ArticleeNeuro2023
Multimodal Brain Signal Complexity Predicts Human Intelligence.
Article in eNeuro, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Cognitive Processing and EEG Complexity.Entropy (Basel, Switzerland) · 2026Review
- Article
- Bilingualism modulates functional connectivity induced by a domain-general artificial grammar learning task.Scientific reports · 2026Article
- Decoding the human brain during intelligence testing.Communications biology · 2025Article
- Sleepiness but neither fluid nor crystallized intelligence can be predicted from resting-state electroencephalography - Evidence from the large scale CoScience EEG-Personality Project.Cognitive, affective & behavioral neuroscience · 2025Article
- Electrophysiological resting-state signatures link polygenic scores to general intelligence.Scientific reports · 2025Observational
- Vertical Topography in EEG Microstates: Physiology or Artifact Manifestation?Human brain mapping · 2025Article
- Reliability of variability and complexity measures for task and task-free BOLD fMRI.Human brain mapping · 2024Article
- What does it mean for consciousness to be multidimensional? A narrative review.Frontiers in psychology · 2024Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven's Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (
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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.