BACKGROUND: To elucidate neural correlates associated with processing of tonic aching pain, the authors used high-field (3-T) functional magnetic resonance imaging with a blocked parametric study design and characterized regional brain responses to electrical stimulation according to stimulus intensity-response functions. METHODS: Pain was induced in six male volunteers using a 5-Hz electrical stimulus applied to the right index finger. Scanning sequences involved different levels of stimulation corresponding to tingling sensation (P1), mild pain (P2), or high pain (P3). Common effects across subjects were sought using a conjunction analyses approach, as implemented in statistical parametric mapping (SPM-99). RESULTS: The contralateral posterior/mid insula and contralateral primary somatosensory cortex were most associated with encoding stimulus intensity because they showed a positive linear relation between blood oxygenation level-dependent signal responses and increasing stimulation intensity (P1 < P2 < P3). The contralateral secondary somatosensory cortex demonstrated a response function most consistent with a role in pain intensity encoding because it had no significant response during the innocuous condition (P1) but proportionally increased activity with increasingly painful stimulus intensities (0 < P2 < P3). Finally, a portion of the anterior cingulate cortex (area 24) and supplementary motor area 6 demonstrated a high pain-specific response (P3). CONCLUSIONS: The use of response function modeling, conjunction analysis, and high-field imaging reveals dissociable regional responses to a tonic aching electrical pain. Most specifically, the primary somatosensory cortex and insula seem to encode stimulus intensity information, whereas the secondary somatosensory cortex encodes pain intensity information. The cingulate findings are consistent with its proposed role in processing affective-motivational aspects of pain.
BACKGROUND: To elucidate neural correlates associated with processing of tonic aching pain, the authors used high-field (3-T) functional magnetic resonance imaging with a blocked parametric study design and characterized regional brain responses to electrical stimulation according to stimulus intensity-response functions. METHODS:Pain was induced in six male volunteers using a 5-Hz electrical stimulus applied to the right index finger. Scanning sequences involved different levels of stimulation corresponding to tingling sensation (P1), mild pain (P2), or high pain (P3). Common effects across subjects were sought using a conjunction analyses approach, as implemented in statistical parametric mapping (SPM-99). RESULTS: The contralateral posterior/mid insula and contralateral primary somatosensory cortex were most associated with encoding stimulus intensity because they showed a positive linear relation between blood oxygenation level-dependent signal responses and increasing stimulation intensity (P1 < P2 < P3). The contralateral secondary somatosensory cortex demonstrated a response function most consistent with a role in pain intensity encoding because it had no significant response during the innocuous condition (P1) but proportionally increased activity with increasingly painful stimulus intensities (0 < P2 < P3). Finally, a portion of the anterior cingulate cortex (area 24) and supplementary motor area 6 demonstrated a high pain-specific response (P3). CONCLUSIONS: The use of response function modeling, conjunction analysis, and high-field imaging reveals dissociable regional responses to a tonic aching electrical pain. Most specifically, the primary somatosensory cortex and insula seem to encode stimulus intensity information, whereas the secondary somatosensory cortex encodes pain intensity information. The cingulate findings are consistent with its proposed role in processing affective-motivational aspects of pain.
Authors: Florian Kurth; Simon B Eickhoff; Axel Schleicher; Lars Hoemke; Karl Zilles; Katrin Amunts Journal: Cereb Cortex Date: 2009-10-12 Impact factor: 5.357
Authors: Jian Kong; Nathan S White; Kenneth K Kwong; Mark G Vangel; Ilana S Rosman; Richard H Gracely; Randy L Gollub Journal: Hum Brain Mapp Date: 2006-09 Impact factor: 5.038
Authors: Meryem A Yücel; Christopher M Aasted; Mihayl P Petkov; David Borsook; David A Boas; Lino Becerra Journal: Sci Rep Date: 2015-03-30 Impact factor: 4.996