Habas C, Cabanis E A
Service de NeuroImagerie, Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts, Paris, France.
AJNR Am J Neuroradiol. 2008 Oct;29(9):1715-21. doi: 10.3174/ajnr.A1191. Epub 2008 Jul 3.
The cerebral and cerebellar networks involved in bimanual object recognition were assessed by blood oxygen level-dependent functional MR imaging by using multivariate model-free analysis, because conventional univariate model-based analysis, such as the general linear model (GLM), does not allow investigation of resting, background, and transiently task-related brain activities.
Data from 14 healthy right-handed volunteers, scanned while successively performing bilateral finger movements and a bimanual tactile-tactile matching discrimination task were analyzed by using tensor-independent component analysis (TICA), which computes statistically independent spatiotemporal processes (P > .7) thought to reflect specific and distinct anatomofunctional neural networks. These results were compared with the network obtained in a previous study by using the same paradigm based on GLM to evaluate the advantages of TICA.
TICA characterized and distinguished the following: 1) resting-state networks such as the default-mode networks, 2) networks transiently synchronized with the beginning and end of the task, such as temporo-pericentral and temporo-pericentral-occipital networks, and 3) task-related networks such as cerebello-fronto-parietal, cerebello-prefrontocingulo-insular, and cerebello-parietal networks.
Bimanual tactile-tactile matching discrimination specifically recruits a complex neural network, which can be dissociated into 3 distinct but cooperative neural subnetworks related to sensorimotor function, salience detection, executive control, and, possibly, sensory expectation. This tripartite network involved in bimanual object recognition could not be demonstrated by GLM. Moreover, TICA allowed monitoring of the temporal succession of the networks recruited during the resting phase, audition of the "go" and "stop" signals, and the tactile discrimination task.
通过血氧水平依赖性功能磁共振成像,采用多变量无模型分析评估参与双手物体识别的大脑和小脑网络,因为传统的基于单变量模型的分析方法,如一般线性模型(GLM),无法研究静息、背景以及与任务相关的瞬态脑活动。
对14名健康右利手志愿者的数据进行分析,这些志愿者在依次进行双侧手指运动和双手触觉-触觉匹配辨别任务时接受扫描,采用张量独立成分分析(TICA),该分析计算统计独立的时空过程(P>.7),被认为反映特定且不同的解剖功能神经网络。将这些结果与之前一项基于GLM使用相同范式获得的网络进行比较,以评估TICA的优势。
TICA对以下方面进行了特征描述和区分:1)静息状态网络,如默认模式网络;2)与任务开始和结束瞬间同步的网络,如颞-中央周和颞-中央周-枕叶网络;3)与任务相关的网络,如小脑-额-顶叶、小脑-前额扣带回-岛叶和小脑-顶叶网络。
双手触觉-触觉匹配辨别特别招募了一个复杂的神经网络,该网络可分解为3个不同但相互协作的神经子网,分别与感觉运动功能、显著性检测、执行控制以及可能的感觉预期相关。GLM无法证明参与双手物体识别的这个三方网络。此外,TICA允许监测静息期招募的网络的时间顺序、“开始”和“停止”信号的听觉以及触觉辨别任务。