Attention deficit hyperactivity disorder (ADHD) is the most commonly diagnosed developmental disorder of childhood, affecting ~5% of children worldwide. ADHD is a great public health concern as its core symptoms, which include increased impulsivity/hyperactivity and difficulty in sustaining attention, increase the risk for por academic achievement, substance abuse, and criminal behavior. Research into the neural basis of ADHD is crucial to improve early detection and treatment of the disorder. ADHD is hypothesized to result from dysfunctional connectivity. However, there are two main limitations with extant research: 1) there is a large emphasis on studying intrinsic connectivity while participants are at rest, despite evidence from the primary mentor's lab that dysfunctional connectivity during impaired cognitive processes is more strongly related to behavioral deficits than intrinsic connectivity; and 2) most research is limited to probing specific networks or connections despite strong evidence that ADHD is associated with a distributed pattern of abnormality across much of the brain. The groundbreaking application of mathematical graph theoretical tools to functional neuroimaging data allows for the first time the quantification of complex properties of large-scale brain network organization that can be assessed during cognitive task performance, when children with ADHD display the greatest behavioral deficits. The candidate has successfully applied graph theoretical analyses to fMRI data in adults, and has conducted preliminary analyses in children with ADHD. This application tests the hypothesis that children with ADHD are impaired in the ability to flexibly adapt network organization to shifting cognitive demands during the exertion of cognitive control, leading to behavioral deficits and observed symptoms. This will be tested by employing innovative functional connectivity and graph theoretical tools to functional neuroimaging data in children with ADHD and typically developing (TD) children.
The first aim (K99) will characterize large-scale neural organization during a response control task in children with ADHD. By the end of year 2 the data for aims 1 and 2 will be collected, the candidate will have received sufficient clinical and methodological training to execute the remaining aims independently, and a manuscript regarding aim 1 will have been submitted.
The second aim (K99/R00) will quantify the change in organization from an intrinsic, resting state to different conditions of a response control task in children with ADHD. The data will be independently analyzed and a manuscript completed during year 3. The candidate will also set up her independent laboratory, submit an IRB application for human subjects testing, and create the infrastructure necessary to recruit ADHD and TD children for aim 3. The third, exploratory aim (R00) will assess the changes in network dynamics that result from stimulant administration in children with ADHD and how those changes relate to changes in behavior. In year 4 data will be collected and initial analyses conducted;
aim 3 will be completed in year 5. Results from these studies will lead to the identification of biomarkers to improve early diagnosis of ADHD and treatments targeting the dysfunctional systems, and will form the basis of an R01 application written during the R00 phase. The candidate is trained in cognitive neuroscience and advanced functional MRI (fMRI) methodology and has conducted functional connectivity and graph theoretical analyses such as those she is proposing. She also has experience working with TD children and clinical populations. The proposed training will fill gaps in the candidate's current knowledge and provide a solid basis for her to independently conduct translational research in functional neuroimaging and developmental disorders. The candidate's clinical training will include formal coursework, seminars, clinics, and individual clinical training in the recruitment and assessment of children with ADHD, behavioral techniques to ensure compliance during behavioral and MRI testing, safe stimulant administration, and theoretical understanding of developmental disorders and therapeutic approaches. It will be led by her primary mentor, Dr. Stewart Mostofsky, a pediatric neurologist and clinical investigator at Kennedy Krieger Institute (KKI) and Johns Hopkins University (JHU) whose research focuses on neuroimaging and cognitive dysfunction in developmental brain disorders, and supplemented by KKI/JHU clinical psychologists Drs. Mark Mahone and Keith Slifer and child psychiatrist Dr. Roma Vasa (consultants). She will receive additional training to supplement her already strong knowledge base of advanced fMRI methodology, with a focus on functional connectivity and graph theoretical analyses, led by her co-mentor, Dr. Mark D'Esposito, a clinical neurologist and researcher at the University of California, Berkeley (UCB) whose research focuses on the effects of disruptions to brain circuitry and developing multivariate fMRI methodology, and supplemented by Dr. Brian Caffo, a biostatistician at JHU, and Dr. Fernando Perez, a physicist and applied mathematician at UCB (consultants). The candidate's training will also enhance her scientific writing and presentation skills. This training will ensure that the proposed R00 studies can be implemented independently. Both the candidate's mentors, who collaborate with each other, have strong track records supervising fellows into becoming independent investigators and are strongly committed to transitioning the candidate to independence. KKI, a world-renowned institute focusing on research of developmental disorders with close ties to JHU, is an ideal location in which the candidate can receive outstanding training in the clinical aspects of research on developmental disorders, as well as training to supplement her methodological skills. Completion of this research application and training plan will enable her to gain proficiency relevant to her goal of becoming an independent investigator in the fields of developmental disorders, cognitive neuroscience, and advanced neuroimaging methodologies.
Attention deficit hyperactivity disorder (ADHD) is a very common and potentially devastating disorder that contributes to poor school performance and impaired social relationships, resulting in long-term dysfunction that is associated with a staggering increase in healthcare costs. We propose to use innovative imaging methods to examine ADHD-associated dysfunction in brain network dynamics and to quantify how stimulant medication normalizes that dysfunction. The findings will lead to the development of biomarkers for early detection and more effective treatments of the disorder.
