使用用于脑磁图的MUSIC（多信号分类）算法分析与疼痛相关的体感诱发磁场。

Analysis of pain-related somatosensory evoked magnetic fields using the MUSIC (multiple signal classification) algorithm for magnetoencephalography.

作者信息

Ninomiya Y, Kitamura Y, Yamamoto S, Okamoto M, Oka H, Yamada N, Kuroda S

机构信息

Department of Neuropsychiatry, Okayama University Medical School, Japan.

出版信息

Neuroreport. 2001 Jun 13;12(8):1657-61. doi: 10.1097/00001756-200106130-00029.

DOI:10.1097/00001756-200106130-00029

PMID:11409735

Abstract

We evaluated the effectiveness of the Multiple Signal Classification (MUSIC) algorithm by analysing pain-related somatosensory-evoked magnetic fields (SEFs) by 148-channel whole-head-type magnetoencephalography. MUSIC peaks of middle latency components were located around the primary somatosensory cortex (SI), contralateral to the stimulated finger. Long latency components were located around the bilateral secondary somatosensory cortices (SII) and cingulate gyri. Peaks at the SII and cingulate gyri were more prominent on very painful and moderately painful stimulation than on weak stimulation. The results were in very good agreement with results from single dipole estimation. These findings suggest that the MUSIC algorithm could be a useful tool for analysis of pain-related SEFs.

摘要

我们通过148通道全头型脑磁图分析疼痛相关的体感诱发磁场（SEF），评估了多重信号分类（MUSIC）算法的有效性。中潜伏期成分的MUSIC峰值位于受刺激手指对侧的初级体感皮层（SI）周围。长潜伏期成分位于双侧次级体感皮层（SII）和扣带回周围。与弱刺激相比，在非常疼痛和中度疼痛刺激下，SII和扣带回的峰值更明显。结果与单偶极子估计的结果非常吻合。这些发现表明，MUSIC算法可能是分析疼痛相关SEF的有用工具。