Deep Learning for Wide-Field Optical Imaging of Calcium Indicators

A multivariate functional connectivity approach to mapping brain networks and imputing neural activity in mice


Imputation of brain activity from optical neuroimaging data using Support Vector Regression (SVR) is highly accurate and reproducible.

MFC maps are topographically similar to FC maps but align better with APC maps while providing more accurate imputations.

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) thus making it difficult to accurately map connectivity in health and disease. 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.

Automated sleep scoring of wide-field optical imaging via multiplex visibility graph and deep learning


    1. Lindsey M Brier, Xiaohui Zhang, Annie R Bice, Seana H Gaines, Eric C Landsness, Jin-Moo Lee, Mark A Anastasio, Joseph P Culver, A Multivariate Functional Connectivity Approach to Mapping Brain Networks and Imputing Neural Activity in Mice, Cerebral Cortex, 2021;, bhab282.