Parcellating cortical functional networks in individuals

• Published in: Nature neuroscience Vol. 18; no. 12; pp. 1853 - 1860
• Main Authors: Wang, Danhong, Buckner, Randy L, Fox, Michael D, Holt, Daphne J, Holmes, Avram J, Stoecklein, Sophia, Langs, Georg, Pan, Ruiqi, Qian, Tianyi, Li, Kuncheng, Baker, Justin T, Stufflebeam, Steven M, Wang, Kai, Wang, Xiaomin, Hong, Bo, Liu, Hesheng
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.12.2015; Nature Publishing Group
• Subjects: 59/36; 631/378; 631/477; 631/553; Adolescent; Adult; Animal Genetics and Genomics; Behavioral Sciences; Biological Techniques; Biomedicine; Brain mapping; Brain Mapping - methods; Cerebral Cortex - physiology; Female; Hospitals; Humans; Magnetic Resonance Imaging - methods; Male; Medical examination; Medical imaging; Medical schools; Methods; Middle Aged; Nerve Net - physiology; Neural circuitry; Neurobiology; Neuroimaging; Neurology; Neurosciences; Observations; Precision medicine; Prefrontal cortex; technical-report; Young Adult; Beijing China; United States > US; China; Massachusetts
• ISSN: 1097-6256; 1546-1726
• DOI: 10.1038/nn.4164

A cortical parcellation technique accurately maps functional organization in individual brains. Functional networks mapped by this approach are highly reproducible and effectively capture individual variability. The algorithm performs well across different populations and data types and is validated by invasive cortical stimulation mapping in surgical patients. The capacity to identify the unique functional architecture of an individual's brain is a crucial step toward personalized medicine and understanding the neural basis of variation in human cognition and behavior. Here we developed a cortical parcellation approach to accurately map functional organization at the individual level using resting-state functional magnetic resonance imaging (fMRI). A population-based functional atlas and a map of inter-individual variability were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and data types, including task fMRI data. The approach was then validated by invasive cortical stimulation mapping in surgical patients, suggesting potential for use in clinical applications.