BackgroundFunctional neuroimaging has great potential to inform clinical decisions, whether by identifying neural biomarkers of illness progression and severity, predicting therapeutic response, or selecting suitable patients for surgical interventions. Yet a persisting barrier to functional neuroimaging's clinical translation is our incomplete understanding of how normative variance in cognition, personality, and behavior shape the brain's structural and functional organization. We propose that modeling individual differences in these brain–behavior relationships is crucial for improving the accuracy of neuroimaging biomarkers for neurologic and psychiatric disorders.MethodsWe addressed this goal by initiating the Cognitive Connectome Project, which bridges neuropsychology and neuroimaging by pairing nine cognitive domains typically assessed by clinically validated neuropsychological measures with those tapped by canonical neuroimaging tasks (motor, visuospatial perception, attention, language, memory, affective processing, decision making, working memory, and executive function). To date, we have recruited a diverse sample of 53 participants (mean [SD], age = 32 [9.7] years, 31 females).ResultsAs a proof of concept, we first demonstrate that our neuroimaging task battery can replicate previous findings that task performance recruits intrinsic brain networks identified during wakeful rest. We then expand upon these previous findings by showing that the extent to which these networks are recruited by task reflects individual differences in cognitive ability. Specifically, performance on the Judgment of Line Orientation task (a clinically validated measure of visuospatial perception) administered outside of the MRI scanner predicts the magnitude of task-induced activity of the dorsal visual network when performing a direct replication of this task within the MRI scanner. Other networks (such as default mode and right frontoparietal) showed task-induced changes in activity that were unrelated to task performance, suggesting these networks to not be involved in visuospatial perception.ConclusionThese findings establish a methodological framework by which clinical neuropsychology and functional neuroimaging may mutually inform one another, thus enhancing the translation of functional neuroimaging into clinical decision making.

The n-back task is a widely used neuroimaging paradigm for studying the neural basis of working memory (WM); however, its neuropsychometric properties have received little empirical investigation. The present study merged clinical neuropsychology and functional magnetic resonance imaging (fMRI) to explore the construct validity of the letter variant of the n-back task (LNB) and to further identify the task-evoked networks involved in WM. Construct validity of the LNB task was investigated using a bootstrapping approach to correlate LNB task performance across clinically validated neuropsychological measures of WM to establish convergent validity, as well as measures of related but distinct cognitive constructs (i.e., attention and short-term memory) to establish discriminant validity. Independent component analysis (ICA) identified brain networks active during the LNB task in 34 healthy control participants, and general linear modeling determined task-relatedness of these networks. Bootstrap correlation analyses revealed moderate to high correlations among measures expected to converge with LNB (|ρ| ≥0.37) and weak correlations among measures expected to discriminate (|ρ| ≤0.29), controlling for age and education. ICA identified 35 independent networks, 17 of which demonstrated engagement significantly related to task condition, controlling for reaction time variability. Of these, the bilateral frontoparietal networks, bilateral dorsolateral prefrontal cortices, bilateral superior parietal lobules including precuneus, and frontoinsular network were preferentially recruited by the 2-back condition compared to 0-back control condition, indicating WM involvement. These results support the use of the LNB as a measure of WM and confirm its use in probing the network-level neural correlates of WM processing.

Growing evidence suggests that intrinsic functional connectivity (i.e. highly structured patterns of communication between brain regions during wakeful rest) may encode cognitive ability. However, the generalizability of these findings is limited by between-study differences in statistical methodology and cognitive domains evaluated. To address this barrier, we evaluated resting-state neural representations of multiple cognitive domains within a relatively large normative adult sample. Forty-four participants (mean(sd) age = 31(10) years; 18 male and 26 female) completed a resting-state functional MRI scan and neuropsychological assessments spanning motor, visuospatial, language, learning, memory, attention, working memory, and executive function performance. Robust linear regression related cognitive performance to resting-state connectivity among 200 a priori determined functional regions of interest (ROIs). Only higher-order cognitions (such as learning and executive function) demonstrated significant relationships between brain function and behavior. Additionally, all significant relationships were negative – characterized by moderately positive correlations among low performers and weak to moderately negative correlations among high performers. These findings suggest that functional independence among brain regions at rest facilitates cognitive performance. Our interpretation is consistent with graph theoretic analyses which represent the brain as independent functional nodes that undergo dynamic reorganization with task demand. Future work will build upon these findings by evaluating domain-specific variance in resting-state neural representations of cognitive impairment among patient populations.