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On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting

View ORCID ProfileBruno Hebling Vieira, View ORCID ProfileGustavo Santo Pedro Pamplona, View ORCID ProfileKarim Fachinello, Alice Kamensek Silva, View ORCID ProfileMaria Paula Foss, View ORCID ProfileCarlos Ernesto Garrido Salmon
doi: https://doi.org/10.1101/2021.10.19.462649
Bruno Hebling Vieira
aInBrain Lab, Departamento de Física, FFCLRP, Universidade de São Paulo, Ribeirão Preto, Brazil
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  • ORCID record for Bruno Hebling Vieira
Gustavo Santo Pedro Pamplona
bSensory-Motor Lab (SeMoLa), Department of Ophthalmology-University of Lausanne, Jules Gonin Eye Hospital-Fondation Asile des Aveugles, Lausanne, Switzerland
cRehabilitation Engineering Laboratory (RELab), Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
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Karim Fachinello
dPsiCog Lab, Psicobiologia, FFCLRP, Universidade de São Paulo, Ribeirão Preto, Brazil
eNeuropsicologia, Setor de Distúrbios do Movimento e Neurologia Comportamental, Departamento de Neurociências e Ciências do Comportamento, FMRP, Universidade de São Paulo, Ribeirão Preto, Brazil
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Alice Kamensek Silva
eNeuropsicologia, Setor de Distúrbios do Movimento e Neurologia Comportamental, Departamento de Neurociências e Ciências do Comportamento, FMRP, Universidade de São Paulo, Ribeirão Preto, Brazil
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Maria Paula Foss
eNeuropsicologia, Setor de Distúrbios do Movimento e Neurologia Comportamental, Departamento de Neurociências e Ciências do Comportamento, FMRP, Universidade de São Paulo, Ribeirão Preto, Brazil
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Carlos Ernesto Garrido Salmon
aInBrain Lab, Departamento de Física, FFCLRP, Universidade de São Paulo, Ribeirão Preto, Brazil
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  • ORCID record for Carlos Ernesto Garrido Salmon
  • For correspondence: bruno.hebling.vieira@usp.br garrido@ffclrp.usp.br
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Abstract

Human intelligence is one of the main objects of study in cognitive neuroscience. Reviews and meta-analyses have proved to be fundamental to establish and cement neuroscientific theories on intelligence. The prediction of intelligence using in vivo neuroimaging data and machine learning has become a widely accepted and replicated result. Here, we present a systematic review of this growing area of research, based on studies that employ structural, functional, and/or diffusion MRI to predict human intelligence in cognitively normal subjects using machine-learning. We performed a systematic assessment of methodological and reporting quality, using the PROBAST and TRIPOD assessment forms and 30 studies identified through a systematic search. We observed that fMRI is the most employed modality, resting-state functional connectivity (RSFC) is the most studied predictor, and the Human Connectome Project is the most employed dataset. A meta-analysis revealed a significant difference between the performance obtained in the prediction of general and fluid intelligence from fMRI data, confirming that the quality of measurement moderates this association. The expected performance of studies predicting general intelligence from fMRI was estimated to be r = 0.42 (CI95% = [0.35, 0.50]) while for studies predicting fluid intelligence obtained from a single test, expected performance was estimated as r = 0.15 (CI95% = [0.13, 0.17]). We further enumerate some virtues and pitfalls we identified in the methods for the assessment of intelligence and machine learning. The lack of treatment of confounder variables, including kinship, and small sample sizes were two common occurrences in the literature which increased risk of bias. Reporting quality was fair across studies, although reporting of results and discussion could be vastly improved. We conclude that the current literature on the prediction of intelligence from neuroimaging data is reaching maturity. Performance has been reliably demonstrated, although extending findings to new populations is imperative. Current results could be used by future works to foment new theories on the biological basis of intelligence differences.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://osf.io/5qe64/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted October 28, 2021.
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On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting
Bruno Hebling Vieira, Gustavo Santo Pedro Pamplona, Karim Fachinello, Alice Kamensek Silva, Maria Paula Foss, Carlos Ernesto Garrido Salmon
bioRxiv 2021.10.19.462649; doi: https://doi.org/10.1101/2021.10.19.462649
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On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting
Bruno Hebling Vieira, Gustavo Santo Pedro Pamplona, Karim Fachinello, Alice Kamensek Silva, Maria Paula Foss, Carlos Ernesto Garrido Salmon
bioRxiv 2021.10.19.462649; doi: https://doi.org/10.1101/2021.10.19.462649

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