4 citations · 10 across the 12 of their papers we have counts for
12 papers
Inferring brain plasticity rule under long-term stimulation with structured recurrent dynamics
Zhichao Liang, Jingzhe Lin, Xinyi Li +2
Understanding how long-term stimulation reshapes neural circuits requires uncovering the rules of brain plasticity. While short-term synaptic modifications have been extensively ch…
Artificial intelligence as a surrogate brain: Bridging neural dynamical models and data
Yinuo Zhang, Demao Liu, Zhichao Liang +6
Recent breakthroughs in artificial intelligence (AI) are reshaping the way we construct computational counterparts of the brain, giving rise to a new class of ``surrogate brains''.…
Uncovering cognitive taskonomy through transfer learning in masked autoencoder-based fMRI reconstruction
Youzhi Qu, Junfeng Xia, Xinyao Jian +5
Data reconstruction is a widely used pre-training task to learn the generalized features for many downstream tasks. Although reconstruction tasks have been applied to neural signal…
Suppressing seizure via optimal electrical stimulation to the hub of epileptic brain network
Zhichao Liang, Guanyi Zhao, Yinuo Zhang +4
The electrical stimulation to the seizure onset zone (SOZ) serves as an efficient approach to seizure suppression. Recently, seizure dynamics have gained widespread attendance in i…
Controlling network-coupled neural dynamics with nonlinear network control theory
Zhongye Xia, Weibin Li, Zhichao Liang +2
This paper addresses the problem of controlling the temporal dynamics of complex nonlinear network-coupled dynamical systems, specifically in terms of neurodynamics. Based on the L…
Reverse engineering the brain input: Network control theory to identify cognitive task-related control nodes
Zhichao Liang, Yinuo Zhang, Jushen Wu +1
The human brain receives complex inputs when performing cognitive tasks, which range from external inputs via the senses to internal inputs from other brain regions. However, the e…
Mapping effective connectivity by virtually perturbing a surrogate brain
Zixiang Luo, Kaining Peng, Zhichao Liang +6
Effective connectivity (EC), indicative of the causal interactions between brain regions, is fundamental to understanding information processing in the brain. Traditional approache…
Network analysis on cortical morphometry in first-episode schizophrenia
Mowen Yin, Weikai Huang, Zhichao Liang +2
First-episode schizophrenia (FES) results in abnormality of brain connectivity at different levels. Despite some successful findings on functional and structural connectivity of FE…
Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Network
Ziyuan Ye, Youzhi Qu, Zhichao Liang +2
Brain decoding, aiming to identify the brain states using neural activity, is important for cognitive neuroscience and neural engineering. However, existing machine learning method…
Partial Least Square Regression via Three-factor SVD-type Manifold Optimization for EEG Decoding
Wanguang Yin, Zhichao Liang, Jianguo Zhang +1
Partial least square regression (PLSR) is a widely-used statistical model to reveal the linear relationships of latent factors that comes from the independent variables and depende…
Kuramoto model based analysis reveals oxytocin effects on brain network dynamics
Shuhan Zheng, Zhichao Liang, Youzhi Qu +3
The oxytocin effects on large-scale brain networks such as Default Mode Network (DMN) and Frontoparietal Network (FPN) have been largely studied using fMRI data. However, these stu…
Online Learning Koopman operator for closed-loop electrical neurostimulation in epilepsy
Zhichao Liang, Zixiang Luo, Keyin Liu +2
Electrical neuromodulation as a palliative treatment has been increasingly used in the control of epilepsy. However, current neuromodulations commonly implement predetermined actua…