Single-subject cortical morphological brain networks: Phenotypic associations and neurobiological substrates

• Published in: NeuroImage (Orlando, Fla.) Vol. 283; p. 120434
• Main Authors: Li, Zhen, Li, Junle, Wang, Ningkai, Lv, Yating, Zou, Qihong, Wang, Jinhui
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.12.2023; Elsevier Limited; Elsevier
• Subjects: Behavior; Brain architecture; Cognition & reasoning; Cognitive ability; Cortical thickness; Datasets; Dopamine transporter; Gene; Glutamic acid receptors (metabotropic); Heritability; Identification; Magnetic resonance imaging; Morphological brain network; Morphology; Neurobiology; Neuroimaging; Neurotransmitter; Neurotransmitter receptors; Sex; Structural magnetic resonance imaging; Transcriptomics; Neurotransmitter; Cortical thickness; Gene; Morphological brain network; Structural magnetic resonance imaging; Sex
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2023.120434

•Single-subject morphological brain networks differed between males and females.•Single-subject morphological brain networks were predictive of individual performance in cognition and motor domains.•Single-subject morphological brain networks provided fingerprints for individual identification.•Different types of single-subject morphological brain networks were distinctively related to genetic, cytoarchitectonic and chemoarchitectonic factors. Although single-subject morphological brain networks provide an important way for human connectome studies, their roles and origins are poorly understood. Combining cross-sectional and repeated structural magnetic resonance imaging scans from adults, children and twins with behavioral and cognitive measures and brain-wide transcriptomic, cytoarchitectonic and chemoarchitectonic data, this study examined phenotypic associations and neurobiological substrates of single-subject morphological brain networks. We found that single-subject morphological brain networks explained inter-individual variance and predicted individual outcomes in Motor and Cognition domains, and distinguished individuals from each other. The performance can be further improved by integrating different morphological indices for network construction. Low-moderate heritability was observed for single-subject morphological brain networks with the highest heritability for sulcal depth-derived networks and higher heritability for inter-module connections. Furthermore, differential roles of genetic, cytoarchitectonic and chemoarchitectonic factors were observed for single-subject morphological brain networks. Cortical thickness-derived networks were related to the three factors with contributions from genes enriched in membrane and transport related functions, genes preferentially located in supragranular and granular layers, overall thickness in the molecular layer and thickness of wall in the infragranular layers, and metabotropic glutamate receptor 5 and dopamine transporter; fractal dimension-, gyrification index- and sulcal depth-derived networks were only associated with the chemoarchitectonic factor with contributions from different sets of neurotransmitter receptors. Most results were reproducible across different parcellation schemes and datasets. Altogether, this study demonstrates phenotypic associations and neurobiological substrates of single-subject morphological brain networks, which provide intermediate endophenotypes to link molecular and cellular architecture and behavior and cognition.