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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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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Related Experiment Video

Updated: Jan 11, 2026

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
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Common pitfalls during model specification in psychophysiological interaction analysis.

Vicky He1,2, Bahman Tahayori1,2, David N Vaughan1,2,3

  • 1The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia.

Imaging Neuroscience (Cambridge, Mass.)
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

Psychophysiological interaction (PPI) analysis in neuroimaging requires careful methodological application. This study corrects common pitfalls in PPI analysis, such as improper prewhitening and mean-centering, to ensure valid connectivity findings.

Keywords:
fMRIfunctional connectivitymean-centeringprewhiteningpsychophysiological interaction

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Statistical Modeling

Background:

  • Psychophysiological interaction (PPI) analysis is a standard regression technique in functional neuroimaging.
  • It is used to identify task-dependent connectivity changes from a seed region.
  • However, common methodological issues can compromise the validity of PPI analyses.

Purpose of the Study:

  • To identify and provide corrections for common methodological pitfalls in PPI analysis.
  • To demonstrate the adverse effects of these issues using simulations and empirical data.
  • To advocate for improved reporting guidelines and appropriate methods for PPI analysis.

Main Methods:

  • The study employed simulations and empirical language fMRI data from the Australian Epilepsy Project.
  • Methodological pitfalls related to prewhitening of seed time series and mean-centering of task regressors were investigated.
  • Corrections for these issues were developed and validated.

Main Results:

  • Prewhitening the seed time series alters signal structure, making subsequent deconvolution suboptimal.
  • Double prewhitening of the seed regressor occurs when prewhitening is applied during model fitting.
  • Failure to mean-center the task regressor leads to model misspecification and potentially spurious inferences.

Conclusions:

  • Correcting prewhitening and mean-centering issues enhances the validity of PPI analyses.
  • A systematic review revealed widespread model misspecification and underreporting in published PPI studies.
  • Clearer reporting guidelines and appropriate methodological practices are advocated to ensure reliable neuroimaging connectivity findings.