scholarly journals Probabilistic Diffusion Tractography and Graph Theory Analysis Reveal Abnormal White Matter Structural Connectivity Networks in Drug-Naive Boys with Attention Deficit/Hyperactivity Disorder

2013 ◽  
Vol 33 (26) ◽  
pp. 10676-10687 ◽  
Author(s):  
Q. Cao ◽  
N. Shu ◽  
L. An ◽  
P. Wang ◽  
L. Sun ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Graph Theory ◽  
White Matter ◽  
Attention Deficit ◽  
Structural Connectivity ◽  
Theory Analysis ◽  
Diffusion Tractography ◽  
Hyperactivity Disorder ◽  
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.

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.

scholarly journals Long-Range White Matter Microstructural Alterations in Drug-Naive Children with Attention Deficit Hyperactivity Disorder: A Tract-Based Spatial Statistics Study

2021 ◽  
Author(s):  
Ronghui Zhou ◽  
Peng Dong ◽  
Shuangli Chen ◽  
Andan Qian ◽  
Jiejie Tao ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
White Matter ◽  
Spatial Statistics ◽  
Long Range ◽  
Attention Deficit ◽  
Children With Adhd ◽  
Hyperactivity Disorder ◽  
Adhd Children ◽  
Drug Naïve ◽  
The Right

Abstract Background Microstructural changes might underlie white matter (WM) pathology in attention deficit hyperactivity disorder (ADHD). To investigate WM alterations, particularly the changes in long-range fibers, in drug-naive children with ADHD, we conducted tract-based spatial statistics (TBSS) analysis on diffusion tensor imaging (DTI) data. Materials and Methods In this study, 57 children with ADHD and 41 healthy controls (HCs) were enrolled. None of the enrolled ADHD children received any medication before data collection. The difference in fractional anisotropy (FA), and in mean (MD), axial (AD), and radial diffusivity (RD) between both groups were calculated using TBSS. WM changes were then correlated with clinical symptoms, including the hyperactivity index score and the impulsivity score. Results Whole-skeleton analysis identified several long-range fibers of decreased FA and increased RD in the ADHD group as compared to the HC group. ADHD children demonstrated decreased FA in the right corpus callosum (CC) splenium, left inferior fronto-occipital fasciculus, and intersection of the anterior and posterior internal capsule. Moreover, higher RD was observed in the right CC splenium, superior longitudinal fasciculus, and posterior corona radiata. No regions of increased FA or reduced RD were observed, and no differences in MD or AD were noted. Conclusion Our results demonstrate that microstructural WM alterations and changes in the long-range WM connections are present in children with ADHD. We speculate that these changes may relate to the symptoms of hyperactivity and impulsivity.

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