorst, V. J., Edwards, R. R., Wasane, A. D., Berna, C., Kong, J., Kaptchuk, T. J., Golluba, R. L., Rosen, B. R., & Napadowa, V. (2019) Machine learning–based prediction of clinical pain using multimodal neuroimaging and autonomic metrics. Pain. 160:550–560.
Brain features significantly contributing to within-patient classification of lower- and higher-clinical pain intensity states. Paired-SVM classification weight maps for rCBF (A) and S1CONN (B) were thresholded (permutation analysis with N=5000, P<0.01) for visualization. N.b. rCBF: regional cerebral blood flow, S1CONN: S1-connectivity, SMA: supplementary motor area, vPCC: ventral posterior cingulate cortex, dlPFC: dorsolateral prefrontal cortex, S1: primary somatosensory cortex, M1: primary motor cortex, mPFC: medial prefrontal cortex, FIC: frontoinsular cortex, R: right hemisphere, L: left hemisphere.
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