ArticleScientific reports2023
Within-subject reproducibility varies in multi-modal, longitudinal brain networks.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Concordance between the fast and efficient mixed-effects algorithm (FEMA) and conventional mixed-effects modeling for whole-brain connectivity in longitudinal chronic pain.bioRxiv : the preprint server for biology · 2026Article
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- An evaluation of the efficacy of single-echo and multi-echo fMRI denoising strategies.Network neuroscience (Cambridge, Mass.) · 2026Article
- Influence of cognitive networks and task performance on fMRI-based state classification using DNN models.Scientific reports · 2025Article
- Review
- Intracranial Voltage Profiles from Untangled Human Deep Sources Reveal Multisource Composition and Source Allocation Bias.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- SNAKE: A modular realistic fMRI data simulator from the space-time domain to k-space and back.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Fully individualized models for cross-sectional and longitudinal network-based tau spread.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Article
- A practical evaluation of measures derived from compressed sensing diffusion spectrum imaging.Human brain mapping · 2024Article
- Simulated brain networks reflecting progression of Parkinson's disease.Network neuroscience (Cambridge, Mass.) · 2024Article
- Quantifying the contribution of subject and group factors in brain activation.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Establishing the Validity of Compressed Sensing Diffusion Spectrum Imaging.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Network neuroscience provides important insights into brain function by analyzing complex networks constructed from diffusion Magnetic Resonance Imaging (dMRI), functional MRI (fMRI) and Electro/Magnetoencephalography (E/MEG) data. However, in order to ensure that results are reproducible, we need a better understanding of within- and between-subject variability over long periods of time. Here, we analyze a longitudinal, 8 session, multi-modal (dMRI, and simultaneous EEG-fMRI), and multiple task imaging data set. We first confirm that across all modalities, within-subject reproducibility is higher than between-subject reproducibility. We see high variability in the reproducibility of individual connections, but observe that in EEG-derived networks, during both rest and task, alpha-band connectivity is consistently more reproducible than connectivity in other frequency bands. Structural networks show a higher reliability than functional networks across network statistics, but synchronizability and eigenvector centrality are consistently less reliable than other network measures across all modalities. Finally, we find that structural dMRI networks outperform functional networks in their ability to identify individuals using a fingerprinting analysis. Our results highlight that functional networks likely reflect state-dependent variability not present in structural networks, and that the type of analysis should depend on whether or not one wants to take into account state-dependent fluctuations in connectivity.
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