A multitaper, causal decomposition for stochastic, multivariate time series: Application to high-frequency calcium imaging data

@article{Sornborger2016AMC,
  title={A multitaper, causal decomposition for stochastic, multivariate time series: Application to high-frequency calcium imaging data},
  author={Andrew T. Sornborger and James D. Lauderdale},
  journal={2016 50th Asilomar Conference on Signals, Systems and Computers},
  year={2016},
  pages={1056-1060}
}
  • A. Sornborger, J. Lauderdale
  • Published 1 November 2016
  • Computer Science, Mathematics
  • 2016 50th Asilomar Conference on Signals, Systems and Computers
Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, C (τ), as opposed to standard methods that decompose the time series, X(t), using only information at zero-lag. In both simulated and neural imaging… 

Figures from this paper

References

SHOWING 1-10 OF 16 REFERENCES
A multiscale analysis of the temporal characteristics of resting-state fMRI data
A multivariate, multitaper approach to detecting and estimating harmonic response in cortical optical imaging data
Analysis of fMRI data by blind separation into independent spatial components
TLDR
This work decomposed eight fMRI data sets from 4 normal subjects performing Stroop color‐naming, the Brown and Peterson word/number task, and control tasks into spatially independent components, and found the ICA algorithm was superior to principal component analysis (PCA) in determining the spatial and temporal extent of task‐related activation.
Independent component analysis: algorithms and applications
A unified view of multitaper multivariate spectral estimation
SUMMARY The orthogonal multitaper framework for cross-spectral estimators provides a simple unifying structure for determining the corresponding statistical properties. Here crossspectral estimators
Spectrum estimation and harmonic analysis
In the choice of an estimator for the spectrum of a stationary time series from a finite sample of the process, the problems of bias control and consistency, or "smoothing," are dominant. In this
Spectral analysis for physical applications : multitaper and conventional univariate techniques
Glossary of symbols 1. Introduction to spectral analysis 2. Stationary stochastic processes 3. Deterministic spectral analysis 4. Foundations for stochastic spectral theory 5. Linear time-invariant
Estimating weak ratiometric signals in imaging data. II. Meta-analysis with multiple, dual-channel datasets.
TLDR
A statistical optimization method is outlined that is designed for the analysis of ratiometric imaging data in which multiple measurements have been taken of systems responding to the same stimulation protocol.
Robust Smoothness Estimation in Statistical Parametric Maps Using Standardized Residuals from the General Linear Model
TLDR
The new approach involves estimating the smoothness of standardized residual fields which approximates the smootherness of the component fields of the associated t-field and eschews bias due to deviation from the null hypothesis.
Time series - data analysis and theory
This book will be most useful to applied mathematicians, communication engineers, signal processors, statisticians, and time series researchers, both applied and theoretical. Readers should have some
...
...