Personalized Neural State Segmentation: Validating the Greedy State Boundary Search Algorithm for Individual-level...

Humans segment experience into a nested series of discrete events, separated by neural state transitions that can be identified in fMRI data collected during passive movie viewing. Current neural state segmentation techniques manage the noisiness of fMRI data by modeling groups of participants at once. However, the perception of event boundaries is itself idiosyncratic. As such, we developed a denoising pipeline to separate meaningful signal from noise and validated the Greedy State Boundary Search algorithm for use in individual participants. We applied the Greedy State Boundary Search to publicly available (1) young adult and (2) developmental fMRI data sets. After extensive denoising, we confirmed that personalized young adult neural state transitions exhibited a canonical temporal cortical hierarchy and were related to normative behavioral boundaries across time in key regions such as posterior parietal cortex. Furthermore, we used machine learning to show that the strongest neural transitions from across cortex could be used to predict the timing of normative boundary judgments. Results from the developmental data set also demonstrated important boundary conditions for estimating personalized neural state transitions. Nonetheless, some brain-behavior relations were still apparent in individually modeled developmental data. Finally, we ran two individual differences analyses demonstrating the utility of our method. These validations pave the way for applying personalized fMRI modeling to the study of event segmentation; what meaningful insights could we be missing when we average away what makes each of us unique?