Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 Jun 3:10:264.
doi: 10.3389/fnhum.2016.00264. eCollection 2016.

Theta Oscillation Reveals the Temporal Involvement of Different Attentional Networks in Contingent Reorienting

Affiliations

Theta Oscillation Reveals the Temporal Involvement of Different Attentional Networks in Contingent Reorienting

Chi-Fu Chang et al. Front Hum Neurosci. .

Abstract

In the visual world, rapidly reorienting to relevant objects outside the focus of attention is vital for survival. This ability from the interaction between goal-directed and stimulus-driven attentional control is termed contingent reorienting. Neuroimaging studies have demonstrated activations of the ventral and dorsal attentional networks (DANs) which exhibit right hemisphere dominance, but the temporal dynamics of the attentional networks still remain unclear. The present study used event-related potential (ERP) to index the locus of spatial attention and Hilbert-Huang transform (HHT) to acquire the time-frequency information during contingent reorienting. The ERP results showed contingent reorienting induced significant N2pc on both hemispheres. In contrast, our time-frequency analysis found further that, unlike the N2pc, theta oscillation during contingent reorienting differed between hemispheres and experimental sessions. The inter-trial coherence (ITC) of the theta oscillation demonstrated that the two sides of the attentional networks became phase-locked to contingent reorienting at different stages. The left attentional networks were associated with contingent reorienting in the first experimental session whereas the bilateral attentional networks play a more important role in this process in the subsequent session. This phase-locked information suggests a dynamic temporal evolution of the involvement of different attentional networks in contingent reorienting and a potential role of the left ventral network in the first session.

Keywords: N2pc; attentional networks; contingent reorienting; inter-trial coherence; theta oscillation.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Illustration of the experimental conditions and procedure. Each stream consisted of 25 frames of three-letter sequences presented in serial order. Each frame was presented for 49.8 ms, followed by a 16.7 ms blank interval. Participant searched for the unique red letter in the central stream. The peripheral flanker streams were mostly gray letters. On one-third of the trials, the target-colored (TC) distractors were presented in the flanker streams two frames before the onset of the central target and lasted for four frames after the offset of the target. On another one third of the trials, these distractors were in a nontarget color. On the remaining one third of the trials, the distractors were all gray.
Figure 2
Figure 2
Illustration of the behavioral performance. The contingent capture effect (lower accuracy of the TC distractor condition, denoted by the red bars) was equal in both visual fields and different experimental sessions. Error bars represent 95% confidence intervals. *p < 0.05.
Figure 3
Figure 3
Results of event-related potential (ERP) waveforms and N2pc scalp distribution map. (A) Grand average waveforms for each distractor condition at contralateral vs. ipsilateral P3/P4 electrode sites. (B) The difference between the contralateral and ipsilateral waveforms. The differential waveforms showed that only TC distractors (red line) induced significant N2pc in comparison with the distractor-absent (gray line) and nontarget-colored (NTC) distractor (green line) conditions. Error shading indicates SEMs. Permutation test results for the contrast of the TC vs. NTC are presented at the bottom. (C) Scalp topography during the N2pc time window (160–240 ms) created by the differential amplitude between contralateral and ipsilateral waveforms.
Figure 4
Figure 4
Intrinsic mode functions from ensemble empirical mode decomposition (EEMD). The electroencephalography (EEG) data were decomposed into eight intrinsic mode functions (IMFs) by using EEMD. Each IMF denotes a different frequency band. The IMFs depicted in the figure were averaged from all electrodes in the distractor-absent condition and arranged by frequency. The theta oscillation is located at the sixth IMF. Error shading indicates SEMs.
Figure 5
Figure 5
Scalp distribution of the theta IMF amplitude. The t value denotes the comparison between the TC and NTC distractor conditions. Each topographic frame denotes the instantaneous time point and is illustrated from 0 to 429 ms relative to the onset of distractor. The inter-stimulus interval of rapid serial visual presentation (RSVP) was 66.4 ms, so every two topographic frames equal an RSVP frame. The permutation results showed that significant theta modulation started from the left hemisphere and then propagated to the right hemisphere. In the first session, the theta amplitude increased from the left hemisphere when the TC distractors appeared in the right visual field (RVF) (A). In contrast, the theta amplitude increased from the right hemisphere in the second session (B). The highlighted concentric circles in the topographic maps denote p < 0.05 in the cluster-based non-parametric permutation (CBnPP).
Figure 6
Figure 6
Scalp distribution and temporal profiles of theta IMF inter-trial coherence (ITC). The t value denotes the comparison between the TC and NTC distractor conditions. Each topographic frame denotes the instantaneous time point and is illustrated from 0 to 429 ms relative to the onset of distractor (separated every 33 ms). The inter-stimulus interval in the RSVP stream was 66.4 ms, so every two topographic frames equal an RSVP frame. (A) In the first session, ITC increased in the left temporal and parietal regions after the onset of RVF distractors to 300 ms after onset. (B) In the second session, ITC increased in the right frontal region in the left visual field (LVF) distractor condition. The ITC in the RVF condition in the first session and in the LVF condition in the second session did not significantly change. The concentric white circles denote p < 0.05 in the CBnPP. (C,D) Illustrate the temporal profiles of ITC in the two regions of interest (bold black circles in (A,B)).
Figure 7
Figure 7
Source reconstruction of the ITC in the theta-band activity. The source reconstruction was based on the conditions which revealed significant ITC changes on the sensor level. The top row of the diagram illustrates the results for the CBnPP test between the right TC and NTC distractor conditions in the time window of 130–230 ms in the first session. The bottom row of the diagram illustrates the results for the CBnPP test between the left TC and NTC distractor conditions in the time window of 230–330 ms in the second session. Only the grid points with p < 0.05 in the CBnPP test will be illustrated in color.

References

    1. Asplund C. L., Todd J. J., Snyder A. P., Marois R. (2010). A central role for the lateral prefrontal cortex in goal-directed and stimulus-driven attention. Nat. Neurosci. 13, 507–512. 10.1038/nn.2509 - DOI - PMC - PubMed
    1. Burnham B. R., Rozell C. A., Kasper A., Bianco N. E., Delliturri A. (2011). The visual hemifield asymmetry in the spatial blink during singleton search and feature search. Brain Cogn. 75, 261–272. 10.1016/j.bandc.2011.01.003 - DOI - PubMed
    1. Buzsáki G., Draguhn A. (2004). Neuronal oscillations in cortical networks. Science 304, 1926–1929. 10.1126/science.1099745 - DOI - PubMed
    1. Chang C.-F., Hsu T.-Y., Tseng P., Liang W.-K., Tzeng O. J. L., Hung D. L., et al. (2013). Right temporoparietal junction and attentional reorienting. Hum. Brain Mapp. 34, 869–877. 10.1002/hbm.21476 - DOI - PMC - PubMed
    1. Chen D., Li D., Xiong M., Bao H., Li X. (2010). GPGPU-aided ensemble empirical-mode decomposition for EEG analysis during anesthesia. IEEE Trans. Inf. Technol. Biomed. 14, 1417–1427. 10.1109/TITB.2010.2072963 - DOI - PubMed