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Related Concept Videos

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
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A method for estimating dynamic functional network connectivity gradients (dFNG) from ICA captures smooth

Najme Soleimani1, Armin Iraji1, Theo G M van Erp2

  • 1Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.

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Summary
This summary is machine-generated.

This study introduces dynamic functional network connectivity gradients (dFNGs) to analyze brain connectivity in schizophrenia. Patients showed distinct connectivity patterns compared to controls, offering new insights into functional dysconnectivity.

Keywords:
Dynamic functional network connectivity (dFNC)Dynamic functional network connectivity gradient (dFNG)GradientIndependent Component Analysis (ICA)Schizophrenia

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Network Analysis

Background:

  • Dynamic functional network connectivity (dFNC) is crucial for understanding brain function over time.
  • Traditional dFNC methods use fixed spatial maps, limiting the analysis of evolving brain network interactions.
  • Schizophrenia is associated with functional dysconnectivity, but its neural underpinnings require further elucidation.

Purpose of the Study:

  • To introduce and validate a novel approach for analyzing dynamic functional network connectivity gradients (dFNGs).
  • To investigate differences in static and dynamic functional network connectivity gradients between schizophrenia patients and healthy controls.
  • To provide a more comprehensive spatiotemporal understanding of brain network alterations in schizophrenia.

Main Methods:

  • Developed a method to dynamically reorder spatial components at each timepoint to optimize for smooth functional network connectivity (FNC) gradients.
  • Applied static FNC gradient (sFNG) and dynamic FNC gradient (dFNG) analyses to resting-state fMRI data from 151 schizophrenia patients and 160 healthy controls.
  • Utilized independent component analysis (ICA) to extract 53 intrinsic connectivity networks (ICNs) and computed Pearson correlation coefficients for static analysis and sliding window approaches for dynamic analysis.

Main Results:

  • Schizophrenia patients exhibited altered connectivity patterns, including stronger subcortical (SC)/auditory (AUD)/visual (VIS) network connectivity and weaker sensorimotor (SM) network connectivity compared to controls.
  • sFNG analysis revealed distinct clustering patterns in patients and controls along cognitive control (CC)/default mode network (DMN) and SC/AUD/SM/cerebellar (CB)/VIS gradients.
  • dFNG analysis indicated that schizophrenia patients spent more time in SC/CB states, while controls favored SM/DMN states, with significant group differences in CB and DMN engagement.

Conclusions:

  • The novel dFNG approach offers a more complete spatiotemporal summary of brain data, advancing the understanding of brain network modulation.
  • dFNG analysis reveals distinct dynamic connectivity patterns in schizophrenia patients, particularly in SC, SM, and CB domains.
  • This study provides a new perspective for capturing large-scale brain fluctuations and understanding functional dysconnectivity in schizophrenia.