ArticlePloS one2015
Mapping Individual Brain Networks Using Statistical Similarity in Regional Morphology from MRI.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05818410 (Human-Prosthetic Interaction), which is not on this map. Cited by 59 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Human-Prosthetic Interaction: Brain & Technology After Lower-Limb Loss
Who cites it
59 citing papers in PubMed, 124 citations in OpenAlex.
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- Altered gray matter network segregation and disrupted interregional connectivity in children with persistent developmental stuttering: a graph-theoretical morphometric study.Brain imaging and behavior · 2026Article
- Similar minds age alike: an MRI similarity approach for predicting age-related cognitive decline.npj aging · 2026Article
- Revealing Structural Brain-Cognition Relationships in Children: A Comparison of Morphometric Similarity and INverse Divergence Networks.Neuroinformatics · 2026Article
- Deteriorated Gray Matter Connectome in Diabetic Kidney Disease: A Graph Theory Analysis of Individual-Level Gray Matter Morphological Networks.Brain and behavior · 2025Article
- Aberrant Cortical Morphological Networks in First-Episode Schizophrenia.Schizophrenia bulletin · 2025Article
- Structural covariance analysis for neurodegenerative and neuroinflammatory brain disorders.Brain : a journal of neurology · 2025Review
- Neural Plasticity Induced by Working Memory Training: Insights From Cortical Microstructure and Transcriptional Profiles.CNS neuroscience & therapeutics · 2025Article
- Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.Schizophrenia bulletin · 2025Review
- Exploring the predictive value of structural covariance networks for the diagnosis of schizophrenia.Frontiers in psychiatry · 2025Article
- Structural MRI of brain similarity networks.Nature reviews. Neuroscience · 2025Review
- Abnormal gray matter volume and structural covariance network of basal ganglia-limbic system in patients with major depression disorder.Frontiers in neurology · 2025Article
- Brain connectome from neuronal morphology.Network neuroscience (Cambridge, Mass.) · 2025Article
- Longitudinal alterations in morphological brain networks and cognitive function in common-type COVID-19: a 3-month follow-up study.Frontiers in neurology · 2025Article
- Age-related changes of node degree in the multiple-demand network predict fluid intelligence.IBRO neuroscience reports · 2024Article
- Morphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Altered individual-level morphological similarity network in children with growth hormone deficiency.Journal of neurodevelopmental disorders · 2024Article
- Multimodal and hemispheric graph-theoretical brain network predictors of learning efficacy for frontal alpha asymmetry neurofeedback.Cognitive neurodynamics · 2024Article
- Disrupted individual-level morphological brain network in spinal muscular atrophy types 2 and 3.CNS neuroscience & therapeutics · 2024Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Representing brain morphology as a network has the advantage that the regional morphology of 'isolated' structures can be described statistically based on graph theory. However, very few studies have investigated brain morphology from the holistic perspective of complex networks, particularly in individual brains. We proposed a new network framework for individual brain morphology. Technically, in the new network, nodes are defined as regions based on a brain atlas, and edges are estimated using our newly-developed inter-regional relation measure based on regional morphological distributions. This implementation allows nodes in the brain network to be functionally/anatomically homogeneous but different with respect to shape and size. We first demonstrated the new network framework in a healthy sample. Thereafter, we studied the graph-theoretical properties of the networks obtained and compared the results with previous morphological, anatomical, and functional networks. The robustness of the method was assessed via measurement of the reliability of the network metrics using a test-retest dataset. Finally, to illustrate potential applications, the networks were used to measure age-related changes in commonly used network metrics. Results suggest that the proposed method could provide a concise description of brain organization at a network level and be used to investigate interindividual variability in brain morphology from the perspective of complex networks. Furthermore, the method could open a new window into modeling the complexly distributed brain and facilitate the emerging field of human connectomics.
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Registered trials
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.