Frequency-specific static and dynamic neural activity indices in children with different attention deficit hyperactivity disorder subtypes: a resting-state fMRI study.

Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders in childhood. Numerous resting-state functional magnetic resonance imaging (rs-fMRI) studies in ADHD have been performed using traditional low-frequency bands (0.01-0.

Predicting individual autistic symptoms for patients with autism spectrum disorder using interregional morphological connectivity.

Intelligent predictive models for autistic symptoms based on neuroimaging datasets were beneficial for the precise intervention of patients with ASD. The goals of this study were twofold: investigating predictive models for autistic symptoms and discovering the brain connectivity patterns for ASD-related behaviors. To achieve these goals, we obtained a cohort of patients with ASD from the ABIDE project.

Corrigendum: Frequency characteristics of temporal and spatial concordance among dynamic indices in inattentive and combined subtypes of attention deficit hyperactivity disorder.

[This corrects the article DOI: 10.3389/fnins.2023.

Aberrant dynamic and static functional connectivity of the striatum across specific low-frequency bands in patients with autism spectrum disorder.

Background: Dysfunctions of the striatum have been repeatedly observed in autism spectrum disorder (ASD). However, previous studies have explored the static functional connectivity (sFC) of the striatum in a single frequency band, ignoring the dynamics and frequency specificity of brain FC. Therefore, we investigated the dynamic FC (dFC) and sFC of the striatum in the slow-4 (0.

Frequency characteristics of temporal and spatial concordance among dynamic indices in inattentive and combined subtypes of attention deficit hyperactivity disorder.

Numerous voxel-based resting-state functional magnetic resonance imaging (rs-fMRI) measurements have been used to characterize spontaneous brain activity in attention deficit hyperactivity disorder (ADHD). However, the practical distinctions and commonalities among these intrinsic brain activity measures remain to be fully explored, and whether the functional concordance is related to frequency is still unknown. The study included 25 ADHD, combined type (ADHD-C); 26 ADHD, inattentive type (ADHD-I); and 28 typically developing (TD) children.

White matter microstructure alterations in idiopathic restless legs syndrome: a study combining crossing fiber-based and tensor-based approaches.

Introduction: Restless legs syndrome (RLS) is a common sensorimotor disorder characterized by an irrepressible urge to move the legs and frequently accompanied by unpleasant sensations in the legs. The pathophysiological mechanisms underlying RLS remain unclear, and RLS is hypothesized to be associated with alterations in white matter tracts.

Methods: Diffusion MRI is a unique noninvasive method widely used to study white matter tracts in the human brain.

Mapping white matter structural covariance connectivity for single subject using wavelet transform with T1-weighted anatomical brain MRI.

Introduction: Current studies of structural covariance networks were focused on the gray matter in the human brain. The structural covariance connectivity in the white matter remains largely unexplored. This paper aimed to build novel metrics that can infer white matter structural covariance connectivity, and to explore the predictive power of the proposed features.

A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning.

Inattention is one of the most significant clinical symptoms for evaluating attention deficit hyperactivity disorder (ADHD). Previous inattention estimations were performed using clinical scales. Recently, predictive models for inattention have been established for brain-behavior estimation using neuroimaging features.

Identifying individuals with autism spectrum disorder based on the principal components of whole-brain phase synchrony.

Autism spectrum disorder (ASD) is a brain disorder that develops during an early stage of childhood. Previous neuroimaging-based diagnostic models for ASD were based on static functional connectivity (FC). The nonlinear complexity of brain connectivity remains unexplored for ASD diagnosis.

Estimating individual scores of inattention and impulsivity based on dynamic features of intrinsic connectivity network.

Inattention and impulsivity are the two most important indices for evaluations of ADHD. Currently, inattention and impulsivity were evaluated by clinical scales. The intelligent evaluation of the two indices using machine learning remains largely unexplored.

Diagnosing autism spectrum disorder using brain entropy: A fast entropy method.

Background And Objective: Previous resting-state fMRI-based diagnostic models for autism spectrum disorder (ASD) were based on traditional linear features. The complexity of the ASD brain remains unexplored.

Methods: To increase our understanding of the nonlinear neural mechanisms in ASD, entropy (i.

A unified framework for mapping individual interregional high-order morphological connectivity based on regional cortical features from anatomical MRI.

Building individual brain networks form the single volume of anatomical MRI is a challenging task. Furthermore, the high-order connectivity of morphological networks remains unexplored. This paper aimed to investigate the individual high-order morphological connectivity from anatomical MRI.

Radiotherapy-related quality of life in patients with head and neck cancers: a meta-analysis.

Objectives: To compare effects of intensity-modulated radiotherapy (IMRT) with those of conventional radiotherapy on quality of life (QoL) and severity of xerostomia in patients with head and neck cancer.

Material And Methods: PubMed, Cochrane, and Embase databases were searched to July 1, 2019, to identify relevant studies, using the following terms: radiotherapy, head and neck cancer, quality of life, cognition, xerostomia, two-/three-dimensional conformal radiation therapy, IMRT, conformal proton beam radiation therapy, stereotactic radiosurgery, and volumetric modulated arc therapy. The outcomes of interest were QoL measured by global health status; emotional, social, and cognitive function; and severity of xerostomia.

Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity.

Attention deficit hyperactivity disorder (ADHD) is a common disorder that emerges in school-age children. The diagnostic model based on neuroimaging features could be beneficial for ADHD in twofold: identifying individuals with ADHD and discovering the discriminative patterns for patients. The dynamic functional connectivity of ADHD remains unclear.

Mapping individual voxel-wise morphological connectivity using wavelet transform of voxel-based morphology.

Mapping individual brain networks has drawn significant research interest in recent years. Most individual brain networks developed to date have been based on fMRI or diffusion MRI. Given recent concerns regarding confounding artifacts, various preprocessing steps are generally included in functional or structural brain networks.

Diagnostic model for attention-deficit hyperactivity disorder based on interregional morphological connectivity.

Previous brain morphology-related diagnostic models for attention-deficit hyperactivity disorder (ADHD) were based on regional features. However, building a model of individual interregional morphological connectivity is a challenging task. This study aimed to identify children with ADHD utilizing a novel interregional morphological connectivity model and discover the discriminative patterns in patients.

Predictive models of minimal hepatic encephalopathy for cirrhotic patients based on large-scale brain intrinsic connectivity networks.

We aimed to find the most representative connectivity patterns for minimal hepatic encephalopathy (MHE) using large-scale intrinsic connectivity networks (ICNs) and machine learning methods. Resting-state fMRI was administered to 33 cirrhotic patients with MHE and 43 cirrhotic patients without MHE (NMHE). The connectivity maps of 20 ICNs for each participant were obtained by dual regression.

Predicting clinical symptoms of attention deficit hyperactivity disorder based on temporal patterns between and within intrinsic connectivity networks.

Attention deficit hyperactivity disorder (ADHD) is a common brain disorder with high prevalence in school-age children. Previously developed machine learning-based methods have discriminated patients with ADHD from normal controls by providing label information of the disease for individuals. Inattention and impulsivity are the two most significant clinical symptoms of ADHD.

[Anterior and posterior default mode networks impairments in minimal hepatic encephalopathy].

Objective: To investigate the functional connectivity (FC) impairments within anterior and posterior default mode networks (aDMN and pDMN) for cirrhosis with minimal hepatic encephalopathy (MHE), and to analyze relationship between DMNs alterations with neurocognitive impairments.

Methods: From March 2009 to December 2011, a total of 43 cirrhotic patients without MHE (NMHE groups), 32 cirrhotic patients with current MHE (MHE groups), and 21 healthy controls were recruited in this study. All resting-state fMRI datasets were preprocessed and normalized into standard brain space.

Investigating the Temporal Patterns within and between Intrinsic Connectivity Networks under Eyes-Open and Eyes-Closed Resting States: A Dynamical Functional Connectivity Study Based on Phase Synchronization.

The brain active patterns were organized differently under resting states of eyes open (EO) and eyes closed (EC). The altered voxel-wise and regional-wise resting state active patterns under EO/EC were found by static analysis. More importantly, dynamical spontaneous functional connectivity has been observed in the resting brain.

Altered temporal features of intrinsic connectivity networks in boys with combined type of attention deficit hyperactivity disorder.

Purpose: Investigating the altered temporal features within and between intrinsic connectivity networks (ICNs) for boys with attention-deficit/hyperactivity disorder (ADHD); and analyzing the relationships between altered temporal features within ICNs and behavior scores.

Materials And Methods: A cohort of boys with combined type of ADHD and a cohort of age-matched healthy boys were recruited from ADHD-200 Consortium. All resting-state fMRI datasets were preprocessed and normalized into standard brain space.

Gender versus brain size effects on subcortical gray matter volumes in the human brain.

Previous studies had reported that volume differences of gray matter (GM) in subcortical regions of the human brain were mainly caused by gender. Meanwhile, other studies had found that the distribution of GM in the human brain varied based on individual brain sizes. Main effects of volume differences of GM in subcortical regions remain unclear.

Altered regional homogeneity patterns in adults with attention-deficit hyperactivity disorder.

Purpose: Investigating the discriminative brain map for patients with attention-deficit/hyperactivity disorder (ADHD) based on feature selection and classifier; and identifying patients with ADHD based on the discriminative model.

Materials And Methods: A dataset of resting state fMRI contains 23 patients with ADHD and 23 healthy subjects were analyzed. Regional homogeneity (ReHo) was extracted from resting state fMRI signals and used as model inputs.

A conditional Granger causality model approach for group analysis in functional magnetic resonance imaging.

Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed to identify effective connectivity in the human brain with functional magnetic resonance imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pair-wise GCM has commonly been applied based on single-voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of fMRI data with GCM.

Automated artifact detection and removal for improved tensor estimation in motion-corrupted DTI data sets using the combination of local binary patterns and 2D partial least squares.

Signal variation in diffusion-weighted images (DWIs) is influenced both by thermal noise and by spatially and temporally varying artifacts, such as rigid-body motion and cardiac pulsation. Motion artifacts are particularly prevalent when scanning difficult patient populations, such as human infants. Although some motion during data acquisition can be corrected using image coregistration procedures, frequently individual DWIs are corrupted beyond repair by sudden, large amplitude motion either within or outside of the imaging plane.