Novel age-dependent cortico-subcortical morphologic interactions predict fluid intelligence: A multi-cohort geometric deep learning study
Novel age-dependent cortico-subcortical morphologic interactions predict fluid intelligence: A multi-cohort geometric deep learning study
Abstract Brain structure is tightly coupled with brain functions, but it remains unclear how cognition is related to brain morphology, and what is consistent across neurodevelopment. In this work, we developed graph convolutional neural networks (gCNNs) to predict Fluid Intelligence (Gf) from shapes of cortical ribbons and subcortical structures. T1-weighted MRIs from two independent cohorts, the Human Connectome Project (HCP; age: 28.81±3.70) and the Adolescent Brain Cognitive Development Study (ABCD; age: 9.93±0.62) were independently analyzed. Cortical and subcortical surfaces were extracted and modeled as surface meshes. Three gCNNs were trained and evaluated using six-fold nested cross-validation. Overall, combining cortical and subcortical surfaces yielded the best predictions on both HCP (R=0.454) and ABCD datasets (R=0.314), and outperformed the current literature. Across both datasets, the morphometry of the amygdala and hippocampus, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a novel reframing of the morphometry underlying Gf.
Katsaggelos Aggelos K.、Breiter Hans C、Besson Pierre、Bandt S. Kathleen、Parrish Todd B、Azcona Emanuel A.、Wu Yunan
Image and Video Processing Lab (IVPL), Department of Electrical Computer Engineering, Northwestern UniversityWarren Wright Adolescent Center Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University||Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital and Harvard School of MedicineAdvanced NeuroImaging and Surgical Epilepsy (ANISE) Lab, Northwestern Memorial HospitalAdvanced NeuroImaging and Surgical Epilepsy (ANISE) Lab, Northwestern Memorial HospitalNeuroimaging Laboratory, Department of Radiology, Northwestern UniversityImage and Video Processing Lab (IVPL), Department of Electrical Computer Engineering, Northwestern UniversityImage and Video Processing Lab (IVPL), Department of Electrical Computer Engineering, Northwestern University
生物科学研究方法、生物科学研究技术生物科学现状、生物科学发展生物物理学
Katsaggelos Aggelos K.,Breiter Hans C,Besson Pierre,Bandt S. Kathleen,Parrish Todd B,Azcona Emanuel A.,Wu Yunan.Novel age-dependent cortico-subcortical morphologic interactions predict fluid intelligence: A multi-cohort geometric deep learning study[EB/OL].(2025-03-28)[2025-05-25].https://www.biorxiv.org/content/10.1101/2020.10.14.331199.点此复制
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