ArticlePLoS computational biology2018
Data-driven brain network models differentiate variability across language tasks.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 46 citations in OpenAlex.
- Physical mechanisms of human brain functions.Quantitative biology (Beijing, China) · 2025Review
- Quantifying the influence of biophysical factors in shaping brain communication through remnant functional networks.Network neuroscience (Cambridge, Mass.) · 2025Article
- Translational network neuroscience: Nine roadblocks and possible solutions.Network neuroscience (Cambridge, Mass.) · 2025Article
- Brain Segregation and Integration Relate to Word-Finding Abilities in Older and Younger Adults.Neurobiology of language (Cambridge, Mass.) · 2025Article
- Noise improves the association between effects of local stimulation and structural degree of brain networks.PLoS computational biology · 2023Article
- Within-subject reproducibility varies in multi-modal, longitudinal brain networks.Scientific reports · 2023Article
- Article
- Social cognitive network neuroscience.Social cognitive and affective neuroscience · 2022Review
- Brain network dynamics codify heterogeneity in seizure evolution.Brain communications · 2022Article
- Basins of attraction of chimera states on networks.Frontiers in physiology · 2022Article
- Relations between large-scale brain connectivity and effects of regional stimulation depend on collective dynamical state.PLoS computational biology · 2020Article
- Personalizing neuromodulation.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2020Review
- Models of communication and control for brain networks: distinctions, convergence, and future outlook.Network neuroscience (Cambridge, Mass.) · 2020Article
- Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks.Network neuroscience (Cambridge, Mass.) · 2020Article
- A two-layered brain network model and its chimera state.Scientific reports · 2019Article
- Cognitive chimera states in human brain networks.Science advances · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 5 institutions in 1 country.
Funding
Abstract
The relationship between brain structure and function has been probed using a variety of approaches, but how the underlying structural connectivity of the human brain drives behavior is far from understood. To investigate the effect of anatomical brain organization on human task performance, we use a data-driven computational modeling approach and explore the functional effects of naturally occurring structural differences in brain networks. We construct personalized brain network models by combining anatomical connectivity estimated from diffusion spectrum imaging of individual subjects with a nonlinear model of brain dynamics. By performing computational experiments in which we measure the excitability of the global brain network and spread of synchronization following a targeted computational stimulation, we quantify how individual variation in the underlying connectivity impacts both local and global brain dynamics. We further relate the computational results to individual variability in the subjects' performance of three language-demanding tasks both before and after transcranial magnetic stimulation to the left-inferior frontal gyrus. Our results show that task performance correlates with either local or global measures of functional activity, depending on the complexity of the task. By emphasizing differences in the underlying structural connectivity, our model serves as a powerful tool to assess individual differences in task performances, to dissociate the effect of targeted stimulation in tasks that differ in cognitive demand, and to pave the way for the development of personalized therapeutics.
Indexed as
Identifiers
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.