echemi logo
Product
  • Product
  • Supplier
  • Inquiry
    Home > Active Ingredient News > Antitumor Therapy > European Radiology: resting state fMRI machine learning to predict individualized hand movements in patients with PRC cortical tumors

    European Radiology: resting state fMRI machine learning to predict individualized hand movements in patients with PRC cortical tumors

    • Last Update: 2021-04-13
    • Source: Internet
    • Author: User
    Search more information of high quality chemicals, good prices and reliable suppliers, visit www.echemi.com

    Accurate brain function area mapping plays a vital role in preoperative planning.


    Yang Wang

    The purpose of this study is to use resting state functional magnetic resonance imaging (rs-fMRI) data to evaluate the effectiveness of neural network (NN) methods in predicting preoperative motor zone positioning.


    Methods 109 patients with craniocerebral tumors underwent rs-fMRI and fMRI (TbfMRI) scan based on hand movement tasks.


    NN_Act ICA_Act

    Schematic diagram of the processing flow of NN-ML prediction model and ICA motion network extraction

    NN-ML prediction model and ICA motion extraction process flow schematic network NN-ML prediction model and ICA motion extraction process flow schematic network

    First, use the resting state data of 98 human connection group project subjects for group ICA to obtain 32 groups of characteristics.


    First, use the resting state data of 98 human connection group project subjects for group ICA to obtain 32 groups of characteristics.


    Neural network (NN) machine learning process flowchart

    Neural network (NN) machine learning process flowchart Neural network (NN) machine learning process flowchart

    A neural network based on individual resting fMRI (RsfMRI) features and a healthy control group task fMRI activation map was used to train 20 models.


    A neural network based on individual resting fMRI (RsfMRI) features and a healthy control group task fMRI activation map was used to train 20 models.


    Compared with the MAP derived by ICA, the CC matrix of the MAP predicted by the neural network shows a higher diagonal value (p<0.


    Regardless of whether there is movement disorder, DC NN_Act is higher than DC ICA_Act (P<0.


    The neural network method can predict individual motor activation based on rs-fMRI data, and has a good clinical application prospect in patients with brain tumor anatomy and functional reconstruction.


    The neural network method can predict individual motor activation based on rs-fMRI data, and has a good clinical application prospect in patients with brain tumor anatomy and functional reconstruction.


    Original source

    springer.


    springer.


    Leave a message here
    This article is an English version of an article which is originally in the Chinese language on echemi.com and is provided for information purposes only. This website makes no representation or warranty of any kind, either expressed or implied, as to the accuracy, completeness ownership or reliability of the article or any translations thereof. If you have any concerns or complaints relating to the article, please send an email, providing a detailed description of the concern or complaint, to service@echemi.com. A staff member will contact you within 5 working days. Once verified, infringing content will be removed immediately.

    Contact Us

    The source of this page with content of products and services is from Internet, which doesn't represent ECHEMI's opinion. If you have any queries, please write to service@echemi.com. It will be replied within 5 days.

    Moreover, if you find any instances of plagiarism from the page, please send email to service@echemi.com with relevant evidence.