Neuroimaging clinics of North America
Sep 17, 2020
Hemorrhagic stroke is a medical emergency. Artificial intelligence techniques and algorithms may be used to automatically detect and quantitate intracranial hemorrhage in a semiautomated fashion. This article reviews the use of deep learning convolut...
Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing. Several studies have utilized state of the art deep learning methods and combined physiological signals, such as the electrocar...
Survivors following very premature birth (i.e., ≤ 32 weeks gestational age) remain at high risk for neurodevelopmental impairments. Recent advances in deep learning techniques have made it possible to aid the early diagnosis and prognosis of neurodev...
Alzheimer's & dementia : the journal of the Alzheimer's Association
Sep 13, 2020
INTRODUCTION: Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging d...
Magnetic resonance imaging (MRI) is one of the most popular techniques in brain science and is important for understanding brain function and neuropsychiatric disorders. However, the processing and analysis of MRI is not a trivial task with lots of c...
A potentially organizing goal of the brain and cognitive sciences is to accurately explain domains of human intelligence as executable, neurally mechanistic models. Years of research have led to models that capture experimental results in individual ...
Association between electroencephalography (EEG) and individually personal information is being explored by the scientific community. Though person identification using EEG is an attraction among researchers, the complexity of sensing limits using su...
Understanding the mechanisms of neural computation and learning will require knowledge of the underlying circuitry. Because it is difficult to directly measure the wiring diagrams of neural circuits, there has long been an interest in estimating them...
Functional MRI (fMRI) is a prominent imaging technique to probe brain function, however, a substantial proportion of noise from multiple sources influences the reliability and reproducibility of fMRI data analysis and limits its clinical applications...
Deep-learning methods based on deep neural networks (DNNs) have recently been successfully utilized in the analysis of neuroimaging data. A convolutional neural network (CNN) is a type of DNN that employs a convolution kernel that covers a local area...
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