AIMC Topic: Brain

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Brain Age Prediction: A Comparison between Machine Learning Models Using Brain Morphometric Data.

Sensors (Basel, Switzerland)
Brain structural morphology varies over the aging trajectory, and the prediction of a person's age using brain morphological features can help the detection of an abnormal aging process. Neuroimaging-based brain age is widely used to quantify an indi...

An explainable deep learning framework for characterizing and interpreting human brain states.

Medical image analysis
Deep learning approaches have been widely adopted in the medical image analysis field. However, a most of existing deep learning approaches focus on achieving promising performances such as classification, detection, and segmentation, and much less e...

Spike-Based Approximate Backpropagation Algorithm of Brain-Inspired Deep SNN for Sonar Target Classification.

Computational intelligence and neuroscience
With the development of neuromorphic computing, more and more attention has been paid to a brain-inspired spiking neural network (SNN) because of its ultralow energy consumption and high-performance spatiotemporal information processing. Due to the d...

Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
Alzheimer's disease (AD) is currently diagnosed using a mixture of psychological tests and clinical observations. However, these diagnoses are not perfect, and additional diagnostic tools (e.g., MRI) can help improve our understanding of AD as well a...

Application of a convolutional neural network to the quality control of MRI defacing.

Computers in biology and medicine
Large-scale neuroimaging datasets present unique challenges for automated processing pipelines. Motivated by a large clinical trials dataset with over 235,000 MRI scans, we consider the challenge of defacing - anonymisation to remove identifying faci...

A Highly Energy-Efficient Hyperdimensional Computing Processor for Biosignal Classification.

IEEE transactions on biomedical circuits and systems
Hyperdimensional computing (HDC) is a brain-inspired computing paradigm that operates on pseudo-random hypervectors to perform high-accuracy classifications for biomedical applications. The energy efficiency of prior HDC processors for this computati...

An Interpretable Deep Learning Model for Speech Activity Detection Using Electrocorticographic Signals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Numerous state-of-the-art solutions for neural speech decoding and synthesis incorporate deep learning into the processing pipeline. These models are typically opaque and can require significant computational resources for training and execution. A d...

A Multiview Deep Learning Method for Brain Functional Connectivity Classification.

Computational intelligence and neuroscience
The brain functional connectivity classification based on deep learning is a research hotspot nowadays. However, the classification performance is far behind the demand of clinical applications. To alleviate the problem, this paper proposes a multivi...

Predicting the evolution trajectory of population-driven connectional brain templates using recurrent multigraph neural networks.

Medical image analysis
The mapping of the time-dependent evolution of the human brain connectivity using longitudinal and multimodal neuroimaging datasets provides insights into the development of neurological disorders and the way they alter the brain morphology, structur...