Are intelligence and creativity distinct abilities, or do they rely on the same cognitive and neural systems?We sought to quantify the extent to which intelligence and creative cognition overlap in brain and behavior by combining machine learning of fMRI data and latent variable modeling of cognitive ability data in a sample of young adults (N = 186) who completed a battery of intelligence and creative thinking tasks. The study had three analytic goals: (a) to assess contributions of specific facets of intelligence (e.g., fluid and crystallized intelligence) and general intelligence to creative ability (i.e., divergent thinking originality), (b) to model whole-brain functional connectivity networks that predict intelligence facets and creative ability, and (c) to quantify the degree to which these predictive networks overlap in the brain. Using structural equation modeling, we found moderate to large correlations between intelligence facets and creative ability, as well as a large correlation between general intelligence and creative ability (r = .63). Using connectomebased predictive modeling, we found that functional brain networks that predict intelligence facets overlap to varying degrees with a network that predicts creative ability, particularly within the prefrontal cortex of the executive control network. Notably, a network that predicted general intelligence shared 46% of its functional connections with a network that predicted creative ability-including connections linking executive control and salience/ventral attention networks-suggesting that intelligence and creative thinking rely on similar neural and cognitive systems.

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Despite the impressive literature describing atypical neural activation in visuoperceptual face processing regions in autism, almost nothing is known about whether these perturbations extend to more affective regions in the circuitry and whether they bear any relationship to symptom severity or atypical behavior. Using fMRI, we compared face-, object-, and house-related activation in adolescent males with high-functioning autism (HFA) and typically developing (TD) matched controls. HFA adolescents exhibited hypo-activation throughout the core visuoperceptual regions, particularly in the right hemisphere, as well as in some of the affective/motivational face-processing regions, including the posterior cingulate cortex and right anterior temporal lobe. Conclusions about the relative hyper- or hypo-activation of the amygdala depended on the nature of the contrast that was used to define the activation. Individual differences in symptom severity predicted the magnitude of face activation, particularly in the right fusiform gyrus. Also, among the HFA adolescents, face recognition performance predicted the magnitude of face activation in the right anterior temporal lobe, a region that supports face individuation in TD adults. Our findings reveal a systematic relation between the magnitude of neural dysfunction, severity of autism symptoms, and variation in face recognition behavior in adolescents with autism. In so doing, we uncover brain–behavior relations that underlie one of the most prominent social deficits in autism and help resolve discrepancies in the literature.

There is increasing appreciation that network‐level interactions among regions produce components of face processing previously ascribed to individual regions. Our goals were to use an exhaustive data‐driven approach to derive and quantify the topology of directed functional connections within a priori defined nodes of the face processing network and evaluate whether the topology is category‐specific. Young adults were scanned with fMRI as they viewed movies of faces, objects, and scenes. We employed GIMME to model effective connectivity among core and extended face processing regions, which allowed us to evaluate all possible directional connections, under each viewing condition (face, object, place). During face processing, we observed directional connections from the right posterior superior temporal sulcus to both the right occipital face area and right fusiform face area (FFA), which does not reflect the topology reported in prior studies. We observed connectivity between core and extended regions during face processing, but this limited to a feed‐forward connection from the FFA to the amygdala. Finally, the topology of connections was unique to face processing. These findings suggest that the pattern of directed functional connections within the face processing network, particularly in the right core regions, may not be as hierarchical and feed‐forward as described previously. Our findings support the notion that topologies of network connections are specialized, emergent, and dynamically responsive to task demands.

Are intelligence and creativity distinct abilities, or do they rely on the same cognitive and neural systems? We sought to quantify the extent to which intelligence and creative cognition overlap in brain and behavior by combining machine learning of fMRI data and latent variable modeling of cognitive ability data in a sample of young adults (N = 186) who completed a battery of intelligence and creative thinking tasks. The study had three analytic goals: (a) to assess contributions of specific facets of intelligence (e.g., fluid and crystallized intelligence) and general intelligence to creative ability (i.e., divergent thinking originality), (b) to model whole-brain functional connectivity networks that predict intelligence facets and creative ability, and (c) to quantify the degree to which these predictive networks overlap in the brain. Using structural equation modeling, we found moderate to large correlations between intelligence facets and creative ability, as well as a large correlation between general intelligence and creative ability (r = .63). Using connectome-based predictive modeling, we found that functional brain networks that predict intelligence facets overlap to varying degrees with a network that predicts creative ability, particularly within the prefrontal cortex of the executive control network. Notably, a network that predicted general intelligence shared 46% of its functional connections with a network that predicted creative ability—including connections linking executive control and salience/ventral attention networks—suggesting that intelligence and creative thinking rely on similar neural and cognitive systems.