2.23 A COMPARISON OF BRAIN NETWORKS BETWEEN ATTENTION-DEFICIT/HYPERACTIVITY DISORDER AND BIPOLAR DISORDER IN MEDICATION-NAïVE ADOLESCENTS: A PRELIMINARY RESTING-STATE FMRI STUDY

2016 ◽  
Vol 55 (10) ◽  
pp. S128
Author(s):  
Doug Hyun Han ◽  
Jeongha Park ◽  
Gi Jung Hyun ◽  
Sun Mi Kim ◽  
Perry F. Renshaw
Keyword(s):  
Bipolar Disorder ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Resting State ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Hyperactivity Disorder ◽  
Fmri Study

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.

scholarly journals Resting-State fMRI to Identify the Brain Correlates of Treatment Response to Medications in Children and Adolescents With Attention-Deficit/Hyperactivity Disorder: Lessons From the CUNMET Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Victor Pereira-Sanchez ◽  
Alexandre R. Franco ◽  
Pilar de Castro-Manglano ◽  
Maria A. Fernandez-Seara ◽  
Maria Vallejo-Valdivielso ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Resting State ◽  
Brain Networks ◽  
Lessons Learned ◽  
Pharmacological Treatments ◽  
Hyperactivity Disorder ◽  
Long Term Treatment ◽  
Treatment Naïve ◽  
The Brain

Neuroimaging research seeks to identify biomarkers to improve the diagnosis, prognosis, and treatment of attention-deficit/hyperactivity disorder (ADHD), although clinical translation of findings remains distant. Resting-state functional magnetic resonance imaging (R-fMRI) is increasingly being used to characterize functional connectivity in the brain. Despite mixed results to date and multiple methodological challenges, dominant hypotheses implicate hyperconnectivity across brain networks in patients with ADHD, which could be the target of pharmacological treatments. We describe the experience and results of the Clínica Universidad de Navarra (Spain) Metilfenidato (CUNMET) pilot study. CUNMET tested the feasibility of identifying R-fMRI markers of clinical response in children with ADHD undergoing naturalistical pharmacological treatments. We analyzed cross-sectional data from 56 patients with ADHD (18 treated with methylphenidate, 18 treated with lisdexamfetamine, and 20 treatment-naive patients). Standard preprocessing and statistical analyses with attention to control for head motion and correction for multiple comparisons were performed. The only results that survived correction were noted in contrasts of children who responded clinically to lisdexamfetamine after long-term treatment vs. treatment-naive patients. In these children, we observed stronger negative correlations (anticorrelations) across nodes in six brain networks, which is consistent with higher across-network functional segregation in patients treated with lisdexamfetamine, i.e., less inter-network interference than in treatment-naive patients. We also note the lessons learned, which could help those pursuing clinically relevant multidisciplinary research in ADHD en route to eventual personalized medicine. To advance reproducible open science, our report is accompanied with links providing access to our data and analytic scripts.

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