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Comparing Multi-Dimensional fNIRS Features Using Bayesian Optimization-Based Neural Networks for Mild Cognitive

Chutian Zhang, Hongjun Yang, Chen-Chen Fan

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |April 5, 2023
    PubMed
    Summary

    This study introduces a streamlined method for processing functional near-infrared spectroscopy (fNIRS) data to detect mild cognitive impairment (MCI). The 3D spatiotemporal oxyhemoglobin feature demonstrated the highest accuracy in identifying MCI patients.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Medical Imaging

    Background:

    • Mild cognitive impairment (MCI) is a precursor to Alzheimer's disease (AD), necessitating early diagnosis for timely intervention.
    • Functional near-infrared spectroscopy (fNIRS) shows promise for MCI detection, but preprocessing and feature selection remain challenging.
    • Understanding the impact of multi-dimensional fNIRS features on classification accuracy is crucial for improving diagnostic tools.

    Purpose of the Study:

    • To develop a streamlined fNIRS preprocessing method for MCI detection.
    • To compare the efficacy of 1D, 2D, and 3D fNIRS features in classifying MCI using neural networks.
    • To identify the most effective fNIRS features for distinguishing MCI patients from cognitively normal individuals.

    Main Methods:

    • A streamlined fNIRS preprocessing pipeline was implemented.
    • Bayesian optimization-based auto hyperparameter tuning neural networks were employed.
    • 1D (channel-wise), 2D (spatial), and 3D (spatiotemporal) fNIRS features were evaluated for MCI classification.
    • A dataset of 127 participants was utilized.

    Main Results:

    • The highest test accuracies achieved were 70.83% for 1D, 76.92% for 2D, and 80.77% for 3D features.
    • The 3D time-point oxyhemoglobin feature demonstrated superior performance in MCI detection.
    • The proposed models eliminated the need for manual hyperparameter tuning.

    Conclusions:

    • The 3D spatiotemporal oxyhemoglobin feature is a highly promising indicator for MCI detection using fNIRS.
    • The developed streamlined preprocessing and automated neural network approach enhances the usability of fNIRS for MCI diagnosis.
    • This study provides a robust framework for leveraging fNIRS and neural networks in clinical settings for early detection of neurodegenerative conditions like MCI.