AIMC Topic: Brain Mapping
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Explainability of three-dimensional convolutional neural networks for functional magnetic resonance imaging of Alzheimer's disease classification based on gradient-weighted class activation mapping.
PloS one
May 21, 2024
Currently, numerous studies focus on employing fMRI-based deep neural networks to diagnose neurological disorders such as Alzheimer's Disease (AD), yet only a handful have provided results regarding explainability. We address this gap by applying sev...
Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network.
Neural plasticity
Apr 30, 2024
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that is characterized by inattention, hyperactivity, and impulsivity. The neural mechanisms underlying ADHD remain inadequately understood, and current approaches...
Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN-LSTM model.
Computer methods and programs in biomedicine
Apr 24, 2024
BACKGROUND AND OBJECTIVE: People with autism spectrum disorder (ASD) often have cognitive impairments. Effective connectivity between different areas of the brain is essential for normal cognition. Electroencephalography (EEG) has been widely used in...
DF-QSM: Data Fidelity based Hybrid Approach for Improved Quantitative Susceptibility Mapping of the Brain.
NMR in biomedicine
Apr 22, 2024
Quantitative Susceptibility Mapping (QSM) is an advanced magnetic resonance imaging (MRI) technique to quantify the magnetic susceptibility of the tissue under investigation. Deep learning methods have shown promising results in deconvolving the susc...
Migraine aura discrimination using machine learning: an fMRI study during ictal and interictal periods.
Medical & biological engineering & computing
Apr 19, 2024
Functional magnetic resonance imaging (fMRI) studies on migraine with aura are challenging due to the rarity of patients with triggered cases. This study optimized methodologies to explore differences in ictal and interictal spatiotemporal activation...
A GRU-CNN model for auditory attention detection using microstate and recurrence quantification analysis.
Scientific reports
Apr 17, 2024
Attention as a cognition ability plays a crucial role in perception which helps humans to concentrate on specific objects of the environment while discarding others. In this paper, auditory attention detection (AAD) is investigated using different dy...
Spinet-QSM: model-based deep learning with schatten p-norm regularization for improved quantitative susceptibility mapping.
Magma (New York, N.Y.)
Apr 10, 2024
OBJECTIVE: Quantitative susceptibility mapping (QSM) provides an estimate of the magnetic susceptibility of tissue using magnetic resonance (MR) phase measurements. The tissue magnetic susceptibility (source) from the measured magnetic field distribu...
A Stepwise Multivariate Granger Causality Method for Constructing Hierarchical Directed Brain Functional Network.
IEEE transactions on neural networks and learning systems
Apr 4, 2024
The directed brain functional network construction gives us the new insights into the relationships between brain regions from the causality point of view. The Granger causality analysis is one of the powerful methods to model the directed network. T...
Identification of autism spectrum disorder using multiple functional connectivity-based graph convolutional network.
Medical & biological engineering & computing
Mar 8, 2024
Presently, the combination of graph convolutional networks (GCN) with resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising approach for early diagnosis of autism spectrum disorder (ASD). However, the prevalent approach in...
Affine medical image registration with fusion feature mapping in local and global.
Physics in medicine and biology
Feb 28, 2024
. Medical image affine registration is a crucial basis before using deformable registration. On the one hand, the traditional affine registration methods based on step-by-step optimization are very time-consuming, so these methods are not compatible ...
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