ArticleNeuroImage2015
Estimating individual contribution from group-based structural correlation networks.
Article in NeuroImage, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Investigating Associations Between Developmental Integration and Physiological Stress.American journal of biological anthropology · 2026Article
- The network neuropsychology of neighborhood deprivation in juvenile myoclonic epilepsy.Scientific reports · 2026Article
- The network neuropsychology of juvenile myoclonic epilepsy.Cortex; a journal devoted to the study of the nervous system and behavior · 2025Article
- Exploring the predictive value of structural covariance networks for the diagnosis of schizophrenia.Frontiers in psychiatry · 2025Article
- Altered cerebellar and caudate gray-matter volumes and structural covariance networks preceding dual cognitive and mobility impairments in older people.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Altered topological structure of the brain white matter in maltreated children through topological data analysis.Network neuroscience (Cambridge, Mass.) · 2024Article
- Exploring the core network of the structural covariance network in childhood absence epilepsy.Heliyon · 2023Article
- Brief intensive social gaze training reorganizes functional brain connectivity in boys with fragile X syndrome.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Brain Metabolic Network Redistribution in Patients with White Matter Hyperintensities on MRI Analyzed with an Individualized Index Derived fromKorean journal of radiology · 2022Article
- Network phenotypes and their clinical significance in temporal lobe epilepsy using machine learning applications to morphological and functional graph theory metrics.Scientific reports · 2022Article
- Rapid processing and quantitative evaluation of structural brain scans for adaptive multimodal imaging.Human brain mapping · 2022Article
- Rich-Club Connectivity of the Structural Covariance Network Relates to Memory Processes in Mild Cognitive Impairment and Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2022Article
- Individual-based morphological brain network organization and its association with autistic symptoms in young children with autism spectrum disorder.Human brain mapping · 2021Article
- Morphological integration of the human brain across adolescence and adulthood.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Glucocorticoid regulation and neuroanatomy in fragile x syndrome.Journal of psychiatric research · 2021Article
- Developmental divergence of structural brain networks as an indicator of future cognitive impairments in childhood brain injury: Executive functions.Developmental cognitive neuroscience · 2020Article
- Network analysis of prospective brain development in youth with benign epilepsy with centrotemporal spikes and its relationship to cognition.Epilepsia · 2019Article
- Characterizing Brain Connectivity From Human Electrocorticography Recordings With Unobserved Inputs During Epileptic Seizures.Neural computation · 2019Article
- Screen media activity and brain structure in youth: Evidence for diverse structural correlation networks from the ABCD study.NeuroImage · 2019Article
- Interpreting and Utilising Intersubject Variability in Brain Function.Trends in cognitive sciences · 2018Review
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Authors and funding
7 authors.
Funding
Abstract
Coordinated variations in brain morphology (e.g., cortical thickness) across individuals have been widely used to infer large-scale population brain networks. These structural correlation networks (SCNs) have been shown to reflect synchronized maturational changes in connected brain regions. Further, evidence suggests that SCNs, to some extent, reflect both anatomical and functional connectivity and hence provide a complementary measure of brain connectivity in addition to diffusion weighted networks and resting-state functional networks. Although widely used to study between-group differences in network properties, SCNs are inferred only at the group-level using brain morphology data from a set of participants, thereby not providing any knowledge regarding how the observed differences in SCNs are associated with individual behavioral, cognitive and disorder states. In the present study, we introduce two novel distance-based approaches to extract information regarding individual differences from the group-level SCNs. We applied the proposed approaches to a moderately large dataset (n=100) consisting of individuals with fragile X syndrome (FXS; n=50) and age-matched typically developing individuals (TD; n=50). We tested the stability of proposed approaches using permutation analysis. Lastly, to test the efficacy of our method, individual contributions extracted from the group-level SCNs were examined for associations with intelligence scores and genetic data. The extracted individual contributions were stable and were significantly related to both genetic and intelligence estimates, in both typically developing individuals and participants with FXS. We anticipate that the approaches developed in this work could be used as a putative biomarker for altered connectivity in individuals with neurodevelopmental disorders.
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