Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …
attention in recent years. Using a variety of neuroimaging modalities such as structural …
Classification and prediction of brain disorders using functional connectivity: promising but challenging
Brain functional imaging data, especially functional magnetic resonance imaging (fMRI)
data, have been employed to reflect functional integration of the brain. Alteration in brain …
data, have been employed to reflect functional integration of the brain. Alteration in brain …
[HTML][HTML] Identification of autism spectrum disorder using deep learning and the ABIDE dataset
The goal of the present study was to apply deep learning algorithms to identify autism
spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the …
spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the …
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis
For the last decade, it has been shown that neuroimaging can be a potential tool for the
diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment …
diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment …
Applications of deep learning to MRI images: A survey
Deep learning provides exciting solutions in many fields, such as image analysis, natural
language processing, and expert system, and is seen as a key method for various future …
language processing, and expert system, and is seen as a key method for various future …
Multimodal fusion of brain imaging data: a key to finding the missing link (s) in complex mental illness
VD Calhoun, J Sui - Biological psychiatry: cognitive neuroscience and …, 2016 - Elsevier
It is becoming increasingly clear that combining multimodal brain imaging data provides
more information for individual subjects by exploiting the rich multimodal information that …
more information for individual subjects by exploiting the rich multimodal information that …
Deep learning for neuroimaging: a validation study
Deep learning methods have recently made notable advances in the tasks of classification
and representation learning. These tasks are important for brain imaging and neuroscience …
and representation learning. These tasks are important for brain imaging and neuroscience …
Studying the manifold structure of Alzheimer's disease: a deep learning approach using convolutional autoencoders
Many classical machine learning techniques have been used to explore Alzheimer's
disease (AD), evolving from image decomposition techniques such as principal component …
disease (AD), evolving from image decomposition techniques such as principal component …
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain …
Functional connectivity (FC) patterns obtained from resting-state functional magnetic
resonance imaging data are commonly employed to study neuropsychiatric conditions by …
resonance imaging data are commonly employed to study neuropsychiatric conditions by …
State-space model with deep learning for functional dynamics estimation in resting-state fMRI
Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that
different brain regions still actively interact with each other while a subject is at rest, and …
different brain regions still actively interact with each other while a subject is at rest, and …