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Article
Deep understanding of big multimedia data
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Article
Fusion of ULS Group Constrained High- and Low-Order Sparse Functional Connectivity Networks for MCI Classification
Functional connectivity networks, derived from resting-state fMRI data, have been found as effective biomarkers for identifying mild cognitive impairment (MCI) from healthy elderly. However, the traditional fu...
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Article
Maternal sensitivity predicts anterior hippocampal functional networks in early childhood
Maternal care influences child hippocampal development. The hippocampus is functionally organized along an anterior–posterior axis. Little is known with regards to the extent maternal care shapes offspring ant...
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Chapter and Conference Paper
Adaptive Functional Connectivity Network Using Parallel Hierarchical BiLSTM for MCI Diagnosis
Most of the existing dynamic functional connectivity (dFC) analytical methods compute the correlation between pairs of time courses with the sliding window. However, there is no clear indication on the standar...
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Article
Fronto-parietal numerical networks in relation with early numeracy in young children
Early numeracy provides the foundation of acquiring mathematical skills that is essential for future academic success. This study examined numerical functional networks in relation to counting and number relat...
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Article
A brief review on multi-task learning
Multi-task learning (MTL), which optimizes multiple related learning tasks at the same time, has been widely used in various applications, including natural language processing, speech recognition, computer vi...
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Chapter and Conference Paper
Fusion of High-Order and Low-Order Effective Connectivity Networks for MCI Classification
Functional connectivity network derived from resting-state fMRI data has been found as effective biomarkers for identifying patients with mild cognitive impairment from healthy elderly. However, the ordinary f...
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Chapter and Conference Paper
Multimodal Hyper-connectivity Networks for MCI Classification
Hyper-connectivity network is a network where every edge is connected to more than two nodes, and can be naturally denoted using a hyper-graph. Hyper-connectivity brain network, either based on structural or ...
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Chapter and Conference Paper
Structural Connectivity Guided Sparse Effective Connectivity for MCI Identification
Recent advances in network modelling techniques have enabled the study of neurological disorders at a whole-brain level based on functional connectivity inferred from resting-state magnetic resonance imaging (...
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Chapter and Conference Paper
Novel Effective Connectivity Network Inference for MCI Identification
Inferring effective brain connectivity network is a challenging task owing to perplexing noise effects, the curse of dimensionality, and inter-subject variability. However, most existing network inference met...
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Article
Open AccessImproving Estimation of Fiber Orientations in Diffusion MRI Using Inter-Subject Information Sharing
Diffusion magnetic resonance imaging is widely used to investigate diffusion patterns of water molecules in the human brain. It provides information that is useful for tracing axonal bundles and inferring brai...
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Article
Identification of progressive mild cognitive impairment patients using incomplete longitudinal MRI scans
Distinguishing progressive mild cognitive impairment (pMCI) from stable mild cognitive impairment (sMCI) is critical for identification of patients who are at risk for Alzheimer’s disease (AD), so that early t...
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Article
Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification
In conventional resting-state functional MRI (R-fMRI) analysis, functional connectivity is assumed to be temporally stationary, overlooking neural activities or interactions that may happen within the scan dur...
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Article
Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder
In this paper, we propose a novel framework for ASD diagnosis using structural magnetic resonance imaging (MRI). Our method deals explicitly with the distributional differences of gray matter (GM) and white ma...
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Chapter and Conference Paper
Angular Resolution Enhancement of Diffusion MRI Data Using Inter-Subject Information Transfer
Diffusion magnetic resonance imaging is widely used to investigate diffusion patterns of water molecules in the human brain. It provides information that is useful for tracing axonal bundles and inferring brai...
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Chapter and Conference Paper
Joint Feature-Sample Selection and Robust Classification for Parkinson’s Disease Diagnosis
Parkinson’s disease (PD) is an overwhelming neurodegenerative disorder caused by deterioration of a neurotransmitter, known as dopamine. Lack of this chemical messenger in the brain impairs several brain regio...
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Article
Supervised Discriminative Group Sparse Representation for Mild Cognitive Impairment Diagnosis
Research on an early detection of Mild Cognitive Impairment (MCI), a prodromal stage of Alzheimer’s Disease (AD), with resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been of great interest f...
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Chapter and Conference Paper
Block-Based Statistics for Robust Non-parametric Morphometry
Automated algorithms designed for comparison of medical images are generally dependent on a sufficiently large dataset and highly accurate registration as they implicitly assume that the comparison is being ma...
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Chapter and Conference Paper
MCI Identification by Joint Learning on Multiple MRI Data
The identification of subtle brain changes that are associated with mild cognitive impairment (MCI), the at-risk stage of Alzheimer’s disease, is still a challenging task. Different from existing works, which ...
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Chapter and Conference Paper
Identification of Infants at Risk for Autism Using Multi-parameter Hierarchical White Matter Connectomes
Autism spectrum disorder (ASD) is a variety of developmental disorders that cause life-long communication and social deficits. However, ASD could only be diagnosed at children as early as 2 years of age, while...
