Brain mapping efforts are time-consuming and benefit from an incremental approach. This allows periodic updates with newly acquired data, analysis methods, and resulting atlases and automated delineation approaches. Here, we present a new release of the Amsterdam Ultra-high field adult lifespan database (AHEAD). The AHEAD dataset is now extended with 105 7 Tesla slab quantitative MRI contrasts covering the subcortex at a 0.5 mm isotropic resolution. The data were collected from 105 participants covering the adult lifespan together with the previously released whole-brain acquisitions. Whole-brain and slab data now have been co-registered, manually delineated, and used for the expansion of the Multi-contrast anatomical subcortical structures parcellation (MASSP) algorithm, which now allows the individual delineation of 19 additional anatomical structures. MASSP2.0 can be used to delineate 35 structures in individual brain contrasts, creating a total of 63 created masks. Quantitative MRI maps, derived probability maps for anatomical structures, as well as MASSP2.0 are now freely available for further analyses.

The estimated latency of the unobservable stop response, the so-called stop-signal reaction time (SSRT), has been the established measure of performance in the stop-signal task. While it is currently debated whether SSRT is a suitable marker of inhibition performance, other markers such as the reliability of triggering the stop process (“stop trigger failures”) are coming into focus. In the present study, we elucidated the mechanisms associated with trigger failures using a model-based neuroscience approach by means of functional magnetic resonance imaging for the first time. To this end, we used a large, open-access fMRI data set to investigate the relationship between the probability of trigger failures and fMRI signal change in a stop-signal task in healthy adults (n = 113). Stop trigger failures were associated with less activity in the substantia nigra during unsuccessful stopping and with less activity in the subthalamic nucleus (STN) region during successful inhibition. Although stop trigger failures strongly correlated with SSRT, we found only little evidence for a correlation between SSRT and stopping-related fMRI signal. Thus, in particular, the reliability of the stop process and not its estimated latency depends on the recruitment of key nodes within the prefrontal-subthalamic hyperdirect pathway. More specifically, stop trigger failures may be linked to inadequate substantia nigra innervation at the neural network level. As current evidence suggests that the hyperdirect pathway is engaged by the processing of salient stimuli, deficiencies in assessing the relevance of the stop signal may represent a phenotype associated with a propensity to trigger the stop process unreliably.