Attention-Deficit Hyperactivity Disorder (ADHD) is one of the most prevalent child psychiatric disorders and is accompanied by significant morbidity. Despite extensive research in this developmental disorder, the etiology and pathophysiology of ADHD remain poorly understood. In response to the specific call by NIMH for R03 proposals (PA-10-64) to further analyze existing data sets, we propose to collaboratively apply innovative data analytic approaches including independent component analysis (ICA) decomposition-based methods to existing EEG data from a large, well-characterized sample of 180 children with ADHD and 60 comparison children collected as part of the ongoing Translational Research in Cognitive Control (TRECC;NIMH P50 MH077248;PI: McCracken) Center. We will use the EEGLAB signal processing environment for electrophysiological data analysis (Delorme &Makeig, 2004;2R01-NS047293-05) to study the neural sources from which the EEG signal originates. Successful application of these and/or related methods to the problem of discovering and testing brain-based biomarkers for ADHD would be highly innovative in the context of current ADHD research, and would also have clear potential for advancing clinical ADHD research and practice. This study proposes to reanalyze EEG data from an existing set obtained during a NIMH supported clinical trial in ADHD: 1) to investigate the EEG brain networks underlying abnormalities in cognitive control in ADHD and 2) to develop brain-based biomarkers for treatment response to medication in ADHD.
The current study proposes to re-analyze electroencephalographic (EEG) data previously collected during a large NIH-funded research study that examined 180 children with ADHD and 60 healthy comparison children. The re-analysis will utilize leading edge statistical analyses such as independent components analysis (ICA) decomposition methods, which will allow us to determine where in the brain the EEG signal originates. These neural sources may potentially be linked to ADHD symptoms and medication response, which may yield biological markers (or biomarkers) that can be used in the diagnosis and treatment of ADHD.
|Ellis, Alissa J; Kinzel, Chantelle; Salgari, Giulia C et al. (2017) Frontal alpha asymmetry predicts inhibitory processing in youth with attention deficit/hyperactivity disorder. Neuropsychologia 102:45-51|
|Loo, Sandra K; Bilder, Robert M; Cho, Alexander L et al. (2016) Effects of d-Methylphenidate, Guanfacine, and Their Combination on Electroencephalogram Resting State Spectral Power in Attention-Deficit/Hyperactivity Disorder. J Am Acad Child Adolesc Psychiatry 55:674-682.e1|
|Lenartowicz, Agatha; Delorme, Arnaud; Walshaw, Patricia D et al. (2014) Electroencephalography correlates of spatial working memory deficits in attention-deficit/hyperactivity disorder: vigilance, encoding, and maintenance. J Neurosci 34:1171-82|
|McGough, James J; McCracken, James T; Cho, Alexander L et al. (2013) A potential electroencephalography and cognitive biosignature for the child behavior checklist-dysregulation profile. J Am Acad Child Adolesc Psychiatry 52:1173-82|
|Loo, Sandra K; Cho, Alexander; Hale, T Sigi et al. (2013) Characterization of the theta to beta ratio in ADHD: identifying potential sources of heterogeneity. J Atten Disord 17:384-92|
|Loo, Sandra K; Makeig, Scott (2012) Clinical utility of EEG in attention-deficit/hyperactivity disorder: a research update. Neurotherapeutics 9:569-87|