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大规模神经认知网络的功能磁共振成像分析

Functional magnetic resonance image analysis of a large-scale neurocognitive network.

作者信息

Bullmore E T, Rabe-Hesketh S, Morris R G, Williams S C, Gregory L, Gray J A, Brammer M J

机构信息

Department of Biostatistics & Computing, Institute of Psychiatry, London, United Kingdom.

出版信息

Neuroimage. 1996 Aug;4(1):16-33. doi: 10.1006/nimg.1996.0026.

Abstract

Many "higher-order" mental functions are subserved by large-scale neurocognitive networks comprising several spatially distributed and functionally specialized brain regions. We here report statistical and graphical methods of functional magnetic resonance imaging data analysis which can be used to elucidate the functional relationships (i.e., connectivity and distance) between elements of a neurocognitive network in a single subject. Data were acquired from a normal right-handed volunteer during periodic performance of a task which demanded visual and semantic processing of words and subvocalization of a decision about the meaning of each word. Major regional foci of activation were identified (by sinusoidal regression modeling and spatiotemporal randomization tests) in left extrastriate cortex, angular gyrus, supramarginal gyrus, superior and middle temporal gyri, lateral premotor cortex, and Broca's area. Principal component (PC) analysis was initially undertaken by singular value decomposition (SVD) of the "raw" time series observed at 170 activated voxels. This revealed a large functional distance (negative connectivity) between visual processing systems and all other brain regions in the space of the first PC. SVD of a matrix of fitted time series, and a matrix of six sinusoidal regression parameters estimated at each activated voxel, were developed as less noisy (more informative) alternatives to SVD of the "raw" data. Canonical variate analysis of denoised data was then used to clarify functional relationships between the major regional foci. Visual input analysis systems (extrastriate cortex and angular gyrus) were colocalized in the space of the first canonical variate (CV) and significantly separated from all other brain regions. Semantic analysis systems (supramarginal and temporal gyri) were colocalized and significantly separated in the space of the second CV from the subvocal output system (Broca's area). These results are provisionally interpreted in terms of underlying hemodynamic events and cognitive psychological theory.

摘要

许多“高阶”心理功能由大规模神经认知网络支持,这些网络由几个空间分布且功能专门化的脑区组成。我们在此报告功能磁共振成像数据分析的统计和图形方法,这些方法可用于阐明单个受试者神经认知网络各元素之间的功能关系(即连通性和距离)。数据是在一名正常右利手志愿者周期性执行一项任务期间采集的,该任务要求对单词进行视觉和语义处理,并对每个单词的含义做出默读决定。通过正弦回归建模和时空随机化测试,在左侧纹外皮层、角回、缘上回、颞上回和颞中回、外侧运动前皮层和布洛卡区确定了主要激活区域。主成分(PC)分析最初通过对在170个激活体素处观察到的“原始”时间序列进行奇异值分解(SVD)来进行。这揭示了在第一主成分空间中视觉处理系统与所有其他脑区之间存在较大的功能距离(负连通性)。拟合时间序列矩阵和在每个激活体素处估计的六个正弦回归参数矩阵的奇异值分解,被开发为比“原始”数据的奇异值分解噪声更小(信息更多)的替代方法。然后使用去噪数据的典型变量分析来阐明主要激活区域之间的功能关系。视觉输入分析系统(纹外皮层和角回)在第一个典型变量(CV)空间中共定位,并与所有其他脑区显著分离。语义分析系统(缘上回和颞回)在第二个CV空间中共定位,并与默读输出系统(布洛卡区)显著分离。这些结果根据潜在的血液动力学事件和认知心理学理论进行了初步解释。

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