There is a specific neurobiological basis underlying Spearman's general factor of intelligence.
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While some studies suggest white matter microstructure correlates with fluid intelligence, other research revisits and questions the foundational hypotheses of general intelligence.
Abstract Purpose The aim of this study is to better understand the neural substrate of intelligence in children, through the characterization of the brain's structural connectivity (graph measures), as well as the major white matter (WM) fibers microstructure. Materials and methods Fourty-three children (8 to 12 years) were include in this study. MRI acquisition included conventional and diffusion tensor imaging sequences. Children also underwent neuropsychological tests, providing the ten WISC-subtests. A factor analysis was performed to explore the inter-correlations of WISC-IV subtests and to extract intelligence domains as well as the general intelligence factor (g-score). Correlations between the intelligence domains and both graphs and diffusivity metrics were also explored. Results Global graph metrics revealed a strong relationship between high intelligence scores and brain network homogeneity (high density and low modularity), mainly in the temporal and parietal lobes. Furthermore, quantitative WM fiber-bundle analysis showed an increased axonal density in the major WM fiber-bundles that was associated to increased intelligence performances. Conclusion These findings demonstrated that intelligence neural substrate is based on a strong WM microstructure of the major intra- and inter- hemispheric fiber-bundles and a well-balanced network organization between local and global scales.
The well-known hypothesis of Sir Francis Galton (1883) posed that individual differences in performance on diverse sensorimotor tasks are rooted in single general sensory discrimination ability. Relatedly, Charles Spearman (1904) hypothesized that this discrimination ability and intelligence share the same neural basis and thus should be statistically equivalent. Despite a century of research, existing evidence for these 2 hypotheses is still inconclusive. Study 1 modeled the factor structure for, to date, the most comprehensive battery of tasks tapping into visual discrimination, and investigated its relationships with iconic and working memory, as well as intelligence. The confirmatory factor analysis (CFA) and structural equation modeling (SEM) models indicated that performance could be grouped into 2 considerably correlated, yet statistically separate factors reflecting temporal versus nontemporal (i.e., featural) sensory discrimination ability. These 2 abilities correlated considerably with working memory and intelligence but, at the same time, were clearly separable. However, the discrimination-intelligence link disappeared when mediated by working memory, suggesting that sensory discrimination plays no explanatory role in intelligence. In Study 2, the above findings were supported and extended by introducing auditory discrimination tasks. The results indicated 3 independent factors reflecting: amodal temporal discrimination, visual featural discrimination, and auditory featural discrimination. Working memory (WM) fully accounted for the shared variance among the three abilities and their relationships with intelligence. Overall, both Galton and Spearman validly guessed that various low-level sensorimotor processes can be mutually linked but, contrary to their ideas, sensory discrimination neither constitutes unitary ability nor is equivalent to intelligence. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
White matter microstructure is a candidate neurobiological substrate underlying individual differences in fluid intelligence, potentially through differences in neural information transfer. Investigating the association between white matter microstructure and fluid intelligence requires precise modeling of these microstructural properties across the brain. Yet, it remains unclear whether MRI-derived markers of white matter microstructure generalize across tracts to support latent modeling approaches. Therefore, our primary objective was to derive measurement models for markers of white matter integrity (fractional anisotropy, FA), neurite density (intra-neurite volume fraction, INVF), and myelin content (magnetization transfer ratio, MTR) across 52 tracts (HCP-1065 atlas) grouped into 10 functional clusters. We investigated data of <i>N</i> = 365 individuals (age range: 18-74 years) drawn from two independent samples (Dortmund Vital Study: <i>N</i> = 150, Clinicaltrials.gov: NCT05155397; Mainz Network Study: <i>N</i> = 215). Confirmatory factor analyses consistently favored hierarchical bifactor models, capturing both a general factor per marker and orthogonal hemisphere-specific factors, independent of participants' age. The general factors FA and MTR of the favored measurement models were significantly associated with fluid intelligence, assessed with matrix reasoning tests, FA: β = 0.26, <i>p</i> < .001 and MTR: β = 0.25, p = .017. When controlling for age, the association of fluid intelligence with FA remained significant, β = 0.14, <i>p</i> < .043, while the association with MTR was no longer significant, β = 0.11, <i>p</i> = .328. These findings establish anatomically informed measurement models for white matter microstructure and provide a scalable framework for investigating the biological underpinnings of cognitive abilities.
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