Deep learning of structural MRI predicts fluid, crystallized, and general intelligence.

Autor: Hussain MA; Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 401 Park Drive, Boston, MA, 02115, USA., LaMay D; Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 401 Park Drive, Boston, MA, 02115, USA.; Khoury College of Computer and Information Science, Northeastern University, 360 Huntington Ave, Boston, MA, 02115, USA., Grant E; Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 401 Park Drive, Boston, MA, 02115, USA.; Department of Radiology, Harvard Medical School, 401 Park Drive, Boston, MA, 02115, USA., Ou Y; Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 401 Park Drive, Boston, MA, 02115, USA. yangming.ou@childrens.harvard.edu.; Department of Radiology, Harvard Medical School, 401 Park Drive, Boston, MA, 02115, USA. yangming.ou@childrens.harvard.edu.; Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School, 401 Park Drive, Boston, MA, 02115, USA. yangming.ou@childrens.harvard.edu.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Nov 14; Vol. 14 (1), pp. 27935. Date of Electronic Publication: 2024 Nov 14.
DOI: 10.1038/s41598-024-78157-0
Abstrakt: 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.
Competing Interests: Declarations Competing interests The authors declare no competing interests.
(© 2024. The Author(s).)
Databáze: MEDLINE
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