Emotion dysregulation and functional brain network topology in adult attention-deficit/hyperactivity disorder

Abstract Emotion dysregulation is common in attention-deficit/hyperactivity disorder (ADHD). It is highly prevalent in adult ADHD and related to reduced well-being and social impairments. Neuroimaging studies reported neural activity changes in ADHD in brain regions associated with emotion processing and regulation. It is however unknown whether deficit in emotion regulation relate to changes in functional brain network topology in these regions. We used a combination of graph analysis and structural equation modelling (SEM) to analyze resting-state functional connectivity in 147 well-characterized young adults with ADHD and age-matched healthy controls from the NeuroMAGE database. Emotion dysregulation was gauged with four scales obtained from questionnaires and operationalized through a latent variable derived from SEM. Graph analysis was applied to resting-state data and network topology measures were entered into SEM models to identify brain regions whose local network integration and connectedness differed between subjects and predicted emotion dysregulation. The latent variable of emotion dysregulation was characterized by scales gauging emotional distress, emotional symptoms, conduct symptoms, and emotional lability. In individuals with ADHD characterized by prominent hyperactivity-impulsivity, the latent emotion dysregulation variable was related to an increased clustering and local efficiency of the right insula. Thus, in the presence of hyperactivity-impulsivity, clustered network formation of the right insula may underpin emotion dysregulation in adult ADHD.

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Functional network topology of the right insula affects emotion dysregulation in hyperactive-impulsive attention-deficit/hyperactivity disorder

Scientific Reports ◽

10.1038/s41598-021-94426-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tammo Viering ◽

Pieter J. Hoekstra ◽

Alexandra Philipsen ◽

Jilly Naaijen ◽

Andrea Dietrich ◽

...

Keyword(s):

Attention Deficit Hyperactivity Disorder ◽

Network Topology ◽

Resting State ◽

Latent Variable ◽

Emotion Dysregulation ◽

Adult Adhd ◽

Brain Regions ◽

Graph Analysis ◽

Hyperactivity Disorder ◽

The Right

AbstractEmotion dysregulation is common in attention-deficit/hyperactivity disorder (ADHD). It is highly prevalent in young adult ADHD and related to reduced well-being and social impairments. Neuroimaging studies reported neural activity changes in ADHD in brain regions associated with emotion processing and regulation. It is however unknown whether deficits in emotion regulation relate to changes in functional brain network topology in these regions. We used a combination of graph analysis and structural equation modelling (SEM) to analyze resting-state functional connectivity in 147 well-characterized young adults with ADHD and age-matched healthy controls from the NeuroIMAGE database. Emotion dysregulation was gauged with four scales obtained from questionnaires and operationalized through a latent variable derived from SEM. Graph analysis was applied to resting-state data and network topology measures were entered into SEM models to identify brain regions whose local network integration and connectedness differed between subjects and was associated with emotion dysregulation. The latent variable of emotion dysregulation was characterized by scales gauging emotional distress, emotional symptoms, conduct symptoms, and emotional lability. In individuals with ADHD characterized by prominent hyperactivity-impulsivity, the latent emotion dysregulation variable was related to an increased clustering and local efficiency of the right insula. Thus, in the presence of hyperactivity-impulsivity, clustered network formation of the right insula may underpin emotion dysregulation in young adult ADHD.

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Graph analysis of functional brain network topology using minimum spanning tree in driver drowsiness

Cognitive Neurodynamics ◽

10.1007/s11571-018-9495-z ◽

2018 ◽

Vol 12 (6) ◽

pp. 569-581 ◽

Cited By ~ 12

Author(s):

Jichi Chen ◽

Hong Wang ◽

Chengcheng Hua ◽

Qiaoxiu Wang ◽

Chong Liu

Keyword(s):

Network Topology ◽

Spanning Tree ◽

Minimum Spanning Tree ◽

Brain Network ◽

Graph Analysis ◽

Functional Brain ◽

Driver Drowsiness ◽

Functional Brain Network

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Rifle Shooting Performance Correlates with Electroencephalogram Beta Rhythm Network Activity during Aiming

Computational Intelligence and Neuroscience ◽

10.1155/2018/4097561 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Anmin Gong ◽

Jianping Liu ◽

Changhao Jiang ◽

Yunfa Fu

Keyword(s):

Network Topology ◽

Information Integration ◽

Correlation Coefficients ◽

Significant Negative Correlation ◽

Brain Network ◽

Network Activity ◽

Functional Coupling ◽

Experimental Conditions ◽

Functional Brain Network ◽

The Right

To study the relationship between brain network and shooting performance during shooting aiming, we collected electroencephalogram (EEG) signals from 40 skilled shooters during rifle shooting and calculated the EEG functional coupling, functional brain network topology, and correlation coefficients between these EEG characteristics and shooting performance. Our result shows a significant negative correlation between shooting performance and functional coupling between the prefrontal, frontal, and temporal regions of the right brain in the Beta1 and Beta2 frequency bands. Global and local brain network topology characteristics were also significantly correlated with shooting performance. These findings indicate that under these experimental conditions, shooters with higher shooting performances exhibit lower functional coupling, higher global, and lower local information integration efficiency during shooting. These conclusions may provide a theoretical basis of the EEG brain network for studying the mental status of shooters while shooting.

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Brain Relatively Inert Network: Taking Adult Attention Deficit Hyperactivity Disorder as an Example

Frontiers in Neuroscience ◽

10.3389/fnins.2021.771947 ◽

2021 ◽

Vol 15 ◽

Author(s):

Hua Zhang ◽

Weiming Zeng ◽

Jin Deng ◽

Yuhu Shi ◽

Le Zhao ◽

...

Keyword(s):

Attention Deficit Hyperactivity Disorder ◽

Functional Connectivity ◽

Attention Deficit ◽

Information Integration ◽

Adult Adhd ◽

Brain Regions ◽

Hyperactivity Disorder ◽

Left Brain ◽

The Right ◽

The Brain

Resting-state functional MRI (rs-fMRI) has been increasingly applied in the research of brain cognitive science and psychiatric diseases. However, previous studies only focused on specific activation areas of the brain, and there are few studies on the inactivation areas. This may overlook much information that explains the brain’s cognitive function. In this paper, we propose a relatively inert network (RIN) and try to explore its important role in understanding the cognitive mechanism of the brain and the study of mental diseases, using adult attention deficit hyperactivity disorder (ADHD) as an example. Here, we utilize methods based on group independent component analysis (GICA) and t-test to identify RIN and calculate its corresponding time series. Through experiments, alterations in the RIN and the corresponding activation network (AN) in adult ADHD patients are observed. And compared with those in the left brain, the activation changes in the right brain are greater. Further, when the RIN functional connectivity is introduced as a feature to classify adult ADHD patients from healthy controls (HCs), the classification accuracy rate is 12% higher than that of the original functional connectivity feature. This was also verified by testing on an independent public dataset. These findings confirm that the RIN of the brain contains much information that will probably be neglected. Moreover, this research provides an effective new means of exploring the information integration between brain regions and the diagnosis of mental illness.

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The Cortical Organization of Writing Sequence: Evidence From Observing Chinese Characters in Motion

10.21203/rs.3.rs-285417/v1 ◽

2021 ◽

Author(s):

Zhaoqi Zhang ◽

Qiming Yuan ◽

Zeping Liu ◽

Man Zhang ◽

Junjie Wu ◽

...

Keyword(s):

Structural Equation ◽

Chinese Character ◽

Inferior Frontal Gyrus ◽

Hierarchical Control ◽

Brain Regions ◽

Brain Network ◽

Equation Model ◽

Chinese Characters ◽

General Motor ◽

The Right

Abstract Writing sequences play an important role in handwriting of Chinese characters. However, little is known regarding the integral brain patterns and network mechanisms of processing Chinese character writing sequences. The present study decoded brain patterns during observing Chinese characters in motion by using multi-voxel pattern analysis (MVPA), meta-analytic decoding analysis, and extended unified structural equation model (euSEM). We found that perception of Chinese character writing sequence recruited brain regions not only for general motor schema processing, i.e., the right inferior frontal gyrus, shifting and inhibition functions, i.e., the right postcentral gyrus and bilateral pre-SMA/dACC, but also for sensorimotor functions specific for writing sequences. More importantly, these brain regions formed a cooperatively top-down brain network where information was transmitted from brain regions for general motor schema processing to those specific for writing sequences. These findings not only shed light on the neural mechanisms of Chinese character writing sequences, but also extend the hierarchical control model on motor schema processing.

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Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features

Computational and Mathematical Methods in Medicine ◽

10.1155/2017/3609821 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 11

Author(s):

Xin Wang ◽

Yanshuang Ren ◽

Wensheng Zhang

Keyword(s):

Pearson Correlation ◽

Brain Regions ◽

Brain Network ◽

Classification Performance ◽

Low Rank ◽

Fmri Data ◽

Healthy Controls ◽

Functional Brain ◽

Functional Brain Network ◽

Depression Disorder

Study of functional brain network (FBN) based on functional magnetic resonance imaging (fMRI) has proved successful in depression disorder classification. One popular approach to construct FBN is Pearson correlation. However, it only captures pairwise relationship between brain regions, while it ignores the influence of other brain regions. Another common issue existing in many depression disorder classification methods is applying only single local feature extracted from constructed FBN. To address these issues, we develop a new method to classify fMRI data of patients with depression and healthy controls. First, we construct the FBN using a sparse low-rank model, which considers the relationship between two brain regions given all the other brain regions. Moreover, it can automatically remove weak relationship and retain the modular structure of FBN. Secondly, FBN are effectively measured by eight graph-based features from different aspects. Tested on fMRI data of 31 patients with depression and 29 healthy controls, our method achieves 95% accuracy, 96.77% sensitivity, and 93.10% specificity, which outperforms the Pearson correlation FBN and sparse FBN. In addition, the combination of graph-based features in our method further improves classification performance. Moreover, we explore the discriminative brain regions that contribute to depression disorder classification, which can help understand the pathogenesis of depression disorder.

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Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking

10.1101/644765 ◽

2019 ◽

Cited By ~ 2

Author(s):

Erlend S. Dørum ◽

Tobias Kaufmann ◽

Dag Alnæs ◽

Geneviève Richard ◽

Knut K. Kolskår ◽

...

Keyword(s):

Brain Function ◽

Repeated Measures ◽

High Sensitivity ◽

Brain Regions ◽

Brain Network ◽

System Level ◽

Cerebral Stroke ◽

Significant Group ◽

Functional Brain Network ◽

Regularized Linear Discriminant Analysis

AbstractA cerebral stroke is characterized by compromised brain function due to an interruption in cerebrovascular blood supply. Although stroke incurs focal damage determined by the vascular territory affected, clinical symptoms commonly involve multiple functions and cognitive faculties that are insufficiently explained by the focal damage alone. Functional connectivity (FC) refers to the synchronous activity between spatially remote brain regions organized in a network of interconnected brain regions. Functional magnetic resonance imaging (fMRI) has advanced this system-level understanding of brain function, elucidating the complexity of stroke outcomes, as well as providing information useful for prognostic and rehabilitation purposes.We tested for differences in brain network connectivity between a group of patients with minor ischemic strokes in sub-acute phase (n=44) and matched controls (n=100). As neural network configuration is dependent on cognitive effort, we obtained fMRI data during rest and two load levels of a multiple object tacking (MOT) task. Network nodes and time-series were estimated using independent component analysis (ICA) and dual regression, with network edges defined as the partial temporal correlations between node pairs. The full set of edgewise FC went into a cross-validated regularized linear discriminant analysis (rLDA) to classify groups and cognitive load.MOT task performance and cognitive tests revealed no significant group differences. While multivariate machine learning revealed high sensitivity to experimental condition, with classification accuracies between rest and attentive tracking approaching 100%, group classification was at chance level, with negligible differences between conditions. Repeated measures ANOVA showed significantly stronger synchronization between a temporal node and a sensorimotor node in patients across conditions. Overall, the results revealed high sensitivity of FC indices to task conditions, and suggest relatively small brain network-level disturbances after clinically mild strokes.

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Exploring Cortical Attentional System by Using fMRI during a Continuous Perfomance Test

Computational Intelligence and Neuroscience ◽

10.1155/2010/329213 ◽

2010 ◽

Vol 2010 ◽

pp. 1-6 ◽

Cited By ~ 31

Author(s):

M. G. Tana ◽

E. Montin ◽

S. Cerutti ◽

A. M. Bianchi

Keyword(s):

Performance Test ◽

Continuous Performance Test ◽

Blood Oxygen Level Dependent ◽

Brain Regions ◽

Brain Network ◽

Anterior Cingulate ◽

Visual Object ◽

Bold Response ◽

Object Processing ◽

The Right

Functional magnetic resonance imaging (fMRI) was performed in eight healthy subjects to identify the localization, magnitude, and volume extent of activation in brain regions that are involved in blood oxygen level-dependent (BOLD) response during the performance of Conners' Continuous Performance Test (CPT). An extensive brain network was activated during the task including frontal, temporal, and occipital cortical areas and left cerebellum. The more activated cluster in terms of volume extent and magnitude was located in the right anterior cingulate cortex (ACC). Analyzing the dynamic trend of the activation in the identified areas during the entire duration of the sustained attention test, we found a progressive decreasing of BOLD response probably due to a habituation effect without any deterioration of the performances. The observed brain network is consistent with existing models of visual object processing and attentional control and may serve as a basis for fMRI studies in clinical populations with neuropsychological deficits in Conners' CPT performance.

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Altered brain network centrality in patients with trigeminal neuralgia: a resting-state fMRI study

Acta Radiologica ◽

10.1177/0284185119847678 ◽

2019 ◽

Vol 61 (1) ◽

pp. 67-75 ◽

Cited By ~ 2

Author(s):

Pei-Wen Zhu ◽

You Chen ◽

Ying-Xin Gong ◽

Nan Jiang ◽

Wen-Feng Liu ◽

...

Keyword(s):

Trigeminal Neuralgia ◽

Brain Activity ◽

Characteristic Curve ◽

Brain Regions ◽

Brain Network ◽

Network Activity ◽

Degree Centrality ◽

Healthy Controls ◽

Postcentral Gyrus ◽

The Right

Background Neuroimaging studies revealed that trigeminal neuralgia was related to alternations in brain anatomical function and regional function. However, the functional characteristics of network organization in the whole brain is unknown. Purpose The aim of the present study was to analyze potential functional network brain-activity changes and their relationships with clinical features in patients with trigeminal neuralgia via the voxel-wise degree centrality method. Material and Methods This study involved a total of 28 trigeminal neuralgia patients (12 men, 16 women) and 28 healthy controls matched in sex, age, and education. Spontaneous brain activity was evaluated by degree centrality. Correlation analysis was used to examine the correlations between behavioral performance and average degree centrality values in several brain regions. Results Compared with healthy controls, trigeminal neuralgia patients had significantly higher degree centrality values in the right lingual gyrus, right postcentral gyrus, left paracentral lobule, and bilateral inferior cerebellum. Receiver operative characteristic curve analysis of each brain region confirmed excellent accuracy of the areas under the curve. There was a positive correlation between the mean degree centrality value of the right postcentral gyrus and VAS score (r = 0.885, P < 0.001). Conclusions Trigeminal neuralgia causes abnormal brain network activity in multiple brain regions, which may be related to underlying disease mechanisms.

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