Identification of high-functioning autism spectrum disorders based on gray-white matter functional network connectivity.

In the field of autism spectrum disorder (ASD), research on functional connectivity between gray matter and white matter remains under-researched. To address this gap, this study innovatively introduced a nested cross-validation method that integrates gray-white matter functional connectivity with an F-Score algorithm. This method calculates the correlation based on signals extracted from functional magnetic resonance imaging data using gray matter and white matter brain region templates.

LCGNet: Local Sequential Feature Coupling Global Representation Learning for Functional Connectivity Network Analysis With fMRI.

Analysis of functional connectivity networks (FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) has greatly advanced our understanding of brain diseases, including Alzheimer's disease (AD) and attention deficit hyperactivity disorder (ADHD). Advanced machine learning techniques, such as convolutional neural networks (CNNs), have been used to learn high-level feature representations of FCNs for automated brain disease classification. Even though convolution operations in CNNs are good at extracting local properties of FCNs, they generally cannot well capture global temporal representations of FCNs.

Weak-Interaction Environment in a Composite Electrolyte Enabling Ultralong-Cycling High-Voltage Solid-State Lithium Batteries.

Poly(vinylidene fluoride) (PVDF)-based solid electrolytes with a Li salt-polymer-little residual solvent configuration are promising candidates for solid-state batteries. Herein, we clarify the microstructure of PVDF-based composite electrolyte at the atomic level and demonstrate that the Li-interaction environment determines both interfacial stability and ion-transport capability. The polymer works as a "solid diluent" and the filler realizes a uniform solvent distribution.

Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification.

Dynamic functional connectivity (dFC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) help us understand fundamental dynamic characteristics of human brains, thereby providing an efficient solution for automated identification of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Existing studies have applied deep learning methods to dFC network analysis and achieved good performance compared with traditional machine learning methods. However, they seldom take advantage of sequential information conveyed in dFC networks that could be informative to improve the diagnosis performance.

Stable Interface Chemistry and Multiple Ion Transport of Composite Electrolyte Contribute to Ultra-long Cycling Solid-State LiNi Co Mn O /Lithium Metal Batteries.

Severe interfacial side reactions of polymer electrolyte with LiNi Co Mn O (NCM811) cathode and Li metal anode restrict the cycling performance of solid-state NCM811/Li batteries. Herein, we propose a chemically stable ceramic-polymer-anchored solvent composite electrolyte with high ionic conductivity of 6.0×10  S cm , which enables the solid-state NCM811/Li batteries to cycle 1500 times.

Distribution-Guided Network Thresholding for Functional Connectivity Analysis in fMRI-Based Brain Disorder Identification.

Functional connectivity (FC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used in automated identification of brain disorders, such as Alzheimer's disease (AD) and attention deficit hyperactivity disorder (ADHD). To generate compact representations of FC networks, various thresholding methods have been designed for FC network analysis. However, these studies usually use a pre-defined threshold or connection percentage to threshold whole FC networks, thus ignoring the diversity of temporal correlation (e.

A novel node-level structure embedding and alignment representation of structural networks for brain disease analysis.

Brain networks based on various neuroimaging technologies, such as diffusion tensor image (DTI) and functional magnetic resonance imaging (fMRI), have been widely applied to brain disease analysis. Currently, there are several node-level structural measures (e.g.

Designing weighted correlation kernels in convolutional neural networks for functional connectivity based brain disease diagnosis.

Functional connectivity networks (FCNs) based on functional magnetic resonance imaging (fMRI) have been widely applied to analyzing and diagnosing brain diseases, such as Alzheimer's disease (AD) and its prodrome stage, i.e., mild cognitive impairment (MCI).

Developing Novel Weighted Correlation Kernels for Convolutional Neural Networks to Extract Hierarchical Functional Connectivities from fMRI for Disease Diagnosis.

Functional magnetic resonance imaging (fMRI) has been widely applied to analysis and diagnosis of brain diseases, including Alzheimer's disease (AD) and its prodrome, , mild cognitive impairment (MCI). Traditional methods usually construct connectivity networks (CNs) by simply calculating Pearson correlation coefficients (PCCs) between time series of brain regions, and then extract low-level network measures as features to train the learning model. However, the valuable observation information in network construction (, specific contributions of different time points) and high-level (, high-order) network properties are neglected in these methods.

Multimodal hyper-connectivity of functional networks using functionally-weighted LASSO for MCI classification.

Recent works have shown that hyper-networks derived from blood-oxygen-level-dependent (BOLD) fMRI, where an edge (called hyper-edge) can be connected to more than two nodes, are effective biomarkers for MCI classification. Although BOLD fMRI is a high temporal resolution fMRI approach to assess alterations in brain networks, it cannot pinpoint to a single correlation of neuronal activity since BOLD signals are composite. In contrast, arterial spin labeling (ASL) is a lower temporal resolution fMRI technique for measuring cerebral blood flow (CBF) that can provide quantitative, direct brain network physiology measurements.

Ordinal Pattern: A New Descriptor for Brain Connectivity Networks.

Brain connectivity networks based on magnetic resonance imaging (MRI) or functional MRI (fMRI) data provide a straightforward way to quantify the structural or functional systems of the brain. Currently, there are several network descriptors developed for representing and analyzing brain connectivity networks. However, most of them are designed for unweighted networks, regardless of the valuable weight information of edges, or do not take advantage of the ordinal relationship of weighted edges (even though they are designed for weighted networks).

Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease.

Functional connectivity networks (FCNs) using resting-state functional magnetic resonance imaging (rs-fMRI) have been applied to the analysis and diagnosis of brain disease, such as Alzheimer's disease (AD) and its prodrome, i.e., mild cognitive impairment (MCI).

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 functional interactions among the brain regions, has been used for brain disease diagnosis. However, the conventional hyper-connectivity network is constructed solely based on single modality data, ignoring potential complementary information conveyed by other modalities.

Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis.

As a simple representation of interactions among distributed brain regions, brain networks have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment (MCI).

Subnetwork mining on functional connectivity network for classification of minimal hepatic encephalopathy.

Hepatic encephalopathy (HE), as a complication of cirrhosis, is a serious brain disease, which may lead to death. Accurate diagnosis of HE and its intermediate stage, i.e.

Network-based classification of ADHD patients using discriminative subnetwork selection and graph kernel PCA.

Background: Attention Deficit Hyperactivity Disorder (ADHD) is one of the most prevalent behavioral disorders in childhood and adolescence. Recently, network-based diagnosis of ADHD has attracted great attentions due to the fact that ADHD disease is related to not only individual brain regions but also the connections among them, while existing methods are hard to discover disorder patterns related with several brain regions.

New Method: To overcome this drawback, a discriminative subnetwork selection method is proposed to directly mine those frequent and discriminative subnetworks from the whole brain networks of ADHD and normal control (NC) groups.

Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease.

Sparse learning has been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment.

Hyper-connectivity of functional networks for brain disease diagnosis.

Exploring structural and functional interactions among various brain regions enables better understanding of pathological underpinnings of neurological disorders. Brain connectivity network, as a simplified representation of those structural and functional interactions, has been widely used for diagnosis and classification of neurodegenerative diseases, especially for Alzheimer's disease (AD) and its early stage - mild cognitive impairment (MCI). However, the conventional functional connectivity network is usually constructed based on the pairwise correlation among different brain regions and thus ignores their higher-order relationships.

Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment.

Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer's disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI).

Discriminative multi-task feature selection for multi-modality classification of Alzheimer's disease.

Recently, multi-task based feature selection methods have been used in multi-modality based classification of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI).

Brain connectivity hyper-network for MCI classification.

Brain connectivity network has been used for diagnosis and classification of neurodegenerative diseases, such as Alzheimer's disease (AD) as well as its early stage, i.e., mild cognitive impairment (MCI).

Manifold regularized multitask feature learning for multimodality disease classification.

Multimodality based methods have shown great advantages in classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently, multitask feature selection methods are typically used for joint selection of common features across multiple modalities. However, one disadvantage of existing multimodality based methods is that they ignore the useful data distribution information in each modality, which is essential for subsequent classification.

Probability method for Cerenkov luminescence tomography based on conformance error minimization.

Cerenkov luminescence tomography (CLT) was developed to reconstruct a three-dimensional (3D) distribution of radioactive probes inside a living animal. Reconstruction methods are generally performed within a unique framework by searching for the optimum solution. However, the ill-posed aspect of the inverse problem usually results in the reconstruction being non-robust.