2019
DOI: 10.1007/978-3-030-31901-4_10
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Predicting Fluid Intelligence from Structural MRI Using Random Forest regression

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Cited by 10 publications
(12 citation statements)
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“…A younger preadolescent cohort, the ABCD dataset, included children from 9 to 11 years, an age at which fluid intelligence has not yet reached its putative maximum. In this cohort, we predicted Gf with R= 0.328, which, to our knowledge, improves the prediction accuracy so far reported using this dataset [21][22][23][24] . Using Kernel Ridge Regression classifiers and CNNs, Mihalik et al used manually extracted voxel-wise brain features (as opposed to automated morphometric analysis) on the ABCD dataset and predicted residualized Gf with an R = 0.17 21 , while Li et al used XGBoost classifiers on brain volumes and cortical curvatures to predict Gf with an R = 0.18 22 .…”
Section: Discussionsupporting
confidence: 64%
“…A younger preadolescent cohort, the ABCD dataset, included children from 9 to 11 years, an age at which fluid intelligence has not yet reached its putative maximum. In this cohort, we predicted Gf with R= 0.328, which, to our knowledge, improves the prediction accuracy so far reported using this dataset [21][22][23][24] . Using Kernel Ridge Regression classifiers and CNNs, Mihalik et al used manually extracted voxel-wise brain features (as opposed to automated morphometric analysis) on the ABCD dataset and predicted residualized Gf with an R = 0.17 21 , while Li et al used XGBoost classifiers on brain volumes and cortical curvatures to predict Gf with an R = 0.18 22 .…”
Section: Discussionsupporting
confidence: 64%
“…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%
“…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%