Sponsored by the UCLA Brain Mapping Center Faculty
The focus of these talks is on advancing the use of brain mapping methods in neuroscience with an emphasis on contemporary issues of neuroplasticity, neurodevelopment, and biomarker development in neuropsychiatric disease.
Hosted By: Mirella Dapretto, Ph.D., Psychiatry and Biobehavioral Sciences, UCLA
|Damien Fair, PA-C, Ph.D.
Assistant Professor, Behavioral Neuroscience and Psychiatry; Assistant Scientist, Advanced Imaging Research Center, Oregon Health and Science University
Background: Research in psychiatry often relies on the assumption that the diagnostic categories identified in the DSM represent homogeneous syndromes. However, the mechanistic heterogeneity that potentially underlies the existing classification scheme might limit discovery of etiology. Another, perhaps less palpable, reality may also be interfering with progress in understanding psychiatric illnesses—heterogeneity in typical populations.
Methods: In our current work we expand on previous brain imaging methods and use graph theory, specifically community detection, to clarifying behavioral and functional heterogeneity in children with and without ADHD.
Results: Using behavioral assays we have been able to identify several unique subgroups of children with ADHD, and importantly, in some cases, in control populations as well. Just as notably, characterizing these unique data driven sub-populations has revealed unique patterns of dysfunction in the children with ADHD. We also show in this longitudinal ADHD sample that this refined nosology is capable of improving our predictive capacity of long-term outcomes relative to current DSM-based nosology. Last, we demonstrate similar phenomena in the form of distinct sub-classifications based on patterns of functional connectivity MRI. As with the behavioral indices, the subgroups yield unique atypical connectivity patterns in the clinical population and shed light on the underlying functional patterns that may contribute to heterogeneity in ADHD.
Conclusions: These findings suggest several principles that have the potential to advance our understanding of typical and atypical developmental trajectories. The first tenet suggests that both children with and without ADHD can be classified into distinct subgroups based on psychometrics or neuroimaging. The second tenet proposes that the information in these data driven neurotypes can assist in predicting future outcomes. We argue that illumination of such phenomena will have significant practical importance for understanding typical development and to identifying the etiologic underpinnings of atypical developmental trajectories.