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    Home > Active Ingredient News > Study of Nervous System > Neuron. Dr. Cui and others used machine learning to study changes in brain network morphology in childhood and adolescence and their relationship to cognitive abilities.

    Neuron. Dr. Cui and others used machine learning to study changes in brain network morphology in childhood and adolescence and their relationship to cognitive abilities.

    • Last Update: 2020-07-22
    • Source: Internet
    • Author: User
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    Previous studies on brain function generally assumed that the functional anatomical units of the brain were in the same position in the cerebral cortex in all people.in recent years, a number of studies using adult fMRI data have proved that the location of cognitive related functional units in the cerebral cortex has great variation for different people [1,2].studies have shown that the size and shape of the joint cortical functional network responsible for advanced executive function vary greatly among adults [2,3], but we still don't know how this individual specific network changes during the development of children and adolescents, and how this change affects cognitive ability.on February 19, 2020, the Ted Satterthwaite research group of the University of Pennsylvania published an article entitled individual variation in functional topology of association networks in youth on February 19, 2020.the corresponding author of this paper is Professor Ted Satterthwaite, and the first author is Dr. Cui Zaixian, postdoctoral student.in this study, 27 minute fMRI data of 693 children and adolescents aged 8-23 years were collected by the Philadelphia neurodevelopmental cohort (PNC) project supported by the National Institute of mental health.they divided the cerebral cortex of each subject into 17 large-scale functional networks using a machine learning method developed by Professor Fan Yong, a collaborator of Penn University, and Dr. Li Hongming, a postdoctoral student.the results show that, similar to adults, every child has a unique functional partition organization pattern, and the functional brain network related to executive function has the largest variation.it is found that the anatomical division of brain functional network changes with the development of adolescents, which mainly shows that the network subordination in the boundary area at the junction of different networks will change with the development.using the information of which functional network the voxels belong to can significantly predict a subject's age and executive ability.children with larger cortical areas occupied by executive function related brain networks performed better in performing function related tasks, while those with poor performance occupied smaller cortical areas.these results shed new light on the plasticity and diversity of the brain during adolescent development, and may assist in personalized diagnosis and treatment.these findings also raise some interesting questions, such as how these functional networks are formed throughout the development process? What potential genetic or environmental factors determine the distribution of cerebral cortex in different functional networks? When using TMS and other neuromodulation techniques, will the effect of acting on individual specific regions be better? Participants in the study also include: Hongming Li, Cedric h. Xia, Bart Larsen, azeez, adebime, Graham L. Baum, Matt cieslak, Raquel E. GUR, Ruben C. GUR, Tyler M. Moore, Desmond J. Oates, Aaron, Alexander Bloch, Armin razahan, David R. roalf, Russell t t T. Shinohara, Daniel H. wolf, Christos davidovzikos, Daniel S. Bassett, Damien, C. GUR, Tyler M. Moore, Desmond J. Oates, Aaron, Alexander Bloch, Armin, razahan, David R, David R, Russell T, T. Shinohara, Daniel h, wolf, Christos, Davids, Daniels, Bassett, Damien, Damien, Damien, Alexander Bloch, Daniel h A. fair, Laumann to, Gordon em, adeyemo B, Snyder AZ, Joo SJ, Chen my, Gilmore aw, McDermott KB, Nelson SM, dosenbach Nu, schlaggar BL, Mumford JA, Poldrack RA, Petersen se. 2015. Functional system and area organization of a highly sampled individual human brain. Neuron. 87:657-670.2, Laumann TO, Gilmore AW, Newbold DJ, Greene DJ, Berg JJ, Ortega M, Hoyt-Drazen C, Gratton C, Sun H, Hampton JM, Coalson RS, Nguyen AL, McDermott KB, Shimony JS, Snyder AZ, Schlaggar BL, Petersen SE, Nelson SM, Dosenbach NUF. 2017. Precision Functional Mapping of Individual Human Brains. Neuron. 95:791-807 e797.3. Li M, Wang D, Ren J, Langs G,Stoecklein S, Brennan BP, Lu J, Chen H, Liu H. 2019. Performing group-level functional image analyses based on homologous functional regions mapped in individuals. PLoS Biol. 17:e2007032.
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