Constructing Brain Network Growth Charts as Objective Biomarkers of Behavioral Disorders

Chandra Sripada, M.D., Ph.D.,

University of Michigan

Funded in September, 2017: $200000 for 3 years
LAY SUMMARY . ABSTRACT .

LAY SUMMARY

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Constructing Brain Network Growth Charts as Objective Biomarkers of Behavioral Disorders

Just as pediatricians use growth charts to track typical and atypical physical development trajectories in children, investigators will work to create “brain network development growth charts.” These charts will be designed to detect early signs in youth of serious behavioral “externalizing” disorders, which include ADHD, substance use, conduct disorder and oppositional defiant disorder.

These disorders have are thought to have their roots in early brain maturation, with a critical window in early childhood and again in mid adolescence. ADHD and substance use disorder are well known. Less commonly known are conduct and oppositional defiant disorders. Youth with conduct disorder are aggressive, bullying, destructive, deceitful, violate rules, throw temper tantrums and fail to appreciate how their behaviors hurt others. Youth with oppositional defiant disorder exhibit prolonged angry, argumentative and vindictive behaviors towards people in authority and blame others for their problems. Affected youth may have more than one of these four externalizing disorders.

The collaborating psychiatrist and statistician will apply statistical analyses to resting state functional MRI (rs-fMRI) “connectomes.” These are whole brain maps of connectivity between all pairs of brain regions that are produced while the person undergoing imaging is undertaking no specific task. Statistical analyses are anticipated to detect cohesive collections of brain regions that mature as a common unit and predict likely emergence of clinically important symptoms such as inattention or poor response inhibition. Such analyses are vital because maps of brain connectivity produced by functional imaging are complex (they encompass hundreds of thousands of connections) and dynamic (the networks change structure dramatically over the course of development). Investigators expect to identify ensembles of brain regions that: 1) share a common developmental trajectory; and 2) maximize the ability to predict those specific aspects of network development that characterize people with clinical externalizing disorders.

Through application of these statistical methods, the investigators will create brain network growth charts that map the typical development of interconnected brain regions that underlie cognitive functions including attention, regulatory control, and reward processing. If a young person’s brain network development is shown to have lagged a lot behind that expected for a child’s age, such charting is expected to provide an objective biomarker indicative of externalizing disorders. They hypothesize that deviations from normative trajectories of development on these growth charts will be highly predictive of externalizing symptom severity scores.

Next, they will determine if the network growth charts are valid and reliable. They will use large existing rs-fMRI databases of typically developing youth and their database of 275 youth with clinically diagnosed externalizing disorders. They anticipate that the comparison will enable them to identify typically versus atypically developing networks involved in regulatory control, voluntary attention, and in linking brain regions responsible for processing both rewarding and aversive stimuli. This approach will not enable them to differentially diagnose one externalizing disorder from another, but rather to characterize development network growth patterns of externalizing disorders in general.  They also anticipate that in behaviorally asymptomatic young children, growth chart scores that deviate from the norms will be predictive of future development of externalizing disorders; in other words abnormalities on these network growth charts can serve pre-clinical markers for later disorder onset. Such children might then benefit from early preventive or therapeutic interventions.

Significance : The study may lay the groundwork for developing objective, brain-based markers of clinically diagnosed externalizing disorders and could enable early identification of youth who have a substantial risk of developing one or more of these disorders. 

ABSTRACT

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Constructing Brain Network Growth Charts as Objective Biomarkers of Behavioral Disorders

The overall aim of this proposal is to develop brain network growth charts that will facilitate reliable detection of youth with externalizing disorders, including attention-deficit/hyperactivity disorder, conduct disorder, oppositional defiant disorder, and substance use disorders. Growth charts are used by pediatricians to map the normative development of key somatic variables—such as height and head circumference—which in turn enables reliable detection of youth following abnormal developmental trajectories. We will extend this basic idea to the brain: we will construct growth charts of brain networks using resting state functional connectomes, whole brain maps of connectivity between all pairs of brain regions. Building on our previous published studies and strong pilot data, we hypothesize that deviations from normative trajectories of development on these growth charts will be highly predictive of externalizing symptom severity scores. The major work executed in this project consists in addressing a key methodological gap that blocks network growth chart construction: the absence of methods that perform supervised community detection. Maps of brain connectivity produced by functional imaging are complex (they encompass hundreds of thousands of connections) and dynamic (the networks change structure dramatically over the course of development). There is critical need for methods that detect cohesive collections of brain regions that mature as a common unit and predict clinically important symptoms (such as inattention or poor response inhibition). To tackle this complex methodological problem, Principal Investigator Sripada (psychiatrist and neuroscientist) will work with Co-Investigator Levina (statistician specializing in graphical methods) as well as two statistics Ph.D. students to formulate the required community detection procedures, produce novel optimization methods, and implement these methods on massively parallel high performance computer systems at the University of Michigan. We will independently validate the resulting growth charts on large youth neuroimaging datasets encompassing thousands of subjects, including the Penn Neurodevelopment Cohort, the Principal Investigator’s own clinical youth dataset, and the Adolescent Brain and Cognitive Development dataset. If we are successful in producing reliable, validated brain network growth charts, this will represent a dramatic step forward in constructing objective markers for detection of externalizing disorders. This will in turn facilitate timely, focused clinical interventions. Finally, the network growth charting methods developed in this project can readily be extended to other neurodevelopmental disorders (e.g., autism, schizophrenia) and thus could have a large clinical impact in many areas of psychiatry.

INVESTIGATOR BIOGRAPHIES

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Chandra Sripada, M.D., Ph.D.,

Dr. Sripada’s research investigates the neurodevelopment of cognitive control, the ability to regulate thoughts, impulses, and emotions. His research uses multiple methods, including neuroimaging and EEG, to map aberrant developmental trajectories of cognitive control circuits across a range of psychiatric disorders, especially attention-deficit/hyperactivity disorder, oppositional defiant disorder, conduct disorder, and substance use disorders. His training includes earning an MD (U. Texas, Houston) and PhD (Rutgers U., New Brunswick). He is currently an Associate Professor in the Department of Psychiatry at the University of Michigan, Ann Arbor, and he is Director of Michigan’s Neuroimaging Methods Core.

KEYWORDS


Conditions: Attention Deficit/Hyperactivity Disorder
Substance Abuse
Development: Early childhood development
Function: Executive function