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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Basics of Multivariate Analysis in Neuroimaging Data
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Network Differential in Gaussian Graphical Models from Multimodal Neuroimaging Data.

Haleh Falakshahi, Hooman Rokham, Robyn Miller

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
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    Summary
    This summary is machine-generated.

    This study introduces a novel multimodal brain network analysis for schizophrenia, identifying disrupted paths as potential biomarkers. This approach moves beyond simple connections to reveal complex network alterations in patients.

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    Area of Science:

    • Neuroscience
    • Computational Psychiatry
    • Network Science

    Background:

    • Multimodal brain network analysis offers potential for understanding brain disorders.
    • Previous studies often focused on unimodal data or limited graph metrics, neglecting disrupted path details.
    • Analyzing disrupted paths in multimodal brain graphs can reveal novel disease biomarkers.

    Purpose of the Study:

    • To develop a method for estimating multimodal brain graphs using static functional network connectivity (sFNC) and gray matter features.
    • To identify path-based biomarkers in schizophrenia by analyzing disrupted network paths.
    • To highlight the importance of multimodal analysis and path-based metrics for understanding brain disorders.

    Main Methods:

    • Estimated multimodal brain graphs using a Gaussian graphical model with sFNC and gray matter data from schizophrenia patients and controls.
    • Applied graph theory to identify "disconnectors" or "connectors" in the patient graph, indicating altered paths compared to controls.
    • Investigated disrupted paths within and between functional connectivity and gray matter networks.

    Main Results:

    • Identified specific edges in the schizophrenia graph associated with missing or additional paths compared to controls.
    • Disrupted paths involved alterations both within and between functional connectivity and gray matter networks.
    • Clinical relevance: A path-based biomarker was identified, with cross-modal edges linked to the middle temporal gyrus and cerebellum.

    Conclusions:

    • Multimodal brain network analysis combined with path-based disruption identification offers a more comprehensive understanding of schizophrenia.
    • This approach can reveal path-based biomarkers that are missed by traditional pairwise edge analyses.
    • The findings underscore the significance of integrating different data modalities and focusing on network path alterations for disease biomarker discovery.