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Abstract #2353

Dynamic autocorrelation as a tool to measure time-dependent changes in neuronal time-scale along the hippocampus axis during resting-state.

Ali Golestani1,2, Lauren Homann3, Nichole Bouffard4, Morris Moscovitch3,5, and Morgan Barense3,5
1University of Calgary, Calgary, AB, Canada, 2Alberta Health Services, Calgary, AB, Canada, 3Department of Psychology, University of Toronto, Toronto, ON, Canada, 4Department of Psychological and Brain Science, Washington University in St Louis, St Louis, MO, United States, 5Rotman Research Institute, Toronto, ON, Canada

Synopsis

Keywords: fMRI Analysis, fMRI (resting state)

Motivation: The autocorrelation (AC) of fMRI signals varies along the hippocampus (HPC), with higher values in the anterior-medial (AM) compared to the posterior-lateral (PL) regions. However, it remains unclear if this pattern is stable over time.

Goal(s): We introduced dynamic autocorrelation (dAC) to investigate temporal changes in the AC pattern. We aimed to determine if the AC along the HPC changes over time.

Approach: Voxel-wise dAC was estimated within the HPC for each subject, and the values were averaged across the PL and AM regions.

Results: The dAC pattern along the HPC is not stable and fluctuates over time.

Impact: We introduced dynamic autocorrelation (dAC) as a measure to investigate dynamic changes in AC patterns and demonstrated that the AC pattern is dynamic during resting-state. The dAC provides a promising approach to examine how neural timescales adapt to environmental changes.

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Keywords