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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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Filtering induces correlation in fMRI resting state data.

Catherine E Davey1, David B Grayden, Gary F Egan

  • 1NeuroEngineering Laboratory, Dept. of Electrical and Electronic Engineering, University of Melbourne, Australia. Catherine.Davey@dsto.defence.gov.au

Neuroimage
|September 4, 2012
PubMed
Summary
This summary is machine-generated.

Temporal filtering in functional MRI (fMRI) can create artificial connectivity. This study derives corrected statistical methods to account for filter-induced sample dependence, ensuring valid fMRI connectivity results.

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Area of Science:

  • Neuroimaging
  • Functional Magnetic Resonance Imaging (fMRI)
  • Statistical Analysis

Background:

  • Functional MRI (fMRI) connectivity analysis often assumes temporal sample independence.
  • Temporal filtering, used to reduce noise in fMRI data, can violate this independence assumption.
  • Unaccounted sample dependence can lead to invalid fMRI connectivity findings.

Purpose of the Study:

  • To develop methods for using temporal filtering in fMRI while preserving connectivity result integrity.
  • To derive the distribution of sample correlation for filtered fMRI time series.
  • To provide corrected statistical inference tests for fMRI connectivity analysis.

Main Methods:

  • Derived the distribution of sample correlation for filtered fMRI time series based on filter frequency response.
  • Developed corrected distributions for statistical tests like Fisher's z-transformation and Student's t-test.
  • Validated corrections using empirical simulations and a resting-state fMRI analysis.

Main Results:

  • Temporal filtering can artificially induce connectivity by introducing sample dependence.
  • The proposed corrections effectively mitigate artificially induced connectivity.
  • Corrected methods in resting-state fMRI yield expected bilateral motor cortex connectivity, unlike uncorrected methods.

Conclusions:

  • Accounting for filter-induced sample dependence is crucial for accurate fMRI connectivity.
  • The derived corrections enable the use of temporal filtering for noise reduction without compromising connectivity validity.
  • This work enhances the reliability of fMRI connectivity analyses, particularly in resting-state studies.