Spatiotemporal Patterns in Traveling Waves during the Oddball Task
Poster No:
1220
Submission Type:
Abstract Submission
Authors:
Dojin Heo1, Duho Sihn2, Yeongseon Choi2, ChaeEun Yoon2, Sung-Phil Kim2
Institutions:
1Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea, 2Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
First Author:
Dojin Heo
Department of Biomedical Engineering, Ulsan National Institute of Science and Technology
Ulsan, Republic of Korea
Department of Biomedical Engineering, Ulsan National Institute of Science and Technology
Ulsan, Republic of Korea
Co-Author(s):
Introduction:
Traveling waves (TWs) represent a spatiotemporal pattern of cortical activity that mediates information transfer in the brain (Muller et al., 2018). Previous human studies have primarily inspected TWs in invasive electrocorticography (ECoG), which limitedly show local cortical activity only. In contrast, using electroencephalography (EEG) enables the analysis of TWs across the whole brain. Studies on the human EEG have reported associations between TWs and visual perception and attention (Alamia et al., 2022) or interference (Klimesch et al., 2007). However, few studies have explored how TWs are related to the interplay among multiple cognitive processes-such as attention, working memory, and behavioral information processing-in a single cognitive task. Therefore, we aim to examine TWs across brain regions using EEG collected during an oddball task, which involves multiple cognitive processes.
Methods:
This study analyzed a publicly available dataset (Dzianok et al., 2022), which offered high-density EEG recordings from 128 active wet electrodes. Forty-two participants performed the oddball task consisting of a frequently presented standard stimulus and rarely presented target/distractor stimuli. Participants were instructed to provide a response by pressing a button only to the target stimulus.
To analyze TWs at each EEG channel, the local phase gradient (LPG) method was employed (Sihn & Kim, 2024). The LPG was obtained from the phase of single-trial EEG signals filtered within 1-12 Hz frequency band. The phase of the filtered signals was estimated using the Hilbert transform. Then, we spatially averaged the LPG vectors within five cortical regions: central, left/right occipito-temporal, and left/right frontal regions. Additionally, direction concordance across trials was analyzed to assess the consistency of TW directionality in response to each stimulus type.
To analyze TWs at each EEG channel, the local phase gradient (LPG) method was employed (Sihn & Kim, 2024). The LPG was obtained from the phase of single-trial EEG signals filtered within 1-12 Hz frequency band. The phase of the filtered signals was estimated using the Hilbert transform. Then, we spatially averaged the LPG vectors within five cortical regions: central, left/right occipito-temporal, and left/right frontal regions. Additionally, direction concordance across trials was analyzed to assess the consistency of TW directionality in response to each stimulus type.
Results:
We compared the direction of LPGs between stimulus types at specific time windows for each region. We found significant differences between the standard and the target/distractor stimuli in some regions during early processing (250–300 ms) and late processing (400–600 ms), respectively (Fig. 1). During early processing, the directionality of TWs in the left/right occipito-temporal regions significantly differed between the standard and target/distractor stimuli (p < 0.05, Watson-Williams test corrected by FDR). Specifically, the standard stimulus drove TWs in the occipital-to-temporal direction, whereas the target/distractor stimuli did in the opposite temporal-to-occipital direction. In contrast to early processing, the TW directionality during late processing reversed: TWs propagated in the temporal-to-occipital direction for the standard, while they did in the occipital-to-temporal direction for the target/distractor stimuli (p < 0.05).
Moreover, during late processing, significant directionality was observed in the central region exclusively for the target stimulus (p < 0.05, Rayleigh's test corrected by FDR). Notably, direction concordance within the central region was significantly higher for the target stimulus compared to the other stimuli (Fig. 2).
Moreover, during late processing, significant directionality was observed in the central region exclusively for the target stimulus (p < 0.05, Rayleigh's test corrected by FDR). Notably, direction concordance within the central region was significantly higher for the target stimulus compared to the other stimuli (Fig. 2).
·Average LPG streamlines across subjects at exampled 250 ms and 550 ms (early and late processing) for each condition—standard, target, and distractor
·Direction concordance across conditions for each of the five cortical regions
Conclusions:
TWs exhibited specific directionality associated with the cognitive processing of different stimuli in the oddball task. During early and late processing, opposing directional patterns were observed in the occipito-temporal regions between the standard and target/distractor stimuli. This spatiotemporal pattern may reflect the involvement of visual perception and attention (Alamia et al., 2023). Also, TWs in the central-to-frontal region became more consistent only for the late processing of the target stimulus. The greater direction concordance in this region compared to the distractor stimulus may suggest information transfer to subsequent behavioral reaction (Stolk et al., 2019).
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Novel Imaging Acquisition Methods:
EEG 2
Perception, Attention and Motor Behavior:
Attention: Visual
Keywords:
Cognition
Data analysis
Electroencephaolography (EEG)
1|2Indicates the priority used for review
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Provide references using APA citation style.
Dzianok, P., Antonova, I., Wojciechowski, J., Dreszer, J., & Kublik, E. (2022). The Nencki-Symfonia electroencephalography/event-related potential dataset: Multiple cognitive tasks and resting-state data collected in a sample of healthy adults. GigaScience, 11, giac015.
Klimesch, W., Hanslmayr, S., Sauseng, P., Gruber, W. R., & Doppelmayr, M. (2007). P1 and Traveling Alpha Waves: Evidence for Evoked Oscillations. Journal of Neurophysiology, 97(2), 1311–1318.
Muller, L., Chavane, F., Reynolds, J., & Sejnowski, T. J. (2018). Cortical travelling waves: mechanisms and computational principles. Nature Reviews Neuroscience, 19(5), 255–268.
Stolk, A., Brinkman, L., Vansteensel, M. J., Aarnoutse, E., Leijten, F. S. S., Dijkerman, C. H., Knight, R. T., de Lange, F. P., & Toni, I. (2019). Electrocorticographic dissociation of alpha and beta rhythmic activity in the human sensorimotor system. ELife, 8, e48065.
Sihn, D., & Kim, S.-P. (2024). Disruption of alpha oscillation propagation in patients with schizophrenia. Clinical Neurophysiology, 162, 262–270.