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Abstract Representing data using time-resolved networks is valuable for analyzing functional data of the human brain. One commonly used method for constructing time-resolved networks from data is sliding window Pearson correlation (SWPC). One major limitation of SWPC is that it applies a high-pass filter to the activity time series. Therefore, if we select a short window (desirable to estimate rapid changes in connectivity), we will remove important low-frequency information. Here, we propose an approach based on single sideband modulation (SSB) in communication theory. This allows us to select shorter windows to capture rapid changes in the time-resolved functional network connectivity (trFNC). We use simulation and real resting-state functional magnetic resonance imaging (fMRI) data to demonstrate the superior performance of SSB+SWPC compared to SWPC. We also compare the recurring trFNC patterns between individuals with the first episode of psychosis (FEP) and typical controls (TC) and show that FEPs stay more in states that show weaker connectivity across the whole brain. A result exclusive to SSB+SWPC is that TCs stay more in a state with negative connectivity between subcortical and cortical regions. Based on all the results, we argue that SSB+SWPC is more sensitive for capturing temporal variation in trFNC.
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SINGLE SIDEBAND MODULATION AS A TOOL TO IMPROVE FUNCTIONAL CONNECTIVITY ESTIMATION
Time-resolved functional network connectivity (trFNC) provides a useful tool for representing functional magnetic resonance imaging (fMRI) data with functional networks that change with time. Partly due to its simplicity, sliding window Pearson correlation (SWPC) is the most widely-used method for trFNC estimation. In SWPC, the window size should be selected long enough to avoid spurious estimates of connectivity values, and short enough to capture meaningful fast variations in connectivity estimates. To solve this issue, we propose a method inspired by single sideband (SSB) modulation that allows us to select small window sizes for SWPC without filtering out important low-frequency activity information. We use simulation to show the improvement offered by the proposed method. Additionally, we use fMRI data to show that SSB-SWPC estimates have reduced spurious variation compared with typical SWPC estimators.
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- Award ID(s):
- 2112455
- PAR ID:
- 10332746
- Date Published:
- Journal Name:
- IEEE International Symposium on Biomedical Imaging (ISBI)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISBI52829.2022.9761427
