echemi logo
  • Product
  • Supplier
  • Inquiry
    Home > Biochemistry News > Biotechnology News > NeuroImage: New technology helps to observe neural activity in the brain in real time

    NeuroImage: New technology helps to observe neural activity in the brain in real time

    • Last Update: 2021-02-23
    • Source: Internet
    • Author: User
    Search more information of high quality chemicals, good prices and reliable suppliers, visit
    Magnetic encephalograms (MEGs) are methods based on measuring very weak magnetic fields (several orders of magnitude weaker than the Earth's magnetic field) caused by electrical activity in the brain.
    MEG, researchers face the complex task of understanding which areas of the brain are active when measuring only sensors around the head.
    this problem is called "anti-problem" and fundamentally there is no universal solution: any set of measurements can be explained by numerous different configurations of neuro-activity sources in the cortical cortical layer.
    in order to make meG application practical, special mathematical methods are used to convert sensor signals into cortical activity maps.
    these methods can be divided into two categories.
    , as part of the so-called "global" approach, narrows down many possible solutions to the problem based on general a priori assumptions about brain activity.
    these constraints, the researchers looked for source distributions in the corties that could explain the measurement data.
    "local" approach, including the algorithms described in the paper (ReciPSIICOS), designed to find separate sources before creating complete images of brain activity.
    ReciPSIICOS uses an adaptive beam-forming device (BF) - a method for processing sensor measurements that detects activity signals in target neuron populations.
    this, it tries to mute signals from other sources, rather than mute signals from all other sources, as the Global method does, but only signals that are currently active.
    this method provides a higher fidelity in the activity visualization than the Global method when only the active signal is suppressed.
    , however, the method can also suppress target signals generated by neuron integrations activated at the same time as neuron populations in other brain regions.
    in real life, this correlation reflects the interaction between neuron populations, an inherent property of the brain, and researchers must look for ways to overcome this barrier.
    information about the active neuron population and its interacting properties is encoded in a special co-variance matrix, which can be calculated from sensor data.
    beamforming algorithm uses this matrix to determine which sources should be suppressed.
    strictly speaking, this approach only works if the source does not interact: information about this interaction is also included in the correlation matrix and negatively affects the performance of the beam-forming algorithm.
    using observed data models and correlation matrix models, researchers have developed a mathematical algorithm that removes information about source interactions from related matrices.
    , they extended the application of beam formation methods to environments with synchronous neuron sources and provided the necessary precision in the group of neurons that visualize interactions.
    " brain magnetogram technology combines the ability to record precise aspects of the evolution of neuron activity time and the ability to locate potentially high fidelity of active neuron populations.
    The first feature comes from the recording of electrical activity, which changes significantly faster than the hemodynamic reactions used by functional magnetic resonance imaging and require complex mathematical methods in order to achieve high accuracy in spatial positioning, said Dr. Alexey Ossadt, author of the study.
    To evaluate the performance of the algorithm, the researchers first generated a data set of signals actually received by analog sensors and tested four methods: two types of ReciPSIICOS and two previously developed algorithms (linear constraint minimum variance (LCMV) beam-former, and minimum paradigm estimation (MNE) methods).
    LCMV and both ReciPSIICOS methods work without correlation between signals, but When correlations exist, ReciPSIICOS is better able to handle tasks than its predecessor.
    under pressure tests for positive modeling accuracy, the results are similar: ReciPSIICOS has been shown to be less sensitive to the inaccuracy of the models used, which is unavoidable in practice.
    scholars have also demonstrated the operability and high performance characteristics of the new method through some actual MEG data sets, which are characterized by the presence of synchronous neurons, which cannot be properly processed by traditional beam formation algorithms.
    source: Researchers find a way to increase spatial resolution in brain activity visualization Original source: Aleksandra Kuznetsova et al, Modified covariance beamformer for solving MEG inverse problem in the environment with correlated sources, NeuroImage (2020). DOI: 10.1016/j.neuroimage.2020.117677
    This article is an English version of an article which is originally in the Chinese language on 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 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 It will be replied within 5 days.

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