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Related Concept Videos

Brain Imaging01:14

Brain Imaging

380
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
380

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Evidence for Transcranial Magnetic Stimulation Induced Functional Connectivity Oscillations in the Brain.

Victor M Vergara, Farshad Rafiei, Martijn E Wokke

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
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    Summary

    This study explored dynamic functional network connectivity (dynFNC) responses to transcranial magnetic stimulation (TMS). We identified distinct brain activity patterns, some linked to TMS and others potentially independent of stimulation.

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    Area of Science:

    • Neuroscience
    • Brain Imaging
    • Cognitive Science

    Background:

    • Transcranial magnetic stimulation (TMS) is a valuable tool for investigating brain function.
    • Limited research exists on dynamic functional connectivity changes following TMS.
    • Understanding brain responses to TMS is crucial for its application in research and therapy.

    Purpose of the Study:

    • To analyze dynamic functional network connectivity (dynFNC) in response to TMS.
    • To identify and characterize brain network dynamics elicited by TMS.
    • To differentiate TMS-evoked responses from intrinsic brain activity patterns.

    Main Methods:

    • Employed an exploratory analysis of dynamic functional network connectivity (dynFNC).
    • Investigated brain responses to transcranial magnetic stimulation (TMS).
    • Categorized observed functional dynamic patterns based on frequency.

    Main Results:

    • Identified distinct, frequency-categorized dynamic functional connectivity patterns.
    • Observed specific patterns potentially linked directly to TMS.
    • Detected a pattern that may represent a TMS-independent response to neural excitation.

    Conclusions:

    • This study presents a novel methodology for analyzing dynFNC after TMS.
    • Results highlight frequency-specific dynamic brain network responses to TMS.
    • Further research is needed to validate TMS-evoked versus independent network dynamics.