scholarly journals Disrupted brain functional networks in drug‐naïve children with attention deficit hyperactivity disorder assessed using graph theory analysis

2019 ◽  
Vol 40 (17) ◽  
pp. 4877-4887
Author(s):  
Ying Chen ◽  
Xiaoqi Huang ◽  
Min Wu ◽  
Kaiming Li ◽  
Xinyu Hu ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Graph Theory ◽  
Attention Deficit ◽  
Functional Networks ◽  
Theory Analysis ◽  
Hyperactivity Disorder ◽  
Brain Functional Networks ◽  
Drug Naïve ◽  
Graph Theory Analysis

scholarly journals Exploring white matter functional networks in children with attention-deficit/hyperactivity disorder

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Xuan Bu ◽  
Kaili Liang ◽  
Qingxia Lin ◽  
Yingxue Gao ◽  
Andan Qian ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
White Matter ◽  
Attention Deficit ◽  
Default Mode Network ◽  
Functional Networks ◽  
Attention Network ◽  
Attention Networks ◽  
Default Mode ◽  
Hyperactivity Disorder ◽  
Drug Naïve

Abstract Attention-deficit/hyperactivity disorder has been identified to involve the impairment of large-scale functional networks within grey matter, and recent studies have suggested that white matter, which also encodes neural activity, can manifest intrinsic functional organization similar to that of grey matter. However, the alterations in white matter functional networks in attention-deficit/hyperactivity disorder remain unknown. We recruited a total of 99 children, including 66 drug-naive patients and 33 typically developing controls aged from 6 to 14, to characterize the alterations in functional networks within white matter in drug-naive children with attention-deficit/hyperactivity disorder. Using clustering analysis, resting-state functional MRI data in the white matter were parsed into different networks. Intrinsic activity within each network and connectivity between networks and the associations between network activity strength and clinical symptoms were assessed. We identified eight distinct white matter functional networks: the default mode network, the somatomotor network, the dorsal attention network, the ventral attention network, the visual network, the deep frontoparietal network, the deep frontal network and the inferior corticospinal-posterior cerebellum network. The default mode, somatomotor, dorsal attention and ventral attention networks showed lower spontaneous neural activity in patients. In particular, the default mode network and the somatomotor network largely showed higher connectivity with other networks, which correlated with more severe hyperactive behaviour, while the dorsal and ventral attention networks mainly had lower connectivity with other networks, which correlated with poor attention performance. In conclusion, there are two distinct patterns of white matter functional networks in children with attention-deficit/hyperactivity disorder, with one being the hyperactivity-related hot networks including default mode network and somatomotor network and the other being inattention-related cold networks including dorsal attention and ventral attention network. These results extended upon our understanding of brain functional networks in attention-deficit/hyperactivity disorder from the perspective of white matter dysfunction.

scholarly journals Altered small-world brain functional networks in children with attention-deficit/hyperactivity disorder

2008 ◽  
Vol 30 (2) ◽  
pp. 638-649 ◽  
Author(s):  
Liang Wang ◽  
Chaozhe Zhu ◽  
Yong He ◽  
Yufeng Zang ◽  
QingJiu Cao ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Small World ◽  
Functional Networks ◽  
Hyperactivity Disorder ◽  
Brain Functional Networks ◽  
World Brain

Graph Theory Analysis of Microstates in Attention-Deficit Hyperactivity Disorder

2020 ◽  
Author(s):  
Lorraine M. Alves ◽  
Klaus F. Côco ◽  
Mariane L. de Souza ◽  
Patrick M. Ciarelli
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Graph Theory ◽  
Attention Deficit ◽  
Image Data ◽  
Symptom Assessment ◽  
Hyperactivity Disorder ◽  
Childhood And Youth ◽  
Functional Image ◽  
Graph Theory Analysis ◽  
Microstate Analysis

Attention-Deficit Hyperactivity Disorder (ADHD) is one of the most common disorders of childhood and youth. The diagnosis of ADHD remains essentially clinical, based on history and questionnaires for symptom assessment, therefore, a biomarker can be of great value to reduce the inherent uncertainty of clinical diagnosis. In recent years, several studies have been carried out to assess the usefulness of neurophysiological (electroencephalography - EEG)and functional image data to assist in the process of diagnosing ADHD. Previous researches have revealed evidences that microstates are selectively affected by ADHD, indicating that their analysis may be a useful tool in methods of automatic disease identication. In this paper is proposed a new methodology for the detection of ADHD using EEG microstate analysis and graph theory. The proposed method allows modeling and interpreting each microstate as a complex network, which permits to identify the effect of ADHD on some characteristics of the built networks. In addition, it provides useful information to identify ADHD and subtypes patients with an accuracy around 99%, indicating that the proposed method is promising.

Sign in / Sign up

Export Citation Format

Share Document