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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.1K

Assessment of EEG Connectivity Patterns in Mild Cognitive Impairment Using Phase Slope Index

Carlos Gomez, Saul J Ruiz-Gomez, Jesus Poza

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 17, 2018
    PubMed

    Insights

    Mild cognitive impairment (MCI) significantly alters brain information flow, showing decreased phase slope index (PSI) in delta and alpha bands. This finding may help understand MCI

    Area of Science:

    • Neuroscience
    • Cognitive Science
    • Medical Imaging

    Background:

    • Mild cognitive impairment (MCI) represents an abnormal cognitive state.
    • MCI patients face an elevated risk of progressing to dementia.
    • Understanding MCI's impact on brain function is crucial for early intervention.

    Purpose of the Study:

    • To investigate alterations in brain information flow patterns associated with MCI.
    • To characterize the changes in brain connectivity in MCI patients.
    • To explore the utility of the phase slope index (PSI) in assessing MCI-related brain dynamics.

    Main Methods:

    • Analysis of spontaneous electroencephalography (EEG) activity.
    • Utilized the phase slope index (PSI) as an effective connectivity measure.
    • Compared EEG data from 41 MCI patients and 37 healthy controls.

    Main Results:

    • Statistically significant reductions in PSI values were observed in MCI patients compared to controls.
    • These decreases were predominantly noted in the delta and alpha frequency bands.
    • Abnormal connectivity patterns may correlate with structural brain changes like hippocampal atrophy.

    Conclusions:

    • The phase slope index (PSI) is a valuable tool for quantifying brain dynamics in MCI.
    • Observed PSI changes offer insights into the neural underpinnings of MCI.
    • This research highlights potential biomarkers for MCI detection and progression monitoring.

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