Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Cereb Cortex. 2013 Jul;23(7):1703-14. doi: 10.1093/cercor/bhs165. Epub 2012 Jun 12.
The brain network underlying speech comprehension is usually described as encompassing fronto-temporal-parietal regions while neuroimaging studies of speech intelligibility have focused on a more spatially restricted network dominated by the superior temporal cortex. Here we use functional magnetic resonance imaging with a novel whole-brain multivariate pattern analysis (MVPA) to more fully characterize neural responses and connectivity to intelligible speech. Consistent with previous univariate findings, intelligible speech elicited greater activity in bilateral superior temporal cortex relative to unintelligible speech. However, MVPA identified a more extensive network that discriminated between intelligible and unintelligible speech, including left-hemisphere middle temporal gyrus, angular gyrus, inferior temporal cortex, and inferior frontal gyrus pars triangularis. These fronto-temporal-parietal areas also showed greater functional connectivity during intelligible, compared with unintelligible, speech. Our results suggest that speech intelligibly is encoded by distinct fine-grained spatial representations and within-task connectivity, rather than differential engagement or disengagement of brain regions, and they provide a more complete view of the brain network serving speech comprehension. Our findings bridge a divide between neural models of speech comprehension and the neuroimaging literature on speech intelligibility, and suggest that speech intelligibility relies on differential multivariate response and connectivity patterns in Wernicke's, Broca's, and Geschwind's areas.
理解言语的大脑网络通常被描述为包含额颞顶叶区域,而言语可理解性的神经影像学研究则侧重于以上颞叶皮层为主的更具空间限制的网络。在这里,我们使用功能磁共振成像和一种新颖的全脑多变量模式分析 (MVPA) 来更全面地描述对可理解言语的神经反应和连接。与之前的单变量发现一致,可理解的言语相对于不可理解的言语在双侧上颞叶皮层引起更大的活动。然而,MVPA 确定了一个更广泛的网络,可以区分可理解和不可理解的言语,包括左半球颞中回、角回、颞下回和额下回三角部。在可理解的言语中,这些额颞顶叶区域的功能连接也比不可理解的言语更大。我们的结果表明,言语的可理解性是由独特的细粒度空间表示和任务内连接来编码的,而不是大脑区域的差异参与或不参与,并且它们提供了一个更完整的言语理解大脑网络视图。我们的发现弥合了言语理解的神经模型与言语可理解性的神经影像学文献之间的鸿沟,并表明言语的可理解性依赖于 Wernicke 区、Broca 区和 Geschwind 区的差异多变量反应和连接模式。