Linking Brain Biology to Intellectual Endowment: A Review on the Associations of Human Intelligence With Neuroimaging Data

Dizaji, Aslan S.; Vieira, Bruno Hebling; Khodaei, Mohmmad Reza; Ashrafi, Mahnaz; Parham, Elahe; Hossein-Zadeh, Gholam-Ali; Salmon, Carlos Ernesto Garrido; Soltanian‐Zadeh, Hamid

doi:10.32598/bcn.12.1.574.1

Cited by 15 publications

(13 citation statements)

References 130 publications

(155 reference statements)

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“…The most successful approaches to the prediction of intelligence are often based on linear modeling with univariate feature filtering. Even though it is not theoretically a required step, it was employed by most of the successful approaches (Dizaji et al, 2021).…”

Section: Predicting "G" From Resting-state Functional Imagingmentioning

confidence: 99%

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A deep learning based approach identifies regions more relevant than resting‐state networks to the prediction of general intelligence from resting‐state fMRI

Vieira

Dubois

Calhoun

et al. 2021

Human Brain Mapping

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Prediction of cognitive ability latent factors such as general intelligence from neuroimaging has elucidated questions pertaining to their neural origins. However, predicting general intelligence from functional connectivity limit hypotheses to that specific domain, being agnostic to time-distributed features and dynamics. We used an ensemble of recurrent neural networks to circumvent this limitation, bypassing feature extraction, to predict general intelligence from resting-state functional magnetic resonance imaging regional signals of a large sample (n = 873) of Human Connectome Project adult subjects. Ablating common resting-state networks (RSNs) and measuring degradation in performance, we show that model reliance can be mostly explained by network size. Using our approach based on the temporal variance of saliencies, that is, gradients of outputs with regards to inputs, we identify a candidate set of networks that more reliably affect performance in the prediction of general intelligence than similarly sized RSNs. Our approach allows us to further test the effect of local alterations on data and the expected changes in derived metrics such as functional connectivity and instantaneous innovations.

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Section: Predicting "G" From Resting-state Functional Imagingmentioning

confidence: 99%

“…Recent advances make neuroimaging a fundamental tool to answer this question. For a recent review, see Dizaji et al ( 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A deep learning based approach identifies regions more relevant than resting‐state networks to the prediction of general intelligence from resting‐state fMRI

Vieira

Dubois

Calhoun

et al. 2021

Human Brain Mapping

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“…Our search strategy identified 689 records in Scopus. Additionally, 17 records were identified from Dizaji et al (2021) and 7 in Fan et al (2020).…”

Section: Resultsmentioning

confidence: 99%

“…We performed a systematized search in Scopus (scopus.com), dating to 15th December 2020. Additional documents were retrieved from a recent literature review (Dizaji et al 2021), co-authored by B.H.V. and C.E.G.S., and another study (Fan et al 2020, Table 1) that provide a comparison between similar studies.…”

Section: Information Sourcesmentioning

confidence: 99%

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

Vieira¹,

Pamplona

Fachinello

et al. 2021

Preprint

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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.

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Brain morphometry and estimation of aging brain in subjects with congenital untreated isolated GH deficiency

Villar-Gouy,

Salmon,

Salvatori

et al. 2024

J Endocrinol Invest

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Linking Brain Biology to Intellectual Endowment: A Review on the Associations of Human Intelligence With Neuroimaging Data

Cited by 15 publications

References 130 publications

A deep learning based approach identifies regions more relevant than resting‐state networks to the prediction of general intelligence from resting‐state fMRI

A deep learning based approach identifies regions more relevant than resting‐state networks to the prediction of general intelligence from resting‐state fMRI

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

Brain morphometry and estimation of aging brain in subjects with congenital untreated isolated GH deficiency

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