An integrative Bayesian approach for linking brain to behavioral phenotype - Abstract Recent advances in human connectome research have led to the development of models that reveal the brain circuits associated with behaviors or symptoms. The networks that define these circuits yield functional phenotypes that can be measured in individuals and are unique to each individual. Such work holds tremendous promise for providing a biological basis for understanding brain function and brain disorders, it allows us to characterize trajectories of growth, development, and aging, to categorize patients according to their functional phenotype, ultimately aiding treatment decisions, and predicting outcomes. Building such connectome based predictive models, involves 3 distinct steps: 1) construction of the connectivity matrix summarizing the connections across the defined nodes/parcellation; 2) a subsequent association step linking edge strength to the behavior or clinical symptom of interest; 3) and finally a predictive model step for validation and to ensure the models generalize and the associations are not spurious. While many atlases are available, there has been no consensus on which atlas to use to define the nodes in building the connectome, making the sharing of models and validation across sites difficult. A second, often overlooked problem, is that the node configuration supporting one behavior may not be the same for a different behavior due to the functional flexibility in brain organization. Thus, while the parcellation and brain modeling steps have historically been treated separately, they are not independent and should not be treated as such. In this work we will develop a joint parcellation/brain-phenotype modeling approach that provides statistically powerful, analytically robust, and biologically interpretable Bayesian models that are not dependent upon the choice of the initial atlas. We will validate the models through measures of predictive power, reliability, and generalizability, and compare to existing state-of-the-art methods. Data for validation will include the healthy adult data from the human connectome project and a transdiagnostic sample of 450 individuals (after adding 150 subjects in this study) collected at Yale, spanning a range from healthy control subjects to those with psychiatric illnesses. Normative models for 16 behavioral measures and 6 clinical scores will be developed and shared with the neuroscience community. A key aspect of validation and reproducibility in research is the sharing of data and models. The use of approximately a dozen or so arbitrary atlases in the field inhibits the sharing of models. This work will move the field forward by improving the methodology of brain-phenotype predictive modeling, identifying the circuits supporting behavior, without a priori imposition of an arbitrary atlas. The results could advance our understanding of the brain networks supporting behavior and impact a wide range of psychiatric illnesses. Facilitating the release of generalized models to the research community will aid in understanding how to use these methods for assigning treatments and monitoring the response to treatment.
