Sub-Second Fluctuations Between Top-Down and Bottom-Up Modes Distinguish Diverse Brain States
Poster No:
1348
Submission Type:
Abstract Submission
Authors:
Joon-Young Moon1, Youngjai Park2, Younghwa Cha2, Han Byul Cho3, Ting Xu4
Institutions:
1Sungkyunkwan University / Institute for Basic Science, Suwon, South Korea, 2Sungkyunkwan University / Institute for Basic Science, Suwon-si, Gyeonggi-do, 3Institute for Basic Science, Suwon, Gyeonggi-do, 4Child Mind Institute, New York, NY
First Author:
Co-Author(s):
Introduction:
A key challenge of understanding the complex dynamics of brain activity is to reliably capture and interpret the directionality and flow of information across brain regions. Traditional measures, such as phase-lag indices (Stam et al. 2012), and traveling wave propagation analysis (Mohan et al. 2024), have provided valuable insights but often fail to reveal the whole brain dynamics or real-time dynamics, by requiring either choosing a subset of signals or time window averaging. To overcome the limitations, we provide a new measure of relative phase, a measure derived from the phase differences between oscillatory signals in different brain regions, as an indicator of neural directionality across the whole brain in real-time. By analyzing i) EEG data from general anesthesia, ii) simultaneous EEG-fMRI recordings with external stimulus, and iii) resting-state EEG datasets of typically developed group vs. ADHD group, we demonstrate the utility of relative phase analysis (RPA) by finding the sub-second fluctuations between top-down modes (where higher order areas phase-lead) and bottom-up modes (where lower order sensory areas phase-lead), with the dynamic characteristics of these fluctuations capable of distinguishing diverse brain states.
Methods:
We propose a relative phase measure that can reveal phase dynamics across the whole brain in real time, without the need for time-window averaging. First, by performing Hibert transform, the phase and amplitude dynamics are extracted from the brain signals. Second, we compute the global mean phase across the whole brain signals at each time point. Third, the global mean phase is then subtracted from the absolute phases of each signal, thus providing the phase-lead/lagness of each signal with respect to the global mean. Fourth, such topography of phrase-lead/lagness of the entire brain is computed for each time point across the whole time series. Finally, the dominant topographic patterns are identified by either performing K-means clustering or PCA on the patterns across the whole time series.
Results:
By performing RPA, we present results from multiple EEG data sets, showing that phase dynamics fluctuate between anterior-to-posterior directionality and posterior-to-anterior directionality at the sub-second scale. First, we analyze brain waves from participants undergoing general anesthesia, demonstrating that phase dynamics become nearly random, lacking distinct directionality patterns during the unconscious state. This suggests that characteristic directionalities emerge only when participants are conscious. Next, we analyze simultaneous EEG-fMRI recordings, comparing BOLD activity patterns from the fMRI with directionality patterns from the EEG. We find that anterior-to-posterior directionality is highly correlated with top-down biased functional networks, whereas posterior-to-anterior directionality correlates strongly with bottom-up biased functional networks. These results indicate that the sub-second anterior-posterior directionality fluctuations observed in the brain wave analysis represent rapid transitions between top-down and bottom-up modes of information flow. Finally, by comparing EEGs from typically developing individuals and those with ADHD, we show that the dynamic characteristics of top-down/bottom-up directionality fluctuations can also distinguish group-level differences. Specifically, the bottom-up directionality pattern exhibits longer dwell times and higher occurrence probabilities in the ADHD group.
Conclusions:
Proposing RPA for brain dynamics analysis, we propose: i) the anterior-to-posterior (top-down) versus posterior-to-anterior (bottom-up) directionality patterns are fundamental components of human brain dynamics, ii) the brain rapidly fluctuates between these two directionalities in sub-second scale, and iii) different brain states (e.g., conscious vs. unconscious, typically developing vs. ADHD) are distinguishable from each other by their unique profiles of the dynamic fluctuations.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism)
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
EEG/MEG Modeling and Analysis 1
Methods Development 2
Keywords:
Attention Deficit Disorder
Consciousness
Electroencephaolography (EEG)
FUNCTIONAL MRI
Modeling
Psychiatric Disorders
Other - Dynamics
1|2Indicates the priority used for review
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Provide references using APA citation style.
Mohan, U.R., Zhang, H., Ermentrout, B. et al. (2024), The direction of theta and alpha travelling waves modulates human memory processing. Nature Human Behaviour 8, 1124–1135.
Zhang, H., Watrous, A. J., Patel, A., & Jacobs, J. (2018), Theta and Alpha Oscillations Are Traveling Waves in the Human Neocortex. Neuron. 98, 1269-1281.
Moon, J. Y., Kim, J., Ko, T. W., Kim, M., Iturria-Medina, Y., Choi, J. H., ... & Lee, U. (2017). Structure shapes dynamics and directionality in diverse brain networks: mathematical principles and empirical confirmation in three species. Scientific reports, 7, 46606.
van Kerkoerle, T., Self, M. W., Dagnino, B., Gariel-Mathis, M.-A., Poort, J., van der Togt, C., & Roelfsema, P. R., (2014), Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex, Proceedings of National Academy of Science, 111, 14332-14341.
Moon, J.-Y., Müsch, K., Schroeder, C. E., Honey, C. J. (2023)., Inter-regional delays fluctuate in the human cerebral cortex, eLife, 13, RP92459.