KEYWORDS: Near infrared spectroscopy, Brain, Hemodynamics, Neuroimaging, Spectroscopy, Brain imaging, Data conversion, Sun, Data analysis, Functional magnetic resonance imaging
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.
We measured hemodynamic activity of the prefrontal cortex (PFC) during a Chinese color-word matching Stroop task using a homemade continuous-wave NIRS system. Two probes were placed separately over the left and the right PFC. Wavelet transform coherence (WTC) analysis was employed to calculate coherences between all channels of the same probe pairwise to obtain the intrahemispheric functional connectivity for each side of the PFC. The intrahemispheric functional connectivities in both sides of PFC were stronger during the incongruent task compared to that of the neutral task, but only the left intrahemispheric functional connectivity showed a significant Stroop effect. In addition to the Stroop effect, for the incongruent or the neutral task, there was also a leftward lateralization. The results indicate that, compared with traditional activation, NIRS-based connectivity is more sensitive for identifying hemispheric lateralization.
Conflict processing is crucial for humans and has been investigated using hemodynamic and electrophysiological measures. However, because most previous research has studied hemodynamic and electrophysiological measures separately, the relationship between these two measures in conflict processing is poorly understood. In our study, we measure near-infrared spectroscopy (NIRS) and event-related potential (ERP) signals simultaneously in a Chinese color-word matching Stroop task and examine the relationship between the conflict-related hemodynamic signal in the prefrontal cortex (PFC) and electrophysiological signal. The results show significant Stroop effects for behavioral, NIRS (oxy-hemoglobin: HbO 2 ), and ERP [N450, late positive complex (LPC)] data. The significant N450 Stroop effect occurs before the behavioral response to incongruent stimuli, while the evident LPC Stroop effect occurs after it, suggesting that only N450 is associated with conflict processing. Additionally, N450 Stroop effects during the early and later phases are negatively correlated with HbO 2 Stroop effects in the left PFC and in the bilateral PFC, respectively. These results suggest that N450 reflects conflict detection and resolution, the left PFC may be involved in conflict detection, and the bilateral PFC is engaged in conflict resolution. Overall, the analysis of the correlation between hemodynamic and electrophysiological signals is useful for studying human brain function.
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