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Related Concept Videos

Brain Imaging01:14

Brain Imaging

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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...
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Neural Regulation01:37

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Related Experiment Video

Updated: Oct 10, 2025

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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Structural Target Controllability of Brain Networks in Dementia.

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    Summary
    This summary is machine-generated.

    This study introduces target controllability to analyze brain network changes in Alzheimer's disease (AD). It identifies key brain regions that steer network transitions, offering insights into neurodegenerative disease evolution.

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    Area of Science:

    • Neuroscience
    • Control Theory
    • Network Science

    Background:

    • Aberrant cognitive functioning in Alzheimer's disease (AD) is linked to large-scale neural circuit dynamics.
    • Understanding neurodegenerative disease evolution requires analyzing disease trajectory changes.
    • Control theory offers tools to analyze brain network dynamics, patient-level disease evolution, and treatment responses.

    Purpose of the Study:

    • To apply target controllability to structural brain connectivity graphs in control (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) subjects.
    • To determine graph-theoretic conditions for structural target controllability in these brain networks.
    • To identify specific brain nodes that can steer network states representative of transitions between CN, MCI, and AD.

    Main Methods:

    • Application of target controllability to structural MRI connectivity graphs.
    • Analysis of graph-theoretic necessary and sufficient conditions for controllability.
    • Identification of driver nodes using local topological information and structural target controllability.

    Main Results:

    • Structural target controllability can be verified using only local topological information of brain networks.
    • Specific brain regions (nodes) were identified as drivers capable of steering network states.
    • The study demonstrates the utility of target controllability in understanding node roles in brain network trajectories relevant to dementia progression.

    Conclusions:

    • Target controllability provides a novel method to describe node roles in controlling brain network trajectories.
    • This approach is relevant for understanding the evolution of neurodegenerative diseases like Alzheimer's.
    • The findings highlight the potential of control theory in analyzing brain connectomics and disease mechanisms.