A primary challenge facing functional neuroimaging is the translation of research findings to the clinical setting. In part, fMRI has struggled as a clinical tool due to the lack of functional phenotypes that characterize patients. To address this, we have developed connectome-based predictive modeling (CPM) to identify and validate predictive models of behavior/symptoms based on functional connectivity data. The promise of this approach is that by developing predictive models based on the functional organization of an individual?s brain, we may be able to extract a rich connectivity phenotypes to aid in the clinical characterization of patients. This approach has the potential to improve our ability to categorize patients in otherwise heterogeneous groups and monitor the effectiveness of treatment interventions. To do this, modeling methods are needed that are designed to generalize across multiple behaviors, symptoms and diagnostic groups. In this proposal, we will push forward several major developments in CPM focused on generating transdiagnostic models for three specific behaviors (attention, working memory, and fluid intelligence) and factors from clinical tests, that will lead to functional phenotypes. We will collect a battery of continuous performance tasks in a spectrum of (N=300) individuals. We propose three specific aims: (1) To characterize node-boundary x dimensional construct effects; (2) To preform unidimensional and multi-dimensional CPM to predict RDoC constructs; (3) To evaluate the extent to which subjects with similar functional phenotypes cluster into symptom based or DSM-5 categorical clusters.
This aim will also allow us to investigate the functional networks that vary with symptom and to investigate categorical subtleties in these symptom based phenotypes. The significance of transdiagnostic predictive models of behavior from functional connectivity data lay in their ability to delineate clinically relevant information from any individual (i.e. patient or control). The current lack of transdiagnostic predictive models limits the clinical utility of fMRI, providing a framework for, and generating, these models could have important implications in translating fMRI into a viable clinical tool. The innovation of this proposal is fourfold: 1) the collection of a novel trans-diagnostic data set to be made publicly available; 2) the development of an approach to generate personalized functional atlases to account for individual differences in anatomy; 3) the development of methods to delineate meaningful functional phenotypes to assess symptoms, and 4) to provide a means for comparing alignment of subjects on symptom dimensions versus DSM-5 categories using these functional phenotypes. These developments will be validated using a combination of novel data to be collected here as well as 3 publicly available data sets. The final deliverables will yield tools for measuring functional phenotypes reflecting symptom scores suitable for an individualized approach to medicine.

Public Health Relevance

We will image 300 adults (ages 18-55 years) from a spectrum of mental health backgrounds using a combination of resting-state, and connectivity-specific, continuous performance tasks for fMRI. We propose to develop and validate transdiagnostic predictive models of psychopathology utilizing multidimensional modeling of connectivity obtained under conditions emphasizing multiple RDoC constructs and using individualized state-specific functional atlases. All data and methods will be made open-source and provide the basis for linking an individual's brain organization to their unique symptoms.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Special Emphasis Panel (ZMH1)
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Ferrante, Michele
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Yale University
Schools of Medicine
New Haven
United States
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