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Increased sensitivity in identifying language-related functional connectivity using jackknife resampling analyses.

Jinqing Liang1, Divesh Thaploo1, Adebiyi Sobitan1

  • 1The Integrative Neuroscience of Communication Research Unit, National Institute on Deafness and Other Communication Disorders, Bethesda, MD, USA.

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PubMed
Summary
This summary is machine-generated.

Jackknife resampling improves the detection of brain networks by revealing more language-related functional connections (FCs) in task-based fMRI (tbfMRI) data. This method enhances sensitivity for robust, task-relevant FCs in neural modeling.

Keywords:
Bonferroni correctionCommon connectionJackknife analysisPearsonSpearmanStatic functional connectivity (traditional)Task-based fMRI

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Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Brain Imaging

Background:

  • Task-based fMRI (tbfMRI) often uses static correlation methods for functional connectivity (FC) analysis.
  • Static methods may overlook transient neural interactions crucial for understanding brain function.

Purpose of the Study:

  • To investigate if jackknife resampling enhances the detection of language-related FC networks in tbfMRI.
  • To compare the sensitivity of jackknife resampling versus static correlation for identifying neural connections.

Main Methods:

  • Analyzed surface-based FC networks in 172 healthy adults using data from the Human Connectome Project.
  • Computed FC matrices across 68 cortical regions of interest, applying Bonferroni correction for statistical significance.
  • Compared static correlation FC networks with those derived from jackknife resampling, using an edge consistency threshold.

Main Results:

  • The static method identified 75 significant language-related FCs.
  • Jackknife resampling identified all 75 static FCs plus 24 additional connections (p < 0.001).
  • These additional connections involved key language regions like the middle temporal gyrus and posterior cingulate cortex.

Conclusions:

  • Jackknife resampling significantly enhances the detection of robust, task-relevant functional connections.
  • This technique offers a promising alternative for modeling language networks and improving neurocomputational representations.
  • Improved FC detection has implications for both research and clinical applications in neuroscience.