Literature DB >> 27184204

Dissociable brain biomarkers of fluid intelligence.

Erick J Paul1, Ryan J Larsen2, Aki Nikolaidis3, Nathan Ward4, Charles H Hillman5, Neal J Cohen6, Arthur F Kramer7, Aron K Barbey8.   

Abstract

Cognitive neuroscience has long sought to understand the biological foundations of human intelligence. Decades of research have revealed that general intelligence is correlated with two brain-based biomarkers: the concentration of the brain biochemical N-acetyl aspartate (NAA) measured by proton magnetic resonance spectroscopy (MRS) and total brain volume measured using structural MR imaging (MRI). However, the relative contribution of these biomarkers in predicting performance on core facets of human intelligence remains to be well characterized. In the present study, we sought to elucidate the role of NAA and brain volume in predicting fluid intelligence (Gf). Three canonical tests of Gf (BOMAT, Number Series, and Letter Sets) and three working memory tasks (Reading, Rotation, and Symmetry span tasks) were administered to a large sample of healthy adults (n=211). We conducted exploratory factor analysis to investigate the factor structure underlying Gf independent from working memory and observed two Gf components (verbal/spatial and quantitative reasoning) and one working memory component. Our findings revealed a dissociation between two brain biomarkers of Gf (controlling for age and sex): NAA concentration correlated with verbal/spatial reasoning, whereas brain volume correlated with quantitative reasoning and working memory. A follow-up analysis revealed that this pattern of findings is observed for males and females when analyzed separately. Our results provide novel evidence that distinct brain biomarkers are associated with specific facets of human intelligence, demonstrating that NAA and brain volume are independent predictors of verbal/spatial and quantitative facets of Gf.
Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Brain volume; Fluid intelligence; MR spectroscopy; N-Acetyl aspartate

Mesh:

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Year:  2016        PMID: 27184204     DOI: 10.1016/j.neuroimage.2016.05.037

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  6 in total

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2.  Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to [corrected] IQ and Gender.

Authors:  Vasileios C Pezoulas; Michalis Zervakis; Sifis Michelogiannis; Manousos A Klados
Journal:  Front Hum Neurosci       Date:  2017-04-26       Impact factor: 3.169

3.  An Investigation of Working Memory Profile and Fluid Intelligence in Children With Neurodevelopmental Difficulties.

Authors:  Maria Sofologi; Vassiliki Pliogou; Eleni Bonti; Maria Efstratopoulou; Georgios A Kougioumtzis; Efthymios Papatzikis; Georgios Ntritsos; Despina Moraitou; Georgia Papantoniou
Journal:  Front Psychol       Date:  2022-03-18

4.  Prediction of fluid intelligence from T1-w MRI images: A precise two-step deep learning framework.

Authors:  Mingliang Li; Mingfeng Jiang; Guangming Zhang; Yujun Liu; Xiaobo Zhou
Journal:  PLoS One       Date:  2022-08-02       Impact factor: 3.752

5.  Cerebral Metabolite Concentrations Are Associated With Cortical and Subcortical Volumes and Cognition in Older Adults.

Authors:  John B Williamson; Damon G Lamb; Eric C Porges; Sarah Bottari; Adam J Woods; Somnath Datta; Kailey Langer; Ronald A Cohen
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6.  The neurobiology of wellness: 1H-MRS correlates of agency, flexibility and neuroaffective reserves in healthy young adults.

Authors:  Tara L White; Meghan A Gonsalves; Ronald A Cohen; Ashley D Harris; Mollie A Monnig; Edward G Walsh; Adam Z Nitenson; Eric C Porges; Damon G Lamb; Adam J Woods; Cara B Borja
Journal:  Neuroimage       Date:  2020-10-27       Impact factor: 6.556

  6 in total

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