This is an application under the NIH K01 Mentored Research Scientist Development Award Mechanism. The overall goal of the research project is to understand brain mechanisms underlying proactive and reactive control and their relation to individual differences in behavioral symptoms associated with childhood attention deficit hyperactivity disorder (ADHD). Childhood ADHD is characterized by significant impairments in academic and social domains, and deficits in cognitive control are at the core of these impairments. Recent research suggests that cognitive control operates via two distinct modes: proactive and reactive. However, the extent to which proactive and reactive control processes influence behavioral symptoms associated with ADHD remains unknown. This proposal will address this fundamental question by assessing how imaging-defined constructs for proactive and reactive control affect inattention and impulsivity in children with and without ADHD using the NIMH Research Domain Criteria (RDoC) strategy. The candidate will use a novel systems neuroscience approach to investigate dynamic brain mechanisms of proactive and reactive control in children and their relation to symptoms associated with ADHD.
The Specific Aims of this project are: (1) To investigate dynamic causal interactions in brain networks during reactive and proactive control in children, (2) To investigate how aberrant dynamic causal interactions during reactive and proactive control affect impulsivity and inattention in children using the RDoC approach, (3) To examine whether dynamic causal interactions during reactive and proactive control can differentiate children with clinically diagnosed ADHD from typically-developing children, and (4) To explore biomarkers for symptom prediction and classification using multivariate imaging-defined constructs of reactive and proactive control. The proposed studies will deepen our understanding of fundamental brain mechanisms underlying individual differences in cognitive control in children with and without ADHD. It will also advance the use of new computational tools in clinical neuroscience research and provide a systems neuroscience framework for future studies of cognitive control in other neurodevelopmental disorders, including autism and schizophrenia. The candidate will undergo a rigorous education and training plan to increase expertise in clinical aspects of ADHD research, advanced brain network analyses and machine learning algorithms for symptom prediction. The candidate will be mentored and trained by leading experts in the fields of clinical psychology, psychiatry, developmental and cognitive neuroscience, brain network analyses and machine learning. The candidate will also gain critical experience in clinical assessments necessary for successfully working with children with ADHD. Formal coursework and attendance at seminars in psychology, psychiatry, connectomics and machine learning will assist in achieving this goal. Completing the proposed project will enable the candidate to become a successful independent investigator in the fields of clinical and developmental cognitive neuroscience.
Childhood ADHD is one of the most common neurodevelopmental disorders, and is characterized by pervasive deficits in cognitive control. This project seeks to uncover dynamic brain mechanisms underlying two different types of cognitive control and their relation to clinical symptoms associated with childhood ADHD. The proposed research will provide new insights into the childhood ADHD and eventually aid in the diagnosis and evaluation of treatments for this disorder.
| Cai, Weidong; Chen, Tianwen; Szegletes, Luca et al. (2018) Aberrant Time-Varying Cross-Network Interactions in Children With Attention-Deficit/Hyperactivity Disorder and the Relation to Attention Deficits. Biol Psychiatry Cogn Neurosci Neuroimaging 3:263-273 |
| Taghia, Jalil; Ryali, Srikanth; Chen, Tianwen et al. (2017) Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI. Neuroimage 155:271-290 |
| Cai, Weidong; Chen, Tianwen; Ide, Jaime S et al. (2017) Dissociable Fronto-Operculum-Insula Control Signals for Anticipation and Detection of Inhibitory Sensory Cue. Cereb Cortex 27:4073-4082 |
| Ryali, Srikanth; Supekar, Kaustubh; Chen, Tianwen et al. (2016) Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling. PLoS Comput Biol 12:e1005138 |
