Meta-analytic connectivity modeling of the human superior temporal sulcus
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The superior temporal sulcus (STS) is a critical region for multiple neural processes in the human brain Hein and Knight (J Cogn Neurosci 20(12): 2125–2136, 2008). To better understand the multiple functions of the STS it would be useful to know more about its consistent functional coactivations with other brain regions. We used the meta-analytic connectivity modeling technique to determine consistent functional coactivation patterns across experiments and behaviors associated with bilateral anterior, middle, and posterior anatomical STS subregions. Based on prevailing models for the cortical organization of audition and language, we broadly hypothesized that across various behaviors the posterior STS (pSTS) would coactivate with dorsal-stream regions, whereas the anterior STS (aSTS) would coactivate with ventral-stream regions. The results revealed distinct coactivation patterns for each STS subregion, with some overlap in the frontal and temporal areas, and generally similar coactivation patterns for the left and right STS. Quantitative comparison of STS subregion coactivation maps demonstrated that the pSTS coactivated more strongly than other STS subregions in the same hemisphere with dorsal-stream regions, such as the inferior parietal lobule (only left pSTS), homotopic pSTS, precentral gyrus and supplementary motor area. In contrast, the aSTS showed more coactivation with some ventral-stream regions, such as the homotopic anterior temporal cortex and left inferior frontal gyrus, pars orbitalis (only right aSTS). These findings demonstrate consistent coactivation maps across experiments and behaviors for different anatomical STS subregions, which may help future studies consider various STS functions in the broader context of generalized coactivations for individuals with and without neurological disorders.
KeywordsSuperior temporal sulcus Coactivation Meta-analytic connectivity modeling Connectivity Network Dorsal stream
This material is based upon work supported by the National Science Foundation (NSF) Graduate Research Fellowship Program under Grant Nos. DGE-0903443 and DGE-1444316 (to LCE) as well as the Achievement Rewards for College Scientists Metropolitan Washington Chapter Noama Wheeler Scholar 2015–2016 (to LCE). This work was supported by grants from the National Institutes of Health (KL2TR000102 to PET; R01-DC03489, R01-NS052494 and R56-NS052494 to JPR), Doris Duke Charitable Foundation (2012062 to PET), the Vernon Family Trust (PET), and a PIRE grant from the NSF (OISE-0730255 to JPR). We would like to thank Rachel Acree and Elizabeth Heeg for their help working with the data.
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Conflict of interest
The authors declare that they have no conflict of interest.
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