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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Phantom oscillations in principal component analysis.

Maxwell Shinn1

  • 1University College London (UCL) Queen Square Institute of Neurology, University College London, London WC1E 6BT, United Kingdom.

Proceedings of the National Academy of Sciences of the United States of America
|November 21, 2023
PubMed
Summary
This summary is machine-generated.

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Principal component analysis (PCA) can create misleading oscillatory patterns, termed "phantom oscillations," in continuous data like neuroscience datasets. These artifacts arise from data smoothness and shifts, impacting analysis and requiring new detection methods beyond traditional cross-validation.

Area of Science:

  • Neuroscience
  • Data Science
  • Statistics

Background:

  • Principal component analysis (PCA) is a widely used dimensionality reduction technique.
  • PCA components are typically interpreted as low-dimensional patterns in high-dimensional data.
  • This interpretation is problematic for continuous data like time series and spatial maps.

Purpose of the Study:

  • To identify the causes of oscillatory principal components in continuous data.
  • To introduce the concept of "phantom oscillations" and their impact on data analysis.
  • To propose methods for detecting these phantom oscillations.

Main Methods:

  • Mathematical proofs to demonstrate the generation of phantom oscillations.
  • Analysis of data properties like smoothness and temporal/spatial shifts.
Keywords:
PCAdata analysisdimensionality reductionoscillations

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  • Evaluation of traditional cross-validation for phantom oscillation detection.

    • Main Results:

    • Smoothness and shifts in time or space are identified as causes of phantom oscillations.
    • These phantom oscillations can affect the interpretation of neuroscience data.
    • Standard cross-validation methods fail to detect phantom oscillations.

    Conclusions:

    • Patterns derived from PCA may not accurately reflect underlying continuous data.
    • Phantom oscillations are an artifact of PCA on smooth, shifted data.
    • New validation procedures are necessary to identify and mitigate phantom oscillations in data analysis.