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Data analysis in behavioral cerebral blood flow activation studies using xenon-133 clearance
1Department of Neurology, University Hospital, Bonn, FRG.
Stroke
|March 1, 1993
Summary
Raw regional cerebral blood flow data is most sensitive for detecting brain activation. Normalization or covariate models can obscure true responses, falsely indicating deactivation in mental activation studies.
Area of Science:
- Neuroscience
- Medical Imaging
- Physiology
Background:
- Functional activation studies rely on detecting regional cerebral blood flow (rCBF) changes.
- Current methods include analyzing raw rCBF, normalized rCBF, or rCBF with global effects as a covariate.
Purpose of the Study:
- To evaluate the sensitivity and potential pitfalls of three rCBF analysis strategies.
- To determine the optimal method for detecting brain activation in behavioral studies.
Main Methods:
- Applied three data analysis strategies to rCBF data from 38 healthy subjects.
- Used the intravenous xenon-133 method with 32 detectors during a visuospatial problem-solving task.
Main Results:
- Mental activation increased blood flow across all regions.
- Raw rCBF data demonstrated the highest sensitivity and reliability.
- Normalization and covariate models were less sensitive and produced false deactivation findings.
Conclusions:
- Raw rCBF data analysis is recommended for behavioral activation studies.
- Avoid normalization or separating global from regional effects to prevent confounding results.
- Complex stimulation studies require careful scrutiny for global CBF effects impacting regional responses.