Neural Transition Metric in fMRI: Categorization and Application

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Lu, Yun
fMRI , Brain Network Dynamics , Novelty , Hippocampus
Recent advances in technology allow us to examine brain network dynamics in fMRI on a large-scale. Our lab has recently developed a data-driven method, the neural transition metric (Tseng & Poppenk, 2020), which allows us to identify transitions in individuals’ mental states. My project employed the neural transition metric to 1. investigate the distinction between the externally and internally driven transitions (i.e., transitions induced by task stimuli vs resulted from internal mind-wandering), and 2. examine cognitive dynamics between the novel and repeated viewings of naturalistic movie stimuli. I found that there was no difference in brain correlates for the internally and externally driven transitions, whereas the event related transitions were associated with distinct profile of brain activations compared to the others. Moreover, through comparing the properties of neural transitions between the novel and repeated viewing, I found that movie stimuli are less effective at aligning viewers’ cognition in repeated viewing. This suggests that with repeated viewing, participants’ thoughts became more idiosyncratic and more internally driven. Additionally, degree of conformities (i.e., the alignment of transitions timing between the individual and the group) in the novel and repeated viewings were correlated with volumes of the anterior/posterior hippocampus (a/pHPC) and amygdala, such that individuals with bigger aHPC and amygdala displayed higher conformity in the novel run, whereas individuals with bigger pHPC have lower conformity in the repeated run. This collectively demonstrated that our neural transition metric could have a broader application in experiments involving naturalistic stimuli by offering unique insights into our cognitive dynamics.
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