Recent advances in magnetic resonance imaging methods, including data acquisition, pre-processing and analysis, have benefited research on the contributions of subcortical brain nuclei to human cognition and behavior. At the same time, these developments have led to an increasing need for a high-resolution probabilistic in vivo anatomical atlas of subcortical nuclei. In order to address this need, we constructed high spatial resolution, three-dimensional templates, using high-accuracy diffeomorphic registration of T1- and T2- weighted structural images from 168 typical adults between 22 and 35 years old. In these templates, many tissue boundaries are clearly visible, which would otherwise be impossible to delineate in data from individual studies. The resulting delineations of subcortical nuclei complement current histology-based atlases. We further created a companion library of software tools for atlas development, to offer an open and evolving resource for the creation of a crowd-sourced in vivo probabilistic anatomical atlas of the human brain.

Recent advances in magnetic resonance imaging (MRI) methods, including data acquisition, pre-processing and analysis, have enabled research on the contributions of subcortical brain nuclei to human cognition and behavior. At the same time, these developments have led to an increasing need for a high-resolution probabilistic in-vivo anatomical atlas of subcortical nuclei. In order to fill this gap, we constructed high spatial resolution, three-dimensional templates, using joint high accuracy diffeomorphic registration of T1-and T2-weighted structural images from 168 typical adults between 22 and 35 years old. In these templates, many tissue boundaries are clearly visible, which would otherwise be impossible to delineate in data from individual studies. The resulting delineation provides a more accurate parcellation of subcortical nuclei than current histology-based atlases. We further created a companion library of software tools for atlas development, to offer an open and evolving resource for the creation of a crowd-sourced in-vivo probabilistic anatomical atlas of the human brain.

Prominent accounts of Pavlovian conditioning successfully approximate the frequency and intensity of conditioned responses under the assumption that learning is exclusively model-free; that animals do not develop a cognitive map of events. However, these model-free approximations fall short of comprehensively capturing learning and behavior in Pavlovian conditioning. We therefore performed multivoxel pattern analysis of high-resolution functional MRI data in human participants to test for the encoding of stimulus-stimulus associations that could support model-based computations during Pavlovian conditioning. We found that dissociable sub-regions of the striatum encode predictions of stimulus-stimulus associations and predictive value, in a manner that is directly related to learning performance. Activity patterns in the orbitofrontal cortex were also found to be related to stimulus-stimulus as well as value encoding. These results suggest that the brain encodes model-based representations during Pavlovian conditioning, and that these representations are utilized in the service of behavior.