4.6 Article

A Multivariate Functional Connectivity Approach to Mapping Brain Networks and Imputing Neural Activity in Mice

期刊

CEREBRAL CORTEX
卷 32, 期 8, 页码 1593-1607

出版社

OXFORD UNIV PRESS INC
DOI: 10.1093/cercor/bhab282

关键词

calcium neuroimaging; multivariate functional connectivity; Pearson functional connectivity; support vector regression

资金

  1. National Institutes of Health [F30AG061932, K08NS109292, R01NS084028, R37NS110699, R01NS102213, R01NS099429, R01NS090874]
  2. American Academy of Sleep Medicine Foundation [183-PA-18, 201-BS-19]

向作者/读者索取更多资源

Temporal correlation analysis of spontaneous brain activity provides insights into the functional organization of the human brain. However, traditional bivariate analysis techniques are susceptible to confounding physiological processes. In contrast, a multivariate approach can accurately determine functional connectivity and provide performance advantages. In a study analyzing neural calcium imaging data from mice, the multivariate functional connectivity showed sparser connections and better connectivity deficit detection compared to traditional functional connectivity.
Temporal correlation analysis of spontaneous brain activity (e.g., Pearson functional connectivity, FC) has provided insights into the functional organization of the human brain. However, bivariate analysis techniques such as this are often susceptible to confounding physiological processes (e.g., sleep, Mayer-waves, breathing, motion), which makes it difficult to accurately map connectivity in health and disease as these physiological processes affect FC. In contrast, a multivariate approach to imputing individual neural networks from spontaneous neuroimaging data could be influential to our conceptual understanding of FC and provide performance advantages. Therefore, we analyzed neural calcium imaging data from Thy1-GCaMP6f mice while either awake, asleep, anesthetized, during low and high bouts of motion, or before and after photothrombotic stroke. A linear support vector regression approach was used to determine the optimal weights for integrating the signals from the remaining pixels to accurately predict neural activity in a region of interest (ROI). The resultant weight maps for each ROI were interpreted as multivariate functional connectivity (MFC), resembled anatomical connectivity, and demonstrated a sparser set of strong focused positive connections than traditional FC. While global variations in data have large effects on standard correlation FC analysis, the MFC mapping methods were mostly impervious. Lastly, MFC analysis provided a more powerful connectivity deficit detection following stroke compared to traditional FC.

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