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At room temperature, the chair conformer of cyclohexane undergoes rapid ring flipping between two equivalent chair conformers at a rate of approximately 105 times per second. These two chair conformers are in equilibrium. The rapid ring flipping results in the interconversion of the axial proton to an equatorial proton and an equatorial to the axial proton. Such interconversions are too rapid and cannot be detected on the NMR timescale. Hence, the NMR spectrometer cannot distinguish between the...
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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Related Experiment Video

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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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A scalable multi-resolution spatio-temporal model for brain activation and connectivity in fMRI data.

Stefano Castruccio1, Hernando Ombao2, Marc G Genton2

  • 1Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, 153 Hurley Hall, Notre Dame, Indiana 46556, U.S.A.

Biometrics
|January 24, 2018
PubMed
Summary

This study introduces a novel multi-resolution model for functional Magnetic Resonance Imaging (fMRI) data. The model accurately detects brain activation and connectivity, improving insights into motor function recovery after stroke.

Keywords:
Big dataBrain imagingFunctional magnetic resonance imageGaussian processesMulti-resolution modelSpace-time statistics

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biostatistics

Background:

  • Functional Magnetic Resonance Imaging (fMRI) is crucial for brain activity studies.
  • Modeling spatial dependencies in high-dimensional fMRI data across scales presents significant challenges.
  • Current methods often simplify by focusing on larger Regions of Interest (ROIs), potentially missing fine-grained spatial information.

Purpose of the Study:

  • To develop a multi-resolution spatio-temporal model for fMRI data analysis.
  • To enable accurate voxel-specific activation testing while considering multi-scale spatial dependencies.
  • To investigate cognitive control-related activation and whole-brain connectivity.

Main Methods:

  • Introduction of a novel multi-resolution spatio-temporal statistical model.
  • Development of a computationally efficient methodology for model estimation.
  • Application of the model to a motor-task fMRI study examining post-stroke motor function recovery.

Main Results:

  • The model successfully estimates voxel-specific brain activation.
  • It accounts for non-stationary local spatial dependence within ROIs and between-ROI regional dependence.
  • The study identified associations between brain activation/connectivity patterns and motor function recovery post-stroke.

Conclusions:

  • The proposed model offers a more comprehensive approach to analyzing fMRI data by incorporating multi-scale spatial dependencies.
  • This methodology enhances the ability to detect subtle brain activity patterns.
  • Findings contribute to understanding neural mechanisms underlying motor recovery and may inform rehabilitation strategies.