ArticleHuman brain mapping2023
Investigating cognitive neuroscience theories of human intelligence: A connectome-based predictive modeling approach.
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 15 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
15 citing papers in PubMed.
- Experience-dependent changes in functional connectome fingerprinting.Nature communications · 2026Article
- Cognitive ability in childhood predicts adolescent structural and functional brain development: A longitudinal study.Developmental cognitive neuroscience · 2026Article
- Whole brain grey matter synaptic terminal density, age and intellectual functioning in schizophrenia: an in vivo [Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- The network architecture of general intelligence in the human connectome.Nature communications · 2026Article
- Decoding the human brain during intelligence testing.Communications biology · 2025Article
- The Functional Connectome Mediates Associations Between Fitness and Cognition Across the Adult Lifespan.Human brain mapping · 2025Article
- Understanding implicature as an inner simulation of the speaker's context retrieval.Frontiers in human neuroscience · 2025Article
- Choosing explanation over performance: Insights from machine learning-based prediction of human intelligence from brain connectivity.PNAS nexus · 2024Article
- Task-specific topology of brain networks supporting working memory and inhibition.Human brain mapping · 2024Article
- Connectome-based predictive modeling of Internet addiction symptomatology.Social cognitive and affective neuroscience · 2024Article
- Investigating cognitive neuroscience theories of human intelligence: A connectome-based predictive modeling approach.Human brain mapping · 2023Article
- Assessing blood oxygen level-dependent signal variability as a biomarker of brain injury in sport-related concussion.Brain communications · 2023Article
- TSP-GNN: a novel neuropsychiatric disorder classification framework based on task-specific prior knowledge and graph neural network.Frontiers in neuroscience · 2023Article
- Time course of effective connectivity associated with perspective taking in utterance comprehension.Frontiers in human neuroscience · 2023Article
- An electroencephalography connectome predictive model of craving for methamphetamine.International journal of clinical and health psychology : IJCHPArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Central to modern neuroscientific theories of human intelligence is the notion that general intelligence depends on a primary brain region or network, engaging spatially localized (rather than global) neural representations. Recent findings in network neuroscience, however, challenge this assumption, providing evidence that general intelligence may depend on system-wide network mechanisms, suggesting that local representations are necessary but not sufficient to account for the neural architecture of human intelligence. Despite the importance of this key theoretical distinction, prior research has not systematically investigated the role of local versus global neural representations in predicting general intelligence. We conducted a large-scale connectome-based predictive modeling study (N = 297), administering resting-state fMRI and a comprehensive cognitive battery to evaluate the efficacy of modern neuroscientific theories of human intelligence, including spatially localized theories (Lateral Prefrontal Cortex Theory, Parieto-Frontal Integration Theory, and Multiple Demand Theory) and recent global accounts (Process Overlap Theory and Network Neuroscience Theory). The results of our study demonstrate that general intelligence can be predicted by local functional connectivity profiles but is most robustly explained by global profiles of whole-brain connectivity. Our findings further suggest that the improved efficacy of global theories is not reducible to a greater strength or number of connections, but instead results from considering both strong and weak connections that provide the basis for intelligence (as predicted by the Network Neuroscience Theory). Our results highlight the importance of considering local neural representations in the context of a global information-processing architecture, suggesting future directions for theory-driven research on system-wide network mechanisms underlying general intelligence.
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.