Secondary Logo

Journal Logo

Beta band patterns in the visible and masked sections of the coincidence-anticipation timing task

Koshizawa, Ryoa; Mori, Akioa; Oki, Kazumaa; Ozawa, Torub; Takayose, Masakic; Minakawa, Nahoko T.a

doi: 10.1097/WNR.0b013e32835b91cf
COGNITIVE NEUROSCIENCE AND NEUROPSYCHOLOGY
Free
SDC

In this study, we examined beta band patterns during the coincidence-anticipation timing task. The tasks were the coincidence-anticipation timing task using a partially masked stimulus runway and a control task using the stimulus runway with no masking. Both tasks were displayed on a computer screen placed 1.3 m in front of the participants while they were seated in an armchair. Ten healthy right-handed adult men were asked to press a holding push button with their right thumb when a downward-moving visual target arrived at the end of each task. The electroencephalogram during both tasks was divided into three segments: the visible section, the first half of the masked section, and the second half of the masked section. The valid epochs were subjected to fast Fourier transform to obtain the power density in the beta bands. Power in the beta bands was expressed as a percentage of the total power (3–30 Hz) in each segment. The results showed that the percentage of beta band activity in Brodmann’s areas 7 and 19 was significantly increased in both the visible and the masked sections of the coincidence-anticipation timing task compared with the control task. These results suggest that Brodmann’s areas 7 and 19 mainly contribute toward attention to visual targets in the visible section and to movement prediction of moving visual targets in the masked section. In addition, Brodmann’s areas 9 and 10, which were inactive, might affect the response time in the masked section during the coincidence-anticipation timing task.

aCollege of Humanities and Sciences, Nihon University, Tokyo

bCollege of Art, Nihon University, Saitama

cCollege of Industrial Technology, Nihon University, Chiba, Japan

Correspondence to Ryo Koshizawa, College of Humanities and Sciences, Nihon University, 3-25-40 Sakurajosui, Setagaya-ku, Tokyo 156-8550, Japan Tel: +81 3 5317 9717; fax: +81 3 5317 9426; e-mail: r-koshi@chs.nihon-u.ac.jp

Received October 10, 2012

Accepted October 15, 2012

Back to Top | Article Outline

Introduction

Coincidence anticipation, which is defined as the ability to predict the arrival of a moving target at a particular point in space and to coordinate a movement response with that arrival 1, is an ability that is required in various activities and sports in daily life. Previous studies have examined the accuracy of such a prediction on the basis of the response times made during the coincidence-anticipation timing task 2,3. However, to clarify the mechanism involved in performing this task, we might also need to consider the role that the central nervous system, such as the cerebral cortex, plays in task processing and how this affects the response time.

Relations have been reported between increased beta band activity observed on electroencephalograms (EEGs) and activity in the cortical region during a cognitive task 4. Moreover, information transmission within the association area is mainly conducted by corticocortical projections not only within the near-association area, such as from the somatosensory cortex to the motor cortex 5–7, but also within the distal association area, such as from the prefrontal cortex to the temporal cortex 8 and from the parietal cortex to various cortical fields 9 in layers II–III. Beta bands on EEGs can be used as a marker of corticocortical coherence 10, and they reflect the corticocortical transmissions related to specific information processing 11. Thus, a higher percentage of beta band activity might reflect not only activity in the cortical region but also the transmission of information within the association area by the corticocortical connections.

In this study, to determine the role of the cerebral cortex in the coincidence-anticipation timing task, we examined beta band patterns in the visible and masked sections of the coincidence-anticipation timing task.

Back to Top | Article Outline

Materials and methods

Participants

Participants were 10 men aged 21–23 years (mean=21.8, SD=0.6). All were right-handed and had no history of seizures, psychiatric illness, or severe head injury. The experiment conformed to the 1964 Declaration of Helsinki and was approved by the institutional ethics committee. Participants provided written informed consent before taking participating in the study and were informed of their rights under the Freedom of Information Act.

Back to Top | Article Outline

Apparatus and experimental procedures

Participants were tested using a Digital-Type Speed Anticipation Reaction Tester (Takei Scientific Instruments Co. Ltd., Niigata, Japan) on a computer display placed 1.3 m in front of them while seated in an armchair and holding a push button in their right hand. They completed the coincidence-anticipation timing task, which used a partially masked stimulus runway (ratio of visible section to masked section=1 : 2), and a control task, which also used the stimulus runway, but with no masking (Fig. 1). All the participants were asked to press the push button with their right thumb at the time they anticipated the arrival of a downward-moving visual target at the end of each task. The stimulus runway was 25 cm long and the moving visual target, traveling at a constant velocity of 16.5 cm/s, took 1500 ms to move from the top to the bottom of the runway. The diameter of the target was 3.3 cm and the angle of vision from the top to the bottom of the runway was 10.8°. Participants completed four blocks of five trials each (20 trials in total). There was a 4 s interval between each trial in a block, an ∼1 min interval between each block of trials, and an ∼3 min interval between tasks. Participants were instructed not to move their eyes during the trials.

Fig. 1

Fig. 1

To initiate testing, participants were seated in a reclining armchair in a quiet and electrically shielded room. They were provided with a set of standardized instructions and performed 10 practice trials of the coincidence-anticipation timing task. After the practice trials, participants were tested first on the coincidence-anticipation timing task and then on the control task.

Back to Top | Article Outline

Electroencephalogram recordings and data analysis

Continuous EEG was recorded with a 128-channel EGI sensor net (Electrical Geodesics Inc., Eugene, Oregon, USA) referenced to the vertex electrode. Electrode positions in the 128-channel EGI sensor net are shown in Fig. 2. A ground electrode was placed at Fpz. The EEG was amplified with a bandwidth of 0.1–200 Hz and digitized at a rate of 500 Hz. The visual target’s starting time, its arrival time, and the participant’s response time were recorded.

Fig. 2

Fig. 2

After recording, data were passed through a digital low-pass filter (30 Hz) and a digital high-pass filter (3 Hz). To investigate beta band patterns in the visible and masked sections, which were of the same duration, the data for both the control and the coincidence-anticipation timing tasks were divided into three categories: the time epoch of 0–500 ms after the visual target’s starting time was defined as the visible section, that of 500–1000 ms after the starting time as the first half of the masked section, and that of 1000 ms after the starting time until the response time as the second half of the masked section. If the data for 10 or more channels exceeded a voltage threshold of 200 μV (absolute) or a transition threshold of 100 μV (sample to sample) or were contaminated with eye blinks or eye movements, each affected segment of the EEG was excluded from signal averaging. The data were re-referenced to the average reference. The valid epochs were subjected to fast Fourier transform analysis using the EMSE Suite (Source Signal Imaging Inc., San Diego, California, USA) to produce the power density of the beta bands that was used as a marker of corticocortical coherence 10. Moreover, as relations have been reported between increased beta band activity observed on EEGs and activity in the cortical region during a cognitive task 4, the power in the beta bands (19–23 Hz) was expressed as a percentage of the total power (3–30 Hz) in each epoch, whereas that calculated in numbers was averaged, with the numbers shown as two-dimensional (2D) topography.

Back to Top | Article Outline

Statistical analysis

A paired t-test was used to compare the response time and the percentage of beta band activity at each electrode and epoch between the control task and the coincidence-anticipation timing task. Statistical significance was set at P value less than 0.05.

Back to Top | Article Outline

Results

Response time

The average response time was 1492±7 ms during the control task and 1573±19 ms during the coincidence-anticipation timing task, indicating a tendency for a delay in the average response time during the coincidence-anticipation timing task compared with the control task (P=0.085).

Back to Top | Article Outline

Percentage of beta band activity

The percentages of beta band activity during the control and the coincidence-anticipation timing tasks are shown in Table 1 for cases wherein there was a significant difference between the two tasks at an electrode. Compared with the control task, the percentage of beta band activity was significantly changed in the coincidence-anticipation timing task as follows: in the visible section, the percentage was significantly increased in Brodmann’s areas 6 (Ch13, 21, 29, 36, 119), 7 (Ch79, 80, 87), 19 (Ch67, 68, 78, 86), and 39 (Ch93); in the first half of the masked section, it was significantly increased in Brodmann’s areas 19 (Ch53, 60), 37 (Ch59), and 40 (Ch43, 48) and significantly decreased in Brodmann’s area 17 (Ch75); and in the second half of the masked section, it was significantly decreased in Brodmann’s areas 9 (Ch24) and 10 (Ch23, 27) 12,13. The percentage of beta band activity during the two tasks is shown in Fig. 3 as 2D topography. Differences between the tasks were observed for electrodes showing significant differences in the left premotor cortex and the parietal cortex in the visible section, in the left parietal cortex and left occipital gyrus in the first half of the masked section, and in the left prefrontal cortex in the second half of the masked section. In addition, the prefrontal cortex was the most active cortical region and was consistently active throughout the target’s motion in both tasks.

Table 1

Table 1

Fig. 3

Fig. 3

Back to Top | Article Outline

Discussion

Visible section

The percentage of beta band activity during the coincidence-anticipation timing task in the visible section was significantly increased in Brodmann’s areas 6, 7, 19, and 39 compared with the control task. The moving visual target was visible in the control task and in the visible section of the coincidence-anticipation timing task, and participants needed to attend to the moving visual target in the visible section of the coincidence-anticipation timing task to make an appropriate response before it entered the masked section a short time later. Brodmann’s areas 7, 19, and 39 have been shown to be active in the control of spatial attention to visual targets 14. Thus, activation of Brodmann’s areas 7, 19, and 39 observed in the present study might reflect attention to the moving visual target in the visible section of the coincidence-anticipation timing task. Moreover, Brodmann’s area 6 is active in perceptual complexity in attended and predicted target motion 15. Such findings indicate that the activation of Brodmann’s area 6 in the present study might reflect attention paid to the moving visual target in the visible section of the coincidence-anticipation timing task, because participants in Schubotz and Cramon’s study 15 also needed to attend to a visual target during tasks. Brodmann’s area 7, which has been shown to connect to Brodmann’s area 6 9, might transmit the information about attending to the moving visual target because the percentage of beta band activity, which was used as a marker of corticocortical connections 10, increased. Therefore, Brodmann’s area 7 likely contributes toward attention to moving visual targets in the visible section of the coincidence-anticipation timing task.

Back to Top | Article Outline

First half of the masked section

The percentage of beta band activity in the first half of the masked section was significantly increased in Brodmann’s areas 19, 37, and 40 and significantly decreased in Brodmann’s area 17 compared with the control task. In the first half of the masked section, the moving visual target was invisible; therefore, the percentage of beta band activity during this time was significantly decreased in Brodmann’s area 17, which has been shown to be active during received visual information 16. Brodmann’s area 19 is active not only in the control of spatial attention to visual targets 14 but also in response to feedforward visual information 17. In addition, transcranial magnetic stimulation of Brodmann’s area 40 caused a significant delay in planning goal-oriented actions when participants were asked to prepare for a goal-specific movement toward a common object with the intent of grasping-to-pour or grasping-to-move it 18. Participants needed to focus on what was to come (i.e. feedforward or planning) during the coincidence-anticipation timing task in the masked section; thus, these regions were active. Brodmann’s area 37, which is active in optic flow 19, was also active, because the participants had to predict the target’s movement.

Back to Top | Article Outline

Second half of the masked section

The percentage of beta band activity in the second half of the masked section was significantly decreased in Brodmann’s areas 9 and 10 compared with the control task. These areas have been shown to be active in the abstract relational integration component of analogical reasoning 20. In the second half of the masked section, participants needed to integrate information on the moving visual target while it was in the visible section and in the first half of the masked section, and then to respond when the target arrived at an end. However, there was a tendency for some delay in the average response time during the coincidence-anticipation timing task compared with the control task (P=0.085). Brodmann’s areas 9 and 10, which were inactive in the second half of the masked section, might therefore affect the response time.

In terms of the results of 2D topographic mapping, although the responses to the control task and the coincidence-anticipation timing task did not show a significant difference in terms of prefrontal cortex activity in the visible section and in the first half of the masked section, the prefrontal cortex was consistently active throughout the target’s motion in both the tasks and was the most active of the cortical regions (Fig. 3). Thus, activation of the prefrontal cortex, which receives information about a moving target 21, might reflect the receiving of information about the target during both types of task.

Our results suggest that Brodmann’s areas 7 and 19 mainly contribute toward attention to a visual target in the visible section and toward movement prediction of a moving visual target in the first half of the masked section. In addition, Brodmann’s areas 9 and 10, which were inactive in the second half of the masked section during the coincidence-anticipation timing task, might affect the response time.

Back to Top | Article Outline

Acknowledgements

Conflicts of interest

There are no conflicts of interest.

Back to Top | Article Outline

References

1. Payne VG. The effects of stimulus runway length on coincidence-anticipation timing performance. J Hum Mov Stud. 1986;12:289–295
2. Isaacs LD. Effects of angle of approach on coincidence-anticipation timing within a two target display. J Hum Mov Stud. 1990;19:171–179
3. Payne VG, Michael D. Effects of location of stimulus occlusion, stimulus velocity, and gender on coincidence-anticipation timing performance. J Hum Mov Stud. 1990;18:243–250
4. Ray WJ, Cole HW. EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive process. Science. 1985;228:750–752
5. Mori A. Cortico-cortical connections from somatosensory areas to the motor area of the cortex following peripheral nerve lesion in the cat. NeuroReport. 1997;8:3723–3726
6. Mori A, Waters RS, Asanuma H. Physiological properties and patterns of projection in the cortico-cortical connections from the second somatosensory cortex to the motor cortex, area 4 gamma, in the cat. Brain Res. 1989;504:206–210
7. Waters RS, Favorov O, Mori A, Asanuma H. Pattern of projection and physiological properties of cortico-cortical connections from the posterior bank of the ansate sulcus to the motor cortex, area 4 gamma, in the cat. Exp Brain Res. 1982;48:335–344
8. Rempel–Clower NL, Barbas H. The laminar pattern of connections between prefrontal and anterior temporal cortices in the Rhesus monkey is related to cortical structure and function. Cereb Cortex. 2000;10:851–865
9. Andersen RA, Asanuma C, Essick G, Siegel RM. Corticocortical connections of anatomically and physiologically defined subdivisions within the inferior parietal lobule. J Comp Neurol. 1990;296:65–113
10. Wheaton LA, Nolte G, Bohlhalter S, Fridman E, Hallett M. Synchronization of parietal and premotor areas during preparation and execution of praxis hand movements. Clin Neurophysiol. 2005;116:1382–1390
11. Hughes JR, John ER. Conventional and quantitative electroencephalography in psychiatry. J Neuropsychiatry Clin Neurosci. 1999;11:190–208
12. Koessler L, Maillard L, Benhadid A, Vignal JP, Felblinger J, Vespignani H, et al. Automated cortical projection of EEG sensors: anatomical correlation via the international 10–10 system. NeuroImage. 2009;46:64–72
13. Luu P, Ferree T Determination of the Geodesic sensor net’s average electrode positions and their 10–10 international equivalents. Technical note. 2000 Eugene, OR Electrical Geodesics Inc.:1–15
14. Serences JT, Schwarzbach J, Courtney SM, Golay X, Yantis S. Control of object-based attention in human cortex. Cereb Cortex. 2004;14:1346–1357
15. Schubotz RI, von Cramon DY. A blueprint for target motion: fMRI reveals perceived sequential complexity to modulate premotor cortex. NeuroImage. 2002;16:920–935
16. Hubel DH, Wiesel TN. Brain mechanisms of vision. Sci Am. 1979;241:150–162
17. Agnew Z, Wise RJ. Separate areas for mirror responses and agency within the parietal operculum. J Neurosci. 2008;28:12268–12273
18. Tunik E, Lo OY, Adamovich SV. Transcranial magnetic stimulation to the frontal operculum and supramarginal gyrus disrupts planning of outcome-based hand-object interactions. J Neurosci. 2008;28:14422–14427
19. Morrone MC, Tosetti M, Montanaro D, Fiorentini A, Cioni G, Burr DC. A cortical area that responds specifically to optic flow, revealed by fMRI. Nat Neurosci. 2000;3:1322–1328
20. Green AE, Fuqelsanq JA, Kraemer DJ, Shamosh NA, Dunbar KN. Frontopolar cortex mediates abstract integration in analogy. Brain Res. 2006;1096:125–137
21. Passingham RE, Toni I. Contrasting the dorsal and ventral visual systems: guidance of movement versus decision making. NeuroImage. 2001;14:S125–S131
Keywords:

beta band; coincidence-anticipation timing task; electroencephalogram; masked section; visible section

© 2013 Lippincott Williams & Wilkins, Inc.