25 September 2013 Correlation between hemodynamic and electrophysiological signals dissociates neural correlates of conflict detection and resolution in a Stroop task: a simultaneous near-infrared spectroscopy and event-related potential study
Author Affiliations +
J. of Biomedical Optics, 18(9), 096014 (2013). doi:10.1117/1.JBO.18.9.096014
Abstract
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.
Sun, Sun, Zhang, Luo, and Gong: Correlation between hemodynamic and electrophysiological signals dissociates neural correlates of conflict detection and resolution in a Stroop task: a simultaneous near-infrared spectroscopy and event-related potential study

1.

Introduction

Cognitive control is a key characteristic of the human cognitive system. It refers to the ability to attend to goal-relevant information, ignore distracting information, overcome conflict, and select the appropriate response. Cognitive control over conflict usually relies on conflict detection and resolution, and it has been widely studied using the Stroop task. In the classic Stroop task,1 subjects are instructed to identify the color in which a word stimulus is printed. Usually, behavioral responses to incongruent stimuli (when the word and the ink color are incongruent, e.g., the word “blue” shown in green ink) are slower and less accurate than responses to neutral or congruent stimuli (when the word and the ink color are the same). The behavioral difference between incongruent and neutral or congruent stimuli is called the Stroop effect, which has been used as an index of cognitive control. Because the Stroop task is also useful for studying neurological/psychiatric disorders,2,3 investigating its neural correlates can both improve our understanding of conflict processing and provide knowledge for practical uses.

A number of functional neuroimaging studies have investigated the neural correlates of the Stroop task and have identified that the prefrontal cortex (PFC) and the anterior cingulate cortex (ACC) are involved in Stroop conflict tasks.45.6.7 The PFC has been proven to play a predominant role in cognitive control.8,9 The PFC implements top-down attention control to bias the neural processing of task-related information and resolve conflict.910.11 Regarding conflict detection, the conflict monitoring theory asserts that the ACC detects the occurrence of conflict and then sends conflict-related information to the PFC, which resolves the conflict.6,12 However, some studies have questioned the ACC’s role in conflict detection,9,13 and an increasing amount of research has suggested that the PFC also plays a role in conflict detection.9,14,15

Regarding electrophysiological measures, the previous event-related potential (ERP) studies have revealed two primary ERP markers in the Stroop task: N450 and the late positive complex (LPC).1617.18.19.20 N450 is more negative in amplitude for incongruent stimuli than for neutral or congruent stimuli. It has been generally considered to be related to conflict detection and resolution,16,21 although some studies have suggested that N450 only reflected conflict detection.22,23 The LPC amplitude is more positive for incongruent stimuli than for neutral or congruent stimuli. The cognitive processes reflected by the LPC are even more ambiguous than those reflected by N450. Some studies have suggested that the LPC was related to the additional processing of word meaning,16,17 while others have proposed that this component was linked to conflict resolution.22,23 Some studies have also suggested that the LPC reflected the conflict adaptation process.24,25

Although the previous neuroimaging studies have provided insight into the neural correlates of the Stroop task, most research has studied the hemodynamic and electrophysiological signals separately and the relationship between these two types of signals is still poorly understood.7,26 Considering that an analysis of the relationship between hemodynamic and electrophysiological signals could inprove our understanding of human brain function,26,27 it is necessary to study the relationship between these two types of signals to better understand the neural correlates of conflict processing.

Near-infrared spectroscopy (NIRS), a noninvasive optical functional neuroimaging method, can measure the concentration changes of oxy-hemoglobin (Δ[HbO2]) and deoxy-hemoglobin (Δ[Hb]) to reflect relative regional brain activity. Previous studies using NIRS have successfully investigated the neural correlates of the Stroop task28,29 and have improved our understanding of the PFC’s role in conflict processing.5,30 In addition, NIRS can be conveniently integrated with simultaneous electroencephalography (EEG) recordings.26,31 Thus, it is appropriate to investigate the relationship between conflict-related hemodynamic and electrophysiological signals by combining NIRS and EEG. Our previous study has combined NIRS and ERP to investigate the relationship between hemodynamic and electrophysiological signals in a Stroop task and obtained some significant results.26 However, the NIRS data were collected from only four detector channels with our portable brain function imager,32 and the ERP data were recorded from only four frontopolar electrodes, which might overlook the presence of the N450 Stroop effect. Such limited measurement of the NIRS and ERP signals restricted the analysis of the relationship between hemodynamic and electrophysiological signals in conflict processing.

In this study, we aimed to examine the relationship between the conflict-related hemodynamic signal and the electrophysiological signal by combining NIRS and ERP. Since a color-word matching Stroop task primarily activates the lateral PFC,5,13 it is more suitable for NIRS due to its low penetration depth.31 We employed a Chinese color-word matching Stroop task and measured the hemodynamic signal from the PFC and the electrophysiological signal simultaneously during the task. Compared with our previous study, the NIRS signal was recorded from a larger prefrontal region using our newly developed brain function imager,33 which emits and detects light with optical fibers. The ERP signal was recorded from anterior to posterior cortical sites.

2.

Methods

2.1.

Subjects

Fifteen students (seven women) aged 20 to 27 years [mean, 23.36 years; standard deviation (SD), 2.09 years] participated in this study as paid volunteers. All the participants were right handed and had normal or corrected-to-normal vision and normal color vision. All the subjects were healthy and had no personal or family history of neurological or psychiatric disorders. The volunteers received a complete description of the experiment and gave their written informed consent before the experiment. This study was approved by the Human Subjects Institutional Review Board at Huazhong University of Science and Technology.

2.2.

Materials

The color-word matching Stroop task was used with a block design adapted from the previous studies.13 Each stimulus consists of two Chinese characters, and the subjects were instructed to judge whether the color of the upper character corresponded to the meaning of the lower character [Fig. 1(a)]. If the answer was “match,” the subjects pressed a button with the index finger of their left hand; if the answer was “unmatch,” they pressed another button with the index finger of their right hand. There were two stimulus conditions: neutral and incongruent [Fig. 1(a)]. For neutral stimuli, the upper characters were noncolor words (涂, 贯, 华, 球, 奖 meaning “scrawl,” “pass through,” “China,” “ball,” and “prize”) presented in red, yellow, blue, green, or purple, and the lower characters were color words (红, 黄, 蓝, 绿, 紫 meaning “red,” “yellow,” “blue,” “green,” and “purple”) printed in white. For incongruent stimuli, the upper characters were color words shown in a disparate color. The subjects were instructed to attend to the upper character first and then make their decision. This instruction was used to prevent subjects from ignoring the upper character. For each stimulus condition, the color of the upper character was the same as the meaning of the lower character in half of the trials, whereas the color of the upper character differed from the meaning of the lower character in the other half of the trials. Moreover, the “same” and “different” trials were semi-randomly mixed within each block to avoid the consecutive appearance of more than three “same” or “different” trials.

Fig. 1

Experimental design. (a) Trial examples for the neutral and incongruent conditions. The subjects were instructed to judge whether the color of the upper character corresponded to the meaning of the lower character. The correct answer was “match” for the upper two trial examples and “unmatch” for the lower two trial examples. 华 means “China” and 蓝, 红 corresponds to “blue, red.” (b) The task sequence in each run. Rest, 1-min rest period before and after each run; B, 15-s baseline between blocks within each run; N, 30-s neutral task block; I, 30-s incongruent task block.

JBO_18_9_096014_f001.png

2.3.

Procedures

The experiment consisted of four runs with four alternating block pairs of neutral and incongruent conditions (i.e., eight blocks) in each run [Fig. 1(b)]. Each block contained 20 trials, and within each trial, the stimulus was shown for 1200 ms, followed by a 300-ms blank screen. Subjects were instructed to respond to the stimuli as quickly and accurately as possible right after the stimuli presentation. A white cross appeared for 15 s between blocks within each run and for 60 s before and after each run, indicating a rest period. One second before each block, a sound alerted the subjects that the task was beginning. Subjects were told to avoid any major body movement during the experiment. Before the formal experiment, there was a practice session to familiarize the subjects with the task instructions.

2.4.

NIRS Recording

The NIRS data were acquired using a continuous-wave NIRS system developed by our laboratory.33 The system uses two wavelengths (785 and 850 nm) to determine the concentration changes of HbO2 (Δ[HbO2]) and Hb (Δ[Hb]) based on the modified Beer–Lambert Law. The NIRS probe was supported by a flexible plastic base and held one source and eight detectors, providing eight detector channels. (The channel was defined as the midpoint of the source-detector pair.) Two such NIRS probes were used to cover the left and right PFC. The right probe was placed so that Channel 3 (Ch 3, the midpoint of the NIRS source and Detector 3) was over the F4 electrode, and the line through the source and Detector 3 was parallel to the midline (i.e., the arc from the nasion to the inion). The left probe was placed symmetrically (Fig. 2). The distance between the source and the detector was 3 cm. To estimate the measured brain region, we first determined the corresponding (i.e., the nearest) electrode in the 10–5 system34 for each NIRS detector channel. Then, based on the standard electrode coordinates for the 10–5 system, we projected all corresponding electrodes to the standard Montreal Neurological Institute (MNI) space using the probabilistic estimation method.35 Finally, we estimated the measured brain region according to the MNI coordinates of all these corresponding electrodes. Thus, we presumed that the NIRS probes covered part of the bilateral dorsolateral prefrontal cortex (DLPFC) and the ventrolateral prefrontal cortex (VLPFC) in this study. The time resolution was 70 Hz.

Fig. 2

Schematic of near-infrared spectroscopy (NIRS) measurement channel locations on the head. The red circle indicates the NIRS light source and the blue rectangle indicates the detector. The number (1 to 16) denotes the NIRS detector channel (midpoint of the source-detector pair). Channels 3 and 11 were over F4 and F3, respectively.

JBO_18_9_096014_f002.png

2.5.

EEG Recording

EEG recordings from 18 scalp sites (F3/Fz/F4, FC3/FCz/FC4, C3/Cz/C4, CP3/CPz/CP4, P3/Pz/P4 and PO3/POz/PO4) and the left mastoid were made using a Neuroscan 64-channel device (Synamps2), with the right mastoid as reference. Four additional bipolar electrodes were used to record the electro-oculograms (EOGs). The horizontal EOG was recorded by placing two electrodes lateral to the left and right orbits, and the vertical EOG was recorded by placing two electrodes in the superior and inferior areas of the left orbit. The EEG and EOG data were band-pass filtered at 0.05 to 100 Hz, notch filtered at 50 Hz and continuously recorded at a sampling rate of 500 Hz. Electrode impedances were kept below 5 kΩ.

2.6.

Data Analyses and Statistics

2.6.1.

Behavioral data

Trials with no responses were rare (mean missed trials: <1 for both stimulus conditions) and were not included in the behavioral data analyses. Only trials with correct responses were used for the response time (RT) analysis, and trials with RTs >3 SD above the mean values were excluded from the analysis (1.11%). Repeated-measures analysis of variance (ANOVA) for RT and error rate were performed with the Condition (neutral, incongruent) and Run (Run 1, Run 2, Run 3, and Run 4) as within-subject factors.

2.6.2.

NIRS data

The raw optical data were low-pass filtered at 3 Hz (least-squares FIR filter with zero-phase distortion; order: 50), down sampled to 10 Hz, and converted to change in optical density (ΔOD) values. The ΔOD was band-pass filtered with a frequency range from 0.015 to 0.5 Hz to eliminate slow drifts and arterial pulse oscillations, and then converted to hemodynamic parameters (Δ[HbO2] and Δ[Hb]) using the differential pathlength factor (DPF) method. The DPF values we used were 6.0 at 785 nm and 5.2 at 850 nm.33 The wavelet minimum description length detrending algorithm was used to suppress unknown global trends in the hemodynamic parameters.36 Blocks contaminated by movement artifacts were removed from the analyses. Then, for each subject, the hemodynamic parameters were block averaged for each task condition separately.

Because the HbO2 signal has a higher amplitude and a better signal-to-noise ratio than the Hb signal does,37 the HbO2 signal was used for the statistical analyses. The mean values of the HbO2 signal of the baseline (the last 5-s rest before the task) and the task (5 to 20 s after the task beginning) were computed for each subject, channel, and task condition. The difference in the mean values of the HbO2 signal between the task and the baseline was used to indicate the hemodynamic response to each task condition. The hemodynamic responses of incongruent and neutral tasks were compared using paired t-tests in a channel-wise manner, and a false discovery rate (FDR) control was used for these channel-wise t-tests.

2.6.3.

EEG data

The EEG data were re-referenced to the average of the left and right mastoid data. Movement artifacts were identified and removed by visual inspection, and eye-blink artifacts were corrected using a regression approach. The EEG data were then low-pass filtered at 40 Hz. Filtered EEG data were segmented into 1200-ms epochs, including a 200-ms prestimulus baseline. Only epochs with correct button presses were included. After the baseline correction, epochs contaminated with artifacts (the threshold for artifact rejection was ±80μV) were rejected. Then, epochs were averaged separately for the two types of stimuli.

Based on our results and previous studies, analyses focused on N450 and LPC. Conflict has to be detected and resolved before the behavioral response to incongruent stimuli occurs.20,38 Thus, to better understand the cognitive processes N450 and LPC reflect, mass univariate analyses were first used to reveal the time intervals showing significant Stroop effects and determine whether the evident N450/LPC Stroop effect occurred before or after the behavioral response to incongruent stimuli. Mass univariate analysis is an approach to analyze data by performing a massive number of univariate hypothesis tests (e.g., t-tests). Then, to compare our results with those of the previous studies and examine the interaction between Condition and other factors, ANOVA was used to analyze N450 and LPC.

For mass univariate analyses, a paired t-test was performed on each point of the time-cortical site matrix to reveal the time range that showed a significant difference between incongruent and neutral stimuli. Time points from 400 to 1000 ms after the stimulus onset were analyzed. For multiple comparisons, no correction was used in order to avoid influencing the judgment of whether the evident N450/LPC Stroop effect occurred before or after the behavioral response. For the ANOVA, time windows of 450 to 650 and 880 to 1000 ms were chosen for N450 and LPC based on the results of mass univariate analyses at each electrode. The mean amplitudes for N450 and LPC were obtained for each electrode in the six regions: frontal (F3, Fz, F4), frontocentral (FC3, FCz, FC4), central (C3, Cz, C4), centroparietal (CP3, CPz, CP4), parietal (P3, Pz, P4), and occipitoparietal (PO3, POz, PO4). Repeated-measures ANOVA with Condition (neutral, incongruent), Region (frontal, frontocentral, central, centroparietal, parietal, occipitoparietal), and Laterality (left, middle, right) as within-subject factors were performed on the mean amplitudes of N450 and LPC. Because the Stroop effect was the primary interest of this study, only significant main effects and interactions involving the factor Condition were reported.

2.6.4.

Correlation between NIRS and ERP data

The results of mass univariate analyses showed that the significant N450 Stroop effect occurred before the behavioral response to incongruent stimuli, while the significant LPC Stroop effect occurred after it (see the third paragraph of Sec. 4). These results indicated that only N450 was associated with conflict processing before the behavioral response. Thus, we analyzed the relationship between N450 and HbO2. Because the conflict-related processing was the focus of this study, the Stroop effect was used as an index in the relationship analysis.

Previous studies have shown that task-related electrophysiological and hemodynamic responses might be correlated.26,39 Therefore, Pearson’s correlation coefficient was used to examine the relationship between HbO2 and N450. In addition, the correlation between the hemodynamic response and the electrophysiological response in different time windows might provide information about the latency at which one brain region processes information.27 We further examined the correlation between the ERP Stroop effect for each 20-ms bin during the period from 400 to 700 ms after the stimulus onset and the HbO2 Stroop effect.

Statistical Package for the Social Sciences 13 software was used for all statistical analyses except the Stroop effect analysis of NIRS data. The level of significance for statistical analyses was set at p<0.05. The Greenhouse–Geisser correction was applied to adjust the degrees of freedom of the F ratios where necessary, and the Bonferroni correction was used for multiple comparisons unless otherwise specified.

3.

Results

3.1.

Behavioral Results

Figure 3 displays the average RT and error rate for each run. Regarding RT, the repeated-measures ANOVA revealed a significant Condition effect [F(1,14)=41.133, p<0.001, ηp2=0.746], suggesting a longer RT for the incongruent condition compared with the neutral condition. There was a significant Run effect [F(1.964,27.493)=23.62, p<0.001, ηp2=0.628]. Follow-up multiple comparisons showed that the RT order was Run1>Run2>Run3Run4. Regarding the error rate, the repeated-measures ANOVA disclosed a significant Condition effect [F(1,14)=42.857, p<0.001, ηp2=0.754], indicating a higher error rate for the incongruent condition. The ANOVA also revealed a significant Run effect [F(3,42)=8.567, p<0.001, ηp2=0.38]. Follow-up multiple comparisons showed that the error rate was higher for Run 1 than for Run 2 and Run 4. Although no significant interaction was observed for either RT or error rate (p>0.05), a paired t-test was used to determine whether the Stroop effect was significant for the RT and error rate in each run. The results showed a significant Stroop effect in each run (RT: t>4.84, p<0.001; error rate: t3.062, p0.008), indicating that the Stroop interference effect remained significant across the entire experiment.

Fig. 3

Response time and error rate for each run. The data are shown as mean±standard error (SE).

JBO_18_9_096014_f003.png

3.2.

NIRS Results

Figure 4(a) shows the grand average time courses of the HbO2 responses to neutral and incongruent tasks at one typical channel (Ch 9). The statistical analyses revealed significant Stroop effects in all eight channels for the left PFC (Ch 9 to 16) and in three channels for the right PFC (Ch 2/6/7; p<0.05, FDR correction), indicating greater HbO2 concentration increase for the incongruent task than for the neutral task. Figures 4(b) and 4(c) show the maps of t-values from the Stroop effect tests for the left and right PFC, respectively. According to the effect size analyses, the Stroop effects were larger in the left PFC than in the right PFC, with the largest Stroop effect at Ch 9 (Cohen’s dz=1.667).

Fig. 4

(a) The grand average time courses of HbO2 responses to neutral (blue) and incongruent (red) tasks at typical Channel 9 (Ch 9). The gray region indicates SE. The black lines at 0 and 30 s indicate the start and end of the task blocks. (b) and (c) show the t-values from the Stroop effect tests for the left and right PFC, respectively.

JBO_18_9_096014_f004.png

3.3.

ERP Results

Figure 5 shows the average ERP waveforms for neutral and incongruent stimuli at the six midline electrodes.

Fig. 5

The grand average of the event-related potential (ERP) waveforms for neutral (blue) and incongruent (red) stimuli at the six midline electrodes. The gray bars at Pz indicate the time windows used in the analysis of variance for N450 (left) and late positive complex (right).

JBO_18_9_096014_f005.png

The mass univariate analyses revealed significant differences between incongruent and neutral stimuli during two distinct time windows (422 to 680 and 870 to 1000 ms) in the six regions. The ERP elicited by incongruent stimuli was more negative during the 422 to 680-ms period and more positive during the 870 to 1000-ms period compared with the neutral stimuli.

3.3.1.

N450 (450 to 650 ms)

The ANOVA revealed a significant Condition effect [F(1,14)=16.001, p=0.001, ηp2=0.533], indicating a more negative N450 response for the incongruent condition. The Condition×Region interaction was evident [F(1.446,20.248)=3.963, p=0.047, ηp2=0.221]. Further simple effect analyses showed that the Stroop effect was significant at each region (Table 1). No other interactions involving Condition were significant (p>0.05).

Table 1

The statistical results of the N450 Stroop effect at each region.

N450FPηp2
Frontal7.410.0170.336
Frontocentral80.0130.364
Central10.710.0060.434
Centroparietal18.20.0010.565
Parietal21.67<0.0010.607
Occipitoparietal12.490.0030.471

3.3.2.

LPC (880 to 1000 ms)

The ANOVA disclosed an evident Condition effect [F(1,14)=10.609, p=0.006, ηp2=0.431], indicating a more positive LPC response for the incongruent condition. The Condition×Laterality interaction was significant [F(2,28)=3.751, p=0.036, ηp2=0.211]. Simple effect analyses revealed that the Condition effect was significant for the left, middle, and right cortical sites (p<0.05), with the largest Condition effect at the left cortical sites indicated by the largest effect size (left: ηp2=0.474; middle: ηp2=0.400; right: ηp2=0.370). No other interactions involving Condition were significant (p>0.05).

3.4.

Correlation Between HbO2 and N450

Since it is difficult to determine the neural generator source location from the ERP scalp voltage distribution, the largest N450 Stroop effect was correlated to the HbO2 Stroop effect. The larger effect size (ηp2) indicated that the N450 Stroop effects were greater in centroparietal and parietal regions than in the other four regions. Thus, the N450 in the centroparietal and parietal regions was used to correlate with HbO2. The N450 Stroop effect in the centroparietal (parietal) region was obtained by averaging the data from the three electrodes in that region, and then correlated with the significant HbO2 Stroop effect (left PFC: Ch 9 to 16; right PFC: Ch 2/6/7).

The N450 Stroop effect in the centroparietal region was significantly negatively correlated with the HbO2 Stroop effects in the left PFC (Ch 9/10/11/16; r ranged from 0.541 to 0.654, p<0.05). The N450 Stroop effect in the parietal region was significantly negatively correlated with the HbO2 Stroop effects in the left (Ch 9; r=0.537, p<0.05) and right PFC (Ch 2; r=0.523, p<0.05) (Table 2). Due to the negativity of N450, this negative correlation indicated that the larger the difference in the N450 amplitude between these two stimulus conditions, the greater the difference in the HbO2 signal. To explore whether the HbO2 responses in the left and right PFC were correlated with the N450 at different time ranges, the correlation between the HbO2 Stoop effect and the ERP Stroop effect for each 20-ms bin from 400 to 700 ms after the stimulus onset was further examined. These analyses revealed that the HbO2 Stroop effect in the left PFC (Ch 9/10/16) was negatively correlated with the N450 Stroop effect during the early phase (from 440 to 580 ms; Fig. 6), and the HbO2 Stroop effect in the left (Ch 11) and right PFC (Ch 2) was negatively correlated with the N450 Stroop effect during the later phase (from 600 to 680 ms; Fig. 6).

Table 2

Correlation between HbO2 Stroop effects and N450 Stroop effects in the centroparietal and parietal regions.

CentroparietalParietal
Ch 2−0.48b−0.523a
Ch 6−0.148−0.241
Ch 7−0.38−0.448b
Ch 9−0.654a−0.537a
Ch 10−0.592a−0.449b
Ch 11−0.541a−0.41
Ch 120.1310.221
Ch 13−0.166−0.14
Ch 14−0.113−0.144
Ch 15−0.174−0.206
Ch 16−0.57a−0.453b

a

Significant correlation (p<0.05).

b

Marginally significant correlation (p<0.1).

Fig. 6

(a) Correlation coefficients between ERP Stroop effects for each 20-ms bin within 400 to 700 ms at the centroparietal region and the HbO2 Stroop effects at Ch 9 (left top), Ch 10 (left middle), and Ch 11 (left bottom). (b) Correlation coefficients between ERP Stroop effects for each 20-ms bin within 400 to 700 ms at the parietal region and the HbO2 Stroop effects at Ch 2 (right top), Ch 7 (right middle), and Ch 10 (right bottom). The red rectangle indicates the time window during which there was a significant correlation between these two Stroop effects (p<0.05).

JBO_18_9_096014_f006.png

In addition, the N450 Stroop effect in the centroparietal region was marginally significantly negatively correlated with the HbO2 Stroop effect in the right PFC (Ch 2; r=0.48, p<0.1). There were also some marginally significant negative correlations between the N450 Stroop effect in the parietal region and the HbO2 Stroop effects in the left (Ch 10: r=0.449, p<0.1; Ch 16: r=0.453, p<0.1) and right PFC (Ch 7; r=0.448, p<0.1; Table 2). Further analyses of the marginally significant correlation between HbO2 and N450 also proved that HbO2 in the left PFC (Ch 10/16) was correlated with the early phase of N450 (Fig. 6), and HbO2 in the right PFC (Ch 2/7) was correlated with the later phase of N450 (Fig. 6).

4.

Discussion

In this study, we examined the relationship between the conflict-related hemodynamic signal in the PFC and the electrophysiological signal during a Chinese color-word matching Stroop task. There were significant Stroop effects in behavioral (RT, error rate), NIRS (HbO2), and ERP (N450, LPC) data, consistent with the previous studies.5,16,17 Furthermore, the HbO2 Stroop effect was negatively correlated with the N450 Stroop effect. More specifically, the HbO2 Stroop effect in the left PFC (Ch 9/10/16) was negatively correlated with the N450 Stroop effect during the early phase (440 to 580 ms), while the HbO2 Stroop effect in the bilateral PFC (left: Ch 11; right: Ch 2/7) was negatively correlated with the N450 Stroop effect during the later phase (600 to 680 ms).

The color-word matching Stroop task was used because this task mainly activates the lateral PFC.5,13 In the present study, the HbO2 response in the bilateral PFC was stronger for the incongruent stimulus task than for the neutral stimulus task, which is consistent with the previous studies reporting the involvement of the bilateral PFC in the Stroop task.5,40,41

The N450 amplitude was more negative and the LPC amplitude was more positive for incongruent stimuli compared with the neutral stimuli, which was consistent with the previous reports.16,17,21 The significant N450 Stroop effect lasted from approximately 420 to 680 ms after the stimulus onset, and the significant LPC Stroop effect began at approximately 870 ms. The start of the evident LPC Stroop effect was somewhat earlier than the behavioral response to incongruent stimuli in Run 1, but later than the behavioral response to incongruent stimuli in Runs 2 to 4. LPC has been associated with conflict resolution,22,23 conflict adaption,24,25 or the re-activation of word meaning.16,17 Because conflict needs to be resolved before a behavioral response occurs,20,38 the significant LPC Stroop effect occurring after the behavioral response indicates that the LPC could not be associated with conflict resolution before a manual response. Additionally, the conflict adaption might be greatly diminished under the conditions of our experimental design.42 Taken together, we suggest that the LPC could be related with word meaning processing.21,22 Combined with the previous studies, we infer that N450 is related to conflict processing.16,21 In addition, N450 Stroop effects during the early and later phases were negatively correlated with HbO2 Stroop effects in the left PFC and the bilateral PFC, respectively. This dissociation of the early and later phases of N450 suggests that there are two stages in N450. Our study supports that N450 reflects both conflict detection and resolution.16,21

The negative correlation between N450 and HbO2 indicates that the larger the difference in the N450 between these two stimulus conditions, the greater the difference in the HbO2 signal. Previous animal studies have suggested a tight coupling between the hemodynamic response and the local field potential (LFP).43,44 The LFP is known to be the basis of scalp EEG/ERP,45 implying that the spatiotemporal data integration can be obtained by examining the correlation between the hemodynamic response and the scalp EEG/ERP in most situations. In addition, human studies have also shown that these two types of measures were correlated during brain function.26,39 Our results fit well with the previous studies that show a coupling between these two measures. The correlation suggests that N450 and activity in certain PFC regions reflect related processing.

The HbO2 Stroop effect in the left PFC (Ch 9/10/16, located approximately in the left DLPFC) was negatively correlated with the N450 Stroop effect during the early phase. The early phase of N450 corresponded with conflict detection. Although some studies have suggested that the ACC detected conflict,6,12 others have suggested that the ACC was associated with response-related processing, but not conflict-related processing.13,46 Given that the ACC activity was not observed in the color-word matching Stroop task,13 this correlation indicated that the left PFC might be involved in conflict detection. Considering the bidirectional connection between the DLPFC and posterior brain regions,47 and the involvement of the left middle frontal gyrus in Chinese semantic processing,48 it is reasonable to conclude that the left DLPFC plays a role in detecting conflict. Furthermore, there is increasing evidence supporting the assumption that the DLPFC can detect conflict. Milham et al. found that practice had different influences on the ACC and the left DLPFC.8 While the ACC activity existed only during the first two cycles, the left DLPFC activity persisted even until the last two cycles (Cycles 5 to 6). Milham et al. suggested that the DLPFC could detect the nonresponse conflict independent of the ACC.8 Morishima et al. reported that the left DLPFC activity covaried with the neutral activity of sensory conflict for incongruent trials in a face-word Stroop task, suggesting the left PFC’s role in detecting conflict.14 Some lesion studies in patients and animals also suggested that the ACC was not necessarily involved in conflict processing,15,49 while DLPFC was involved in conflict detection.9,15,50 Given our findings and those of the previous studies, we suggest that the left PFC is involved in conflict detection.

The HbO2 Stroop effect in the bilateral PFC was negatively correlated with the N450 Stroop effect during the later phase. This correlation supports the generally accepted viewpoint that the PFC is involved in conflict resolution. The PFC exerts the cognitive control to resolve conflict by favoring the processing of task-relevant information and/or inhibiting the processing of task-irrelevant information.51,52 Participation of both the left and right PFC in conflict resolution has been proven in many previous studies.11,40,41

NIRS measures the hemodynamic response and indirectly reflect neural activity at a reasonable spatial resolution, while ERP measures the electrical response and directly reflect neural activity at a high temporal resolution. Our study shows that the analysis of the relationship between these two complementary responses can not only provide information about the processing stage in which one brain region is involved, but also distinguish the different processing stages in the ERP response with the help of brain activity.

This study examined the relationship between hemodynamic and electrophysiological signals to investigate the neural correlates of conflict processing in a Chinese color-word matching Stroop task. The significant LPC Stroop effect occurring after the behavioral response to incongruent stimuli suggests that the LPC is associated with the re-activation of word meaning. The evident N450 Stroop effect occurred before the behavioral response to incongruent stimuli. Besides, the N450 Stroop effects during the early and later phases were negatively correlated with HbO2 Stroop effects in the left PFC and in the bilateral PFC, respectively. These results indicate the following: (1) there are two stages in N450: conflict detection and resolution; (2) the left PFC may be involved in conflict detection and the bilateral PFC is engaged in conflict resolution. Our study promotes the understanding of the neural correlates of conflict processing and shows that an analysis of the correlation between hemodynamic and electrophysiological measures can be an effective method for studying human brain function.

Acknowledgments

We would like to thank all subjects for their participation. We thank Qianzhu Luo for proofreading the manuscript. This study was supported by the Science Fund for Creative Research Group of China (Grant No. 61121004), National Natural Science Foundation of China (Grant No. 61078072), and 863 Program (Grant No. 2012AA011602).

References

1. 

J. R. Stroop, “Studies of interference in serial verbal reactions,” J. Exp. Psychol. 18(6), 643–662 (1935).JEPSAK0022-1015http://dx.doi.org/10.1037/h0054651Google Scholar

2. 

A. J. HolmesD. A. Pizzagalli, “Response conflict and frontocingulate dysfunction in unmedicated participants with major depression,” Neuropsychologia 46(12), 2904–2913 (2008).NUPSA60028-3932http://dx.doi.org/10.1016/j.neuropsychologia.2008.05.028Google Scholar

3. 

J. Sunet al., “Reduced prefrontal cortex activation in the color-word Stroop task for Chinese dyslexic children: a near-infrared spectroscopy study,” J. Phys. Conf. Ser. 277(1), 012034 (2011).1742-6588http://dx.doi.org/10.1088/1742-6596/277/1/012034Google Scholar

4. 

A. W. MacDonaldet al., “Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control,” Science 288(5472), 1835–1838 (2000).SCIEAS0036-8075http://dx.doi.org/10.1126/science.288.5472.1835Google Scholar

5. 

M. L. Schroeteret al., “Near-infrared spectroscopy can detect brain activity during a color-word matching Stroop task in an event-related design,” Hum. Brain Mapp. 17(1), 61–71 (2002).HBRME71065-9471http://dx.doi.org/10.1002/(ISSN)1097-0193Google Scholar

6. 

M. M. BotvinickJ. D. CohenC. S. Carter, “Conflict monitoring and anterior cingulate cortex: an update,” Trends Cogn. Sci. 8(12), 539–546 (2004).TCSCFK1364-6613http://dx.doi.org/10.1016/j.tics.2004.10.003Google Scholar

7. 

R. L. Siltonet al., “The time course of activity in dorsolateral prefrontal cortex and anterior cingulate cortex during top-down attentional control,” Neuroimage 50(3), 1292–1302 (2010).NEIMEF1053-8119http://dx.doi.org/10.1016/j.neuroimage.2009.12.061Google Scholar

8. 

M. P. Milhamet al., “Practice-related effects demonstrate complementary roles of anterior cingulate and prefrontal cortices in attentional control,” Neuroimage 18(2), 483–493 (2003).NEIMEF1053-8119http://dx.doi.org/10.1016/S1053-8119(02)00050-2Google Scholar

9. 

F. A. MansouriK. TanakaM. J. Buckley, “Conflict-induced behavioural adjustment: a clue to the executive functions of the prefrontal cortex,” Nat. Rev. Neurosci. 10(2), 141–152 (2009).NRNAAN1471-0048http://dx.doi.org/10.1038/nrn2538Google Scholar

10. 

D. E. NeeT. D. WagerJ. Jonides, “Interference resolution: insights from a meta-analysis of neuroimaging tasks,” Cogn. Affect. Behav. Neurosci. 7(1), 1–17 (2007).1530-7026http://dx.doi.org/10.3758/CABN.7.1.1Google Scholar

11. 

V. van VeenC. S. Carter, “Separating semantic conflict and response conflict in the Stroop task: a functional MRI study,” Neuroimage 27(3), 497–504 (2005).NEIMEF1053-8119http://dx.doi.org/10.1016/j.neuroimage.2005.04.042Google Scholar

12. 

C. S. CarterV. van Veen, “Anterior cingulate cortex and conflict detection: an update of theory and data,” Cogn. Affect. Behav. Neurosci. 7(4), 367–379 (2007).1530-7026http://dx.doi.org/10.3758/CABN.7.4.367Google Scholar

13. 

S. Zyssetet al., “Color-word matching stroop task: separating interference and response conflict,” Neuroimage 13(1), 29–36 (2001).NEIMEF1053-8119http://dx.doi.org/10.1006/nimg.2000.0665Google Scholar

14. 

Y. MorishimaJ. OkudaK. Sakai, “Reactive mechanism of cognitive control system,” Cereb. Cortex 20(11), 2675–2683 (2010).53OPAV1047-3211http://dx.doi.org/10.1093/cercor/bhq013Google Scholar

15. 

F. A. MansouriM. J. BuckleyK. Tanaka, “Mnemonic function of the dorsolateral prefrontal cortex in conflict-induced behavioral adjustment,” Science 318(5852), 987–990 (2007).SCIEAS0036-8075http://dx.doi.org/10.1126/science.1146384Google Scholar

16. 

M. Liottiet al., “An ERP study of the temporal course of the Stroop color-word interference effect,” Neuropsychologia 38(5), 701–711 (2000).NUPSA60028-3932http://dx.doi.org/10.1016/S0028-3932(99)00106-2Google Scholar

17. 

L. G. AppelbaumK. L. MeyerhoffM. G. Woldorff, “Priming and backward influences in the human brain: processing interactions during the Stroop interference effect,” Cereb. Cortex 19(11), 2508–2521 (2009).53OPAV1047-3211http://dx.doi.org/10.1093/cercor/bhp036Google Scholar

18. 

D. SzucsF. Soltesz, “Functional definition of the N450 event-related brain potential marker of conflict processing: a numerical Stroop study,” BMC Neurosci. 13(1), 35–48 (2012).1471-2202http://dx.doi.org/10.1186/1471-2202-13-35Google Scholar

19. 

D. SzucsF. Soltesz, “Stimulus and response conflict in the color-word Stroop task: a combined electro-myography and event-related potential study,” Brain Res. 1325, 63–76 (2010).BRREAP1385-299Xhttp://dx.doi.org/10.1016/j.brainres.2010.02.011Google Scholar

20. 

S. Hanslmayret al., “The electrophysiological dynamics of interference during the Stroop task,” J. Cogn. Neurosci. 20(2), 215–225 (2008).JCONEO0898-929Xhttp://dx.doi.org/10.1162/jocn.2008.20020Google Scholar

21. 

J. Qiuet al., “Brain mechanism of Stroop interference effect in Chinese characters,” Brain Res. 1072(1), 186–193 (2006).BRREAP1385-299Xhttp://dx.doi.org/10.1016/j.brainres.2005.12.029Google Scholar

22. 

E. CoderreK. ConklinW. J. van Heuven, “Electrophysiological measures of conflict detection and resolution in the Stroop task,” Brain Res. 1413, 51–59 (2011).BRREAP1385-299Xhttp://dx.doi.org/10.1016/j.brainres.2011.07.017Google Scholar

23. 

R. West, “Neural correlates of cognitive control and conflict detection in the Stroop and digit-location tasks,” Neuropsychologia 41(8), 1122–1135 (2003).NUPSA60028-3932http://dx.doi.org/10.1016/S0028-3932(02)00297-XGoogle Scholar

24. 

M. J. LarsonD. A. KaufmanW. M. Perlstein, “Neural time course of conflict adaptation effects on the Stroop task,” Neuropsychologia 47(3), 663–670 (2009).NUPSA60028-3932http://dx.doi.org/10.1016/j.neuropsychologia.2008.11.013Google Scholar

25. 

D. Tanget al., “The neural dynamics of conflict adaptation within a look-to-do transition,” PLoS One 8(2), e57912 (2013).1932-6203http://dx.doi.org/10.1371/journal.pone.0057912Google Scholar

26. 

J. Zhaiet al., “Hemodynamic and electrophysiological signals of conflict processing in the Chinese-character Stroop task: a simultaneous near-infrared spectroscopy and event-related potential study,” J. Biomed. Opt. 14(5), 054022 (2009).JBOPFO1083-3668http://dx.doi.org/10.1117/1.3247152Google Scholar

27. 

B. Sadehet al., “Event-related potential and functional MRI measures of face-selectivity are highly correlated: a simultaneous ERP-fMRI investigation,” Hum. Brain Mapp. 31(10), 1490–1501 (2010).HBRME71065-9471http://dx.doi.org/10.1002/hbm.v31:10Google Scholar

28. 

K. Ciftciet al., “Multilevel statistical inference from functional near-infrared spectroscopy data during stroop interference,” IEEE Trans. Biomed. Eng. 55(9), 2212–2220 (2008).IEBEAX0018-9294http://dx.doi.org/10.1109/TBME.2008.923918Google Scholar

29. 

D. SzucsC. KillikellyS. Cutini, “Event-related near-infrared spectroscopy detects conflict in the motor cortex in a Stroop task,” Brain Res. 1477, 27–36 (2012).BRREAP1385-299Xhttp://dx.doi.org/10.1016/j.brainres.2012.08.023Google Scholar

30. 

J. Leon-Carrionet al., “The hemodynamics of cognitive control: the level of concentration of oxygenated hemoglobin in the superior prefrontal cortex varies as a function of performance in a modified Stroop task,” Behav. Brain Res. 193(2), 248–256 (2008).BBREDI0166-4328http://dx.doi.org/10.1016/j.bbr.2008.06.013Google Scholar

31. 

Y. Hoshi, “Functional near-infrared spectroscopy: current status and future prospects,” J. Biomed. Opt. 12(6), 062106 (2007).JBOPFO1083-3668http://dx.doi.org/10.1117/1.2804911Google Scholar

32. 

X. Lvet al., “A portable functional imaging instrument for psychology research based on near-infrared spectroscopy,” Front. Optoelectron. China 1(3–4), 279–284 (2008).1674-4128http://dx.doi.org/10.1007/s12200-008-0057-6Google Scholar

33. 

Z. Zhanget al., “A fast neuronal signal-sensitive continuous-wave near-infrared imaging system,” Rev. Sci. Instrum. 83(9), 094301 (2012).RSINAK0034-6748http://dx.doi.org/10.1063/1.4752021Google Scholar

34. 

R. OostenveldP. Praamstra, “The five percent electrode system for high-resolution EEG and ERP measurements,” Clin. Neurophysiol. 112(4), 713–719 (2001).CNEUFU1388-2457http://dx.doi.org/10.1016/S1388-2457(00)00527-7Google Scholar

35. 

A. K. Singhet al., “Spatial registration of multichannel multi-subject fNIRS data to MNI space without MRI,” Neuroimage 27(4), 842–851 (2005).NEIMEF1053-8119http://dx.doi.org/10.1016/j.neuroimage.2005.05.019Google Scholar

36. 

K. E. Janget al., “Wavelet minimum description length detrending for near-infrared spectroscopy,” J. Biomed. Opt. 14(3), 034004 (2009).JBOPFO1083-3668http://dx.doi.org/10.1117/1.3127204Google Scholar

37. 

G. Strangmanet al., “A quantitative comparison of simultaneous BOLD fMRI and NIRS recordings during functional brain activation,” Neuroimage 17(2), 719–731 (2002).NEIMEF1053-8119http://dx.doi.org/10.1006/nimg.2002.1227Google Scholar

38. 

J. S. Brittainet al., “A role for the subthalamic nucleus in response inhibition during conflict,” J. Neurosci. 32(39), 13396–13401 (2012).JNRSDS0270-6474http://dx.doi.org/10.1523/JNEUROSCI.2259-12.2012Google Scholar

39. 

H. Yuanet al., “Negative covariation between task-related responses in alpha/beta-band activity and BOLD in human sensorimotor cortex: an EEG and fMRI study of motor imagery and movements,” Neuroimage 49(3), 2596–2606 (2010).NEIMEF1053-8119http://dx.doi.org/10.1016/j.neuroimage.2009.10.028Google Scholar

40. 

T. Egneret al., “Dissociable neural systems resolve conflict from emotional versus nonemotional distracters,” Cereb. Cortex 18(6), 1475–1484 (2008).53OPAV1047-3211http://dx.doi.org/10.1093/cercor/bhm179Google Scholar

41. 

M. P. MilhamM. T. BanichV. Barad, “Competition for priority in processing increases prefrontal cortex’s involvement in top-down control: an event-related fMRI study of the stroop task,” Brain Res. Cogn. Brain Res. 17(2), 212–222 (2003).CBRREZ0926-6410http://dx.doi.org/10.1016/S0926-6410(03)00108-3Google Scholar

42. 

U. MayrE. Awh, “The elusive link between conflict and conflict adaptation,” Psychol. Res. 73(6), 794–802 (2009).0340-0727http://dx.doi.org/10.1007/s00426-008-0191-1Google Scholar

43. 

N. K. Logothetiset al., “Neurophysiological investigation of the basis of the fMRI signal,” Nature 412(6843), 150–157 (2001).NATUAS0028-0836http://dx.doi.org/10.1038/35084005Google Scholar

44. 

N. K. Logothetis, “What we can do and what we cannot do with fMRI,” Nature 453(7197), 869–878 (2008).NATUAS0028-0836http://dx.doi.org/10.1038/nature06976Google Scholar

45. 

P. L. Nunez, Neocortical Dynamics and Human EEG Rhythms, Oxford University Press, New York (1995).Google Scholar

46. 

X. Liuet al., “Functional dissociation of attentional selection within PFC: response and non-response related aspects of attentional selection as ascertained by fMRI,” Cereb. Cortex 16(6), 827–834 (2006).53OPAV1047-3211http://dx.doi.org/10.1093/cercor/bhj026Google Scholar

47. 

H. Barbas, “Connections underlying the synthesis of cognition, memory, and emotion in primate prefrontal cortices,” Brain Res. Bull. 52(5), 319–330 (2000).BRBUDU0361-9230http://dx.doi.org/10.1016/S0361-9230(99)00245-2Google Scholar

48. 

C. Y. WuM. H. HoS. H. Chen, “A meta-analysis of fMRI studies on Chinese orthographic, phonological, and semantic processing,” Neuroimage 63(1), 381–391 (2012).NEIMEF1053-8119http://dx.doi.org/10.1016/j.neuroimage.2012.06.047Google Scholar

49. 

G. di PellegrinoE. CiaramelliE. Ladavas, “The regulation of cognitive control following rostral anterior cingulate cortex lesion in humans,” J. Cogn. Neurosci. 19(2), 275–286 (2007).JCONEO0898-929Xhttp://dx.doi.org/10.1162/jocn.2007.19.2.275Google Scholar

50. 

M. A. FunesJ. LupianezG. Humphreys, “Top-down and bottom-up deficits in conflict adaptation after frontal lobe damage,” Cogn. Neuropsychol. 27(4), 1–16 (2010).COGNEP1464-0627http://dx.doi.org/10.1080/02643294.2010.532618Google Scholar

51. 

T. EgnerJ. Hirsch, “Cognitive control mechanisms resolve conflict through cortical amplification of task-relevant information,” Nat. Neurosci. 8(12), 1784–1790 (2005).NANEFN1097-6256http://dx.doi.org/10.1038/nn1594Google Scholar

52. 

T. EgnerM. DelanoJ. Hirsch, “Separate conflict-specific cognitive control mechanisms in the human brain,” Neuroimage 35(2), 940–948 (2007).NEIMEF1053-8119http://dx.doi.org/10.1016/j.neuroimage.2006.11.061Google Scholar

Jinyan Sun, Bailei Sun, Lei Zhang, Qingming Luo, Hui Gong, "Correlation between hemodynamic and electrophysiological signals dissociates neural correlates of conflict detection and resolution in a Stroop task: a simultaneous near-infrared spectroscopy and event-related potential study," Journal of Biomedical Optics 18(9), 096014 (25 September 2013). http://dx.doi.org/10.1117/1.JBO.18.9.096014
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KEYWORDS
Near infrared spectroscopy

Hemodynamics

Electrodes

Brain

Statistical analysis

Sensors

Electroencephalography

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