@article{ThieleRichterHilger2023,
  author    = {Thiele, Jonas A. and Richter, Aylin and Hilger, Kirsten},
  title     = {Multimodal brain signal complexity predicts human intelligence},
  series = {eNeuro},
  volume    = {10},
  journal   = {eNeuro},
  number    = {2},
  doi       = {10.1523/ENEURO.0345-22.2022},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-312949},
  year      = {2023},
  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 (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.},
  language  = {en}
}
@article{SchneiderNiklas2017,
  author    = {Schneider, Wolfgang and Niklas, Frank},
  title     = {Intelligence and verbal short-term memory/working memory: their interrelationships from childhood to young adulthood and their impact on academic achievement},
  series = {Journal of Intelligence},
  volume    = {5},
  journal   = {Journal of Intelligence},
  number    = {2},
  issn      = {2079-3200},
  doi       = {10.3390/jintelligence5020026},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-198004},
  pages     = {26},
  year      = {2017},
  abstract  = {Although recent developmental studies exploring the predictive power of intelligence and working memory (WM) for educational achievement in children have provided evidence for the importance of both variables, findings concerning the relative impact of IQ and WM on achievement have been inconsistent. Whereas IQ has been identified as the major predictor variable in a few studies, results from several other developmental investigations suggest that WM may be the stronger predictor of academic achievement. In the present study, data from the Munich Longitudinal Study on the Genesis of Individual Competencies (LOGIC) were used to explore this issue further. The secondary data analysis included data from about 200 participants whose IQ and WM was first assessed at the age of six and repeatedly measured until the ages of 18 and 23. Measures of reading, spelling, and math were also repeatedly assessed for this age range. Both regression analyses based on observed variables and latent variable structural equation modeling (SEM) were carried out to explore whether the predictive power of IQ and WM would differ as a function of time point of measurement (i.e., early vs. late assessment). As a main result of various regression analyses, IQ and WM turned out to be reliable predictors of academic achievement, both in early and later developmental stages, when previous domain knowledge was not included as additional predictor. The latter variable accounted for most of the variance in more comprehensive regression models, reducing the impact of both IQ and WM considerably. Findings from SEM analyses basically confirmed this outcome, indicating IQ impacts on educational achievement in the early phase, and illustrating the strong additional impact of previous domain knowledge on achievement at later stages of development.},
  language  = {en}
}