ArticleNetwork neuroscience (Cambridge, Mass.)2021
Predicting MEG resting-state functional connectivity from microstructural information.
Article in Network neuroscience (Cambridge, Mass.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Distinct EEG functional network alterations in Parkinson's disease and multiple system atrophy.NeuroImage. Clinical · 2026Article
- Structure-function coupling of large-scale cortical networks across the lifespan is spectrally specific.Communications biology · 2026Article
- Altered structural and functional brain networks in children with primary nocturnal enuresis: a DTI and resting-state fMRI study.European child & adolescent psychiatry · 2026Article
- The role of white matter myelin in structural-functional network coupling.Communications biology · 2026Article
- Decoding brain structure-function dynamics in health and in psychosis via an autoencoder.Scientific reports · 2025Article
- Association of 206 Brain Structural Connectivity with Different Types of Strokes: A Mendelian Randomization Study.eNeuro · 2025Article
- Prediction of cognitive outcome after mild traumatic brain injury from acute measures of communication within brain networks.Cortex; a journal devoted to the study of the nervous system and behavior · 2024Article
- Bayesian inference of frequency-specific functional connectivity in MEG imaging using a spectral graph model.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Atypical cortical networks in children at high-genetic risk of psychiatric and neurodevelopmental disorders.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Article
- Interictal MEG abnormalities to guide intracranial electrode implantation and predict surgical outcome.ArXiv · 2023Article
- Article
- Interictal magnetoencephalography abnormalities to guide intracranial electrode implantation and predict surgical outcome.Brain communications · 2023Article
- The human brain connectome weighted by the myelin content and total intra-axonal cross-sectional area of white matter tracts.Network neuroscience (Cambridge, Mass.) · 2023Article
- Increased structural connectivity in high schizotypy.Network neuroscience (Cambridge, Mass.) · 2023Article
- Structure-function coupling as a correlate and potential biomarker of cognitive impairment in multiple sclerosis.Network neuroscience (Cambridge, Mass.) · 2022Article
- Social cognitive network neuroscience.Social cognitive and affective neuroscience · 2022Review
- Dementia Risk Factors Modify Hubs but Leave Other Connectivity Measures Unchanged in Asymptomatic Individuals: A Graph Theoretical Analysis.Brain connectivity · 2022Article
- The impact of genetic risk for Alzheimer's disease on the structural brain networks of young adults.Frontiers in neuroscience · 2022Article
- The impact of graph construction scheme and community detection algorithm on the repeatability of community and hub identification in structural brain networks.Human brain mapping · 2021Article
- Composite reaction time and variability correlate with whole-brain white-matter characteristics.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
7 authors.
Funding
Abstract
Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from 90 healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity, and a myelin measure (derived from multicomponent relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer time series in the delta, theta, alpha, and beta frequency bands. Nonnegative matrix factorization was performed to identify the components of the functional connectivity. Shortest path length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest path length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.
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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.