ArticleHuman brain mapping2023
Using graph theory as a common language to combine neural structure and function in models of healthy cognitive performance.
Article in Human brain mapping, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Structural covariance analysis for neurodegenerative and neuroinflammatory brain disorders.Brain : a journal of neurology · 2025Review
- Credibility revolution: pursuing a balanced and sustainable approach, without dogmas, without magic elixirs.Frontiers in psychology · 2025Article
- Task-guided generative adversarial networks for synthesizing and augmenting structural connectivity matrices for connectivity-based prediction.Network neuroscience (Cambridge, Mass.) · 2025Article
- Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future Directions.Brain sciences · 2024Review
- Elevated Activity in Left Homologous Music Circuits Is Inhibitory for Music Perception but Mediated by Structure-Function Coupling.CNS neuroscience & therapeutics · 2024Article
- Using graph theory as a common language to combine neural structure and function in models of healthy cognitive performance.Human brain mapping · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Graph theory has been used in cognitive neuroscience to understand how organisational properties of structural and functional brain networks relate to cognitive function. Graph theory may bridge the gap in integration of structural and functional connectivity by introducing common measures of network characteristics. However, the explanatory and predictive value of combined structural and functional graph theory have not been investigated in modelling of cognitive performance of healthy adults. In this work, a Principal Component Regression approach with embedded Step-Wise Regression was used to fit multiple regression models of Executive Function, Self-regulation, Language, Encoding and Sequence Processing with a collection of 20 different graph theoretic measures of structural and functional network organisation used as regressors. The predictive ability of graph theory-based models was compared to that of connectivity-based models. The present work shows that using combinations of graph theory metrics to predict cognition in healthy populations does not produce a consistent benefit relative to making predictions based on structural and functional connectivity values directly.
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What OpenQuestion holds
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.