Analysis and visualization methods for detecting functional activation using laser speckle contrast imaging. (21st September 2022)

• Record Type: Journal Article
• Authors: Hu, Peng; Niu, Bochao; Yang, Hang; Xia, Yang; Chen, Donna; Meng, Chun; Chen, Ke; Biswal, Bharat
• Is Part Of: Microcirculation. Volume 29:Number 6/7(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-21
• Subjects: brain functional activation -- cerebral blood flow -- correlation coefficient -- fast Fourier transform -- general linear model -- laser speckle contrast imaging; Biological transport -- Periodicals; Microcirculation -- Physiology -- Periodicals; 612.135
• DOI: 10.1111/micc.12783
• Languages: English
• ISSNs: 1073-9688

Abstract

Abstract: Background: Previous studies have used regional cerebral blood flow (CBF) hemodynamic response to measure brain activities. In this work, we use a laser speckle contrast imaging (LSCI) apparatus to sample the CBF activation in somatosensory cortex (S1BF) with repetitive whisker stimulation. Traditionally, the CBF activations were processed by depicting the change percentage above baseline; however, it is not clear how different methods influence the detection of activations. Aims: Thus, in this work we investigate the influence of different methods to detect activations in LSCI. Materials & Methods: First, principal component analysis (PCA) was performed to denoise the CBF signal. As the signal of the first principal component (PC1) showed the highest correlation with the S1BF CBF response curve, PC1 was used in the subsequent analyses. Then, we used fast Fourier transform (FFT) to evaluate the frequency properties of the LSCI images and the activation map was generated based on the amplitude of the central frequency. Furthermore, Pearson's correlation coefficient (C–C) analysis and a general linear model (GLM) were performed to estimate the S1BF activation based on the time series of PC1. Results: We found that GLM performed better in identifying activation than C–C. Additionally, the activation maps generated by FFT were similar to those obtained by GLM. Particularly, the superficial vein and arterial vessels separated the activation region as segmented activated areas, and the regions with unresolved vessels showed a common activation for whisker stimulation. Discussion and Conclusion: Our research analyzed the extent to which PCA can extract meaningful information from the signal and we compared the performance for detecting brain functional activation between different methods that rely on LSCI. This can be used as a reference for LSCI researchers on choosing the best method to estimate brain activation.