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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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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Multiple Kernel Learning Based Classification of Parkinson's Disease With Multi-Modal Transcranial Sonography.

Jun Shi, Minjun Yan, Yun Dong

    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
    Summary
    This summary is machine-generated.

    This study introduces a new computer-aided diagnosis (CAD) for Parkinson's Disease (PD) using multi-modal transcranial sonography (TCS). Combining transcranial B-mode sonography (TBS) and transcranial Doppler sonography (TDS) improves PD detection accuracy.

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    Area of Science:

    • Neuroimaging
    • Medical Informatics
    • Biomedical Engineering

    Background:

    • Parkinson's Disease (PD) is a prevalent motor neurodegenerative disease in the elderly.
    • Transcranial sonography (TCS) is a key imaging tool for PD diagnosis, with existing computer-aided diagnosis (CAD) systems utilizing transcranial B-mode sonography (TBS).
    • Transcranial Doppler sonography (TDS), another TCS modality, assesses cerebral blood flow and has been used for PD-related orthostatic hypotension, but not yet for PD-specific CAD.

    Purpose of the Study:

    • To develop and evaluate a novel multi-modal computer-aided diagnosis (CAD) system for Parkinson's Disease (PD).
    • To integrate structural information from transcranial B-mode sonography (TBS) and functional information from transcranial Doppler sonography (TDS) for enhanced PD detection.
    • To investigate the effectiveness of a multiple kernel learning (MKL) approach for classifying PD using combined TBS and TDS data.

    Main Methods:

    • Extraction of statistical and texture features from the midbrain region of TBS images.
    • Calculation of blood flow-related features from spectrum curves obtained via TDS.
    • Development of a multiple kernel learning (MKL) classifier to integrate multi-modal features for PD classification.

    Main Results:

    • The proposed multi-modal TCS-based CAD system demonstrated superior performance compared to single-modal TBS-only and TDS-only algorithms.
    • Integration of TBS and TDS data significantly improved the accuracy of Parkinson's Disease diagnosis.
    • The MKL approach effectively combined complementary information from both imaging modalities.

    Conclusions:

    • Combining TBS and TDS via a multi-modal TCS approach offers a feasible and effective strategy for the computer-aided diagnosis of Parkinson's Disease.
    • This multi-modal strategy enhances diagnostic accuracy beyond single-modality methods.
    • The developed MKL-based CAD system shows promise for clinical application in PD diagnosis.