Investigating cognitive neuroscience theories of human intelligence: A connectome‐based predictive modeling approach

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.

Task-specific topology of brain networks supporting working memory and inhibition

“…The theory further suggests that the manifestation of crystallized and fluid intelligence can be traced to the nature of connections within these networks—strong, stable connections and easy-to-reach brain states facilitate verbal intelligence, while weaker, more variable connections and hard-to-reach states might underpin nonverbal intelligence. The theory aligns with findings that associate efficient network reconfiguration with fluid intelligence, suggesting that the brain’s ability to transition between different network states in response to cognitive demands is a substrate of cognitive ability (Anderson & Barbey, 2023; Bassett et al, 2011; Dubois et al, 2018; Wen et al, 2015). Some recent studies, however, have found the opposite associations between brain reconfiguration and intelligence, namely the increased stability of brain networks was related to higher intelligence (Hilger et al, 2017, 2020; Pamplona et al, 2015; Thiele et al, 2022).…”

Assessing blood oxygen level–dependent signal variability as a biomarker of brain injury in sport-related concussion

“…These findings emphasize the need for future research to examine how network topology and dynamics are altered by TBI. Recent research has highlighted the importance of global brain network topology for facilitating cognitive abilities, 73 consistent with the present findings. Executive dysfunction can be a pernicious consequence of TBI, 74 , 75 implicating alterations to network topology and dynamics in neurological sequela following mTBI.…”

Can machine learning-based predictive modelling improve our understanding of human cognition?

“…In applying theory-driven functional brain link selection, we demonstrated that models trained with links between brain regions proposed in neurocognitive intelligence theories [PFIT: ( 13 , 15 ), MD: ( 14 , 38 ), LPFC: ( 39 )] only partly outperformed models trained with links between the same number of randomly chosen regions and performed significantly worse than models trained with all brain links ( 60 ), while performance generally improved with increasing numbers of links. Taken together, this suggests that links between theoretically proposed brain regions include important but not complete intelligence-predictive information.…”