A precision functional atlas of personalized network topography and probabilities.

Journal: Nature neuroscience

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Affiliated Institutions:  Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA. hermosir@umn.edu. Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA. Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA. Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA. Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA. Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA. Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA. Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, USA. Joint Doctoral Program in Clinical Psychology, San Diego State University, San Diego, CA, USA. Department of Psychology, Northwestern University, Evanston, IL, USA. Department of Psychology, University of Rhode Island, Kingston, RI, USA. Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA.

Abstract summary 

Although the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. To capture this, we implemented precision brain mapping functional magnetic resonance imaging methods to establish an open-source, method-flexible set of precision functional network atlases-the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. This atlas is an evolving resource comprising 53,273 individual-specific network maps, from more than 9,900 individuals, across ages and cohorts, including the Adolescent Brain Cognitive Development study, the Developmental Human Connectome Project and others. We also generated probabilistic network maps across multiple ages and integration zones (using a new overlapping mapping technique, Overlapping MultiNetwork Imaging). Using regions of high network invariance improved the reproducibility of executive function statistical maps in brain-wide associations compared to group average-based parcellations. Finally, we provide a potential use case for probabilistic maps for targeted neuromodulation. The atlas is expandable to alternative datasets with an online interface encouraging the scientific community to explore and contribute to understanding the human brain function more precisely.

Authors & Co-authors:  Hermosillo Moore Feczko Miranda-Domínguez Pines Dworetsky Conan Mooney Randolph Graham Adeyemo Earl Perrone Carrasco Uriarte-Lopez Snider Doyle Cordova Koirala Grimsrud Byington Nelson Gratton Petersen Feldstein Ewing Nagel Dosenbach Satterthwaite Fair

Study Outcome 

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Citations :  Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).
Authors :  29
Identifiers
Doi : 10.1038/s41593-024-01596-5
SSN : 1546-1726
Study Population
Male,Female
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Publication Country
United States