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The neural processes that support inhibitory control in the face of stimuli with a history of reward association are not yet well understood. Yet, the ability to flexibly adapt behavior to changing reward-contingency contexts is important for daily functioning and warrants further investigation. This study aimed to characterize neural and behavioral impacts of stimuli with a history of conditioned reward association on motor inhibitory control in healthy young adults by investigating group-level effects as well as individual variation in the ability to inhibit responses to stimuli with a reward history. Participants (N = 41) first completed a reward conditioning phase, during which responses to rewarded stimuli were associated with money and responses to unrewarded stimuli were not. Rewarded and unrewarded stimuli from training were carried forward as No-Go targets in a subsequent go/no-go task to test the effect of reward history on inhibitory control. Participants underwent functional brain imaging during the go/no-go portion of the task. On average, a history of reward conditioning disrupted inhibitory control. Compared to inhibition of responses to stimuli with no reward history, trials that required inhibition of responses to previously rewarded stimuli were associated with greater activity in frontal and striatal regions, including the inferior frontal gyrus, insula, striatum, and thalamus. Activity in the insula and thalamus during false alarms and in the ventromedial prefrontal cortex during correctly withheld trials predicted behavioral performance on the task. Overall, these results suggest that reward history serves to disrupt inhibitory control and provide evidence for diverging roles of the insula and ventromedial prefrontal cortex while inhibiting responses to stimuli with a reward history.

Diffusion MRI tractography produces massive sets of streamlines that need to be clustered into anatomically meaningful white-matter bundles. Conventional clustering techniques group streamlines based on their proximity in Euclidean space. We have developed AnatomiCuts, an unsupervised method for clustering tractography streamlines based on their neighboring anatomical structures, rather than their coordinates in Euclidean space. In this work, we show that the anatomical similarity metric used in AnatomiCuts can be extended to find corresponding clusters across subjects and across hemispheres, without inter-subject or inter-hemispheric registration. Our proposed approach enables group-wise tract cluster analysis, as well as studies of hemispheric asymmetry. We evaluate our approach on data from the pilot MGH-Harvard-USC Lifespan Human Connectome project, showing improved correspondence in tract clusters across 184 subjects aged 8-90. Our method shows up to 38% improvement in the overlap of corresponding clusters when comparing subjects with large age differences. The techniques presented here do not require registration to a template and can thus be applied to populations with large inter-subject variability, e.g., due to brain development, aging, or neurological disorders.

The MGH-USC CONNECTOM MRI scanner housed at the Massachusetts General Hospital (MGH) is a major hardware innovation of the Human Connectome Project (HCP). The 3T CONNECTOM scanner is capable of producing a magnetic field gradient of up to 300 mT/m strength for in vivo human brain imaging, which greatly shortens the time spent on diffusion encoding, and decreases the signal loss due to T2 decay. To demonstrate the capability of the novel gradient system, data of healthy adult participants were acquired for this MGH-USC Adult Diffusion Dataset (N=35), minimally preprocessed, and shared through the Laboratory of Neuro Imaging Image Data Archive (LONI IDA) and the WU-Minn Connectome Database (ConnectomeDB). Another purpose of sharing the data is to facilitate methodological studies of diffusion MRI (dMRI) analyses utilizing high diffusion contrast, which perhaps is not easily feasible with standard MR gradient system. In addition, acquisition of the MGH-Harvard-USC Lifespan Dataset is currently underway to include 120 healthy participants ranging from 8 to 90 years old, which will also be shared through LONI IDA and ConnectomeDB. Here we describe the efforts of the MGH-USC HCP consortium in acquiring and sharing the ultra-high b-value diffusion MRI data and provide a report on data preprocessing and access. We conclude with a demonstration of the example data, along with results of standard diffusion analyses, including q-ball Orientation Distribution Function (ODF) reconstruction and tractography.