White matter integrity predicts individual differences in (fluid) intelligence through working memory

Privado, Jesús; Urturi, C. Sáenz de; Dávila, Jorge; López, Cristina Sáez; Burgaleta, Miguel; Román, Francisco J.; Escorial, Sergio; Colom, Roberto

doi:10.1016/j.paid.2013.07.347

Cited by 2 publications

(1 citation statement)

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“…Previous studies suggested that FI is associated with corpus callosum integrity (Nikolaidis et al, 2017;Wolf et al, 2014) and could be improved via increasing corpus callosum integrity during working memory training (Aydin, Uysal, Yakut, Emiroglu, & Yilmaz, 2012;Peng, Mo, Huang, & Zhou, 2017;Takeuchi et al, 2010). It was also observed that corpus callosum integrity could predict FI through working memory (Privado et al, 2014). Studies have shown that corona radiata integrity was associated with FI (Colom, Karama, Jung, & Haier, 2010;Nikolaidis et al, 2017).…”

Section: Identified Potential Neuromarkersmentioning

confidence: 98%

Predicting visual working memory with multimodal magnetic resonance imaging

Lin

et al. 2020

Human Brain Mapping

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The indispensability of visual working memory (VWM) in human daily life suggests its importance in higher cognitive functions and neurological diseases. However, despite the extensive research efforts, most findings on the neural basis of VWM are limited to a unimodal context (either structure or function) and have low generalization. To address the above issues, this study proposed the usage of multimodal neuroimaging in combination with machine learning to reveal the neural mechanism of VWM across a large cohort (N = 547). Specifically, multimodal magnetic resonance imaging features extracted from voxel‐wise amplitude of low‐frequency fluctuations, gray matter volume, and fractional anisotropy were used to build an individual VWM capacity prediction model through a machine learning pipeline, including the steps of feature selection, relevance vector regression, cross‐validation, and model fusion. The resulting model exhibited promising predictive performance on VWM (r = .402, p < .001), and identified features within the subcortical‐cerebellum network, default mode network, motor network, corpus callosum, anterior corona radiata, and external capsule as significant predictors. The main results were then compared with those obtained on emotional regulation and fluid intelligence using the same pipeline, confirming the specificity of our findings. Moreover, the main results maintained well under different cross‐validation regimes and preprocess strategies. These findings, while providing richer evidence for the importance of multimodality in understanding cognitive functions, offer a solid and general foundation for comprehensively understanding the VWM process from the top down.

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Section: Identified Potential Neuromarkersmentioning

confidence: 98%

Predicting visual working memory with multimodal magnetic resonance imaging

Lin

et al. 2020

Human Brain Mapping

View full text Add to dashboard Cite

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Technical cognition, working memory and creativity

Wynn

Coolidge²

2014

P&C

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This essay explores the nature and neurological basis of creativity in technical production. After presenting a model of expert technical cognition based in cognitive anthropology and cognitive psychology, the authors propose that craft production has three inherent sources of novelty — procedural drift, serendipitous error and fiddling. However, these are quite limited in their creative potential, which may help explain the virtual absence of innovation over the long millennia of the Palaeolithic. Innovation can be far more rapid and effective via invention, which requires folk theories of causation and adequate working memory capacity, all fairly recent evolutionary developments. The neurological basis of expert technical cognition lies in well-known cortical and sub-cortical structures, but recent research has established a provocative role for the cerebellum in the formulation of novel arrangements.

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White matter integrity predicts individual differences in (fluid) intelligence through working memory

Cited by 2 publications

References 0 publications

Predicting visual working memory with multimodal magnetic resonance imaging

Predicting visual working memory with multimodal magnetic resonance imaging

Technical cognition, working memory and creativity

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