Selected Publications

Inter-Regional Delays Fluctuate in the Human Cerebral Cortex
Joon-Young Moon, Kathrin Müsch, Charles E. Schroeder, Taufik A. Valiante, and Christopher J. Honey,
eLife 13: RP92459.

We measure changes in global oscillations and inter-regional couplings from human ECoG recordings. The strength and delays of inter-regional couplings continuously fluctuate in the presence of auditory narrative stimulus. Increases in low-frequency power (4-14Hz) were associated with stronger and more delayed inter-regional couplings, whereas increases in high-frequency power (65+ Hz) were assoicated with weaker couplings and zero-lag delays. Computational models suggest increases in latency could be explained by global increases in the effective influence of inter-regional signaling. These coupling dynamics can reflect a cortex-wide modulation of the relative influence of top-down and bottom-up signals in the human cerebral cortex.

Phase and Amplitude Dynamics of Coupled Oscillator Systems on Complex Networks
Jae Hyung Woo, Christopher J Honey, and Joon-Young Moon,
Chaos 30, 121102.

As a continuation of previous study (Chaos 29), we now investigate phase and amplitude dynamics of the coupled oscillator system. The expansion is made to include amplitude dynamics. By applying mean-field theory, we find in oscillator systems in which individual nodes can independently vary their ampitude over time, qualitatively different dynamics can be produced via vaiations on the coupling form and the underlying network structure. By applying coupled oscillator systems on brain networks, we predict that there are four possible modes of information flow: high/low activity top-down modes and high/low activity bottom-up modes.

Various Synchronous States Due to Coupling Strength Inhomogeneity and Coupling Functions in Systems of Coupled Identical Oscillators
Junhyeok Kim*, Joon-Young Moon*, UnCheol Lee, Seunghwan Kim, and Tae-Wook Ko,

Chaos 29, 011106.

We investigate phase dynamics of the coupled oscillator system on various complex networks. Applying mean-field theory, we find in oscillator systems with coupling strength inhomogeneity qualitatively different dynamics can emerge depending on their underlying network structure. Hub nodes can either phase-lead (becoming source of the information), or phase-lag (becoming sink of the information). This study is the basis for the expansion to include amplitude dynamics and applications to brain networks in Chaos, 30.

Mechanism of Hysteresis in Human Brain Networks During Transitions of Consciousness and Unconsciousness: Theoretical Principles and Empirical Evidence
Hyoungkyu Kim*, Joon-Young Moon*, George A. Mashour, and UnCheol Lee,

PLoS Computational Biology 14(8), e1006424.

Hysteresis, characterized by distinct forward and reverse phase transitions, is ubiquitous phenomena apearing in many complex systems of the nature. We take a network-based approach and show that anesthetic state transitions share the same underlying mechanism of other hysteresis in nature: nucleation and percolation. Indeed, via computational modeling, analytic study, and human EEG analysis, we show various hysteresis phenomena of conscious-unconscious transition can be explained by genetic network features. This study provide a possbility for a unified framework for radically different conscious state transitions associated with sleep, anesthesia and disorders of consciousness.

Structure Shapes Dynamics and Directionality in Diverse Brain Networks: Mathematical Principles and Empirical Confirmation in Three Species
Joon-Young Moon, Junhyeok Kim, Tae-Wook Ko, Minkyung Kim, Yasser Inturria-Medina, Jee-Hyun Choi, Joseph Lee, George A. Mashour, and UnCheol Lee,

Scientific Reports 7, 46606.

As a continuation of previous study (PloS Comp. Biol. 11), we refine our mathematical principle explaining the emergence of directionality from the underlying brain network structure. We apply our methods to brain networks of human, macaque, and mouse, successfully predicting information flow dynamics of the empirical EEG/ECoG data. The unique global directionality patterns in resting state brain networks of each species can be predicted by their distinctive brain network structures. This comprehensive study forms a foundation for a understanding of how neural information is drected and integrated in complex brain networks across three mammalian species.

General Relationship of Global Topology, Local Dynamics, and Directionality in Large-Scale Brain Networks
Joon-Young Moon, UnCheol Lee, Stefanie Blain-Mraes, and George A. Mashour,
PloS Computational Biology 11(4), e1004225.

How does the brain network organization determine local functions and information transfer patters? In this study, we show inter-node directionality arises naturally from the topology of the network. Analytical, computational, and empirical results all demonstrate that, on average, network nodes with more connections lag in phase, while ower-degree nodes lead. We demonstrate our novel phase analysis method can predict the directionality patterns in human brain networks across different states of consciousness. Our findings provide a straighforward method to dissect how directionality between interaction node is shapes in brain networks. Furthermore, the underlying mathematical relationship between node connections and directionality patterns has the potential to advance network science across multiple disciplines.