ArticleNetwork neuroscience (Cambridge, Mass.)2024
Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study.
Article in Network neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Dynamic Co-Modulation (DyCoM): A Unified Operator Framework for Dynamic Connectivity in Neuroimaging.bioRxiv : the preprint server for biology · 2026Article
- Aberrant recovery of timescale-aligned amplitude balance links to symptoms and cognition in schizophrenia.Translational psychiatry · 2026Article
- Aberrant Recovery of Timescale-Aligned Amplitude Balance Links to Symptoms and Cognition in Schizophrenia.Research square · 2026Article
- Functional Inertia Index of Memory-Retaining Brain Dynamics: A Measure of Large-Scale Brain Adaptability.bioRxiv : the preprint server for biology · 2025Article
- Studying Time-Resolved Functional Connectivity via Communication Theory: On the Complementary Nature of Phase Synchronization and Sliding Window Pearson Correlation.Brain connectivity · 2025Article
- Neural flexibility in metabolic demand dynamics reveals sex-specific differences and supports cognition in late childhood.bioRxiv : the preprint server for biology · 2025Article
- Timescale-normalized fMRI reveals disrupted dynamic signal-energy balance in schizophrenia.bioRxiv : the preprint server for biology · 2025Article
- Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation.bioRxiv : the preprint server for biology · 2025Article
- The dynamics of dynamic time warping in fMRI data: A method to capture inter-network stretching and shrinking via warp elasticity.Imaging neuroscience (Cambridge, Mass.) · 2024Article
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Authors and funding
7 authors.
Funding
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.