Abstract

A multimodal neuroimaging technique based on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) was used with horizontal hemifield visual stimuli with graded contrasts to investigate the retinotopic mapping more fully as well as to explore hemispheric differences in neuronal activity, the hemodynamic response, and the neurovascular coupling relationship in the visual cortex. The fNIRS results showed the expected activation over the contralateral hemisphere for both the left and right hemifield visual stimulations. However, the EEG results presented a paradoxical lateralization, with the maximal response located over the ipsilateral hemisphere but with the polarity inversed components located over the contralateral hemisphere. Our results suggest that the polarity inversion as well as the latency advantage over the contralateral hemisphere cause the amplitude of the VEP over the contralateral hemisphere to be smaller than that over the ipsilateral hemisphere. Both the neuronal and hemodynamic responses changed logarithmically with the level of contrast in the hemifield visual stimulations. Moreover, the amplitudes and latencies of the visual evoked potentials (VEPs) were linearly correlated with the hemodynamic responses despite differences in the slopes.

© 2017 Optical Society of America

1. Introduction

In the past few decades, improved neuroimaging techniques and the necessity for clinical applications have considerably improved the investigation of the brain functional activity. Electroencephalography (EEG) has been used to measure the oscillations in the brain electrical dynamics with sub-millisecond temporal resolution but at a relatively low spatial resolution due to the semi-conductance of head tissues [1]. Using functional magnetic resonance imaging (fMRI) from the perspective of hemodynamics has become a common way to measure brain activity indirectly by recording the blood-oxygen-level-dependent (BOLD) signals [2,3]. The BOLD signal primarily reflects changes in paramagnetic deoxygenated hemoglobin in tissues. The technique can provide maps of the brain activations at good spatial resolution, but its temporal resolution is limited to seconds [4]. Because EEG and fMRI can respectively measure electro- and hemo- dynamics, they have been combined to measure signals from neuronal activity and the corresponding hemodynamic response of the brain [5,6]. However, serious cross interference between the electrical field of an EEG system and the magnetic field of an fMRI scanner has limited the application of this combination [7].

Alternatively, functional near infrared spectroscopy (fNIRS) [8] has emerged as a non-invasive optical neuroimaging technique, which provides more information about the hemodynamic responses with improved ecological validity by measuring deoxygenated (HbR), oxygenated (HbO), and total (HbT) hemoglobin [9]. This makes it useful for distinguishing differences in the amplitude, timing, and location of these components [10] at appropriate temporal and spatial resolutions. Because of the complementary strengths of EEG and fNIRS and because both modalities are non-invasive, cost-effective, portable, wearable, and ecological and can be used for longitudinal monitoring, a multimodal neuroimaging method based on these two techniques is uniquely suited for investigating brain activity in natural and clinical environments. Moreover, the electrical signals of EEG and the optical signals of fNIRS can be used to record neuronal activity and the hemodynamic response of the brain without cross interference. Therefore, this combination was employed in this study to explore non-invasively the relationship between neuronal electrical activity and the hemodynamics of the brain using hemifield stimulation with graded contrasts.

The study of hemispheric differences in the brain has a long history in neuroscience. The left and right hemispheres of the brain are not identical, but show hemispheric differences in both structure and function [11, 12]. The left hemisphere has been reported to be dominant in verbal processing, while the right hemisphere is considered to be dominant in nonverbal and visuospatial processing [13]. With respect to the sensitivity of each side to various stimuli, the left hemisphere tends to process high spatial frequencies, local information, and categorical information from visual stimuli; whereas the right hemisphere tends to process low spatial frequencies, global information, and coordinate information from visual stimuli [14].

Because retinotopy is the mapping of visual inputs from the retina to the visual cortex, it has been used extensively to explore the mechanisms of the primary sensory cortex [15]. According to the original retinotopic model (cruciform model) [16], in response to hemifield visual stimuli, the left and right visual fields are projected to the corresponding contralateral hemispheres, and the upper and lower visual fields are projected to the ventral and dorsal regions of the visual areas, respectively. Several studies have used fNIRS to investigate this retinotopic organization. For instance, Colier et al., 2001 measured contralateral hemodynamic changes in response to hemifield visual stimulation using a multi-channel system [17]. Bastien et al., 2012 studied the locations and magnitudes of the hemodynamic responses to quadrant visual stimulations and reported the expected retinotopic distribution of the responses with respect to the visual field stimulated [18]. Other studies [10, 19] showed the reliability of retinotopic mapping in a single subject using a high-density fNIRS system. Eggebrecht et al., 2012, further confirmed that high-density optical systems provide excellent image quality for retinotopic mapping. Moreover, a quantitative spatial comparison with fMRI results suggested that a high-density optical system could be used as an alternative neuroimaging technique to fMRI in clinical applications [20].

Visual evoked potentials (VEPs) have also been recorded in EEG studies involving the visual cortex but have provided conflicting results. On the one hand, some studies suggested that the maximal response (P100 or C1) in the checkerboard reversal task was distributed in the hemisphere that was contralateral to the stimulated visual field [21–23]. On the other hand, Barrett reported a paradoxical lateralization of the P100 component in the checkerboard reversal task [24] in that the P100 of the hemifield visual stimulus was located in the hemisphere that was ipsilateral to the stimulated visual field. This paradoxical lateralization was also found in several other studies [25–27].

Because of the functional asymmetry of the hemispheres of the brain, spatial resolution and contrast sensitivity have been found to be asymmetric under stimuli in distinct visual fields [14]. Because visual stimuli with a variety of contrasts present significant advantages as a paradigm for investigating ways in which the visual information is organized and encoded [28], they have been employed in several studies to probe the relationship between stimulus contrasts and the corresponding responses [29–31]. Most of the studies in this field have either concentrated on distinct stimulus contrasts in the full visual field or on distinct visual fields with a constant contrast (usually, a high contrast stimulus) [10, 21–23, 32]. Moreover,due to advancements in multimodal neuroimaging techniques, the relationship between neuronal activities and the corresponding hemodynamic responses (also known as neurovascular coupling) in visual stimulation has been studied extensively using full field visual stimulation [30, 31, 33, 34]. However, because, as far as we are aware, no one has simultaneously investigated hemifield stimulations with graded contrasts, hemispheric differences in the relationship between neuronal activities and the corresponding hemodynamic responses are not sufficiently clear, and the underlying mechanism in neurovascular coupling under hemifield visual stimuli with graded contrasts remains unknown.

To better understand these issues, in this study, hemifield visual stimulations with graded contrasts were used, and a multimodal neuroimaging technique based on EEG and fNIRS was employed to explore the asymmetry of neuronal activity and the vascular response in the two hemispheres in response to hemifield visual stimulations. The research goals were three-fold. The first was to study how hemifield visual stimuli with distinct contrasts affect both the neuronal and the hemodynamic responses. The second goal was to investigate the relationship between the neuronal activity and the corresponding hemodynamic response to hemifield visual stimulations. The last, but not least, was to identify hemispheric differences in neuronal activity, the hemodynamic response, and neurovascular coupling.

2. Materials and methods

2.1 Participants and protocol

Fourteen healthy volunteers (12 males and 2 females, ages 22-30 years) were recruited for this study. All the participants had normal or corrected-to-normal vision. They were in good health and had no neurological or psychiatric disorders. The visual stimuli used here were full-field, left, and right hemifield windmill checkerboard reversal designs with three different levels of contrast (1%, 10%, and 100%), resulting in nine conditions. The stimulus contrast (C) was defined according to C=(LmaxLmin)/(Lmax+Lmin), where Lmax and Lminare the maximum and minimum luminances, respectively [35]. The hemifield visual stimuli were absent from a central 2.1°circular zone to avoid ambiguities caused by fixation instability [21]. The stimuli were presented one at a time in random order at a reversal rate of 4 reversals/s. The entire experimental protocol consisted of an initial baseline period (30 s) followed by 27 blocks (comprised of 3 blocks for each of the 9 conditions); each block consisted of a stimulation period (25 s) and a resting period (30 s). During the experiment, the participants were seated comfortably and stayed 80 cm away from the front of a computer monitor with a visual angle of 19°. To help the participants keep a high level of attention and to minimize fatigue, the participants were instructed to maintain a stable fixation on a red circle in the middle of the monitor and to press a button whenever the circle turned green, which occurred only rarely (8 to 15 s inter-stimulus interval). EEG and fNIRS were used to record the neuronal electrophysiological signals and vascular microcirculation, respectively. The experimental configuration is illustrated in Fig. 1.

 figure: Fig. 1

Fig. 1 Experimental configuration. a) Experimental paradigm of the hemifield visual task. The entire experimental paradigm consisted of an initial baseline period (30 s) followed by 27 blocks. Each block consisted of a stimulation period (25 s, reversal rate: 4 reversals/s) and a resting period (30 s). b) The diagram of the head illustrates the placement of the electrode Oz (green hexagon) and optodes, specifically 6 sources (red stars) and 12 detectors (blue circles), yielding 20 optical channels (black lines marked with channel numbers). The distance between a neighboring source and the detector pairs was 3 cm and the probe covered an area of approximately 6 × 6 cm2 for each hemisphere.

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In addition, the effect of the size of the spared circular zone on the polarity of the components for the hemifield stimulations was investigated. This portion of the study was conducted using a subset of three of the participants. Hemifield stimuli at the 100% contrast level were used, and the spared circular zones had the following visual angles: 2.1°, 6.3°, and 10.5°.

It has been well established that the functional activation of unilateral hand movement is lateralized into the contralateral hemisphere. Therefore, as an example, a classical finger tapping experiment was used to discover whether the polarity would be inversed in the motor cortex. Ten subjects were recruited for this study. They were instructed to tap their left or right index finger as quickly and correctly as possible when a character ‘L’ or ‘R’ was presented at the center of the screen. The experimental paradigm was also block-designed. In each block, there were 24 trials with an inter-stimulus interval (ISI) of 1,000 ms. After each task period, there was a 20 s resting period. In total, there were 30 blocks. Equal numbers of left and right finger tapping tasks were presented in random order.

The experimental protocols were approved by the ethics committee of the Institute of Automation, Chinese Academy of Sciences. Written informed consent was obtained after the experimental paradigm was explained. All the experiments were carried out in a quiet, dimly illuminated, acoustically shielded room. Before the formal measurement session, the participants were trained in a practice session until they were familiar with the paradigm. All experimental stimuli were presented using E-prime 2.0 (Psychology Software Tools, Inc., Pittsburgh, PA, USA).

2.2 EEG recording and data analysis

Visual evoked potentials (VEPs) are electrophysiological signals evoked by visual stimuli that can be extracted from the electroencephalographic activity in the visual cortex recorded from the overlying scalp [35]. The VEPs were recorded using the Brain Vision system (Brain Products Ltd., Munich, Germany) with 64 channels at a sampling rate of 5000 Hz. Oz and four electrodes in each of the left (P7, P5, PO7, and PO3) and right (P8, P6, PO8, and PO4) hemispheres were used to record the VEPs. The electrodes were placed based on the international 10-20 system of electrode placement. An electrooculographic (EOG) electrode was placed over the outer canthus of the left eye to correct for blink artifacts. All electrodes were referred to a common reference, FCz. The impedances of the electrodes were maintained below 10 kΩ.

Brain Vision Analyzer 2.0 was used for the off-line analysis of the EEG data. First, the data were down-sampled to 1000 Hz and band-filtered between 1 and 100 Hz with an additional 50 Hz notch filter. Moreover, an ocular correction using independent component analysis (ICA) was done for the data that was seriously affected by EOG artifacts. Then, the data was linearly detrended, taking into account the DC offset. The data were segmented into epochs that started 50 ms before the stimulus onset and ended 200 ms after the stimulus. Epochs with a magnitude greater than ± 50μV were automatically rejected as artifacts. Before averaging, the data was baseline corrected by subtracting the 50 ms pre-stimulus baseline. Reliable VEP waveforms were obtained from 12 out of 14 participants. One participant’s data was discarded because of large motion artifacts. Another’s was discarded because of poor data quality. For the remaining EEG data, the average trial rejection rate was less than 6.5%. The average accuracy of the participants in pressing the button was 64%.

For the finger tapping task, Cz and two electrodes in each of the left (T7 and C3) and right (T8 and C4) hemispheres were used to record the somatosensory evoked potentials (SEPs). The arrangement of the electrodes was based on the international 10-20 system. An EOG electrode was used to correct for blink artifacts. All electrodes were referred to a common reference, FCz. The impedances of the electrodes were maintained below 10 kΩ. The data processing methods for the finger tapping task were similar to those from the hemifield stimulations except that the data were segmented into epochs that started 50 ms before the stimulus onset and ended 450 ms after the stimulus.

2.3 fNIRS recording and data analysis

The hemodynamic responses were recorded using the CW6 system (TechEn, Inc., USA). Optodes were placed on the surface above the visual cortex guided by the EEG electrode positions. Six sources and twelve detectors were arranged geometrically to obtain 20 optical channels, as shown in Fig. 1. The distance between the source and detector pairs was 3 cm, and the probe covered an area of approximately 6 × 6 cm2 for each hemisphere. The sampling rate for the fNIRS was 50 Hz.

Homer 2 software was used for data processing. First, the relative concentration changes of oxygenated (HbO), deoxygenated (HbR), and total (HbT) hemoglobin were calculated from the original raw data using the modified Beer-Lambert Law (MBLL) [36]. The differential pathlength factors (DPF) were 6.51 and 5.86 for 690 and 830 nm [37]. Next, the data were band-pass filtered between 0.01 and 0.1 Hz to remove task-unrelated noise. The data were then segmented into blocks, starting 10 s before the activation onset and ending 20 s after the activation, and epochs with apparent artifacts were rejected. The 10 s period before the activation onset was regarded as the baseline. Finally, the block-averaged hemodynamic responses were calculated. A typical cerebral activation is characterized by a significant increase in HbO concentration or a significant decrease in HbR concentration with respect to the baseline after the activation onset [38]. The spatial distribution maps of the group-averaged hemodynamic responses were calculated based on the maximum values of the group-averaged hemodynamic responses from 5 seconds after the stimulation onset until the end of the stimulation. In total, reliable hemodynamic responses were obtained in 11 out of 14 participants; the data from the other three participants were discarded. One participant’s data was discarded because of large motion artifacts. Another was discarded because of bad optical coupling between the optodes and the scalp. The third was rejected because of poor data quality resulting from the subject’s relatively thick, black hair. For the remaining fNIRS data, the average trial rejection rate was 4.3%. The average accuracy of the participants in pressing the button was 71%.

2.4 Correlation analysis and statistical analysis

The correlation analysis was conducted on MATLAB (The Mathworks Inc., Natick, Massachusetts, USA) 2013a platform. In this paper, all the experimental results are indicated as means ± standard error (SE), unless otherwise noted. To characterize the differences between different conditions, a paired t-test was conducted. The differences were accepted as significant when p < 0.05. All the statistical analyses were performed using SPSS software.

3. Results

3.1 EEG results

The group-averaged VEP results of the hemifield visual tasks with graded contrast levels across all 9 electrodes are shown in Fig. 2. Hemispheric differences in the neuronal activities during visual stimulation with graded contrasts are illustrated in Fig. 3.

 figure: Fig. 2

Fig. 2 The group-averaged VEP results for a) the left visual field and b) the right visual field stimulations at different contrast levels: 1% (left column, blue lines), 10% (middle column, green lines), and 100% (right column, red lines), recorded from electrodes P7, P5, PO7, PO3, Oz, PO4, PO8, P6, and P8, respectively. The gray dotted lines indicate the stimulus onset. In comparison, both the amplitude and latency of the VEPs varied with contrast level with the amplitude increasing and the latency decreasing as the stimulus contrast increased. Moreover, the main VEP components across the three contrast levels indicated opposite polarities over the left and the right hemispheres for the hemifield visual stimulations.

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 figure: Fig. 3

Fig. 3 The amplitudes of Peak2 for a) the left visual field and d) the right visual field against the stimulus contrast levels. The amplitudes of Peak2-Peak1 for b) the left visual field and e) the right visual field against the stimulus contrast levels. The latencies of Peak2 for c) the left visual field and f) the right visual field in response to the varying contrast levels. Error bars show the SE.

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VEP, which has two critical parameters, amplitude and latency, can provide important diagnostic information about the functional integrity of the visual system. The amplitude represents the amount of information received by the visual cortex, whereas the latency indicates how long the electrical signal took to travel from the retina to the visual cortex [39]. In this paper, peaks were determined by finding maxima and minima of the VEPs between 40 and 180 ms after the stimulus onset [27, 34, 40, 41]. Moreover, the nomenclature of the VEP components was based on the major peak in the order of their place in the sequence; for instance, the first peak was named Peak1; the second peak was named Peak2. The difference between Peak2 and Peak1 (Peak2-Peak1) was reported to have a greater dynamic range than those of the Peak2 and Peak1 amplitudes alone [42]. Therefore, in this paper, we used not only the Peak2 amplitude but also the Peak2-Peak1 amplitude for further analysis. For latency, the Peak2 was used for analysis because of its relatively good stability [43].

As Figs. 2 and 3 show, both the amplitude and the latency of the VEPs varied with contrast level in response to the hemifield visual stimulations; specifically, an increase in the contrast level resulted in an increase in the amplitude and a reduction in the latency of the VEPs. Moreover, the main VEP components across the three contrast levels indicated opposite polarities over the left and the right hemispheres in response to the hemifield visual stimulations.

Distribution of the VEP over the electrodes at distinct contrast levels

At the 100% contrast level, the VEP components N75 (at about 75 ms), P100 (at about 100 ms), and N145 (at about 145 ms) were almost consistently obtained over the midline and the lateral areas ipsilateral to the hemifield stimulation; however, the VEP components P75, N100, and P145 were consistently recorded over the lateral areas contralateral to the hemifield stimulation. Specifically, for the 100% contrast level stimulus in the left hemifield (see the right column in Fig. 2(a)), the VEPs from the central electrode Oz and the electrodes (P7, P5, PO7, and PO3) above the ipsilateral hemisphere presented a typical “NPN” pattern reversal VEP, specifically, N75, P100, and N145. However, the VEP components from the electrodes (P8, P6, PO8 and PO4) above the contralateral hemisphere were inverted in polarity, that is, they showed a “PNP” pattern, which consisted of P75, N100, and P145. It is interesting to note that this “PNP” pattern was more obvious over the more contralaterally placed electrodes (P8, P6, and PO8); however, the electrode near the midline (PO4) did not provide effective lateralizing information. For the stimuli at the 10% and 1% contrast levels in the left visual field (see the left and the middle columns in Fig. 2(a)), the VEP amplitudes of the positive peaks from the central electrode Oz and the electrodes (P7, P5, PO7, and PO3) above the ipsilateral hemisphere were obviously reduced and the latencies were noticeably prolonged. Moreover, the VEPs components from the electrodes (P8, P6, PO8, and PO4) above the contralateral hemisphere were also inverted in polarity. In comparison, the VEPs of the right visual field stimulation showed similar distributions across the three contrast levels. The paracentral electrodes (PO4 or PO3) contralateral to the stimulated hemifield for all three contrast levels were excluded from further analysis because they lacked effective lateralizing information.

The effect of the size of the spared circular zone on the polarity of the components for the hemifield stimulations was also investigated. Hemifield stimuli at the 100% contrast level with the visual angles of the spared circular zones varying at 2.1°, 6.3°, and 10.5° were used. The result showed that the three sizes of the spared circular zone consistently showed the same inverted polarity of the components.

In addition, whether such a polarity inversion only occurs in the visual cortex or can be found in other areas was also investigated. A classical finger tapping experiment was used to discover whether the polarity would be inversed in the motor cortex. As Fig. 4 shows, the SEP of the electrodes over the two hemispheres presented similar components: P120 and N200 for both the left and right hand finger tapping tasks. The polarity of the waveform over the contralateral hemisphere was not inversed.

 figure: Fig. 4

Fig. 4 The group-averaged SEP results for finger tapping of a) the left hand (left column, blue lines) and b) the right hand (right column, red lines), recorded from electrodes T7, C3, Cz, C4, and T8, respectively. The gray dotted lines indicate the stimulus onset. A comparison of the graphs shows that the main components across the five electrodes had a similar polarity over the left and the right hemispheres for unilateral finger tapping tasks.

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To test whether these changing trends in response to hemifield stimulations were statistically significant (paired t-test, p < 0.05), the amplitudes and the latencies of the three contrast levels were compared with each other, see Table 1.

Tables Icon

Table 1. Overview of the peak amplitudes and peak time of the VEP and the corresponding hemodynamic responses at three contrast levels (mean ± SE)

Differences in the VEP amplitudes across the three contrast levels

The amplitudes of Peak2 and Peak2-Peak1 over the ipsilateral and the contralateral hemisphere were significantly different between the three contrast levels for both the left and right visual field stimulations, except for the 10% vs. 100% contrast levels of Peak2 over the contralateral hemisphere in the right visual field. Moreover, because of the polarity inversion, the amplitudes over the ipsilateral and the contralateral hemisphere presented opposite polarities. Additionally, for each of the contrast levels, the amplitudes of Peak2 and Peak2-Peak1 between the ipsilateral and the contralateral hemisphere were also significantly different from each other for both the left and right hemifield stimulations.

Differences in the VEP latency across the three contrast levels

The latencies of Peak2 over the ipsilateral and contralateral hemispheres were significantly different between the three contrast levels for both the left and right visual field stimulations. Moreover, the latencies decreased monotonically as the contrast levels of the hemifield stimulations increased from 1% to 100%. Note that, for each of the contrast levels, the latencies of Peak2 for the contralateral hemisphere were significantly shorter than those for the ipsilateral hemisphere for both the left and right visual field stimulations. In addition, there was a trend for the contralateral latency advantage to increase with decreasing contrast levels.

3.2 fNIRS results

The group-averaged spatial distribution maps of the HbO concentration changes for the visual checkerboard reversal tasks at different contrast levels for the hemifield visual tasks are illustrated in Fig. 5. The hemispheric differences in the hemodynamic responses during visual stimulation with graded contrasts are shown in Fig. 6. The hemodynamic response showed the expected contralateral activations as well as similar distribution patterns for both the left and right hemifield stimulations.

 figure: Fig. 5

Fig. 5 The group-averaged spatial distribution maps of the hemodynamic changes for the visual checkerboard reversal tasks at different contrast levels: 1% (left column), 10% (middle column), and 100% (right column) for the left visual field (top row) and the right visual field (bottom row). The color bar indicates the HbO/ HbR concentration changes in μmol/l.

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 figure: Fig. 6

Fig. 6 Hemispheric differences in the hemodynamic responses during hemifield visual stimulations with graded contrasts. Comparison of the changes in a) HbO and b) HbR concentration over the left hemisphere (LH) and the right hemisphere (RH) of the visual cortex across the 1%, 10%, and 100% contrast levels for the left hemifield visual stimulation; Comparison of the changes in c) HbO and d) HbR concentration over the left hemisphere and the right hemisphere of the visual cortex across the 1%, 10%, and 100% contrast levels for the right hemifield visual stimulation; error bars show the SE. For the hemifield visual stimulations, the functional activated areas were consistently located over the lateral areas contralateral to the stimulated hemifield. The amplitudes of the HbO concentration monotonically increased as the contrast level increased. The changes in the HbO amplitudes are more apparent than those of the HbR.

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Spatial distribution of the hemodynamic response at distinct contrast levels

As is illustrated in Figs. 5 and 6, the functional activated areas of the hemifield visual tasks were consistently located over the lateral areas contralateral to the stimulated hemifield. Specifically, the amplitudes of the HbO concentration for the hemifield visual stimulations were significantly higher in the contralateral hemisphere than those in the ipsilateral hemisphere for all three contrast levels (paired t-test, p < 0.05) except for the 1% contrast level in the right visual field (see Table 1).

Differences in the hemodynamic responses across the three contrast levels

The amplitudes of the HbO concentration over the contralateral hemisphere at all three contrast levels differed significantly from each other (paired t-test, p < 0.05) for both the left and right hemifield visual stimulations. Moreover, the HbO concentration over the contralateral hemisphere monotonically increased as the contrast level increased from 1% to 100% for both the left and right hemifield visual stimulations. However, the amplitudes of the HbO concentration over the ipsilateral hemisphere showed no significant differences between the three contrast levels, except for the 1% vs. 10% and 1% vs. 100% contrast level pairs in the left visual field. Note that the changes in HbO concentration over the contralateral hemisphere were larger for the left field stimulation than for the right field stimulation, but the trends were not statistically significant (paired t-test, p < 0.05) for any of the three contrast levels.

Differences in the HbO and HbR concentrations across the three contrast levels

The amplitudes of the HbO concentration for the hemifield stimulations increased monotonically with contrast level; however, there were no obvious changes in the amplitudes of the HbR concentration. Additionally, the changes in the HbO amplitudes were more apparent than those in the HbR amplitudes.

3.3 Correlation results

When correlating VEPs and hemodynamic responses, an important issue is which features of the VEPs should be used to represent the neuronal activity [42]. In this paper, we performed correlation analyses for both the neuronal and hemodynamic responses in the hemisphere that was contralateral to the stimulated hemifield. For the VEP amplitude, the Peak2 amplitude as well as the Peak2-Peak1 amplitude was used for the correlation analysis. For the VEP latency, Peak2 was used for the correlation analysis.

The correlations between the VEPs and the corresponding hemodynamic responses at the different contrast levels for the hemifield visual stimulations are illustrated in Fig. 7, which shows that, for the stimulated hemifields, the relationships between the VEP amplitude and the hemodynamic response were linear, although the slopes for the Peak2 and Peak2-Peak1 amplitudes were different. Specifically, because of the polarity inversion, increases in the Peak2 amplitude were related to increases in the amplitude of the HbO concentration, indicating a positive correlation. However, decreases in the Peak2-Peak1 amplitude were related to increases in the amplitude of the HbO concentration, indicating a negative correlation. The changing trends for the HbR had similar forms to those of the HbO despite the differences in the slopes and signs of the amplitudes. As for the latency, decreases in the Peak2 latency were related to increases in the amplitude of the hemodynamic response, indicating a negative correlation.

 figure: Fig. 7

Fig. 7 Correlations between the VEPs and the corresponding hemodynamic responses at different contrast levels for the hemifield visual stimulations. a), b), and c) respectively show the relationship between the Peak2 amplitude, Peak2-Peak1 amplitude, and Peak2 latency of the VEP against the hemodynamic response at different contrast levels for the left hemifield visual stimulations. d), e), and f) respectively show the relationship between the Peak2 amplitude, Peak2-Peak1 amplitude, and Peak2 latency of the VEP against the hemodynamic response at different contrast levels for the right hemifield visual stimulation. The dot, triangle, and square represent the 1%, 10%, and 100% contrast levels, respectively. The red and blue lines indicate HbO and HbR, respectively. Error bars show the SE.

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In comparison, the correlations between the VEPs and the corresponding hemodynamic responses at the three contrast levels showed similar forms for the left and right hemifield visual stimulations, although the slopes were different from each other.

4. Discussion and conclusions

It is well documented that responses to visual stimuli vary with the location of each stimulus in the visual field. Retinotopy maps the visual information from the retina to the visual cortex. It has been demonstrated that stimuli that are adjacent in the visual fields are represented in adjacent areas in the visual cortex [15]. Typically, the left and right visual fields are represented in the hemisphere that is contralateral to the stimulated hemifield while the upper and lower visual fields are represented in the ventral and dorsal areas of the visual cortex, respectively. Because of the significance of visually evoked functional activity in clinical applications, numerous studies have been conducted to investigate the retinotopy as well as to explore the relationship between the neuronal activity and the corresponding hemodynamic response [23, 30, 34, 44]. However, most of the previous studies focused on either distinct stimulus contrasts in the full visual field or distinct visual fields with constant contrast. The hemispheric differences in neuronal activities and the hemodynamic responses under distinct stimuli with graded contrasts remain unknown. Moreover, the hemispheric differences in neurovascular coupling are especially worth exploring. A multimodal neuroimaging method based on EEG and fNIRS is appropriate for studying the topic as it provides complementary advantages for investigating the spatial-temporal correlation between electrophysiological activities and the hemodynamic responses of the brain.

4.1 Neuronal activity in response to hemifield stimulation

In previous studies the VEP waveforms were distributed approximately symmetrically across the left and right hemispheres when full-field visual stimulations were used [30, 32, 33, 35]. When hemifield visual stimuli were used, as in this work, the VEP waveforms over the left and right hemispheres were distributed asymmetrically. Another study reported that a cruciform-based arrangement of the visual field projection to the striate cortex could produce potentials of opposite polarity in response to the left and right hemifield stimuli and in response to upper and lower hemifield stimuli [45].

The VEP waveform for the 100% contrast level presented a typical N75-P100-N145 complex over the ipsilateral hemisphere but showed an inverted polarity P75-N100-P145 complex over the contralateral hemisphere for both the left and right hemifield visual stimulations. The critical difference between these two complex waveforms lies in their completely opposite polarities. This result is consistent with a previous finding, which was described as “paradoxical lateralization” [24]. That study reported that the P100 of the left visual field stimulus was located in the left (ipsilateral) hemisphere. The finding was completely opposite to the common belief that retinotopy crosses the brain and that hemifield visual stimulation induces significant brain functional activity in the contralateral visual cortex. This paradoxical lateralization has also been reported in other publications despite using different experimental configurations [25–27].

Neuroscientists attempted to explain the phenomenon, but they have not reached a consensus on this issue. Barett et al., 1976 inferred that the ipsilateral P100 originated from the hemisphere that was contralateral to the stimulated hemifield and concluded that the component was able to be measured over the ipsilateral hemisphere because of the orientation of the dipoles [24]. However, Arruga et al., 1980 suggested that the absence of the P100 in the contralateral hemisphere could be due to the emergence of a polarity-inverted N100 component. The P100 over the ipsilateral hemisphere and the N100 over the contralateral hemisphere should be combined to determine the lateralization because the combined feature may be more reliable than either component alone [27]. Therefore, in this work, we took both of these perspectives into account when we performed the hemifield visual stimulations. It is worth noting that the polarity inverted components are more obvious when using laterally arranged electrodes than when using paracentral electrodes, even when they are located contralateral to the stimulated hemifield. The paracentral electrodes did not provide effective lateralizing information, results which were in agreement with previous studies [27, 46]. The effect of the size of the spared circular zone was also investigated in this study. The results showed that different sizes of the spared circular zone consistently resulted in the components being inverted in polarity in response to the hemifield stimulations. Further, to test whether this polarity inversion was unique to the visual cortex, a unilateral finger tapping task was conducted. The SEP waveform over the contralateral motor cortex did not invert the polarity. Therefore, it appears likely that this polarity inversion is a characteristic of the visual cortex.

The VEP components for the 10% and 1% contrast levels showed similar distributions to that of the 100% contrast level, but the amplitudes were obviously reduced and the latencies noticeably prolonged compared to the 100% contrast level. Considering all of these results, we concluded that the influence of the stimulus contrast is consistent for the left and right hemifield visual stimulations, that is, that they show higher amplitudes and shorter latencies for higher contrast level stimulus [41]. In accordance with previous findings based on full field visual stimulations [29, 30, 33], we observed a similar relationship between the hemodynamic responses and the stimulation contrast levels in the hemifield visual stimulations. In addition, our results suggested that this relationship may be able to be depicted as a logarithmic function.

For a given contrast level, the amplitudes of the VEPs elicited by the hemifield visual stimulation over the contralateral hemisphere were smaller than those over the ipsilateral hemisphere. However, the latencies of the VEPs elicited by the hemifield visual stimulation over the contralateral hemisphere were shorter than those over the ipsilateral hemisphere. The contralateral latency advantage increased noticeably at the lower contrast level. These results were in accordance with previous studies [47, 48]. The latency advantage over the directly stimulated (contralateral) hemisphere compared to the indirectly stimulated (ipsilateral) hemisphere is possibly due to the transcallosal relay [47].

4.2 Hemodynamic response to hemifield stimulation

Hemodynamic responses showed the expected activation over the hemisphere that was contralateral to the stimulated hemifield for both the left and right hemifield visual stimulations. Our results were compatible with the accepted retinotopy of the visual cortex. The spatial distributions of the hemodynamic responses for the hemifield stimulations confirm the previous findings [10, 17, 18].

Under the hemifield visual stimulations, the hemodynamic responses over the contralateral hemisphere showed significant increases in the HbO concentration and corresponding decreases in the HbR concentration with respect to the baseline with an increasing level of stimulus contrast. The relationship between the hemodynamic responses and the contrast levels for the hemifield stimulation is non-linear and could potentially be best fitted to a logarithmic function. The trend of the amplitudes with respect to the contrast levels in this paper showed a form that was similar to the full-field visual stimulations reported in previous publications [30, 33]. Moreover, the rate of increase in the HbO concentration was greater than the rate of decrease in the HbR concentration. This may be due to a relatively higher signal-to-noise ratio for HbO, which can improve its robustness to cross-talk [38]. For a given stimulus, the changes in the hemodynamic response are produced by a series of complex processes, including the neuronal activity responding to the stimulus, the neurovascular coupling between the neuronal and the hemodynamic responses, and the characteristics of the hemodynamic response itself [49]. Therefore, the hemodynamic nonlinearity could have resulted from any one or a combination of these processes, rather than from the vascular effect alone [31]. In this study, the consistency of the logarithmic changes in the VEP and the corresponding hemodynamic response to the graded contrast levels seems to imply that the hemodynamic nonlinearity resulted from neuronal nonlinearity. This result is in agreement with previous works [31, 33].

The response on the right hemisphere for the left hemifield stimulation was found to be stronger, but not statistically significantly stronger, than the response on the left hemisphere for the right hemifield visual stimulation. This is likely due to the superiority of the right hemisphere in spatial attention [14]. The asymmetry between the left and the right visual field has been attributed to stimulus-driven attentional bias or to the functional specialization of the corresponding hemispheres [50]. Ocular dominance has been reported as being related to this asymmetry [51, 52].

4.3 Correlation between the neuronal and hemodynamic responses

VEPs are the synchronized potentials from millions of neurons in response to a given stimulus, but they can be cancelled out by opposing current sources under certain conditions [40, 53]. Hemodynamic responses are induced by integrating the synaptic inputs of the entire neuronal population of a region of interest regardless of the orientation or the excitatory/inhibitory role of those neurons [40]. A hemodynamic response can be regarded as a low-pass filter, which integrates both the temporal and the spatial information of the underlying neuronal activity [3]. A strong stimulus, such as our 100% contrast level, should be expected to take less time for the neurons to become synchronized and should produce a higher amplitude of response in the visual cortex.

This study also explored the relationship between neuronal and hemodynamic responses to see what they could reveal at the macro-scale of brain regions. The correlation analysis of the neuronal and hemodynamic response was analyzed over the contralateral hemisphere, instead of the hemisphere that was ipsilateral to the stimulated hemifield. There are three reasons. First, the fNIRS results presented significant contralateral activation. Second, although the VEP responses recorded over the contralateral hemisphere had smaller amplitudes than those over the ipsilateral hemisphere, the trends toward the contrast level showed a form that was similar to that of the ipsilateral despite having the opposite sign. Finally, the latency of the contralateral hemisphere was significantly shorter than that of the ipsilateral hemisphere. Our results also suggest that the polarity inversion as well as the latency advantage over the contralateral hemisphere cause the amplitude of the VEP over the contralateral hemisphere to be smaller than that over the ipsilateral hemisphere.

With an increase in the contrast level, both the amplitude of the neuronal activity and the hemodynamic response monotonically increased, although the slopes were different. This suggests that the neurovascular coupling function can be described as a linear function. Our hemifield neurovascular coupling relationships are in agreement with previous publications based on full-field visual stimulation, although the different studies reported different slopes [30, 33, 34, 44]. In comparison, the correlations between the neuronal activity and the hemodynamic response show similar forms for both the left and right hemifield visual stimulations. The generality of these results is uncertain, and our current finding cannot fully conclude that such a linear function will exist for all experimental paradigms. Nevertheless, we suggest that a linear relationship is the primary way to explain the relationship between the hemodynamics and the electro-dynamics from neurons, especially at the macro-scale.

Our previous paper demonstrated the advantages of using latency to correlate with the hemodynamic responses [33]. This current study provided similar results. The latency of the VEP decreased when the stimulus contrast increased, implying a negative correlation between the VEP latency and the hemodynamic responses. In comparison, the correlation forms were similar for both hemifield stimulations even though the slopes were different. A possible physiological mechanism was mentioned in our previous work [33]. Briefly, a decreased response latency of the VEP could be related to a greater amplitude of the hemodynamic response because the neurons become synchronized more rapidly in response to a strong stimulus, resulting in a shorter response latency. Moreover, it is well established that neuronal activity consumes energy. More energy is required for neurons to generate action potentials in a shorter time leading to stronger hemodynamic responses. This mechanism does not conflict with the strength mechanism but rather illustrates a new perspective that shows that the time relationship can provide supplementary information about neurovascular coupling.

4.4 Limitations

One of the limitations of this study is that only three contrast levels were used. Future studies using more contrast levels would be helpful for addressing the relationship between the neural and hemodynamic responses in greater detail. A second limitation is that the EEG and fNIRS measurements were recorded separately. Therefore, the experimental conditions, arousal state, and attention level of the participants could have differed during the electrophysiological recordings and the hemodynamic measurements. In further exploratory studies, simultaneous recording might be helpful for overcoming this limitation. Additionally, whether the hemispheric differences are influenced, and to what extent they are influenced, by factors such as ocular dominance, handedness, and attentional bias towards one visual field need further investigation.

Although there are certain limitations, this study should be a meaningful step in the exploration of the hemispheric differences in neuronal activity, hemodynamic response, and neurovascular coupling. To make the hemispheric differences clearer and more applicable clinically, we are not going to stop at this stage, but will adopt more imaging modalities and advanced technologies for measuring the functional signals from both the neurons and the microcircuits. Moreover, advanced data processing methods will be utilized in further studies to investigate the hemispheric differences in brain activation.

Funding

National Key Scientific Instrument and Equipment Development Project of China (2012YQ120046); National Natural Science Foundation of China (Grant No. 31571003 and No. 81501550).

Acknowledgments

We appreciate the assistance that Rhoda and Edmund Perozzi, PhDs, provided in proofreading and critiquing this work.

References and links

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5. H. Laufs, A. Kleinschmidt, A. Beyerle, E. Eger, A. Salek-Haddadi, C. Preibisch, and K. Krakow, “EEG-correlated fMRI of human alpha activity,” Neuroimage 19(4), 1463–1476 (2003). [CrossRef]   [PubMed]  

6. R. J. Huster, S. Debener, T. Eichele, and C. S. Herrmann, “Methods for simultaneous EEG-fMRI: an introductory review,” J. Neurosci. 32(18), 6053–6060 (2012). [CrossRef]   [PubMed]  

7. C. Bénar, Y. Aghakhani, Y. Wang, A. Izenberg, A. Al-Asmi, F. Dubeau, and J. Gotman, “Quality of EEG in simultaneous EEG-fMRI for epilepsy,” Clin. Neurophysiol. 114(3), 569–580 (2003). [CrossRef]   [PubMed]  

8. F. F. Jöbsis, “Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters,” Science 198(4323), 1264–1267 (1977). [CrossRef]   [PubMed]  

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References

  • View by:

  1. C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
    [Crossref] [PubMed]
  2. S. Ogawa, T. M. Lee, A. R. Kay, and D. W. Tank, “Brain magnetic resonance imaging with contrast dependent on blood oxygenation,” Proc. Natl. Acad. Sci. U.S.A. 87(24), 9868–9872 (1990).
    [Crossref] [PubMed]
  3. N. K. Logothetis and J. Pfeuffer, “On the nature of the BOLD fMRI contrast mechanism,” Magn. Reson. Imaging 22(10), 1517–1531 (2004).
    [Crossref] [PubMed]
  4. B. Yeşilyurt, K. Whittingstall, K. Uğurbil, N. K. Logothetis, and K. Uludağ, “Relationship of the BOLD signal with VEP for ultrashort duration visual stimuli (0.1 to 5 ms) in humans,” J. Cereb. Blood Flow Metab. 30(2), 449–458 (2010).
    [Crossref] [PubMed]
  5. H. Laufs, A. Kleinschmidt, A. Beyerle, E. Eger, A. Salek-Haddadi, C. Preibisch, and K. Krakow, “EEG-correlated fMRI of human alpha activity,” Neuroimage 19(4), 1463–1476 (2003).
    [Crossref] [PubMed]
  6. R. J. Huster, S. Debener, T. Eichele, and C. S. Herrmann, “Methods for simultaneous EEG-fMRI: an introductory review,” J. Neurosci. 32(18), 6053–6060 (2012).
    [Crossref] [PubMed]
  7. C. Bénar, Y. Aghakhani, Y. Wang, A. Izenberg, A. Al-Asmi, F. Dubeau, and J. Gotman, “Quality of EEG in simultaneous EEG-fMRI for epilepsy,” Clin. Neurophysiol. 114(3), 569–580 (2003).
    [Crossref] [PubMed]
  8. F. F. Jöbsis, “Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters,” Science 198(4323), 1264–1267 (1977).
    [Crossref] [PubMed]
  9. M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
    [Crossref] [PubMed]
  10. B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
    [Crossref] [PubMed]
  11. F. Homae, “A brain of two halves: insights into interhemispheric organization provided by near-infrared spectroscopy,” Neuroimage 85(Pt 1), 354–362 (2014).
    [Crossref] [PubMed]
  12. K. Hugdahl, “Hemispheric asymmetry: contributions from brain imaging,” Wiley Interdiscip. Rev. Cogn. Sci. 2(5), 461–478 (2011).
    [Crossref] [PubMed]
  13. B. Lee, Y. Kaneoke, R. Kakigi, and Y. Sakai, “Human brain response to visual stimulus between lower/upper visual fields and cerebral hemispheres,” Int. J. Psychophysiol. 74(2), 81–87 (2009).
    [Crossref] [PubMed]
  14. A. K. Karim and H. Kojima, “The what and why of perceptual asymmetries in the visual domain,” Adv. Cogn. Psychol. 6(-1), 103–115 (2010).
    [Crossref] [PubMed]
  15. B. A. Wandell, S. O. Dumoulin, and A. A. Brewer, “Visual field maps in human cortex,” Neuron 56(2), 366–383 (2007).
    [Crossref] [PubMed]
  16. D. A. Jeffreys and J. G. Axford, “Source locations of pattern-specific components of human visual evoked potentials. I. Component of striate cortical origin,” Exp. Brain Res. 16(1), 1–21 (1972).
    [PubMed]
  17. W. N. Colier, V. Quaresima, R. Wenzel, M. C. van der Sluijs, B. Oeseburg, M. Ferrari, and A. Villringer, “Simultaneous near-infrared spectroscopy monitoring of left and right occipital areas reveals contra-lateral hemodynamic changes upon hemi-field paradigm,” Vision Res. 41(1), 97–102 (2001).
    [Crossref] [PubMed]
  18. D. Bastien, A. Gallagher, J. Tremblay, P. Vannasing, M. Thériault, M. Lassonde, and F. Lepore, “Specific functional asymmetries of the human visual cortex revealed by functional near-infrared spectroscopy,” Brain Res. 1431, 62–68 (2012).
    [Crossref] [PubMed]
  19. B. R. White and J. P. Culver, “Quantitative evaluation of high-density diffuse optical tomography: in vivo resolution and mapping performance,” J. Biomed. Opt. 15(2), 026006 (2010).
    [Crossref] [PubMed]
  20. A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. Chen, Y. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping,” Neuroimage 61(4), 1120–1128 (2012).
    [Crossref] [PubMed]
  21. F. Di Russo, S. Pitzalis, G. Spitoni, T. Aprile, F. Patria, D. Spinelli, and S. A. Hillyard, “Identification of the neural sources of the pattern-reversal VEP,” Neuroimage 24(3), 874–886 (2005).
    [Crossref] [PubMed]
  22. F. Di Russo, A. Martínez, M. I. Sereno, S. Pitzalis, and S. A. Hillyard, “Cortical sources of the early components of the visual evoked potential,” Hum. Brain Mapp. 15(2), 95–111 (2002).
    [Crossref] [PubMed]
  23. N. Novitskiy, J. R. Ramautar, K. Vanderperren, M. De Vos, M. Mennes, B. Mijovic, B. Vanrumste, P. Stiers, B. Van den Bergh, L. Lagae, S. Sunaert, S. Van Huffel, and J. Wagemans, “The BOLD correlates of the visual P1 and N1 in single-trial analysis of simultaneous EEG-fMRI recordings during a spatial detection task,” Neuroimage 54(2), 824–835 (2011).
    [Crossref] [PubMed]
  24. G. Barett, L. Blumhardt, A. M. Halliday, E. Halliday, and A. Kriss, “A paradox in the lateralisation of the visual evoked response,” Nature 261(5557), 253–255 (1976).
    [Crossref] [PubMed]
  25. A. Nakamura, R. Kakigi, M. Hoshiyama, S. Koyama, Y. Kitamura, and M. Shimojo, “Visual evoked cortical magnetic fields to pattern reversal stimulation,” Brain Res. Cogn. Brain Res. 6(1), 9–22 (1997).
    [Crossref] [PubMed]
  26. V. L. Towle, M. Brigell, and J. P. Spire, “Hemi-field pattern visual evoked potentials: a comparison of display and analysis techniques,” Brain Topogr. 1(4), 263–270 (1989).
    [Crossref] [PubMed]
  27. J. Arruga, S. E. Feldon, W. F. Hoyt, and M. J. Aminoff, “Monocularly and binocularly evoked visual responses to patterned half-field stimulation,” J. Neurol. Sci. 46(3), 281–290 (1980).
    [Crossref] [PubMed]
  28. D. S. Reich, F. Mechler, and J. D. Victor, “Temporal coding of contrast in primary visual cortex: when, what, and why,” J. Neurophysiol. 85(3), 1039–1050 (2001).
    [PubMed]
  29. J. Schadow, D. Lenz, S. Thaerig, N. A. Busch, I. Fründ, J. W. Rieger, and C. S. Herrmann, “Stimulus intensity affects early sensory processing: visual contrast modulates evoked gamma-band activity in human EEG,” Int. J. Psychophysiol. 66(1), 28–36 (2007).
    [Crossref] [PubMed]
  30. L. Rovati, G. Salvatori, L. Bulf, and S. Fonda, “Optical and electrical recording of neural activity evoked by graded contrast visual stimulus,” Biomed. Eng. Online 6(1), 28 (2007).
    [Crossref] [PubMed]
  31. X. Wan, J. Riera, K. Iwata, M. Takahashi, T. Wakabayashi, and R. Kawashima, “The neural basis of the hemodynamic response nonlinearity in human primary visual cortex: Implications for neurovascular coupling mechanism,” Neuroimage 32(2), 616–625 (2006).
    [Crossref] [PubMed]
  32. B. Sun, L. Zhang, H. Gong, J. Sun, and Q. Luo, “Detection of optical neuronal signals in the visual cortex using continuous wave near-infrared spectroscopy,” Neuroimage 87, 190–198 (2014).
    [Crossref] [PubMed]
  33. J. Si, X. Zhang, Y. Li, Y. Zhang, N. Zuo, and T. Jiang, “Correlation between electrical and hemodynamic responses during visual stimulation with graded contrasts,” J. Biomed. Opt. 21(9), 091315 (2016).
    [Crossref] [PubMed]
  34. H. Obrig, H. Israel, M. Kohl-Bareis, K. Uludag, R. Wenzel, B. Müller, G. Arnold, and A. Villringer, “Habituation of the visually evoked potential and its vascular response: implications for neurovascular coupling in the healthy adult,” Neuroimage 17(1), 1–18 (2002).
    [Crossref] [PubMed]
  35. J. V. Odom, M. Bach, M. Brigell, G. E. Holder, D. L. McCulloch, A. P. Tormene, and Vaegan, “ISCEV standard for clinical visual evoked potentials (2009 update),” Doc. Ophthalmol. 120(1), 111–119 (2010).
    [Crossref] [PubMed]
  36. A. Bozkurt, A. Rosen, H. Rosen, and B. Onaral, “A portable near infrared spectroscopy system for bedside monitoring of newborn brain,” Biomed. Eng. Online 4(1), 29 (2005).
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  37. A. Duncan, J. H. Meek, M. Clemence, C. E. Elwell, P. Fallon, L. Tyszczuk, M. Cope, and D. T. Delpy, “Measurement of cranial optical path length as a function of age using phase resolved near infrared spectroscopy,” Pediatr. Res. 39(5), 889–894 (1996).
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  38. S. K. Piper, A. Krueger, S. P. Koch, J. Mehnert, C. Habermehl, J. Steinbrink, H. Obrig, and C. H. Schmitz, “A wearable multi-channel fNIRS system for brain imaging in freely moving subjects,” Neuroimage 85(Pt 1), 64–71 (2014).
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  39. Diopsys, “The ABCs of VEP office based visual evoked potential: descriptions, methods and applications,” 2nd Ed (2013).
  40. S. D. Mayhew, B. J. Macintosh, S. G. Dirckx, G. D. Iannetti, and R. G. Wise, “Coupling of simultaneously acquired electrophysiological and haemodynamic responses during visual stimulation,” Magn. Reson. Imaging 28(8), 1066–1077 (2010).
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  41. D. J. Hagler., “Visual field asymmetries in visual evoked responses,” J. Vis. 14(14), 13 (2014).
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  42. D. Fuglø, H. Pedersen, E. Rostrup, A. E. Hansen, and H. B. Larsson, “Correlation between single-trial visual evoked potentials and the blood oxygenation level dependent response in simultaneously recorded electroencephalography-functional magnetic resonance imaging,” Magn. Reson. Med. 68(1), 252–260 (2012).
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  43. K. Kunita and K. Fujiwara, “Changes in the P100 latency of the visual evoked potential and the saccadic reaction time during isometric contraction of the shoulder girdle elevators,” Eur. J. Appl. Physiol. 92(4-5), 421–424 (2004).
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  44. S. P. Koch, P. Werner, J. Steinbrink, P. Fries, and H. Obrig, “Stimulus-induced and state-dependent sustained gamma activity is tightly coupled to the hemodynamic response in humans,” J. Neurosci. 29(44), 13962–13970 (2009).
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  45. K. Seki, N. Nakasato, S. Fujita, K. Hatanaka, T. Kawamura, A. Kanno, and T. Yoshimoto, “Neuromagnetic evidence that the P100 component of the pattern reversal visual evoked response originates in the bottom of the calcarine fissure,” Electroencephalogr. Clin. Neurophysiol. 100(5), 436–442 (1996).
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  47. C. R. Lines, M. D. Rugg, and A. D. Milner, “The effect of stimulus intensity on visual evoked potential estimates of interhemispheric transmission time,” Exp. Brain Res. 57(1), 89–98 (1984).
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  48. P. E. Moes, W. S. Brown, and M. T. Minnema, “Individual differences in interhemispheric transfer time (IHTT) as measured by event related potentials,” Neuropsychologia 45(11), 2626–2630 (2007).
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  49. O. J. Arthurs and S. Boniface, “How well do we understand the neural origins of the fMRI BOLD signal?” Trends Neurosci. 25(1), 27–31 (2002).
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  50. A. Hougaard, B. H. Jensen, F. M. Amin, E. Rostrup, M. B. Hoffmann, and M. Ashina, “Cerebral Asymmetry of fMRI-BOLD Responses to Visual Stimulation,” PLoS One 10(5), e0126477 (2015).
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  51. J. D. Mendola and I. P. Conner, “Eye dominance predicts fMRI signals in human retinotopic cortex,” Neurosci. Lett. 414(1), 30–34 (2007).
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  52. A. Taghavy and C. F. Kügler, “Pattern reversal visual evoked potentials (white-black- and colour-black-PVEPs) in the study of eye dominance,” Eur. Arch. Psychiatry Neurol. Sci. 236(6), 329–332 (1987).
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2016 (1)

J. Si, X. Zhang, Y. Li, Y. Zhang, N. Zuo, and T. Jiang, “Correlation between electrical and hemodynamic responses during visual stimulation with graded contrasts,” J. Biomed. Opt. 21(9), 091315 (2016).
[Crossref] [PubMed]

2015 (1)

A. Hougaard, B. H. Jensen, F. M. Amin, E. Rostrup, M. B. Hoffmann, and M. Ashina, “Cerebral Asymmetry of fMRI-BOLD Responses to Visual Stimulation,” PLoS One 10(5), e0126477 (2015).
[Crossref] [PubMed]

2014 (4)

D. J. Hagler., “Visual field asymmetries in visual evoked responses,” J. Vis. 14(14), 13 (2014).
[Crossref] [PubMed]

B. Sun, L. Zhang, H. Gong, J. Sun, and Q. Luo, “Detection of optical neuronal signals in the visual cortex using continuous wave near-infrared spectroscopy,” Neuroimage 87, 190–198 (2014).
[Crossref] [PubMed]

S. K. Piper, A. Krueger, S. P. Koch, J. Mehnert, C. Habermehl, J. Steinbrink, H. Obrig, and C. H. Schmitz, “A wearable multi-channel fNIRS system for brain imaging in freely moving subjects,” Neuroimage 85(Pt 1), 64–71 (2014).
[Crossref] [PubMed]

F. Homae, “A brain of two halves: insights into interhemispheric organization provided by near-infrared spectroscopy,” Neuroimage 85(Pt 1), 354–362 (2014).
[Crossref] [PubMed]

2012 (5)

D. Bastien, A. Gallagher, J. Tremblay, P. Vannasing, M. Thériault, M. Lassonde, and F. Lepore, “Specific functional asymmetries of the human visual cortex revealed by functional near-infrared spectroscopy,” Brain Res. 1431, 62–68 (2012).
[Crossref] [PubMed]

R. J. Huster, S. Debener, T. Eichele, and C. S. Herrmann, “Methods for simultaneous EEG-fMRI: an introductory review,” J. Neurosci. 32(18), 6053–6060 (2012).
[Crossref] [PubMed]

M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
[Crossref] [PubMed]

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. Chen, Y. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping,” Neuroimage 61(4), 1120–1128 (2012).
[Crossref] [PubMed]

D. Fuglø, H. Pedersen, E. Rostrup, A. E. Hansen, and H. B. Larsson, “Correlation between single-trial visual evoked potentials and the blood oxygenation level dependent response in simultaneously recorded electroencephalography-functional magnetic resonance imaging,” Magn. Reson. Med. 68(1), 252–260 (2012).
[Crossref] [PubMed]

2011 (2)

N. Novitskiy, J. R. Ramautar, K. Vanderperren, M. De Vos, M. Mennes, B. Mijovic, B. Vanrumste, P. Stiers, B. Van den Bergh, L. Lagae, S. Sunaert, S. Van Huffel, and J. Wagemans, “The BOLD correlates of the visual P1 and N1 in single-trial analysis of simultaneous EEG-fMRI recordings during a spatial detection task,” Neuroimage 54(2), 824–835 (2011).
[Crossref] [PubMed]

K. Hugdahl, “Hemispheric asymmetry: contributions from brain imaging,” Wiley Interdiscip. Rev. Cogn. Sci. 2(5), 461–478 (2011).
[Crossref] [PubMed]

2010 (5)

A. K. Karim and H. Kojima, “The what and why of perceptual asymmetries in the visual domain,” Adv. Cogn. Psychol. 6(-1), 103–115 (2010).
[Crossref] [PubMed]

B. R. White and J. P. Culver, “Quantitative evaluation of high-density diffuse optical tomography: in vivo resolution and mapping performance,” J. Biomed. Opt. 15(2), 026006 (2010).
[Crossref] [PubMed]

B. Yeşilyurt, K. Whittingstall, K. Uğurbil, N. K. Logothetis, and K. Uludağ, “Relationship of the BOLD signal with VEP for ultrashort duration visual stimuli (0.1 to 5 ms) in humans,” J. Cereb. Blood Flow Metab. 30(2), 449–458 (2010).
[Crossref] [PubMed]

S. D. Mayhew, B. J. Macintosh, S. G. Dirckx, G. D. Iannetti, and R. G. Wise, “Coupling of simultaneously acquired electrophysiological and haemodynamic responses during visual stimulation,” Magn. Reson. Imaging 28(8), 1066–1077 (2010).
[Crossref] [PubMed]

J. V. Odom, M. Bach, M. Brigell, G. E. Holder, D. L. McCulloch, A. P. Tormene, and Vaegan, “ISCEV standard for clinical visual evoked potentials (2009 update),” Doc. Ophthalmol. 120(1), 111–119 (2010).
[Crossref] [PubMed]

2009 (2)

B. Lee, Y. Kaneoke, R. Kakigi, and Y. Sakai, “Human brain response to visual stimulus between lower/upper visual fields and cerebral hemispheres,” Int. J. Psychophysiol. 74(2), 81–87 (2009).
[Crossref] [PubMed]

S. P. Koch, P. Werner, J. Steinbrink, P. Fries, and H. Obrig, “Stimulus-induced and state-dependent sustained gamma activity is tightly coupled to the hemodynamic response in humans,” J. Neurosci. 29(44), 13962–13970 (2009).
[Crossref] [PubMed]

2007 (6)

P. E. Moes, W. S. Brown, and M. T. Minnema, “Individual differences in interhemispheric transfer time (IHTT) as measured by event related potentials,” Neuropsychologia 45(11), 2626–2630 (2007).
[Crossref] [PubMed]

J. D. Mendola and I. P. Conner, “Eye dominance predicts fMRI signals in human retinotopic cortex,” Neurosci. Lett. 414(1), 30–34 (2007).
[Crossref] [PubMed]

B. A. Wandell, S. O. Dumoulin, and A. A. Brewer, “Visual field maps in human cortex,” Neuron 56(2), 366–383 (2007).
[Crossref] [PubMed]

B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
[Crossref] [PubMed]

J. Schadow, D. Lenz, S. Thaerig, N. A. Busch, I. Fründ, J. W. Rieger, and C. S. Herrmann, “Stimulus intensity affects early sensory processing: visual contrast modulates evoked gamma-band activity in human EEG,” Int. J. Psychophysiol. 66(1), 28–36 (2007).
[Crossref] [PubMed]

L. Rovati, G. Salvatori, L. Bulf, and S. Fonda, “Optical and electrical recording of neural activity evoked by graded contrast visual stimulus,” Biomed. Eng. Online 6(1), 28 (2007).
[Crossref] [PubMed]

2006 (3)

X. Wan, J. Riera, K. Iwata, M. Takahashi, T. Wakabayashi, and R. Kawashima, “The neural basis of the hemodynamic response nonlinearity in human primary visual cortex: Implications for neurovascular coupling mechanism,” Neuroimage 32(2), 616–625 (2006).
[Crossref] [PubMed]

Y. Kaneoke, “Magnetoencephalography: in search of neural processes for visual motion information,” Prog. Neurobiol. 80(5), 219–240 (2006).
[Crossref] [PubMed]

American Clinical Neurophysiology Society, “Guideline 9B: Guidelines on Visual Evoked Potentials,” J. Clin. Neurophysiol. 23(2), 138–156 (2006).
[Crossref] [PubMed]

2005 (2)

F. Di Russo, S. Pitzalis, G. Spitoni, T. Aprile, F. Patria, D. Spinelli, and S. A. Hillyard, “Identification of the neural sources of the pattern-reversal VEP,” Neuroimage 24(3), 874–886 (2005).
[Crossref] [PubMed]

A. Bozkurt, A. Rosen, H. Rosen, and B. Onaral, “A portable near infrared spectroscopy system for bedside monitoring of newborn brain,” Biomed. Eng. Online 4(1), 29 (2005).
[Crossref] [PubMed]

2004 (3)

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

N. K. Logothetis and J. Pfeuffer, “On the nature of the BOLD fMRI contrast mechanism,” Magn. Reson. Imaging 22(10), 1517–1531 (2004).
[Crossref] [PubMed]

K. Kunita and K. Fujiwara, “Changes in the P100 latency of the visual evoked potential and the saccadic reaction time during isometric contraction of the shoulder girdle elevators,” Eur. J. Appl. Physiol. 92(4-5), 421–424 (2004).
[Crossref] [PubMed]

2003 (2)

H. Laufs, A. Kleinschmidt, A. Beyerle, E. Eger, A. Salek-Haddadi, C. Preibisch, and K. Krakow, “EEG-correlated fMRI of human alpha activity,” Neuroimage 19(4), 1463–1476 (2003).
[Crossref] [PubMed]

C. Bénar, Y. Aghakhani, Y. Wang, A. Izenberg, A. Al-Asmi, F. Dubeau, and J. Gotman, “Quality of EEG in simultaneous EEG-fMRI for epilepsy,” Clin. Neurophysiol. 114(3), 569–580 (2003).
[Crossref] [PubMed]

2002 (3)

H. Obrig, H. Israel, M. Kohl-Bareis, K. Uludag, R. Wenzel, B. Müller, G. Arnold, and A. Villringer, “Habituation of the visually evoked potential and its vascular response: implications for neurovascular coupling in the healthy adult,” Neuroimage 17(1), 1–18 (2002).
[Crossref] [PubMed]

F. Di Russo, A. Martínez, M. I. Sereno, S. Pitzalis, and S. A. Hillyard, “Cortical sources of the early components of the visual evoked potential,” Hum. Brain Mapp. 15(2), 95–111 (2002).
[Crossref] [PubMed]

O. J. Arthurs and S. Boniface, “How well do we understand the neural origins of the fMRI BOLD signal?” Trends Neurosci. 25(1), 27–31 (2002).
[Crossref] [PubMed]

2001 (2)

D. S. Reich, F. Mechler, and J. D. Victor, “Temporal coding of contrast in primary visual cortex: when, what, and why,” J. Neurophysiol. 85(3), 1039–1050 (2001).
[PubMed]

W. N. Colier, V. Quaresima, R. Wenzel, M. C. van der Sluijs, B. Oeseburg, M. Ferrari, and A. Villringer, “Simultaneous near-infrared spectroscopy monitoring of left and right occipital areas reveals contra-lateral hemodynamic changes upon hemi-field paradigm,” Vision Res. 41(1), 97–102 (2001).
[Crossref] [PubMed]

1997 (1)

A. Nakamura, R. Kakigi, M. Hoshiyama, S. Koyama, Y. Kitamura, and M. Shimojo, “Visual evoked cortical magnetic fields to pattern reversal stimulation,” Brain Res. Cogn. Brain Res. 6(1), 9–22 (1997).
[Crossref] [PubMed]

1996 (2)

A. Duncan, J. H. Meek, M. Clemence, C. E. Elwell, P. Fallon, L. Tyszczuk, M. Cope, and D. T. Delpy, “Measurement of cranial optical path length as a function of age using phase resolved near infrared spectroscopy,” Pediatr. Res. 39(5), 889–894 (1996).
[Crossref] [PubMed]

K. Seki, N. Nakasato, S. Fujita, K. Hatanaka, T. Kawamura, A. Kanno, and T. Yoshimoto, “Neuromagnetic evidence that the P100 component of the pattern reversal visual evoked response originates in the bottom of the calcarine fissure,” Electroencephalogr. Clin. Neurophysiol. 100(5), 436–442 (1996).
[Crossref] [PubMed]

1990 (1)

S. Ogawa, T. M. Lee, A. R. Kay, and D. W. Tank, “Brain magnetic resonance imaging with contrast dependent on blood oxygenation,” Proc. Natl. Acad. Sci. U.S.A. 87(24), 9868–9872 (1990).
[Crossref] [PubMed]

1989 (1)

V. L. Towle, M. Brigell, and J. P. Spire, “Hemi-field pattern visual evoked potentials: a comparison of display and analysis techniques,” Brain Topogr. 1(4), 263–270 (1989).
[Crossref] [PubMed]

1987 (1)

A. Taghavy and C. F. Kügler, “Pattern reversal visual evoked potentials (white-black- and colour-black-PVEPs) in the study of eye dominance,” Eur. Arch. Psychiatry Neurol. Sci. 236(6), 329–332 (1987).
[Crossref] [PubMed]

1984 (1)

C. R. Lines, M. D. Rugg, and A. D. Milner, “The effect of stimulus intensity on visual evoked potential estimates of interhemispheric transmission time,” Exp. Brain Res. 57(1), 89–98 (1984).
[Crossref] [PubMed]

1980 (1)

J. Arruga, S. E. Feldon, W. F. Hoyt, and M. J. Aminoff, “Monocularly and binocularly evoked visual responses to patterned half-field stimulation,” J. Neurol. Sci. 46(3), 281–290 (1980).
[Crossref] [PubMed]

1977 (1)

F. F. Jöbsis, “Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters,” Science 198(4323), 1264–1267 (1977).
[Crossref] [PubMed]

1976 (1)

G. Barett, L. Blumhardt, A. M. Halliday, E. Halliday, and A. Kriss, “A paradox in the lateralisation of the visual evoked response,” Nature 261(5557), 253–255 (1976).
[Crossref] [PubMed]

1972 (1)

D. A. Jeffreys and J. G. Axford, “Source locations of pattern-specific components of human visual evoked potentials. I. Component of striate cortical origin,” Exp. Brain Res. 16(1), 1–21 (1972).
[PubMed]

Aghakhani, Y.

C. Bénar, Y. Aghakhani, Y. Wang, A. Izenberg, A. Al-Asmi, F. Dubeau, and J. Gotman, “Quality of EEG in simultaneous EEG-fMRI for epilepsy,” Clin. Neurophysiol. 114(3), 569–580 (2003).
[Crossref] [PubMed]

Al-Asmi, A.

C. Bénar, Y. Aghakhani, Y. Wang, A. Izenberg, A. Al-Asmi, F. Dubeau, and J. Gotman, “Quality of EEG in simultaneous EEG-fMRI for epilepsy,” Clin. Neurophysiol. 114(3), 569–580 (2003).
[Crossref] [PubMed]

Amin, F. M.

A. Hougaard, B. H. Jensen, F. M. Amin, E. Rostrup, M. B. Hoffmann, and M. Ashina, “Cerebral Asymmetry of fMRI-BOLD Responses to Visual Stimulation,” PLoS One 10(5), e0126477 (2015).
[Crossref] [PubMed]

Aminoff, M. J.

J. Arruga, S. E. Feldon, W. F. Hoyt, and M. J. Aminoff, “Monocularly and binocularly evoked visual responses to patterned half-field stimulation,” J. Neurol. Sci. 46(3), 281–290 (1980).
[Crossref] [PubMed]

Aprile, T.

F. Di Russo, S. Pitzalis, G. Spitoni, T. Aprile, F. Patria, D. Spinelli, and S. A. Hillyard, “Identification of the neural sources of the pattern-reversal VEP,” Neuroimage 24(3), 874–886 (2005).
[Crossref] [PubMed]

Arnold, G.

H. Obrig, H. Israel, M. Kohl-Bareis, K. Uludag, R. Wenzel, B. Müller, G. Arnold, and A. Villringer, “Habituation of the visually evoked potential and its vascular response: implications for neurovascular coupling in the healthy adult,” Neuroimage 17(1), 1–18 (2002).
[Crossref] [PubMed]

Arruga, J.

J. Arruga, S. E. Feldon, W. F. Hoyt, and M. J. Aminoff, “Monocularly and binocularly evoked visual responses to patterned half-field stimulation,” J. Neurol. Sci. 46(3), 281–290 (1980).
[Crossref] [PubMed]

Arthurs, O. J.

O. J. Arthurs and S. Boniface, “How well do we understand the neural origins of the fMRI BOLD signal?” Trends Neurosci. 25(1), 27–31 (2002).
[Crossref] [PubMed]

Ashina, M.

A. Hougaard, B. H. Jensen, F. M. Amin, E. Rostrup, M. B. Hoffmann, and M. Ashina, “Cerebral Asymmetry of fMRI-BOLD Responses to Visual Stimulation,” PLoS One 10(5), e0126477 (2015).
[Crossref] [PubMed]

Axford, J. G.

D. A. Jeffreys and J. G. Axford, “Source locations of pattern-specific components of human visual evoked potentials. I. Component of striate cortical origin,” Exp. Brain Res. 16(1), 1–21 (1972).
[PubMed]

Bach, M.

J. V. Odom, M. Bach, M. Brigell, G. E. Holder, D. L. McCulloch, A. P. Tormene, and Vaegan, “ISCEV standard for clinical visual evoked potentials (2009 update),” Doc. Ophthalmol. 120(1), 111–119 (2010).
[Crossref] [PubMed]

Barett, G.

G. Barett, L. Blumhardt, A. M. Halliday, E. Halliday, and A. Kriss, “A paradox in the lateralisation of the visual evoked response,” Nature 261(5557), 253–255 (1976).
[Crossref] [PubMed]

Bastien, D.

D. Bastien, A. Gallagher, J. Tremblay, P. Vannasing, M. Thériault, M. Lassonde, and F. Lepore, “Specific functional asymmetries of the human visual cortex revealed by functional near-infrared spectroscopy,” Brain Res. 1431, 62–68 (2012).
[Crossref] [PubMed]

Bénar, C.

C. Bénar, Y. Aghakhani, Y. Wang, A. Izenberg, A. Al-Asmi, F. Dubeau, and J. Gotman, “Quality of EEG in simultaneous EEG-fMRI for epilepsy,” Clin. Neurophysiol. 114(3), 569–580 (2003).
[Crossref] [PubMed]

Beyerle, A.

H. Laufs, A. Kleinschmidt, A. Beyerle, E. Eger, A. Salek-Haddadi, C. Preibisch, and K. Krakow, “EEG-correlated fMRI of human alpha activity,” Neuroimage 19(4), 1463–1476 (2003).
[Crossref] [PubMed]

Blumhardt, L.

G. Barett, L. Blumhardt, A. M. Halliday, E. Halliday, and A. Kriss, “A paradox in the lateralisation of the visual evoked response,” Nature 261(5557), 253–255 (1976).
[Crossref] [PubMed]

Boniface, S.

O. J. Arthurs and S. Boniface, “How well do we understand the neural origins of the fMRI BOLD signal?” Trends Neurosci. 25(1), 27–31 (2002).
[Crossref] [PubMed]

Bozkurt, A.

A. Bozkurt, A. Rosen, H. Rosen, and B. Onaral, “A portable near infrared spectroscopy system for bedside monitoring of newborn brain,” Biomed. Eng. Online 4(1), 29 (2005).
[Crossref] [PubMed]

Brewer, A. A.

B. A. Wandell, S. O. Dumoulin, and A. A. Brewer, “Visual field maps in human cortex,” Neuron 56(2), 366–383 (2007).
[Crossref] [PubMed]

Brigell, M.

J. V. Odom, M. Bach, M. Brigell, G. E. Holder, D. L. McCulloch, A. P. Tormene, and Vaegan, “ISCEV standard for clinical visual evoked potentials (2009 update),” Doc. Ophthalmol. 120(1), 111–119 (2010).
[Crossref] [PubMed]

V. L. Towle, M. Brigell, and J. P. Spire, “Hemi-field pattern visual evoked potentials: a comparison of display and analysis techniques,” Brain Topogr. 1(4), 263–270 (1989).
[Crossref] [PubMed]

Brown, W. S.

P. E. Moes, W. S. Brown, and M. T. Minnema, “Individual differences in interhemispheric transfer time (IHTT) as measured by event related potentials,” Neuropsychologia 45(11), 2626–2630 (2007).
[Crossref] [PubMed]

Bulf, L.

L. Rovati, G. Salvatori, L. Bulf, and S. Fonda, “Optical and electrical recording of neural activity evoked by graded contrast visual stimulus,” Biomed. Eng. Online 6(1), 28 (2007).
[Crossref] [PubMed]

Busch, N. A.

J. Schadow, D. Lenz, S. Thaerig, N. A. Busch, I. Fründ, J. W. Rieger, and C. S. Herrmann, “Stimulus intensity affects early sensory processing: visual contrast modulates evoked gamma-band activity in human EEG,” Int. J. Psychophysiol. 66(1), 28–36 (2007).
[Crossref] [PubMed]

Chen, C.

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. Chen, Y. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping,” Neuroimage 61(4), 1120–1128 (2012).
[Crossref] [PubMed]

Clemence, M.

A. Duncan, J. H. Meek, M. Clemence, C. E. Elwell, P. Fallon, L. Tyszczuk, M. Cope, and D. T. Delpy, “Measurement of cranial optical path length as a function of age using phase resolved near infrared spectroscopy,” Pediatr. Res. 39(5), 889–894 (1996).
[Crossref] [PubMed]

Colier, W. N.

W. N. Colier, V. Quaresima, R. Wenzel, M. C. van der Sluijs, B. Oeseburg, M. Ferrari, and A. Villringer, “Simultaneous near-infrared spectroscopy monitoring of left and right occipital areas reveals contra-lateral hemodynamic changes upon hemi-field paradigm,” Vision Res. 41(1), 97–102 (2001).
[Crossref] [PubMed]

Conner, I. P.

J. D. Mendola and I. P. Conner, “Eye dominance predicts fMRI signals in human retinotopic cortex,” Neurosci. Lett. 414(1), 30–34 (2007).
[Crossref] [PubMed]

Cope, M.

A. Duncan, J. H. Meek, M. Clemence, C. E. Elwell, P. Fallon, L. Tyszczuk, M. Cope, and D. T. Delpy, “Measurement of cranial optical path length as a function of age using phase resolved near infrared spectroscopy,” Pediatr. Res. 39(5), 889–894 (1996).
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S. Ogawa, T. M. Lee, A. R. Kay, and D. W. Tank, “Brain magnetic resonance imaging with contrast dependent on blood oxygenation,” Proc. Natl. Acad. Sci. U.S.A. 87(24), 9868–9872 (1990).
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Li, Y.

J. Si, X. Zhang, Y. Li, Y. Zhang, N. Zuo, and T. Jiang, “Correlation between electrical and hemodynamic responses during visual stimulation with graded contrasts,” J. Biomed. Opt. 21(9), 091315 (2016).
[Crossref] [PubMed]

Lines, C. R.

C. R. Lines, M. D. Rugg, and A. D. Milner, “The effect of stimulus intensity on visual evoked potential estimates of interhemispheric transmission time,” Exp. Brain Res. 57(1), 89–98 (1984).
[Crossref] [PubMed]

Logothetis, N. K.

B. Yeşilyurt, K. Whittingstall, K. Uğurbil, N. K. Logothetis, and K. Uludağ, “Relationship of the BOLD signal with VEP for ultrashort duration visual stimuli (0.1 to 5 ms) in humans,” J. Cereb. Blood Flow Metab. 30(2), 449–458 (2010).
[Crossref] [PubMed]

N. K. Logothetis and J. Pfeuffer, “On the nature of the BOLD fMRI contrast mechanism,” Magn. Reson. Imaging 22(10), 1517–1531 (2004).
[Crossref] [PubMed]

Luo, Q.

B. Sun, L. Zhang, H. Gong, J. Sun, and Q. Luo, “Detection of optical neuronal signals in the visual cortex using continuous wave near-infrared spectroscopy,” Neuroimage 87, 190–198 (2014).
[Crossref] [PubMed]

Macintosh, B. J.

S. D. Mayhew, B. J. Macintosh, S. G. Dirckx, G. D. Iannetti, and R. G. Wise, “Coupling of simultaneously acquired electrophysiological and haemodynamic responses during visual stimulation,” Magn. Reson. Imaging 28(8), 1066–1077 (2010).
[Crossref] [PubMed]

Martínez, A.

F. Di Russo, A. Martínez, M. I. Sereno, S. Pitzalis, and S. A. Hillyard, “Cortical sources of the early components of the visual evoked potential,” Hum. Brain Mapp. 15(2), 95–111 (2002).
[Crossref] [PubMed]

Mayhew, S. D.

S. D. Mayhew, B. J. Macintosh, S. G. Dirckx, G. D. Iannetti, and R. G. Wise, “Coupling of simultaneously acquired electrophysiological and haemodynamic responses during visual stimulation,” Magn. Reson. Imaging 28(8), 1066–1077 (2010).
[Crossref] [PubMed]

McCulloch, D. L.

J. V. Odom, M. Bach, M. Brigell, G. E. Holder, D. L. McCulloch, A. P. Tormene, and Vaegan, “ISCEV standard for clinical visual evoked potentials (2009 update),” Doc. Ophthalmol. 120(1), 111–119 (2010).
[Crossref] [PubMed]

Mechler, F.

D. S. Reich, F. Mechler, and J. D. Victor, “Temporal coding of contrast in primary visual cortex: when, what, and why,” J. Neurophysiol. 85(3), 1039–1050 (2001).
[PubMed]

Meek, J. H.

A. Duncan, J. H. Meek, M. Clemence, C. E. Elwell, P. Fallon, L. Tyszczuk, M. Cope, and D. T. Delpy, “Measurement of cranial optical path length as a function of age using phase resolved near infrared spectroscopy,” Pediatr. Res. 39(5), 889–894 (1996).
[Crossref] [PubMed]

Mehnert, J.

S. K. Piper, A. Krueger, S. P. Koch, J. Mehnert, C. Habermehl, J. Steinbrink, H. Obrig, and C. H. Schmitz, “A wearable multi-channel fNIRS system for brain imaging in freely moving subjects,” Neuroimage 85(Pt 1), 64–71 (2014).
[Crossref] [PubMed]

Mendola, J. D.

J. D. Mendola and I. P. Conner, “Eye dominance predicts fMRI signals in human retinotopic cortex,” Neurosci. Lett. 414(1), 30–34 (2007).
[Crossref] [PubMed]

Mennes, M.

N. Novitskiy, J. R. Ramautar, K. Vanderperren, M. De Vos, M. Mennes, B. Mijovic, B. Vanrumste, P. Stiers, B. Van den Bergh, L. Lagae, S. Sunaert, S. Van Huffel, and J. Wagemans, “The BOLD correlates of the visual P1 and N1 in single-trial analysis of simultaneous EEG-fMRI recordings during a spatial detection task,” Neuroimage 54(2), 824–835 (2011).
[Crossref] [PubMed]

Michel, C. M.

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

Mijovic, B.

N. Novitskiy, J. R. Ramautar, K. Vanderperren, M. De Vos, M. Mennes, B. Mijovic, B. Vanrumste, P. Stiers, B. Van den Bergh, L. Lagae, S. Sunaert, S. Van Huffel, and J. Wagemans, “The BOLD correlates of the visual P1 and N1 in single-trial analysis of simultaneous EEG-fMRI recordings during a spatial detection task,” Neuroimage 54(2), 824–835 (2011).
[Crossref] [PubMed]

Milner, A. D.

C. R. Lines, M. D. Rugg, and A. D. Milner, “The effect of stimulus intensity on visual evoked potential estimates of interhemispheric transmission time,” Exp. Brain Res. 57(1), 89–98 (1984).
[Crossref] [PubMed]

Minnema, M. T.

P. E. Moes, W. S. Brown, and M. T. Minnema, “Individual differences in interhemispheric transfer time (IHTT) as measured by event related potentials,” Neuropsychologia 45(11), 2626–2630 (2007).
[Crossref] [PubMed]

Moes, P. E.

P. E. Moes, W. S. Brown, and M. T. Minnema, “Individual differences in interhemispheric transfer time (IHTT) as measured by event related potentials,” Neuropsychologia 45(11), 2626–2630 (2007).
[Crossref] [PubMed]

Müller, B.

H. Obrig, H. Israel, M. Kohl-Bareis, K. Uludag, R. Wenzel, B. Müller, G. Arnold, and A. Villringer, “Habituation of the visually evoked potential and its vascular response: implications for neurovascular coupling in the healthy adult,” Neuroimage 17(1), 1–18 (2002).
[Crossref] [PubMed]

Murray, M. M.

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

Nakamura, A.

A. Nakamura, R. Kakigi, M. Hoshiyama, S. Koyama, Y. Kitamura, and M. Shimojo, “Visual evoked cortical magnetic fields to pattern reversal stimulation,” Brain Res. Cogn. Brain Res. 6(1), 9–22 (1997).
[Crossref] [PubMed]

Nakasato, N.

K. Seki, N. Nakasato, S. Fujita, K. Hatanaka, T. Kawamura, A. Kanno, and T. Yoshimoto, “Neuromagnetic evidence that the P100 component of the pattern reversal visual evoked response originates in the bottom of the calcarine fissure,” Electroencephalogr. Clin. Neurophysiol. 100(5), 436–442 (1996).
[Crossref] [PubMed]

Novitskiy, N.

N. Novitskiy, J. R. Ramautar, K. Vanderperren, M. De Vos, M. Mennes, B. Mijovic, B. Vanrumste, P. Stiers, B. Van den Bergh, L. Lagae, S. Sunaert, S. Van Huffel, and J. Wagemans, “The BOLD correlates of the visual P1 and N1 in single-trial analysis of simultaneous EEG-fMRI recordings during a spatial detection task,” Neuroimage 54(2), 824–835 (2011).
[Crossref] [PubMed]

Obrig, H.

S. K. Piper, A. Krueger, S. P. Koch, J. Mehnert, C. Habermehl, J. Steinbrink, H. Obrig, and C. H. Schmitz, “A wearable multi-channel fNIRS system for brain imaging in freely moving subjects,” Neuroimage 85(Pt 1), 64–71 (2014).
[Crossref] [PubMed]

S. P. Koch, P. Werner, J. Steinbrink, P. Fries, and H. Obrig, “Stimulus-induced and state-dependent sustained gamma activity is tightly coupled to the hemodynamic response in humans,” J. Neurosci. 29(44), 13962–13970 (2009).
[Crossref] [PubMed]

H. Obrig, H. Israel, M. Kohl-Bareis, K. Uludag, R. Wenzel, B. Müller, G. Arnold, and A. Villringer, “Habituation of the visually evoked potential and its vascular response: implications for neurovascular coupling in the healthy adult,” Neuroimage 17(1), 1–18 (2002).
[Crossref] [PubMed]

Odom, J. V.

J. V. Odom, M. Bach, M. Brigell, G. E. Holder, D. L. McCulloch, A. P. Tormene, and Vaegan, “ISCEV standard for clinical visual evoked potentials (2009 update),” Doc. Ophthalmol. 120(1), 111–119 (2010).
[Crossref] [PubMed]

Oeseburg, B.

W. N. Colier, V. Quaresima, R. Wenzel, M. C. van der Sluijs, B. Oeseburg, M. Ferrari, and A. Villringer, “Simultaneous near-infrared spectroscopy monitoring of left and right occipital areas reveals contra-lateral hemodynamic changes upon hemi-field paradigm,” Vision Res. 41(1), 97–102 (2001).
[Crossref] [PubMed]

Ogawa, S.

S. Ogawa, T. M. Lee, A. R. Kay, and D. W. Tank, “Brain magnetic resonance imaging with contrast dependent on blood oxygenation,” Proc. Natl. Acad. Sci. U.S.A. 87(24), 9868–9872 (1990).
[Crossref] [PubMed]

Onaral, B.

A. Bozkurt, A. Rosen, H. Rosen, and B. Onaral, “A portable near infrared spectroscopy system for bedside monitoring of newborn brain,” Biomed. Eng. Online 4(1), 29 (2005).
[Crossref] [PubMed]

Patria, F.

F. Di Russo, S. Pitzalis, G. Spitoni, T. Aprile, F. Patria, D. Spinelli, and S. A. Hillyard, “Identification of the neural sources of the pattern-reversal VEP,” Neuroimage 24(3), 874–886 (2005).
[Crossref] [PubMed]

Pedersen, H.

D. Fuglø, H. Pedersen, E. Rostrup, A. E. Hansen, and H. B. Larsson, “Correlation between single-trial visual evoked potentials and the blood oxygenation level dependent response in simultaneously recorded electroencephalography-functional magnetic resonance imaging,” Magn. Reson. Med. 68(1), 252–260 (2012).
[Crossref] [PubMed]

Pfeuffer, J.

N. K. Logothetis and J. Pfeuffer, “On the nature of the BOLD fMRI contrast mechanism,” Magn. Reson. Imaging 22(10), 1517–1531 (2004).
[Crossref] [PubMed]

Piper, S. K.

S. K. Piper, A. Krueger, S. P. Koch, J. Mehnert, C. Habermehl, J. Steinbrink, H. Obrig, and C. H. Schmitz, “A wearable multi-channel fNIRS system for brain imaging in freely moving subjects,” Neuroimage 85(Pt 1), 64–71 (2014).
[Crossref] [PubMed]

Pitzalis, S.

F. Di Russo, S. Pitzalis, G. Spitoni, T. Aprile, F. Patria, D. Spinelli, and S. A. Hillyard, “Identification of the neural sources of the pattern-reversal VEP,” Neuroimage 24(3), 874–886 (2005).
[Crossref] [PubMed]

F. Di Russo, A. Martínez, M. I. Sereno, S. Pitzalis, and S. A. Hillyard, “Cortical sources of the early components of the visual evoked potential,” Hum. Brain Mapp. 15(2), 95–111 (2002).
[Crossref] [PubMed]

Preibisch, C.

H. Laufs, A. Kleinschmidt, A. Beyerle, E. Eger, A. Salek-Haddadi, C. Preibisch, and K. Krakow, “EEG-correlated fMRI of human alpha activity,” Neuroimage 19(4), 1463–1476 (2003).
[Crossref] [PubMed]

Quaresima, V.

M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
[Crossref] [PubMed]

W. N. Colier, V. Quaresima, R. Wenzel, M. C. van der Sluijs, B. Oeseburg, M. Ferrari, and A. Villringer, “Simultaneous near-infrared spectroscopy monitoring of left and right occipital areas reveals contra-lateral hemodynamic changes upon hemi-field paradigm,” Vision Res. 41(1), 97–102 (2001).
[Crossref] [PubMed]

Ramautar, J. R.

N. Novitskiy, J. R. Ramautar, K. Vanderperren, M. De Vos, M. Mennes, B. Mijovic, B. Vanrumste, P. Stiers, B. Van den Bergh, L. Lagae, S. Sunaert, S. Van Huffel, and J. Wagemans, “The BOLD correlates of the visual P1 and N1 in single-trial analysis of simultaneous EEG-fMRI recordings during a spatial detection task,” Neuroimage 54(2), 824–835 (2011).
[Crossref] [PubMed]

Reich, D. S.

D. S. Reich, F. Mechler, and J. D. Victor, “Temporal coding of contrast in primary visual cortex: when, what, and why,” J. Neurophysiol. 85(3), 1039–1050 (2001).
[PubMed]

Rieger, J. W.

J. Schadow, D. Lenz, S. Thaerig, N. A. Busch, I. Fründ, J. W. Rieger, and C. S. Herrmann, “Stimulus intensity affects early sensory processing: visual contrast modulates evoked gamma-band activity in human EEG,” Int. J. Psychophysiol. 66(1), 28–36 (2007).
[Crossref] [PubMed]

Riera, J.

X. Wan, J. Riera, K. Iwata, M. Takahashi, T. Wakabayashi, and R. Kawashima, “The neural basis of the hemodynamic response nonlinearity in human primary visual cortex: Implications for neurovascular coupling mechanism,” Neuroimage 32(2), 616–625 (2006).
[Crossref] [PubMed]

Rosen, A.

A. Bozkurt, A. Rosen, H. Rosen, and B. Onaral, “A portable near infrared spectroscopy system for bedside monitoring of newborn brain,” Biomed. Eng. Online 4(1), 29 (2005).
[Crossref] [PubMed]

Rosen, H.

A. Bozkurt, A. Rosen, H. Rosen, and B. Onaral, “A portable near infrared spectroscopy system for bedside monitoring of newborn brain,” Biomed. Eng. Online 4(1), 29 (2005).
[Crossref] [PubMed]

Rostrup, E.

A. Hougaard, B. H. Jensen, F. M. Amin, E. Rostrup, M. B. Hoffmann, and M. Ashina, “Cerebral Asymmetry of fMRI-BOLD Responses to Visual Stimulation,” PLoS One 10(5), e0126477 (2015).
[Crossref] [PubMed]

D. Fuglø, H. Pedersen, E. Rostrup, A. E. Hansen, and H. B. Larsson, “Correlation between single-trial visual evoked potentials and the blood oxygenation level dependent response in simultaneously recorded electroencephalography-functional magnetic resonance imaging,” Magn. Reson. Med. 68(1), 252–260 (2012).
[Crossref] [PubMed]

Rovati, L.

L. Rovati, G. Salvatori, L. Bulf, and S. Fonda, “Optical and electrical recording of neural activity evoked by graded contrast visual stimulus,” Biomed. Eng. Online 6(1), 28 (2007).
[Crossref] [PubMed]

Rugg, M. D.

C. R. Lines, M. D. Rugg, and A. D. Milner, “The effect of stimulus intensity on visual evoked potential estimates of interhemispheric transmission time,” Exp. Brain Res. 57(1), 89–98 (1984).
[Crossref] [PubMed]

Sakai, Y.

B. Lee, Y. Kaneoke, R. Kakigi, and Y. Sakai, “Human brain response to visual stimulus between lower/upper visual fields and cerebral hemispheres,” Int. J. Psychophysiol. 74(2), 81–87 (2009).
[Crossref] [PubMed]

Salek-Haddadi, A.

H. Laufs, A. Kleinschmidt, A. Beyerle, E. Eger, A. Salek-Haddadi, C. Preibisch, and K. Krakow, “EEG-correlated fMRI of human alpha activity,” Neuroimage 19(4), 1463–1476 (2003).
[Crossref] [PubMed]

Salvatori, G.

L. Rovati, G. Salvatori, L. Bulf, and S. Fonda, “Optical and electrical recording of neural activity evoked by graded contrast visual stimulus,” Biomed. Eng. Online 6(1), 28 (2007).
[Crossref] [PubMed]

Schadow, J.

J. Schadow, D. Lenz, S. Thaerig, N. A. Busch, I. Fründ, J. W. Rieger, and C. S. Herrmann, “Stimulus intensity affects early sensory processing: visual contrast modulates evoked gamma-band activity in human EEG,” Int. J. Psychophysiol. 66(1), 28–36 (2007).
[Crossref] [PubMed]

Schlaggar, B. L.

B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
[Crossref] [PubMed]

Schmitz, C. H.

S. K. Piper, A. Krueger, S. P. Koch, J. Mehnert, C. Habermehl, J. Steinbrink, H. Obrig, and C. H. Schmitz, “A wearable multi-channel fNIRS system for brain imaging in freely moving subjects,” Neuroimage 85(Pt 1), 64–71 (2014).
[Crossref] [PubMed]

Seki, K.

K. Seki, N. Nakasato, S. Fujita, K. Hatanaka, T. Kawamura, A. Kanno, and T. Yoshimoto, “Neuromagnetic evidence that the P100 component of the pattern reversal visual evoked response originates in the bottom of the calcarine fissure,” Electroencephalogr. Clin. Neurophysiol. 100(5), 436–442 (1996).
[Crossref] [PubMed]

Sereno, M. I.

F. Di Russo, A. Martínez, M. I. Sereno, S. Pitzalis, and S. A. Hillyard, “Cortical sources of the early components of the visual evoked potential,” Hum. Brain Mapp. 15(2), 95–111 (2002).
[Crossref] [PubMed]

Shimojo, M.

A. Nakamura, R. Kakigi, M. Hoshiyama, S. Koyama, Y. Kitamura, and M. Shimojo, “Visual evoked cortical magnetic fields to pattern reversal stimulation,” Brain Res. Cogn. Brain Res. 6(1), 9–22 (1997).
[Crossref] [PubMed]

Si, J.

J. Si, X. Zhang, Y. Li, Y. Zhang, N. Zuo, and T. Jiang, “Correlation between electrical and hemodynamic responses during visual stimulation with graded contrasts,” J. Biomed. Opt. 21(9), 091315 (2016).
[Crossref] [PubMed]

Snyder, A. Z.

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. Chen, Y. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping,” Neuroimage 61(4), 1120–1128 (2012).
[Crossref] [PubMed]

Spinelli, D.

F. Di Russo, S. Pitzalis, G. Spitoni, T. Aprile, F. Patria, D. Spinelli, and S. A. Hillyard, “Identification of the neural sources of the pattern-reversal VEP,” Neuroimage 24(3), 874–886 (2005).
[Crossref] [PubMed]

Spinelli, L.

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

Spire, J. P.

V. L. Towle, M. Brigell, and J. P. Spire, “Hemi-field pattern visual evoked potentials: a comparison of display and analysis techniques,” Brain Topogr. 1(4), 263–270 (1989).
[Crossref] [PubMed]

Spitoni, G.

F. Di Russo, S. Pitzalis, G. Spitoni, T. Aprile, F. Patria, D. Spinelli, and S. A. Hillyard, “Identification of the neural sources of the pattern-reversal VEP,” Neuroimage 24(3), 874–886 (2005).
[Crossref] [PubMed]

Steinbrink, J.

S. K. Piper, A. Krueger, S. P. Koch, J. Mehnert, C. Habermehl, J. Steinbrink, H. Obrig, and C. H. Schmitz, “A wearable multi-channel fNIRS system for brain imaging in freely moving subjects,” Neuroimage 85(Pt 1), 64–71 (2014).
[Crossref] [PubMed]

S. P. Koch, P. Werner, J. Steinbrink, P. Fries, and H. Obrig, “Stimulus-induced and state-dependent sustained gamma activity is tightly coupled to the hemodynamic response in humans,” J. Neurosci. 29(44), 13962–13970 (2009).
[Crossref] [PubMed]

Stiers, P.

N. Novitskiy, J. R. Ramautar, K. Vanderperren, M. De Vos, M. Mennes, B. Mijovic, B. Vanrumste, P. Stiers, B. Van den Bergh, L. Lagae, S. Sunaert, S. Van Huffel, and J. Wagemans, “The BOLD correlates of the visual P1 and N1 in single-trial analysis of simultaneous EEG-fMRI recordings during a spatial detection task,” Neuroimage 54(2), 824–835 (2011).
[Crossref] [PubMed]

Sun, B.

B. Sun, L. Zhang, H. Gong, J. Sun, and Q. Luo, “Detection of optical neuronal signals in the visual cortex using continuous wave near-infrared spectroscopy,” Neuroimage 87, 190–198 (2014).
[Crossref] [PubMed]

Sun, J.

B. Sun, L. Zhang, H. Gong, J. Sun, and Q. Luo, “Detection of optical neuronal signals in the visual cortex using continuous wave near-infrared spectroscopy,” Neuroimage 87, 190–198 (2014).
[Crossref] [PubMed]

Sunaert, S.

N. Novitskiy, J. R. Ramautar, K. Vanderperren, M. De Vos, M. Mennes, B. Mijovic, B. Vanrumste, P. Stiers, B. Van den Bergh, L. Lagae, S. Sunaert, S. Van Huffel, and J. Wagemans, “The BOLD correlates of the visual P1 and N1 in single-trial analysis of simultaneous EEG-fMRI recordings during a spatial detection task,” Neuroimage 54(2), 824–835 (2011).
[Crossref] [PubMed]

Taghavy, A.

A. Taghavy and C. F. Kügler, “Pattern reversal visual evoked potentials (white-black- and colour-black-PVEPs) in the study of eye dominance,” Eur. Arch. Psychiatry Neurol. Sci. 236(6), 329–332 (1987).
[Crossref] [PubMed]

Takahashi, M.

X. Wan, J. Riera, K. Iwata, M. Takahashi, T. Wakabayashi, and R. Kawashima, “The neural basis of the hemodynamic response nonlinearity in human primary visual cortex: Implications for neurovascular coupling mechanism,” Neuroimage 32(2), 616–625 (2006).
[Crossref] [PubMed]

Tank, D. W.

S. Ogawa, T. M. Lee, A. R. Kay, and D. W. Tank, “Brain magnetic resonance imaging with contrast dependent on blood oxygenation,” Proc. Natl. Acad. Sci. U.S.A. 87(24), 9868–9872 (1990).
[Crossref] [PubMed]

Thaerig, S.

J. Schadow, D. Lenz, S. Thaerig, N. A. Busch, I. Fründ, J. W. Rieger, and C. S. Herrmann, “Stimulus intensity affects early sensory processing: visual contrast modulates evoked gamma-band activity in human EEG,” Int. J. Psychophysiol. 66(1), 28–36 (2007).
[Crossref] [PubMed]

Thériault, M.

D. Bastien, A. Gallagher, J. Tremblay, P. Vannasing, M. Thériault, M. Lassonde, and F. Lepore, “Specific functional asymmetries of the human visual cortex revealed by functional near-infrared spectroscopy,” Brain Res. 1431, 62–68 (2012).
[Crossref] [PubMed]

Tormene, A. P.

J. V. Odom, M. Bach, M. Brigell, G. E. Holder, D. L. McCulloch, A. P. Tormene, and Vaegan, “ISCEV standard for clinical visual evoked potentials (2009 update),” Doc. Ophthalmol. 120(1), 111–119 (2010).
[Crossref] [PubMed]

Towle, V. L.

V. L. Towle, M. Brigell, and J. P. Spire, “Hemi-field pattern visual evoked potentials: a comparison of display and analysis techniques,” Brain Topogr. 1(4), 263–270 (1989).
[Crossref] [PubMed]

Tremblay, J.

D. Bastien, A. Gallagher, J. Tremblay, P. Vannasing, M. Thériault, M. Lassonde, and F. Lepore, “Specific functional asymmetries of the human visual cortex revealed by functional near-infrared spectroscopy,” Brain Res. 1431, 62–68 (2012).
[Crossref] [PubMed]

Tyszczuk, L.

A. Duncan, J. H. Meek, M. Clemence, C. E. Elwell, P. Fallon, L. Tyszczuk, M. Cope, and D. T. Delpy, “Measurement of cranial optical path length as a function of age using phase resolved near infrared spectroscopy,” Pediatr. Res. 39(5), 889–894 (1996).
[Crossref] [PubMed]

Ugurbil, K.

B. Yeşilyurt, K. Whittingstall, K. Uğurbil, N. K. Logothetis, and K. Uludağ, “Relationship of the BOLD signal with VEP for ultrashort duration visual stimuli (0.1 to 5 ms) in humans,” J. Cereb. Blood Flow Metab. 30(2), 449–458 (2010).
[Crossref] [PubMed]

Uludag, K.

B. Yeşilyurt, K. Whittingstall, K. Uğurbil, N. K. Logothetis, and K. Uludağ, “Relationship of the BOLD signal with VEP for ultrashort duration visual stimuli (0.1 to 5 ms) in humans,” J. Cereb. Blood Flow Metab. 30(2), 449–458 (2010).
[Crossref] [PubMed]

H. Obrig, H. Israel, M. Kohl-Bareis, K. Uludag, R. Wenzel, B. Müller, G. Arnold, and A. Villringer, “Habituation of the visually evoked potential and its vascular response: implications for neurovascular coupling in the healthy adult,” Neuroimage 17(1), 1–18 (2002).
[Crossref] [PubMed]

Vaegan,

J. V. Odom, M. Bach, M. Brigell, G. E. Holder, D. L. McCulloch, A. P. Tormene, and Vaegan, “ISCEV standard for clinical visual evoked potentials (2009 update),” Doc. Ophthalmol. 120(1), 111–119 (2010).
[Crossref] [PubMed]

Van den Bergh, B.

N. Novitskiy, J. R. Ramautar, K. Vanderperren, M. De Vos, M. Mennes, B. Mijovic, B. Vanrumste, P. Stiers, B. Van den Bergh, L. Lagae, S. Sunaert, S. Van Huffel, and J. Wagemans, “The BOLD correlates of the visual P1 and N1 in single-trial analysis of simultaneous EEG-fMRI recordings during a spatial detection task,” Neuroimage 54(2), 824–835 (2011).
[Crossref] [PubMed]

van der Sluijs, M. C.

W. N. Colier, V. Quaresima, R. Wenzel, M. C. van der Sluijs, B. Oeseburg, M. Ferrari, and A. Villringer, “Simultaneous near-infrared spectroscopy monitoring of left and right occipital areas reveals contra-lateral hemodynamic changes upon hemi-field paradigm,” Vision Res. 41(1), 97–102 (2001).
[Crossref] [PubMed]

Van Huffel, S.

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Figures (7)

Fig. 1
Fig. 1 Experimental configuration. a) Experimental paradigm of the hemifield visual task. The entire experimental paradigm consisted of an initial baseline period (30 s) followed by 27 blocks. Each block consisted of a stimulation period (25 s, reversal rate: 4 reversals/s) and a resting period (30 s). b) The diagram of the head illustrates the placement of the electrode Oz (green hexagon) and optodes, specifically 6 sources (red stars) and 12 detectors (blue circles), yielding 20 optical channels (black lines marked with channel numbers). The distance between a neighboring source and the detector pairs was 3 cm and the probe covered an area of approximately 6 × 6 cm2 for each hemisphere.
Fig. 2
Fig. 2 The group-averaged VEP results for a) the left visual field and b) the right visual field stimulations at different contrast levels: 1% (left column, blue lines), 10% (middle column, green lines), and 100% (right column, red lines), recorded from electrodes P7, P5, PO7, PO3, Oz, PO4, PO8, P6, and P8, respectively. The gray dotted lines indicate the stimulus onset. In comparison, both the amplitude and latency of the VEPs varied with contrast level with the amplitude increasing and the latency decreasing as the stimulus contrast increased. Moreover, the main VEP components across the three contrast levels indicated opposite polarities over the left and the right hemispheres for the hemifield visual stimulations.
Fig. 3
Fig. 3 The amplitudes of Peak2 for a) the left visual field and d) the right visual field against the stimulus contrast levels. The amplitudes of Peak2-Peak1 for b) the left visual field and e) the right visual field against the stimulus contrast levels. The latencies of Peak2 for c) the left visual field and f) the right visual field in response to the varying contrast levels. Error bars show the SE.
Fig. 4
Fig. 4 The group-averaged SEP results for finger tapping of a) the left hand (left column, blue lines) and b) the right hand (right column, red lines), recorded from electrodes T7, C3, Cz, C4, and T8, respectively. The gray dotted lines indicate the stimulus onset. A comparison of the graphs shows that the main components across the five electrodes had a similar polarity over the left and the right hemispheres for unilateral finger tapping tasks.
Fig. 5
Fig. 5 The group-averaged spatial distribution maps of the hemodynamic changes for the visual checkerboard reversal tasks at different contrast levels: 1% (left column), 10% (middle column), and 100% (right column) for the left visual field (top row) and the right visual field (bottom row). The color bar indicates the HbO/ HbR concentration changes in μmol/l.
Fig. 6
Fig. 6 Hemispheric differences in the hemodynamic responses during hemifield visual stimulations with graded contrasts. Comparison of the changes in a) HbO and b) HbR concentration over the left hemisphere (LH) and the right hemisphere (RH) of the visual cortex across the 1%, 10%, and 100% contrast levels for the left hemifield visual stimulation; Comparison of the changes in c) HbO and d) HbR concentration over the left hemisphere and the right hemisphere of the visual cortex across the 1%, 10%, and 100% contrast levels for the right hemifield visual stimulation; error bars show the SE. For the hemifield visual stimulations, the functional activated areas were consistently located over the lateral areas contralateral to the stimulated hemifield. The amplitudes of the HbO concentration monotonically increased as the contrast level increased. The changes in the HbO amplitudes are more apparent than those of the HbR.
Fig. 7
Fig. 7 Correlations between the VEPs and the corresponding hemodynamic responses at different contrast levels for the hemifield visual stimulations. a), b), and c) respectively show the relationship between the Peak2 amplitude, Peak2-Peak1 amplitude, and Peak2 latency of the VEP against the hemodynamic response at different contrast levels for the left hemifield visual stimulations. d), e), and f) respectively show the relationship between the Peak2 amplitude, Peak2-Peak1 amplitude, and Peak2 latency of the VEP against the hemodynamic response at different contrast levels for the right hemifield visual stimulation. The dot, triangle, and square represent the 1%, 10%, and 100% contrast levels, respectively. The red and blue lines indicate HbO and HbR, respectively. Error bars show the SE.

Tables (1)

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Table 1 Overview of the peak amplitudes and peak time of the VEP and the corresponding hemodynamic responses at three contrast levels (mean ± SE)

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