ArticleNeuroImage2019
Optimization of graph construction can significantly increase the power of structural brain network studies.
Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Shared white matter connectome-based correlates of self-reproach, response failure, spatial planning, and resilience in patients with subthreshold depression: a graph theory approach.Scientific reports · 2026Article
- Decoding brain structure-function dynamics in health and in psychosis via an autoencoder.Scientific reports · 2025Article
- Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics.Nature communications · 2024Article
- Similarity between structural and proxy estimates of brain connectivity.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2024Article
- Modeling the impact of MRI acquisition bias on structural connectomes: Harmonizing structural connectomes.Network neuroscience (Cambridge, Mass.) · 2024Article
- Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge.NeuroImage · 2023Article
- Genetic risk for schizophrenia is associated with increased proportion of indirect connections in brain networks revealed by a semi-metric analysis: evidence from population sample stratified for polygenic risk.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths.Neuroinformatics · 2023Article
- Increased structural connectivity in high schizotypy.Network neuroscience (Cambridge, Mass.) · 2023Article
- Accuracy and reliability of diffusion imaging models.NeuroImage · 2022Article
- Impact of thresholding on the consistency and sensitivity of diffusion MRI-based brain networks in patients with cerebral small vessel disease.Brain and behavior · 2022Article
- Dementia Risk Factors Modify Hubs but Leave Other Connectivity Measures Unchanged in Asymptomatic Individuals: A Graph Theoretical Analysis.Brain connectivity · 2022Article
- Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis.Frontiers in neuroinformatics · 2022Article
- Diffusion Tensor MRI Structural Connectivity and PET Amyloid Burden in Preclinical Autosomal Dominant Alzheimer Disease: The DIAN Cohort.Radiology · 2022Article
- The impact of genetic risk for Alzheimer's disease on the structural brain networks of young adults.Frontiers in neuroscience · 2022Article
- Dyconnmap: Dynamic connectome mapping-A neuroimaging python module.Human brain mapping · 2021Article
- 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
- Mapping Structural Connectivity Using Diffusion MRI: Challenges and Opportunities.Journal of magnetic resonance imaging : JMRI · 2021Review
- Combining network topology and information theory to construct representative brain networks.Network neuroscience (Cambridge, Mass.) · 2021Article
- Predicting MEG resting-state functional connectivity from microstructural information.Network neuroscience (Cambridge, Mass.) · 2021Article
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3 authors.
Funding
Abstract
Structural brain networks derived from diffusion magnetic resonance imaging data have been used extensively to describe the human brain, and graph theory has allowed quantification of their network properties. Schemes used to construct the graphs that represent the structural brain networks differ in the metrics they use as edge weights and the algorithms they use to define the network topologies. In this work, twenty graph construction schemes were considered. The schemes use the number of streamlines, the fractional anisotropy, the mean diffusivity or other attributes of the tracts to define the edge weights, and either an absolute threshold or a data-driven algorithm to define the graph topology. The test-retest data of the Human Connectome Project were used to compare the reproducibility of the graphs and their various attributes (edges, topologies, graph theoretical metrics) derived through those schemes, for diffusion images acquired with three different diffusion weightings. The impact of the scheme on the statistical power of the study and on the number of participants required to detect a difference between populations or an effect of an intervention was also calculated. The reproducibility of the graphs and their attributes depended heavily on the graph construction scheme. Graph reproducibility was higher for schemes that used thresholding to define the graph topology, while data-driven schemes performed better at topology reproducibility (mean similarities of 0.962 and 0.984 respectively, for graphs derived from diffusion images with b=2000 s/mm
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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.