Ensemble of 3D CNN Regressors with Data Fusion for Fluid Intelligence Prediction

Pominova, Marina; Kuzina, Anna; Kondrateva, Ekaterina; Sushchinskaya, Svetlana; Burnaev, Evgeny; Yarkin, V.E.; Sharaev, Maxim

doi:10.1007/978-3-030-31901-4_19

Cited by 9 publications

(10 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As we mentioned in section that several studies used sMRI-based regional brain volumes as features in different machine learning methods to predict intelligence scores 4,[6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][27][28][29][30][31] . These studies used ∼8,500 healthy subjects for model training and then predicted the residual PIQ score of more than 3,500 adolescents with a mean square error (MSE) ranging from 86 to 103 (for a range of true residual PIQ score of [−40, 30]), or a correlation of 10% (p < 0.05).…”

Section: Discussionmentioning

confidence: 99%

Can deep learning predict human intelligence from structural brain MRI?

Hussain

LaMay

Grant

et al. 2023

Preprint

View full text Add to dashboard Cite

Can brain structure predict human intelligence? T1-weighted structural brain magnetic resonance images (sMRI) have been correlated with intelligence. Nevertheless, population-level association does not fully account for individual variability in intelligence. To address this, individual prediction studies emerge recently. However, they are mostly on predicting fluid intelligence (the ability to solve new problems). Studies are lacking to predict crystallized intelligence (the ability to accumulate knowledge) or general intelligence (fluid and crystallized intelligence combined). This study tests whether deep learning of sMRI can predict an individual verbal, comprehensive, and full-scale intelligence quotients (VIQ, PIQ, FSIQ), which reflect both fluid and crystallized intelligence. We performed a comprehensive set of 432 experiments, using different input images, six deep learning models, and two outcome settings, on 850 autistic and healthy subjects 6-64 years of age. Results show promise with statistical significance, and also open up questions inviting further future studies.

show abstract

Section: Discussionmentioning

confidence: 99%

Can deep learning predict human intelligence from structural brain MRI?

Hussain

LaMay

Grant

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…We also computed the residual scores to account for the influence of covariates (see below). Deep learning strategies that predict absolute and residual scores have been used in other machine learning studies 4,[6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][35][36][37][38][39] .…”

Section: Residual Intelligence Scorementioning

confidence: 99%

Can deep learning predict human intelligence from structural brain MRI?

Hussain,

LaMay,

Grant

et al. 2024

Preprint

View full text Add to dashboard Cite

Can brain structure predict human intelligence? T1-weighted structural brain magnetic resonance images (sMRI) have been correlated with intelligence. However, the population-level association does not fully account for individual variability in intelligence. To address this, studies have emerged recently to predict individual subject’s intelligence or neurocognitive scores. However, they are mostly on predicting fluid intelligence (the ability to solve new problems). Studies are lacking to predict crystallized intelligence (the ability to accumulate knowledge) or general intelligence (fluid and crystallized intelligence combined). This study tests whether deep learning of sMRI can predict an individual subject’s verbal, comprehensive, and full-scale intelligence quotients (VIQ, PIQ, and FSIQ), which reflect fluid and crystallized intelligence. We performed a comprehensive set of 432 experiments, using different input image channels, six deep learning models, and two outcome settings, in 850 healthy and autistic subjects 6-64 years of age. Our findings indicate a statistically significant potential of T1-weighted sMRI in predicting intelligence, with a Pearson correlation exceeding 0.21 (p < 0.001). Interestingly, we observed that an increase in the complexity of deep learning models does not necessarily translate to higher accuracy in intelligence prediction. The interpretations of our 2D and 3D CNNs, based on GradCAM, align well with the Parieto-Frontal Integration Theory (P-FIT), reinforcing the theory’s suggestion that human intelligence is a result of interactions among various brain regions, including the occipital, temporal, parietal, and frontal lobes. These promising results invite further studies and open new questions in the field.

show abstract

“…Those deep features are extracted from convolutions of images with filters (3×3×3, 5×5×5, or other sizes). Several studies 60,66,[107][108][109][110] used a convolutional neural network (CNN), a specific type of image-based deep learning technique, on T1-MRI to predict fluid intelligence in adolescents. They predicted the residual fluid intelligence score of more than 4500 adolescents with an MSE ranging from 92 to 103 (for a range of true residual fluid intelligence score of [-40, 30]), as summarized in Table 6 of the supplementary materials.…”

Section: Structural Mri To Infer Intelligence and Neurocognitionmentioning

confidence: 99%

Inferring Neurocognition and Intelligence using Brain MRI

Hussain¹,

Grant²,

Ou³

2023

Preprint

View full text Add to dashboard Cite

Brain magnetic resonance imaging (MRI) offers a unique lens to study neuroanatomic support of human neurocognition and intelligence. A core mystery is the MRI explanation of individual differences in neurocognition and intelligence. The past four decades have seen great advancement in studying this century-long mystery, but the sample size and population-level studies limit the explanation at the individual level. The recent rise of big data and artificial intelligence offers novel opportunities. Yet, data sources, harmonization, study design, and interpretation need to be carefully considered. This review aims to summarize past work, discuss rising opportunities and challenges, and facilitate further investigations on machine intelligence inferring human intelligence.

show abstract

Ensemble of 3D CNN Regressors with Data Fusion for Fluid Intelligence Prediction

Cited by 9 publications

References 32 publications

Can deep learning predict human intelligence from structural brain MRI?

Can deep learning predict human intelligence from structural brain MRI?

Can deep learning predict human intelligence from structural brain MRI?

Inferring Neurocognition and Intelligence using Brain MRI

Contact Info

Product

Resources

About