Sex Differences in Predicting Fluid Intelligence of Adolescent Brain from T1-Weighted MRIs

Abstract

Fluid intelligence (Gf) has been defined as the ability to reason and solve previously unseen problems. Links to Gf have been found in magnetic resonance imaging (MRI) sequences such as functional MRI and diffusion tensor imaging. As part of the Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge 2019, we sought to predict Gf in children aged 9-10 from T1-weighted (T1W) MRIs. The data included atlas-aligned volumetric T1W images, atlas-defined segmented regions, age, and sex for 3739 subjects used for training and internal validation and 415 subjects used for external validation. We trained sex-specific convolutional neural net (CNN) and random forest models to predict Gf. For the convolutional model, skull-stripped volumetric T1W images aligned to the SRI24 brain atlas were used for training. Volumes of segmented atlas regions along with each subject's age were used to train the random forest regressor models. Performance was measured using the mean squared error (MSE) of the predictions. Random forest models achieved lower MSEs than CNNs. Further, the external validation data had a better MSE for females than males (60.68 vs. 80.74), with a combined MSE of 70.83. Our results suggest that predictive models of Gf from volumetric T1W MRI features alone may perform better when trained separately on male and female data. However, the performance of our models indicates that more information is necessary beyond the available data to make accurate predictions of Gf.

Can deep learning predict human intelligence from structural brain MRI?

“…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).…”

“…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.…”

Inferring Neurocognition and Intelligence using Brain MRI

“…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.…”

“…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).…”

“…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.…”