Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach

Srivastava, Shikhar; Eitel, Fabian; Ritter, Kerstin

doi:10.1007/978-3-030-31901-4_9

Cited by 5 publications

(11 citation statements)

References 26 publications

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“…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%

“…Recent studies have started to use the machine especially deep learning to predict intelligence for individuals but left many open questions. In over 20 studies corresponding to a 2019 Grand Challenge, the predicted fluid intelligence had a mean square error ranging from 86 to 103 (for a range of true residual fluid intelligence score of [−40, 30]) [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] . The modest accuracy may suggest the need for more sophisticated deep learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Can deep learning predict human intelligence from structural brain MRI?

Hussain

LaMay

Grant

et al. 2023

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Can deep learning predict human intelligence from structural brain MRI?

Hussain

LaMay

Grant

et al. 2023

Preprint

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“…Early studies argued that the attentional control mechanism, the linkage between sensory discrimination and intelligence, 57 corresponds to the volumes in brain regions such as lateral fronto-parietal cortex 58 (includes BAs 6, 8, 9), dorsal anterior cingulate 58,59 (includes BA 32), and lateral posterior cerebellum. 58 As summarized in Table 3 of the supplementary materials, recent structural MRI-based predictive methods 55,[60][61][62][63][64][65][66][67][68][69][70] that used brain regional volumes found that the fronto-parietal (includes BAs 6, 8, and 9), cingulo opercular (includes BAs 22, 41, and 42), visual (includes BAs 17, 18, and 19), somatosensory (includes BAs 1, 2, 3, 5, and 7), right posterior cingulate gyrus (BAs 23, 31), left caudate nucleus, entorhinal white matter (BA 28), globus pallidus, precentral gyrus (BA 4), corpus callosum, left/right hippocampus, parahippocampal gyrus (BA 34), thalamus, precentral gyrus (BA 4), caudate nucleus, pons, and motor (includes BAs 4 and 6) cortex areas are related to the fluid intelligence in adolescents. This study predicted the residual fluid intelligence score of more than 3500 adolescents with a mean square error (MSE) ranging from 92 to 101 (for a range of true residual fluid intelligence score of [-40, 30]), 55,[60][61][62][63][64][65][66][67][68][69] or a correlation of 10% (p <0.05), 70 which further strengthens the arguments from the previous studies 58,71,72 as well as the P-FIT theory.…”

Section: Structural Mri To Infer Intelligence and Neurocognitionmentioning

confidence: 99%

“…58 As summarized in Table 3 of the supplementary materials, recent structural MRI-based predictive methods 55,[60][61][62][63][64][65][66][67][68][69][70] that used brain regional volumes found that the fronto-parietal (includes BAs 6, 8, and 9), cingulo opercular (includes BAs 22, 41, and 42), visual (includes BAs 17, 18, and 19), somatosensory (includes BAs 1, 2, 3, 5, and 7), right posterior cingulate gyrus (BAs 23, 31), left caudate nucleus, entorhinal white matter (BA 28), globus pallidus, precentral gyrus (BA 4), corpus callosum, left/right hippocampus, parahippocampal gyrus (BA 34), thalamus, precentral gyrus (BA 4), caudate nucleus, pons, and motor (includes BAs 4 and 6) cortex areas are related to the fluid intelligence in adolescents. This study predicted the residual fluid intelligence score of more than 3500 adolescents with a mean square error (MSE) ranging from 92 to 101 (for a range of true residual fluid intelligence score of [-40, 30]), 55,[60][61][62][63][64][65][66][67][68][69] or a correlation of 10% (p <0.05), 70 which further strengthens the arguments from the previous studies 58,71,72 as well as the P-FIT theory. Another study 73 involving a comparatively smaller adult data cohort (N = 211) reported a positive correlation of overall gray matter volume with fluid intelligence (r = 0.16; p < 0.01), working memory (r = 0.21; p < 0.01), and quantitative reasoning (r = 0.26; p < 0.01).…”

Section: Structural Mri To Infer Intelligence and Neurocognitionmentioning

confidence: 99%

Inferring Neurocognition and Intelligence using Brain MRI

Hussain¹,

Grant²,

Ou³

2023

Preprint

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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.

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“…An intermediate solution would be to train models on regions-of-interest (ROIs) or, more generally, image patches (Greenstein et al, 2012;Srivastava et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Harnessing spatial homogeneity of neuroimaging data: patch individual filter layers for CNNs

Eitel,

Albrecht,

Weygandt

et al. 2020

Preprint

Self Cite

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Neuroimaging data, e.g. obtained from magnetic resonance imaging (MRI), is comparably homogeneous due to (1) the uniform structure of the brain and (2) additional efforts to spatially normalize the data to a standard template using linear and non-linear transformations. Convolutional neural networks (CNNs), in contrast, have been specifically designed for highly heterogeneous data, such as natural images, by sliding convolutional filters over different positions in an image. Here, we suggest a new CNN architecture that combines the idea of hierarchical abstraction in neural networks with a prior on the spatial homogeneity of neuroimaging data: Whereas early layers are trained globally using standard convolutional layers, we introduce for higher, more abstract layers patch individual filters (PIF). By learning filters in individual image regions (patches) without sharing weights, PIF layers can learn abstract features faster and with fewer samples. We thoroughly evaluated PIF layers for three different tasks and data sets, namely sex classification on UK Biobank data, Alzheimer's disease detection on ADNI data and multiple sclerosis detection on private hospital data. We demonstrate that CNNs using PIF layers result in higher accuracies, especially in low sample size settings, and need fewer training epochs for convergence. To the best of our knowledge, this is the first study which introduces a prior on brain MRI for CNN learning.

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Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach

Cited by 5 publications

References 26 publications

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

Harnessing spatial homogeneity of neuroimaging data: patch individual filter layers for CNNs

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