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Generative AI for brain image computing and brain network computing: a review
Recent years have witnessed a significant advancement in brain imaging techniques that
offer a non-invasive approach to map** the structure and function of the brain …
offer a non-invasive approach to map** the structure and function of the brain …
The role of generative adversarial networks in brain MRI: a sco** review
The performance of artificial intelligence (AI) for brain MRI can improve if enough data are
made available. Generative adversarial networks (GANs) showed a lot of potential to …
made available. Generative adversarial networks (GANs) showed a lot of potential to …
Causal recurrent variational autoencoder for medical time series generation
We propose causal recurrent variational autoencoder (CR-VAE), a novel generative model
that is able to learn a Granger causal graph from a multivariate time series x and …
that is able to learn a Granger causal graph from a multivariate time series x and …
MCAN: multimodal causal adversarial networks for dynamic effective connectivity learning from fMRI and EEG data
Dynamic effective connectivity (DEC) is the accumulation of effective connectivity in the time
dimension, which can describe the continuous neural activities in the brain. Recently …
dimension, which can describe the continuous neural activities in the brain. Recently …
A survey on brain effective connectivity network learning
Human brain effective connectivity characterizes the causal effects of neural activities
among different brain regions. Studies of brain effective connectivity networks (ECNs) for …
among different brain regions. Studies of brain effective connectivity networks (ECNs) for …
Exploring brain effective connectivity networks through spatiotemporal graph convolutional models
Learning brain effective connectivity networks (ECN) from functional magnetic resonance
imaging (fMRI) data has gained much attention in recent years. With the successful …
imaging (fMRI) data has gained much attention in recent years. With the successful …
Metarlec: Meta-reinforcement learning for discovery of brain effective connectivity
In recent years, the discovery of brain effective connectivity (EC) networks through
computational analysis of functional magnetic resonance imaging (fMRI) data has gained …
computational analysis of functional magnetic resonance imaging (fMRI) data has gained …
Decgan: decoupling generative adversarial network for detecting abnormal neural circuits in Alzheimer's disease
One of the main reasons for Alzheimer's disease (AD) is the disorder of some neural circuits.
Existing methods for AD prediction have achieved great success, however, detecting …
Existing methods for AD prediction have achieved great success, however, detecting …
Ccam: Cross-channel association mining for ubiquitous sleep staging
Accurate sleep staging is crucial for wearable sensor-based sleep monitoring and health
interventions. Polysomnography (PSG) signals, rich in information from multiple …
interventions. Polysomnography (PSG) signals, rich in information from multiple …
Estimating effective connectivity by recurrent generative adversarial networks
Estimating effective connectivity from functional magnetic resonance imaging (fMRI) time
series data has become a very hot topic in neuroinformatics and brain informatics. However …
series data has become a very hot topic in neuroinformatics and brain informatics. However …