26 May 2014 Studying hemispheric lateralization during a Stroop task through near-infrared spectroscopy-based connectivity
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Near-infrared spectroscopy (NIRS) is a developing and promising functional brain imaging technology. Developing data analysis methods to effectively extract meaningful information from collected data is the major bottleneck in popularizing this technology. In this study, we measured hemodynamic activity of the prefrontal cortex (PFC) during a color-word matching Stroop task using NIRS. Hemispheric lateralization was examined by employing traditional activation and novel NIRS-based connectivity analyses simultaneously. Wavelet transform coherence was used to assess intrahemispheric functional connectivity. Spearman correlation analysis was used to examine the relationship between behavioral performance and activation/functional connectivity, respectively. In agreement with activation analysis, functional connectivity analysis revealed leftward lateralization for the Stroop effect and correlation with behavioral performance. However, functional connectivity was more sensitive than activation for identifying hemispheric lateralization. Granger causality was used to evaluate the effective connectivity between hemispheres. The results showed increased information flow from the left to the right hemispheres for the incongruent versus the neutral task, indicating a leading role of the left PFC. This study demonstrates that the NIRS-based connectivity can reveal the functional architecture of the brain more comprehensively than traditional activation, helping to better utilize the advantages of NIRS.
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
Lei Zhang, Lei Zhang, Jinyan Sun, Jinyan Sun, Bailei Sun, Bailei Sun, Qingming Luo, Qingming Luo, Hui Gong, Hui Gong, } "Studying hemispheric lateralization during a Stroop task through near-infrared spectroscopy-based connectivity," Journal of Biomedical Optics 19(5), 057012 (26 May 2014). https://doi.org/10.1117/1.JBO.19.5.057012 . Submission:

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