Brain Topogr DOI 10.1007/s10548-016-0540-0

ORIGINAL PAPER

Brain Areas Responsible for Vigilance: An EEG Source Imaging Study Jung-Hoon Kim1 · Do-Won Kim2 · Chang-Hwan Im1  

Received: 9 July 2016 / Accepted: 15 December 2016 © Springer Science+Business Media New York 2017

Abstract Vigilance, sometimes referred to as sustained attention, is an important type of human attention as it is closely associated with cognitive activities required in various daily-life situations. Although many researchers have investigated which brain areas control the maintenance of vigilance, findings have been inconsistent. We hypothesized that this inconsistency might be due to the use of different experimental paradigms in the various studies. We found that most of the previous studies used paradigms that included specific cognitive tasks requiring a high cognitive load, which could complicate identification of brain areas associated only with vigilance. To minimize the influence of cognitive processes other than vigilance on the analysis results, we adopted the d2-test of attention, which is a wellknown neuropsychological test of attention that does not require high cognitive load, and searched for brain areas at which EEG source activities were temporally correlated with fluctuation of vigilance over a prolonged period of time. EEG experiments conducted with 31 young adults showed that left prefrontal cortex activity was significantly correlated with vigilance variation in the delta, beta1, beta2, and gamma frequency bands, but not the theta and alpha frequency bands. Our study results suggest that the left prefrontal cortex plays a key role in vigilance modulation, and can therefore be used to monitor individual

* Chang-Hwan Im [email protected] 1

Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea

2

Department of Biomedical Engineering, Chonnam National University, 50 Daehak-ro, Yeosu 59626, Republic of Korea

vigilance changes over time or serve as a potential target of noninvasive brain stimulation. Keywords Vigilance · Sustained attention · Electroencephalography · Source imaging · D2 test of attention

Introduction Attention, a basic human cognitive ability, can be categorized into different types. For example, selective attention is the ability to focus on a specific target signal among many different signals, such as lights, sounds, and scents, whereas divided attention enables one to concentrate on two or more signals simultaneously (Corbetta et  al. 1991; Desimone and Duncan 1995). Among the various types of attention, vigilance, also referred to as sustained attention, is the ability to maintain concentrated attention on specific target stimuli over a prolonged period of time (Mackworth 1964; Oken et  al. 2006; Corkum and Siegel 1993; Paus et al. 1997). Vigilance is regarded as one of the most important types of attention as it determines effectiveness in a variety of situations involving long-term monitoring of events, such as lifeguarding, quality control, and air traffic control (DeGangi and Porges 1990; Sarter et al. 2001). Several neurophysiological studies have attempted to identify brain areas responsible for vigilance using various functional brain mapping modalities such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and electroencephalography (EEG) (Coull and Nobre 1998; Olbrich et  al. 2009; Park et  al. 2013). These studies showed that prefrontal and parietal brain areas play important roles in regulating vigilance; however, there was considerable inconsistency among

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studies in the identities of brain areas associated with vigilance (Freeman et al. 2004; Klimesch 1999). For example, Ishii et  al. reported that the central cingulate gyrus was meaningfully activated during a prolonged mental arithmetic task (Ishii et  al. 1999). Later, Lawrence et  al. demonstrated that the temporal lobe and occipital gyrus were associated with vigilance (Lawrence et al. 2003) using the same mental arithmetic paradigm as Ishii and colleagues. Gevins et al. demonstrated that left frontal lobe activity was modulated by vigilance changes during working memory tasks, whereas a study by Klimesch reported that vigilance with respect to a similar working memory task was controlled by temporal and occipital lobes (Gevins et al. 1997; Klimesch 2012). Many previous studies on vigilance have not followed the general definition of vigilance (Bekhtereva et al. 2014; Coull et al. 1996; Hinds et al. 2013), which is a “temporal change” in task performance when a person continuously detects the appearance of a specific infrequent stimulus over a prolonged time period (Rueckert et al. 1999). Instead of using temporal variation, many studies have averaged performance scores to identify brain areas responsible for vigilance. We believe that the inconsistency in previously reported brain areas associated with vigilance might originate in part from not considering temporal changes in task performance. In such cases, additional cognitive processes other than vigilance (e.g., working memory or mental arithmetic tasks) might influence the identity of the brain area determined to be directly associated with vigilance. Practically, however, it is not easy to design an experiment that contrasts brain areas related to vigilance while excluding other cognitive processes such as working memory or decision-making, because these cognitive processes are also essential to accomplish specific goal-directed tasks. According to a neurophysiological study, variability in working memory capacity among individuals directly affects the decrement rate of vigilance (Parasuraman 1979). A working memory task might therefore not be appropriate for investigating vigilance, because these two cognitive processes use the same resource pools in specific brain areas. Another study showed that decision-making can also be a factor that can change the decrement rate of vigilance (Caggiano and Parasuraman 2004). Due to these limitations, brain areas directly associated with vigilance have not been confidently identified. In this study, we investigated whether it is possible to identify brain regions responsible for vigilance using EEG. We searched for brain areas for which EEG source activities were temporally correlated with fluctuations of d2-test scores to identify brain areas responsible for vigilance, based on the hypothesis that temporal fluctuation of d2-test scores would be related only to changes in the degree of mental concentration. To the best of our knowledge, this is

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the first study to use the d2-test paradigm to investigate the brain areas associated with vigilance.

Materials and Methods Participants A total of 41 undergraduate students from Hanyang University were recruited for the current study, among which 21 were female. Any participant with a history of alcohol or drug abuse, electroconvulsive therapy, mental retardation, head injury with loss of consciousness, or any other neurological disorders that might have affected the experiment were excluded. All participants had normal or corrected vision, and all of them answered that they are right-handed. Participants who showed severely artifact-contaminated EEG data (n = 8) and who showed extremely low behavioral performance (n = 2) were excluded before the analysis; consequently, EEG data from 31 subjects (females: 18, age: 20–25 years) were analyzed in this study. All participants were given monetary reimbursement for their participation in the experiments. Ethical approval was obtained from the Institutional Review Board of Hanyang University. All experiments were performed between 3  pm and 5  pm of each day to avoid potential diurnal effects. The participants were asked not to intake any caffeine or alcohol on the day of experiment. Stimuli and Experimental Paradigm To circumvent the aforementioned issues with regard to selection of a task paradigm, we adopted a paradigm called “the d2 test of attention” to exclude the potential involvement of cognitive processes other than vigilance to the greatest possible extent (Brickenkamp and Zillmer 1998). The d2-test was originally a paper-based mental task, and is one of the most frequently used tools for diagnosing attention-deficit hyperactivity disorder (ADHD). Participants are asked to quickly and consistently discriminate target characters (“d” with two dots above or below the character) from many similar distracters (e.g., “d” with only one dot, or “p” instead of the letter “d”). Behavioral performance of the d2 test is known to be affected mainly by simple cognitive factors requiring a relatively light cognitive load, such as mental concentration (also referred to as attentional control), visual perception, visual scanning ability, and perceptual speed. All of those cognitive factors are not directly associated with higher cognitive processes such as working memory (Brickenkamp and Zillmer 1998; Drechsler et  al. 2007). Moreover, the temporal changes of the task performance (e.g., concentration performance) can be thought to be mostly related to the changes in mental concentration

Brain Topogr

was 11.90 ± 0.89 min. To accustom the participants to the task, they underwent sufficient practice sessions before the main experiments. An index CP was calculated by,

CP =

Fig. 1 Paradigm of the d2 test of attention. “d with 2 dots regardless of location” is the target stimulus, whereas the others (d with 1 dot, p regardless of location and number of dots) are non-target stimuli. Participants had to press either the left or right button based on their perception

because the other factors except mental concentration (visual perception, visual scanning ability, and perceptual speed) are individual traits that do not fluctuate over short task time. The score of the d2-test is usually evaluated using two quantitative measures called concentration performance (CP) and deterioration rate (DETER), which have been proven to be reliable in practical applications (Bates and Lemay 2004). The definition of CP and DETER indices are overall task performance during a certain period of time and the decrement rate of vigilance, respectively. Participants were seated in a comfortable armchair, facing a 17-inch LCD monitor in a sound-attenuated room. In this study, we used a computerized d2-test paradigm, which was based on the paper-based d2-test. A schematic illustration of our task paradigm is provided in Fig. 1. The study participants were asked to quickly discriminate target characters (“d” with two dots above or below the character) from many similar distracters (e.g., “d” with only one dot, or “p” instead of the letter “d”) by pressing one of the two response buttons designated as target stimulus (left button) and non-target stimulus (right button). The total number of stimuli was 1316, and the target-to-nontarget ratio was set to 1:3. The order of stimuli presentation was randomized. The maximum inter-stimulus interval (ISI) was set to 800  ms; if a study participant failed to respond within 800  ms, the next stimulus was presented. The maximum ISI was determined empirically based on the preliminary experiments prior to the main experiments. If the participant made a decision within 800 ms, the next stimulus was presented after a 50-ms delay time. The total experimental time differed from participant to participant; the average



∑ NC − E2 , ∑ NC

(1)

where NC is the number of correct responses, and E2 is the number of commission errors (wrong responses). Degree of vigilance decrement was calculated by DETER, which is the correlation between trial number and the sum of two types of errors, omission error (no response) and commission error. Both types of errors are associated with rule compliance and accuracy of visual scanning. However, error of omission is known to be more sensitive to attentional control and quality of performance, whereas error of commission is more closely related to inhibitory control and cognitive flexibility (Brickenkamp and Zillmer 1998). The CP index was evaluated continuously for each moving window consisting of 94 visual stimuli with an overlapping rate of 50%, resulting in 27 data points that represented temporal changes in vigilance. EEG Acquisition Continuous EEG was recorded with a 32-channel EEG recording system (Active Two, Biosemi Instrumentations, Amsterdam, Netherlands). Electrodes were attached on each participant’s scalp surface according to a modified 10–20 configuration. A CMS active electrode and a DRL passive electrode were used to form a feedback loop for the amplifier reference. Details of this feedback loop can be found on the company website (http://www.biosemi.com/faq/cms&drl. htm). A pair of electrodes was attached above and below the right eye to acquire a vertical electrooculogram (EOG). The sampling rate was set at 2048 Hz. E-prime (Psychology Software Tools, Pittsburgh, PA, USA) was used to synchronize the stimulus onset with the recorded signal. Data were preprocessed using CURRY7 for Windows (Compumedics USA, El Paso, TX, USA). The raw signal was downsampled to 1,024 Hz, and re-referenced to an average reference. EOG artifact was corrected automatically by the CURRY7 software using an established principal component analysis (PCA) process. Data were filtered using a band pass filter with cutoff frequencies of 0.1 and 55 Hz. Source Imaging Using sLORETA and Correlation Analysis EEG source imaging was conducted using standardized low resolution electromagnetic tomography (sLORETA) software, which calculates a particular solution of the non-unique EEG inverse problem (Pascual-Marqui 2002)

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Brain Topogr Fig. 2 A schematic diagram of the correlation analysis we performed. The correlation coefficient for temporal perturbation of source activation of each voxel and temporal changes of CP (behavioral data) was calculated. Statistical significance was evaluated using a permutation test (p < 0.05)

using a realistic standard head model extracted from the MNI 152 template (Fuchs et  al. 2002). The source space was restricted to cortical gray matter and hippocampus, and divided into 6239 voxels with 5-mm resolution. Details of this software can be found in (Pascual-Marqui 2002). Source images were continuously estimated for every 2  s time window, and then averaged over moving windows that were used to evaluate CP index to match the behavioral results, which also resulted in 27 time points that represented temporal changes of cortical source activities over whole experimental time. Source activities for five different frequency bands (delta (1–4 Hz), theta (4–8 Hz), alpha (8–12  Hz), beta1 (12–22  Hz), beta2 (22–30), and gamma (30–55 Hz)) were evaluated for each overlapping time window. A schematic diagram of our source imaging protocol is provided in Fig. 2. To investigate the brain areas responsible for vigilance, the correlation coefficient between temporal perturbation of source activation of each voxel and temporal changes of CP was calculated. To evaluate the statistical significance of the correlation, we applied a permutation test. Null distributions of correlation were calculated for each voxel by calculating the correlation between individual CP and source activations of the other participants. In other words, we compared two groups, 31 correlation coefficient values (same as number of subjects) and 930 null correlation coefficient values (number of subjects × number of subjects −1) per each voxels. We first assessed the significance of correlation of each voxel independently using a permutation test, and then adjacent significant voxels were clustered. Then, we only reported brain areas clustered with more than five voxels.

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Results Behavioral Performance The behavioral performance of each participant was evaluated using two indices, CP and DETER (Brickenkamp and Zillmer 1998).The average CP of the 31 participants was 77.78 ± 10.32%. The average DETER value was 0.27 ± 0.21, which implies that the vigilance of the study participants decreased during the task (p < 0.001). The high standard deviation of DETER suggested that participants had different vigilance abilities from one another. Figure 3 shows the average change in CP over time; CP decreased monotonically, as expected. The average number of E1 (omission error) was 55.24 ± 26.25 and the average number of E2 (commission error) was 16.06 ± 13.56. These results are similar to those in the original paper (Brickenkamp and Zillmer 1998) in that E2 type error is less common than E1 type error. The average reaction time in correct responses was 535.42 ± 25.17 ms.1

1

Trial-to-trial reaction time variability (RTV) was also evaluated for each of 27 segments because increased RTV might be potentially associated with decreased attention. RTV of each segment was defined as the standard deviation of response speed within the segment. However, no significant correlation was found between temporal changes in RTV and CP. The temporal variability of RTV was consistent over the entire experiment (mean of RTV over 27 time segments: 89.13 ms, standard deviation of RTV over 27 segments: 6.38 ms).

Brain Topogr Fig. 3 Temporal variation in concentrated performance (CP) averaged over all study participants

Brain Areas Associated with Vigilance The time series of CP and time series of EEG sources at some voxels showed significant negative correlations in the delta, beta1, beta2, and gamma frequency bands. In the delta frequency band, source activities at the left middle frontal gyrus and superior frontal gyrus showed significant correlations with temporal changes in vigilance (p < 0.05) (Fig.  4a). Likewise, left inferior frontal gyrus was significantly correlated with vigilance in both beta1 (Fig. 4b) and beta2 (Fig.  4c) frequency bands. In the gamma band, the left inferior frontal gyrus showed a significant association with vigilance (Fig.  4d). Detailed analyses results (MNI coordinates and t-values) are provided in Table 1.

Discussions and Conclusions In the present study, we looked for brain areas responsible for vigilance using EEG source imaging. We designed a digitalized d2-test paradigm, which is a modification of a well-known paper-based neuropsychological test of vigilance (Brickenkamp and Zillmer 1998). Focusing on the fact that d2-test scores are influenced by mental concentration, visual perception, visual scanning ability, and perceptual speed, we hypothesized that temporal fluctuation of d2-test scores would be related only to changes in the degree of mental concentration because the other factors except mental concentration are individual traits that do not fluctuate much over short time. EEG source activities at the left prefrontal cortex showed a significant correlation with temporal changes of vigilance in the delta, beta1, beta2,

and gamma frequency bands, but not the theta and alpha frequency bands. Our EEG source imaging results showed that the source activities of the left inferior, middle, or superior frontal gyrus were significantly negatively correlated with task performance changes in the delta, beta1, beta2, and gamma frequency bands. These results are consistent with previous EEG studies (Babiloni et  al. 2006; Dussault et  al. 2005; Harmony et al. 1996; Smit et al. 2004, 2005), a PET study (Coull et al. 1996), and a near infrared spectroscopy (NIRS) study (Bogler et al. 2014), which all reported that the left frontal lobe is tightly associated with vigilance. However, even though we found significantly strong correlations between vigilance fluctuation and temporal source activity changes in delta, beta1, beta2, and gamma bands, no brain area showed significant correlation between vigilance and source activities in the theta and alpha bands. These results are inconsistent with some previous studies that reported that theta and alpha EEG activities might play key roles in maintaining vigilance (Kelly et al. 2006; Sauseng et al. 2007). This discrepancy may originate from differences in the task paradigms and analysis methods used in this study versus those used in previous studies, as we chose to use a task paradigm and an analysis method that would minimize potential involvement of cognitive processes other than vigilance. Note that many previous physiological studies have reported that theta and alpha frequency bands have close relationships with higher cognitive processes such as working memory (Dockree et  al. 2007; Gevins et al. 1997; Klimesch 1999), which suggests that some of the results reported previously could be due to misinterpretation of other cognitive processes as vigilance (Rueckert et al. 1999).

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Brain Topogr Fig. 4 Brain areas that showed significant negative correlation with fluctuation of vigilance in each frequency band: a delta, b beta1, c beta2, and d gamma bands

The neural mechanisms underlying modulation of vigilance have been studied by various research groups. Nowadays, two pathways, the top-down pathway and bottom-up pathway, are widely used to elucidate the neural mechanism

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of vigilance control. The top-down pathway, an exogenous way to respond to signals (Hopfinger et  al. 2000), is relevant to the consciousness of individuals (Wright and Ward 2008). In contrast to the top-down mechanism, the

Brain Topogr Table 1 Brain areas strongly correlated with vigilance in EEG delta, beta1, beta2, and gamma frequency bands

Structures

Delta  Inferior frontal gyrus  Superior frontal gyrus  Medial frontal gyrus  Middle frontal gyrus  Anterior cingulate Beta1  Inferior frontal gyrus  Precentral gyrus Beta2  Inferior frontal gyrus  Middle frontal gyrus Gamma  Inferior frontal gyrus  Middle frontal gyrus

Cluster

MNI coordinates

Talairach coordinates

t

X

Y

Z

X

Y

Z

7 32 22 41 8

−55 −20 −15 −25 −10

35 50 50 50 50

0 0 0 0 0

−54 −20 −15 −25 −10

34 48 48 48 48

−2 −2 −2 −2 −2

−2.03* −2.09* −2.07* −2.08* −2.08*

32 7

−50 −45

25 45

−10 20

−50 −45

24 45

−10 16

−2.19* −2.05*

12 13

−50 −10

35 65

−5 10

−50 −10

34 63

−6 6

−2.04* −1.98*

43 16

−50 −40

30 35

−10 −10

−50 −40

29 33

−10 −10

−2.25* −2.13*

*p < 0.05

bottom-up mechanism is irrelevant to consciousness, and thus an endogenous way to respond to signals (Treisman and Gelade 1980). A neurophysiological study revealed that vigilance is modulated not by dichotomous interaction between top-down and bottom-up processes but, in many situations, by complementary interactions between them (Egeth and Yantis 1997). Several neurophysiological studies have attempted to verify which brain areas are responsible for top-down and bottom-up processes, and have reported that the top-down process mainly involves frontal and temporal areas, whereas the bottom-up process is associated with temporal and occipital areas (Corbetta et  al. 2002, 2000, 2008; He et  al. 2007; Hopfinger et  al. 2000; Kastner et al. 1999; Kincade et al. 2005). Our present results support the theory that, for the tasks stressing faster processing speed, a top-down mechanism is more dominantly involved in fluctuation of vigilance than a bottomup mechanism, because source activity in the frontal brain area only showed a significant correlation with temporal changes in vigilance. There are some limitations in this study. Only 32 channel EEG data were used for the source imaging. Better spatial resolution is expected if a larger number of channels is used for the source imaging although a recent study showed that 32 channels could also give reasonable spatial resolution when sLORETA was used (Song et  al. 2015). Investigating brain areas responsible for vigilance can also be explored using fMRI or simultaneously recorded EEG and fMRI. Other than the mental concentration, arousal level is also known as a factor that might affect performance of vigilance tasks (Corkum and Siegel 1993; Paus et al. 1997).

However, it is generally difficult to design the task that can keep subjects to maintain a specific level of arousal during the whole task period. Nonetheless, since most vigilance studies including ours use continuous and simple tasks, it is expected that arousal levels would not be drastically changed over time during the experiments. We are planning to analyze the same dataset with different analysis methods and different epochs (e.g., epochs segmented based on concentration performance, error types, and response time) in future studies. We will also investigate other factors that might influence the vigilance performance, e.g., inter-stimulus interval and arousal level, in our future studies. We used the d2-test to track vigilance changes in order to minimize potential involvement of higher cognitive processes other than vigilance. In addition, while many previous studies on vigilance simply used averaged behavioral scores to evaluate vigilance, we considered temporal changes in the behavioral index to identify brain areas that showed a significant association with vigilance changes. By minimizing the potential involvement of other higher cognitive processes, we were able to conclude that the left prefrontal brain area plays a significant role in the modulation of vigilance. This area can therefore potentially be used to monitor changes in individual vigilance over time with the aid of real-time source imaging, or serve as a potential target of noninvasive brain stimulation to prevent vigilance decrement. Acknowledgements This research was supported by the National Research Foundation of Korea(NRF) grants funded by Korea government (MSIP) (NRF-2015M3C7A1031969 and 2015R1A2A1A15054662).

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Brain Topogr Compliance with Ethical Standards Conflict of interest of interest.

All the authors declare that they have no conflict

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Brain Areas Responsible for Vigilance: An EEG Source Imaging Study.

Vigilance, sometimes referred to as sustained attention, is an important type of human attention as it is closely associated with cognitive activities...
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