PLoS Computational Biology
April 14, 2015
Joon-Young Moon, UnCheol Lee, Stefanie Blain‐moraes et al.
143 citations
Efficient brain networks balance global integration with functional specialization, but how global topology, local node dynamics, and information flow relate has been unclear. Using analytical solutions of oscillator models, computational simulations on model and anatomical brain networks, and high-density electroencephalography from conscious and anesthetized humans, the authors demonstrate that network nodes with more connections (higher degree) have larger amplitudes and are directional targets (phase lag) rather than sources (phase lead). This degree–directionality relationship appears to be a fundamental network property with direct applicability to brain function. Changes in directionality patterns across states of human consciousness are driven by alterations in brain network topology.
PLoS Computational Biology
August 30, 2018
Hyoungkyu Kim, Joon-Young Moon, George A. Mashour et al.
79 citations
Hysteresis—the difference between the forward and reverse paths of state transitions—occurs as people lose and regain consciousness. Analyzing high-density EEG from healthy volunteers given sevoflurane or ketamine, the authors found that functional brain networks exhibit hysteresis during these transitions. The principle of explosive synchronization, which governs abrupt state shifts in many complex networks, also explains hysteresis in the brain. More potent anesthetics produce larger hysteresis; a broader range of EEG frequencies hastens the loss of consciousness but delays its return; connectivity shows greater hysteresis than EEG power; and network structure and strength reconfigure differently during loss versus recovery. These results indicate that hysteresis in conscious state transitions is a generic network feature, potentially allowing prediction and modulation of such transitions.
Current biology : CB
July 2, 2026
Youngjai Park, Younghwa Cha, Hyoungkyu Kim et al.
The human brain's information flow alternates between two dominant modes roughly every 200 milliseconds: a top-down mode where anterior brain regions drive posterior activity, and a bottom-up mode with reverse directionality. These sub-second alternations are most prominent during wakefulness, gradually diminish under anesthesia, and show pathological imbalance in attention-deficit/hyperactivity disorder (ADHD). Simultaneous EEG-fMRI recordings reveal that top-down dynamics coincide with increased activity in higher-order cognitive networks, while bottom-up dynamics correspond to heightened sensory network activity. A connectome-based coupled-oscillator model reproduces these transitions, suggesting they emerge naturally from structural connectivity. Relative phase analysis (RPA) enables tracking these whole-brain dynamics with millisecond precision in real time from electroencephalography.
bioRxiv : the preprint server for biology
March 28, 2025
Youngjai Park, Younghwa Cha, Hyoungkyu Kim et al.
preprint
The human brain shifts between two directional modes on a sub-second timescale: a top-down mode where anterior regions drive posterior activity and a bottom-up mode with reverse directionality. These shifts are most distinct during full consciousness and become less pronounced as awareness fades. Simultaneous EEG-fMRI recordings show the top-down mode coincides with higher-order cognitive network activity, while the bottom-up mode aligns with sensory system activity. An inattentive ADHD cohort exhibited imbalances in these transition dynamics compared to typically developing individuals. A coupled-oscillator model of the structural brain network reproduced these patterns, suggesting they arise naturally from inter-regional neural interactions.