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Analysis and visualization methods for detecting functional activation using laser speckle contrast imaging.

Peng Hu1, Bochao Niu1, Hang Yang1

  • 1University of Electronic Science & Technology of China, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.

Microcirculation (New York, N.Y. : 1994)
|September 7, 2022
PubMed
Summary
This summary is machine-generated.

This study compares methods for detecting brain activity using laser speckle contrast imaging (LSCI). The general linear model (GLM) and fast Fourier transform (FFT) show promise for analyzing cerebral blood flow (CBF) signals.

Keywords:
brain functional activationcerebral blood flowcorrelation coefficientfast Fourier transformgeneral linear modellaser speckle contrast imaging

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Regional cerebral blood flow (CBF) is used to measure brain activity.
  • Laser speckle contrast imaging (LSCI) samples CBF activation in the somatosensory cortex (S1BF).
  • Current methods for detecting CBF activation lack clarity on their influence.

Purpose of the Study:

  • Investigate the influence of different signal processing methods on LSCI-based brain activation detection.
  • Compare the efficacy of principal component analysis (PCA), fast Fourier transform (FFT), Pearson's correlation coefficient (C-C), and general linear model (GLM).
  • Provide a reference for LSCI researchers in selecting optimal methods for brain activation estimation.

Main Methods:

  • Denoised cerebral blood flow (CBF) signals using principal component analysis (PCA), selecting the first principal component (PC1).
  • Applied fast Fourier transform (FFT) to analyze frequency properties and generate activation maps.
  • Utilized Pearson's correlation coefficient (C-C) and general linear model (GLM) for activation estimation based on PC1 time series.

Main Results:

  • The general linear model (GLM) demonstrated superior performance in identifying brain activation compared to C-C analysis.
  • Activation maps generated by FFT showed similarities to those obtained via GLM.
  • Vascular structures influenced activation region segmentation, with unresolved vessel areas exhibiting common activation patterns.

Conclusions:

  • Principal component analysis (PCA) effectively extracts meaningful information from LSCI signals.
  • GLM and FFT are effective methods for detecting brain functional activation using LSCI.
  • This comparative analysis serves as a guide for LSCI researchers in choosing appropriate methods for brain activation studies.