ArticleNetwork neuroscience (Cambridge, Mass.)2021
Parcellation-induced variation of empirical and simulated brain connectomes at group and subject levels.
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 26 papers.
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26 citing papers in PubMed.
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- Adolescent maturation of cortical excitation-inhibition ratio based on individualized biophysical network modeling.Science advances · 2025Article
- Structural Balance of Resting-state Brain Network in Attention Deficit Hyperactivity Disorder.Basic and clinical neuroscience · 2025Article
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- Modelling low-dimensional interacting brain networks reveals organising principle in human cognition.Network neuroscience (Cambridge, Mass.) · 2025Article
- Symmetry breaking organizes the brain's resting state manifold.Scientific reports · 2024Article
- Local but not global graph theoretic measures of semantic networks generalize across tasks.Behavior research methods · 2024Article
- Fast connectivity gradient approximation: maintaining spatially fine-grained connectivity gradients while reducing computational costs.Communications biology · 2024Article
- Edge time series components of functional connectivity and cognitive function in Alzheimer's disease.Brain imaging and behavior · 2024Article
- Edge Time Series Components of Functional Connectivity and Cognitive Function in Alzheimer's Disease.medRxiv : the preprint server for health sciences · 2023Article
- White-matter degradation and dynamical compensation support age-related functional alterations in human brain.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Prenatal depressive symptoms and childhood development of brain limbic and default mode network structure.Human brain mapping · 2023Article
- Whole-brain dynamical modelling for classification of Parkinson's disease.Brain communications · 2023Article
- Normalizing the brain connectome for communication through synchronization.Network neuroscience (Cambridge, Mass.) · 2022Article
- Towards an efficient validation of dynamical whole-brain models.Scientific reports · 2022Article
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Abstract
Recent developments of whole-brain models have demonstrated their potential when investigating resting-state brain activity. However, it has not been systematically investigated how alternating derivations of the empirical structural and functional connectivity, serving as the model input, from MRI data influence modeling results. Here, we study the influence from one major element: the brain parcellation scheme that reduces the dimensionality of brain networks by grouping thousands of voxels into a few hundred brain regions. We show graph-theoretical statistics derived from the empirical data and modeling results exhibiting a high heterogeneity across parcellations. Furthermore, the network properties of empirical brain connectomes explain the lion's share of the variance in the modeling results with respect to the parcellation variation. Such a clear-cut relationship is not observed at the subject-resolved level per parcellation. Finally, the graph-theoretical statistics of the simulated connectome correlate with those of the empirical functional connectivity across parcellations. However, this relation is not one-to-one, and its precision can vary between models. Our results imply that network properties of both empirical connectomes can explain the goodness-of-fit of whole-brain models to empirical data at a global group level but not at a single-subject level, which provides further insights into the personalization of whole-brain models.
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