Background: Pain is one of the most disabling symptoms in multiple sclerosis. Chronic pain in multiple sclerosis is often neuropathic in nature, although a clear-cut distinction with nociceptive pain is not easy. Objective: The aim of our study was to analyze the MRIs of multiple sclerosis patients with chronic pain in order to explore possible associations with lesion sites, on a voxel-by-voxel basis. Materials and methods: We enrolled patients aged > 18 years with multiple sclerosis in accordance with the 2010 McDonald criteria. Patients meeting criteria for persistent pain (frequent or constant pain lasting > 3 months) were included in the “pain group”. The other patients were included in the “no pain group”. We outlined lesions on FLAIR MRI scans using a semi-automated edge finding tool. To detect the association between lesion localization and persistent pain, images were analysed with the voxel-based lesion symptom mapping methods implemented in the (nonparametric mapping software included into the MRIcron. Results: We enrolled 208 MS patients (140 F, mean age 55.2 ± 9.4 years; 176 RR, 28 progressive MS; mean EDSS 2.0 + 2.0). Pain group included 96 patients and no pain group 112 patients. Lesions of the right dorsolateral prefrontal area were significantly more prevalent in patients without pain, whereas periventricular posterior lesions were significantly more prevalent in patients with persistent pain. Conclusion: Our data suggest a role of the right dorsolateral prefrontal cortex in the modulation of pain perception and in the occurrence of chronic pain in MS patients. Our data also support a hemispheric asymmetry in pain perception and modulation.

Plantone, D., Vollono, C., Pardini, M., Primiano, G., Myftari, V., Vitetta, F., et al. (2020). A voxel-based lesion symptom mapping analysis of chronic pain in multiple sclerosis. NEUROLOGICAL SCIENCES, 42(5), 1941-1947 [10.1007/s10072-020-04745-3].

A voxel-based lesion symptom mapping analysis of chronic pain in multiple sclerosis

Plantone D
;
2020-01-01

Abstract

Background: Pain is one of the most disabling symptoms in multiple sclerosis. Chronic pain in multiple sclerosis is often neuropathic in nature, although a clear-cut distinction with nociceptive pain is not easy. Objective: The aim of our study was to analyze the MRIs of multiple sclerosis patients with chronic pain in order to explore possible associations with lesion sites, on a voxel-by-voxel basis. Materials and methods: We enrolled patients aged > 18 years with multiple sclerosis in accordance with the 2010 McDonald criteria. Patients meeting criteria for persistent pain (frequent or constant pain lasting > 3 months) were included in the “pain group”. The other patients were included in the “no pain group”. We outlined lesions on FLAIR MRI scans using a semi-automated edge finding tool. To detect the association between lesion localization and persistent pain, images were analysed with the voxel-based lesion symptom mapping methods implemented in the (nonparametric mapping software included into the MRIcron. Results: We enrolled 208 MS patients (140 F, mean age 55.2 ± 9.4 years; 176 RR, 28 progressive MS; mean EDSS 2.0 + 2.0). Pain group included 96 patients and no pain group 112 patients. Lesions of the right dorsolateral prefrontal area were significantly more prevalent in patients without pain, whereas periventricular posterior lesions were significantly more prevalent in patients with persistent pain. Conclusion: Our data suggest a role of the right dorsolateral prefrontal cortex in the modulation of pain perception and in the occurrence of chronic pain in MS patients. Our data also support a hemispheric asymmetry in pain perception and modulation.
2020
Plantone, D., Vollono, C., Pardini, M., Primiano, G., Myftari, V., Vitetta, F., et al. (2020). A voxel-based lesion symptom mapping analysis of chronic pain in multiple sclerosis. NEUROLOGICAL SCIENCES, 42(5), 1941-1947 [10.1007/s10072-020-04745-3].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1152678