Personalized neural state segmentation: validating the GSBS algorithm for individual-level fMRI data
Year: 2024
Type: preprint
Abstract: 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 modelling groups of participants at once. However, the perception of event boundaries is itself idiosyncratic. As such, we developed a novel denoising pipeline to separate meaningful signal from noise and validated the Greedy State Boundary Search (GSBS) algorithm for use in individual participants. We applied the GSBS to publicly available (1) young adult (YA) and (2) developmental fMRI datasets. After extensive denoising, we confirmed that personalized YA neural state transitions exhibited a canonical temporal cortical hierarchy and were related to normative behavioural boundaries across time in key regions such as the posterior parietal cortex. Further, we used machine learning to show that the strongest neural transitions could be used to predict the timing of normative boundary judgements. Results from the developmental dataset also demonstrated important boundary conditions for estimating personalized neural state transitions. Nonetheless, some brain-behaviour relations were still apparent in individually modelled developmental data. These validations pave the way for applying personalized fMRI modelling to the study of event segmentation; what meaningful insights could we be missing when we average away what makes each of us unique?
Institution: University of Chicago
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4403018418