AIMC Topic: Brain
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Multitask Learning for Joint Diagnosis of Multiple Mental Disorders in Resting-State fMRI.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
Facing the increasing worldwide prevalence of mental disorders, the symptom-based diagnostic criteria struggle to address the urgent public health concern due to the global shortfall in well-qualified professionals. Thanks to the recent advances in n...
Gradient Matching Federated Domain Adaptation for Brain Image Classification.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
Federated learning has shown its unique advantages in many different tasks, including brain image analysis. It provides a new way to train deep learning models while protecting the privacy of medical image data from multiple sites. However, previous ...
Exploring Brain Effective Connectivity Networks Through Spatiotemporal Graph Convolutional Models.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
Learning brain effective connectivity networks (ECN) from functional magnetic resonance imaging (fMRI) data has gained much attention in recent years. With the successful applications of deep learning in numerous fields, several brain ECN learning me...
Attention-Like Multimodality Fusion With Data Augmentation for Diagnosis of Mental Disorders Using MRI.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
The globally rising prevalence of mental disorders leads to shortfalls in timely diagnosis and therapy to reduce patients' suffering. Facing such an urgent public health problem, professional efforts based on symptom criteria are seriously overstretc...
An Explainable and Generalizable Recurrent Neural Network Approach for Differentiating Human Brain States on EEG Dataset.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
Electroencephalogram (EEG) is one of the most widely used brain computer interface (BCI) approaches. Despite the success of existing EEG approaches in brain state recognition studies, it is still challenging to differentiate brain states via explaina...
BAI-Net: Individualized Anatomical Cerebral Cartography Using Graph Neural Network.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
Brain atlas is an important tool in the diagnosis and treatment of neurological disorders. However, due to large variations in the organizational principles of individual brains, many challenges remain in clinical applications. Brain atlas individual...
GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
Toward the development of effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by an electroencephalogram (EEG) is highly demanded. Traditional works classify EEG signals without considering the ...
Anatomy-Guided Spatio-Temporal Graph Convolutional Networks (AG-STGCNs) for Modeling Functional Connectivity Between Gyri and Sulci Across Multiple Task Domains.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
The cerebral cortex is folded as gyri and sulci, which provide the foundation to unveil anatomo-functional relationship of brain. Previous studies have extensively demonstrated that gyri and sulci exhibit intrinsic functional difference, which is fur...
Adversarial Learning Based Node-Edge Graph Attention Networks for Autism Spectrum Disorder Identification.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
Graph neural networks (GNNs) have received increasing interest in the medical imaging field given their powerful graph embedding ability to characterize the non-Euclidean structure of brain networks based on magnetic resonance imaging (MRI) data. How...
Spiking generative adversarial network with attention scoring decoding.
Neural networks : the official journal of the International Neural Network Society
Jun 1, 2024
Generative models based on neural networks present a substantial challenge within deep learning. As it stands, such models are primarily limited to the domain of artificial neural networks. Spiking neural networks, as the third generation of neural n...
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