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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Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study
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 sub-cortical and cortical regions. Based on all the results, we argue that SSB+SWPC is more sensitive for capturing temporal variation in trFNC.
more » « less- NSF-PAR ID:
- 10495618
- Publisher / Repository:
- DOI PREFIX: 10.1162
- Date Published:
- Journal Name:
- Network Neuroscience
- ISSN:
- 2472-1751
- Format(s):
- Medium: X Size: p. 1-82
- Size(s):
- ["p. 1-82"]
- Sponsoring Org:
- National Science Foundation
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