Importance:We show that three common approaches to clinical deficits (cognitive phenotype, disease group, disease severity) each offer useful and perhaps complimentary explanations for the brain's underlying functional architecture as affected by psychiatric disease. Objective:To understand how different clinical frameworks are represented in the brain's functional connectome. Design:We use an openly available dataset to create predictive models based on multiple connectomes built from task-based functional MRI data. We use these models to predict individual traits corresponding to multiple cognitive constructs across disease category. We also show that these same connectomes statistically differ depending on disease category and symptom burden.Setting: This was a population-based study with data collected in UCLA.Participants: Healthy adults were recruited by community advertisements from the Los Angeles area. Participants with adult ADHD, bipolar disorder, and schizophrenia were recruited using a patient-oriented strategy involving outreach to local clinics and online portals (separate from the methods used to recruit healthy volunteers)

There is extensive evidence that human brain functional organization is dynamic, varying within a subject as the brain switches between tasks demands. This functional organization also varies across subjects, even when they are all engaged in similar tasks. Currently, we lack a comprehensive model that unifies the two dimensions of variation (brain state and subject). Using fMRI data obtained across multiple task-evoked and rest conditions (which we operationally define as brain states) and across multiple subjects, we develop a state-and subject-specific functional network parcellation (the assignment of nodes to networks). Our parcellation approach provides a measure of how node-to-network assignment (NNA) changes across states and across subjects. We demonstrate that the brain's functional networks are not spatially fixed, but reconfigure with brain state. This reconfiguration is robust and reliable to such an extent that it can be used to predict brain state with accuracies up to 97%.Recent studies have made significant progress in characterizing state-evoked changes in functional connectivity elicited by task performance (20-28). However, none of these works explicitly examined the possibility that networks spatially reconfigure: (i) The majority of these studies explicitly assumed that networks remain spatially unchanged across tasks (21,22,29). These analyses were restricted to investigating differences in connectivity between networks, and not whether networks spatially reconfigure across tasks. (ii) Another line of research takes a more abstract perspective by computing global network measures (such as modularity and participation coefficient) and comparing these measures across different states (27,28,(30)(31)(32). Such approaches address the modular reconfiguration of the brain as a whole by defining a new set of networks for each state, but they do not quantify how or whether the same networks change across states. Such studies also typically define a small number of networks (~3-7) whose correspondence to the putative resting-state networks are unclear. These approaches cannot answer how specific networks reorganize as a function of brain state. (iii) Finally, many studies do not directly examine cross-subject variations in network reorganization (20), or treat crosssubject and cross-session changes as similar notions for defining reorganization (33). However, functional organization varies significantly across subjects, yielding large individual differences in network definitions (34,35). There is a clear need to account for both cross-subject and crossstate variability in functional network organization when characterizing brain dynamics.Here, we use a novel approach to dynamically map the brain's functional networks across taskevoked and resting states and across individuals. We operationalize the brain's functional subunits as a set of 268 pre-defined nodes (6). We then apply our recently developed exemplarbased parcellation method (36) to assign a set of pre-defined nodes to individualized, stat...