Network modulation during complex syntactic processing

Abstract

Complex sentence processing is supported by a left-lateralized neural network including inferior frontal cortex and posterior superior temporal cortex. This study investigates the pattern of connectivity and information flow within this network. We used fMRI BOLD data derived from 12 healthy participants reported in an earlier study (Thompson, C. K., Den Ouden, D. B., Bonakdarpour, B., Garibaldi, K., & Parrish, T. B. (2010b). Neural plasticity and treatment-induced recovery of sentence processing in agrammatism. Neuropsychologia, 48(11), 3211-3227) to identify activation peaks associated with object-cleft over syntactically less complex subject-cleft processing. Directed Partial Correlation Analysis was conducted on time series extracted from participant-specific activation peaks and showed evidence of functional connectivity between four regions, linearly between premotor cortex, inferior frontal gyrus, posterior superior temporal sulcus and anterior middle temporal gyrus. This pattern served as the basis for Dynamic Causal Modeling of networks with a driving input to posterior superior temporal cortex, which likely supports thematic role assignment, and networks with a driving input to inferior frontal cortex, a core region associated with syntactic computation. The optimal model was determined through both frequentist and Bayesian Model Selection and turned out to reflect a network with a primary drive from inferior frontal cortex and modulation of the connection between inferior frontal cortex and posterior superior temporal cortex by complex sentence processing. The winning model also showed a substantive role for a feedback mechanism from posterior superior temporal cortex back to inferior frontal cortex. We suggest that complex syntactic processing is driven by word-order analysis, supported by inferior frontal cortex, in an interactive relation with posterior superior temporal cortex, which supports verb argument structure processing.

Figure 1

(A) Rendering of the four clusters of voxels showing increased activation associated with processing of object-cleft sentences, relative to subject-cleft sentences (N = 12; p<.05, FDR corrected, k>15). (B) Functional connectivity between four activation peaks, based on directed partial correlation (dPC) analysis. Mean dPC values are given for each significant interaction; two standard deviations of the mean (SDM) are given in brackets. An interaction was defined significant if the mean dPC value minus two SDM were larger than 1. Note: PM = premotor cortex; IFG = inferior frontal gyrus; aMTG = anterior middle temporal gyrus ; pSTS = posterior superior temporal sulcus.

Figure 2

The 12 DCMs that were compared. All have bidirectional connectivity between PM-IFG, IFG-pSTS and pSTS-aMTG. Models 1-6 have driving input on pSTS, while models 7-12 have driving input on IFG. Modulation by object clefts (OC) is tested on all connections. Note: PM = premotor cortex; IFG = inferior frontal gyrus; aMTG = anterior middle temporal gyrus ; pSTS = posterior superior temporal sulcus.

Figure 3

Mean negative free energy (F) values, with standard errors (SE), for the 12 models. The left six models are those with driving input from posterior superior temporal sulcus (pSTS). The right six models have driving input from the inferior frontal gyrus (IFG).

Figure 4

Exceedance probabilities for the 12 models compared with variational Bayesian Model Selection.

Figure 5

Posterior model probabilities for the 12 models compared with variational Bayesian Model Selection.

Figure 6

The winning model #12, with driving input on the IFG node and modulation of the connection between IFG and pSTS by object-cleft processing. Mean parameter estimates are given alongside the connections and the modulation. Values that exceed the statistical threshold (p<.05, uncorrected) are listed in bold print. Note: PM = premotor cortex; IFG = inferior frontal gyrus; aMTG = anterior middle temporal gyrus ; pSTS = posterior superior temporal sulcus.