AIMC Topic: Brain

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Convolutional neural network (CNN) configuration using a learning automaton model for neonatal brain image segmentation.

PloS one
CNN is considered an efficient tool in brain image segmentation. However, neonatal brain images require specific methods due to their nature and structural differences from adult brain images. Hence, it is necessary to determine the optimal structure...

Towards parameter-free attentional spiking neural networks.

Neural networks : the official journal of the International Neural Network Society
Brain-inspired spiking neural networks (SNNs) are increasingly explored for their potential in spatiotemporal information modeling and energy efficiency on emerging neuromorphic hardware. Recent works incorporate attentional modules into SNNs, greatl...

Illuminating the unseen: Advancing MRI domain generalization through causality.

Medical image analysis
Deep learning methods have shown promise in accelerated MRI reconstruction but face significant challenges under domain shifts between training and testing datasets, such as changes in image contrasts, anatomical regions, and acquisition strategies. ...

Identifying multilayer network hub by graph representation learning.

Medical image analysis
The recent advances in neuroimaging technology allow us to understand how the human brain is wired in vivo and how functional activity is synchronized across multiple regions. Growing evidence shows that the complexity of the functional connectivity ...

Characterizing brain network alterations in cervical spondylotic myelopathy using static and dynamic functional network connectivity and machine learning.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND: Cervical spondylotic myelopathy (CSM) is a debilitating condition that affects the cervical spine, leading to neurological impairments. While the neural mechanisms underlying CSM remain poorly understood, changes in brain network connecti...

Adapting to evolving MRI data: A transfer learning approach for Alzheimer's disease prediction.

NeuroImage
Integrating 3D magnetic resonance imaging (MRI) with machine learning has shown promising results in healthcare, especially in detecting Alzheimer's Disease (AD). However, changes in MRI technologies and acquisition protocols often yield limited data...

Predicting Paediatric Brain Disorders from MRI Images Using Advanced Deep Learning Techniques.

Neuroinformatics
The problem at hand is the significant global health challenge posed by children's diseases, where timely and accurate diagnosis is crucial for effective treatment and management. Conventional diagnosis techniques are typical, use tedious processes a...

Exploring continual learning strategies in artificial neural networks through graph-based analysis of connectivity: Insights from a brain-inspired perspective.

Neural networks : the official journal of the International Neural Network Society
Artificial Neural Networks (ANNs) aim at mimicking information processing in biological networks. In cognitive neuroscience, graph modeling is a powerful framework widely used to study brain structural and functional connectivity. Yet, the extension ...

Working-memory load decoding model inspired by brain cognition based on cross-frequency coupling.

Brain research bulletin
Working memory, a fundamental cognitive function of the brain, necessitates the evaluation of cognitive load intensity due to limited cognitive resources. Optimizing cognitive load can enhance task performance efficiency by preventing resource waste ...

Developing a brain inspired multilobar neural networks architecture for rapidly and accurately estimating concrete compressive strength.

Scientific reports
Concrete compressive strength is a critical parameter in construction and structural engineering. Destructive experimental methods that offer a reliable approach to obtaining this property involve time-consuming procedures. Recent advancements in art...