Farewell to the explore-exploit trade-off in large-scale datasets

LSVNDs are a very powerful tool for discovery science. Due to their suitability for exploration, large datasets synergize well when supplemented with more exploitative datasets focused on small-scale hypothesis testing that can confirm exploratory findings. Similar synergy can be attained when combining findings across datasets, where one LSVND can be used to confirm and extend discoveries from another LSVND. I will showcase how we have recently leveraged several large-scale datasets in unison to discover principles of topographic visual processing throughout the brain. These examples demonstrate how LSVNDs can be used to great effect, especially in combination across datasets. In our most recent example, we combined the HCP 7T fMRI dataset (a "wide" dataset with 180 participants, 2.5 hrs of whole-brain fMRI each) with NSD (a "deep" dataset with 8 participants, 40 hrs of whole-brain fMRI each) to investigate visual body-part selectivity. We discovered homuncular maps in high-level visual cortex through connectivity with primary somatosensory cortex in HCP, and validated the body-part tuning of these maps using NSD. This integration of wide and deep LSVNDs allows inference about computational mechanisms at both the individual and population levels. For this reason, we believe the field needs a variety of LSVNDs. I will briefly present ongoing work from my lab collecting new ‘deep’ LSVND contributions: a brief (2.5-s) video watching dataset and a retinotopic mapping dataset, each with up to 10 sessions of 7T fMRI in 8 subjects.