Altered Resting-state Functional Connectivity Patterns of Several Frontal and DMN Related Areas in Children with Attention Deficit Hyperactivity Disorder

2014 ◽  
Vol 11 (2) ◽  
pp. 761-766
Author(s):  
B. Shekarchi ◽  
M. Hosein Lashkari ◽  
A. Mehrvar ◽  
A. Akbarian Aghdam ◽  
S. Fartook Zadeh
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Functional Connectivity ◽  
Attention Deficit ◽  
Resting State ◽  
Resting State Functional Connectivity ◽  
Hyperactivity Disorder ◽  
Connectivity Patterns

scholarly journals Beyond massive univariate tests: Covariance regression reveals complex patterns of functional connectivity related to attention-deficit/hyperactivity disorder, age, sex, and response control

2021 ◽  
Author(s):  
Yi Zhao ◽  
Mary Beth Nebel ◽  
Brian S. Caffo ◽  
Stewart H. Mostofsky ◽  
Keri S. Rosch
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Functional Connectivity ◽  
Attention Deficit ◽  
Resting State ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Resting State Functional Connectivity ◽  
Response Control ◽  
Hyperactivity Disorder ◽  
Regression Approach

AbstractWe applied a novel Covariate Assisted Principal (CAP) whole-matrix regression approach to identify resting-state functional connectivity (FC) brain networks associated with attention-deficit/hyperactivity disorder (ADHD) and response control. Participants included 8-12 year-old children with ADHD (n=115, 29 girls) and typically developing controls (n=102, 35 girls) with a resting-state fMRI scan and go/no-go task behavioral data. We modeled three sets of covariates to identify resting-state networks associated with ADHD, age, sex, and response control. Four networks were identified across models revealing complex interactions between subregions of cognitive control, default mode, subcortical, visual, and somatomotor networks that relate to age, response control, and a diagnosis of ADHD among girls and boys. Unique networks were also identified in each of the three models suggesting some specificity to the covariates of interest. These findings demonstrate the utility of our novel covariance regression approach to studying functional brain networks relevant for development, behavior, and psychopathology.

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