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使用带和不带生理混杂因素的统计参数映射 (SPM) 方法进行功能光学拓扑分析。

Functional optical topography analysis using statistical parametric mapping (SPM) methodology with and without physiological confounds.

机构信息

Biomedical Optics Research Laboratory, Department of Medical Physics and Bioengineering, University College London, Gower Street, London WC1E 6BT, UK.

出版信息

Adv Exp Med Biol. 2010;662:237-43. doi: 10.1007/978-1-4419-1241-1_34.

DOI:10.1007/978-1-4419-1241-1_34
PMID:20204798
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4038021/
Abstract

Functional optical topography (OT) measures the changes in oxygenated and deoxygenated hemoglobin (HbO(2), HHb) across multiple brain sites which occur in response to neuronal activation of the cerebral cortex. However, identification of areas of cortical activation is a complex task due to intrinsic physiological noise and systemic interference and careful statistical analysis is therefore required. A total of 10 young healthy adults were studied. The activation paradigm comprised of anagrams followed by finger tapping. 12 channels of the OT system were positioned over the frontal cortex and 12 channels over the motor cortex while the systemic physiology (mean blood pressure (MBP), heart rate (HR), scalp flux) was simultaneously monitored. Analysis was done using the functional Optical Signal Analysis (fOSA) software and Statistical Parametric Mapping (SPM), where we utilized two approaches: (i) using only HbO(2) as a regressor in the general linear model (GLM) and (ii) using all of the explanatory variables (HbO(2), MBP, HR and scalp flux) as regressors. Group analysis using SPM showed significant correlation in a large number of OT channels between HbO(2) and systemic regressors; however no differences in activation areas were seen between the two approaches.

摘要

功能光学拓扑(OT)测量的是含氧和去氧血红蛋白(HbO(2),HHb)在大脑多个部位的变化,这些变化是由于大脑皮层的神经元激活而发生的。然而,由于内在的生理噪声和系统干扰,识别皮质激活区域是一项复杂的任务,因此需要进行仔细的统计分析。共有 10 名年轻健康的成年人参与了这项研究。激活范式包括字谜游戏和手指敲击。OT 系统的 12 个通道被放置在前额皮层上,12 个通道被放置在运动皮层上,同时监测系统生理学(平均血压(MBP)、心率(HR)、头皮通量)。使用功能光学信号分析(fOSA)软件和统计参数映射(SPM)进行分析,我们利用了两种方法:(i)仅使用 HbO(2)作为广义线性模型(GLM)中的回归变量,(ii)使用所有解释变量(HbO(2)、MBP、HR 和头皮通量)作为回归变量。使用 SPM 的组分析显示,在大量 OT 通道中,HbO(2)与系统回归变量之间存在显著相关性;然而,两种方法之间没有观察到激活区域的差异。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f745/4038021/e35e33eb2222/AdvExpMedBiol-662-237-g00unctional.f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f745/4038021/0bfa120ae461/AdvExpMedBiol-662-237-g00unctional.f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f745/4038021/cbe2783f1e75/AdvExpMedBiol-662-237-g00unctional.f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f745/4038021/8e7b1c3adb40/AdvExpMedBiol-662-237-g00unctional.f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f745/4038021/4a6ce6ecb313/AdvExpMedBiol-662-237-g00unctional.f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f745/4038021/e35e33eb2222/AdvExpMedBiol-662-237-g00unctional.f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f745/4038021/0bfa120ae461/AdvExpMedBiol-662-237-g00unctional.f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f745/4038021/cbe2783f1e75/AdvExpMedBiol-662-237-g00unctional.f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f745/4038021/8e7b1c3adb40/AdvExpMedBiol-662-237-g00unctional.f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f745/4038021/4a6ce6ecb313/AdvExpMedBiol-662-237-g00unctional.f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f745/4038021/e35e33eb2222/AdvExpMedBiol-662-237-g00unctional.f5.jpg

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