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Found 7 papers, 0 papers with code
Deep learning acceleration of iterative model-based light fluence correction for photoacoustic tomography
Photoacoustic tomography (PAT) is a promising imaging technique that can visualize the distribution of chromophores within biological tissue.
Mapping the whole-brain effective connectome with excitatory-inhibitory causal relationship
Understanding the large-scale causal relationship among brain regions is crucial for elucidating the information flow that the brain integrates external stimuli and generates behaviors.
Network analysis on cortical morphometry in first-episode schizophrenia
Our work supports that cortical morphological connectivity, which is constructed based on correlations across subjects' cortical thickness, may serve as a tool to study topological abnormalities in neurological disorders.
Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Network
The results show that STpGCN significantly improves brain decoding performance compared to competing baseline models; BrainNetX successfully annotates task-relevant brain regions.
Partial Least Square Regression via Three-factor SVD-type Manifold Optimization for EEG Decoding
To this end, we propose a new method to solve the partial least square regression, named PLSR via optimization on bi-Grassmann manifold (PLSRbiGr).
Kuramoto model based analysis reveals oxytocin effects on brain network dynamics
Here, we propose a physics-based framework of Kuramoto model to investigate oxytocin effects on the phase dynamic neural coupling in DMN and FPN.
Online Learning Koopman operator for closed-loop electrical neurostimulation in epilepsy
In this work, rooted in optimal control theory, we propose a Koopman-MPC framework for real-time closed-loop electrical neuromodulation in epilepsy, which integrates i) a deep Koopman operator based dynamical model to predict the temporal evolution of epileptic EEG with an approximate finite-dimensional linear dynamics and ii) a model predictive control (MPC) module to design optimal seizure suppression strategies.
