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Modeling the Evolution of Neurophysiological Signals

This work explores the evolution of neurophysiological signals, focusing on data characteristics such as small signal-to-noise ratios and nonstationary time series. It examines evolving replicates across experiments, particularly in a learning association framework. Models such as weakly stationary and locally stationary time series are analyzed. The study emphasizes the importance of understanding frequency power differences in familiar versus novel trials, leveraging the Log Periodogram methods. It highlights the significance of advanced statistical techniques for neuroimaging data to address complex scientific questions.

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Modeling the Evolution of Neurophysiological Signals

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  1. Modeling the Evolution of Neurophysiological Signals Mark Fiecas Hernando Ombao

  2. Data Characteristics Small signal-to-noise ratios

  3. Data Characteristics Nonstationarytime series data

  4. Data Characteristics Evolving over time within a replicate Nonidentical replicates across the experiment

  5. Example

  6. A Learning Association Experiment Time

  7. A Learning Association Experiment

  8. Evolving Evolutionary Coherence

  9. Evolving Evolutionary Coherence

  10. Evolving Evolutionary Spectrum

  11. Evolving Evolutionary Spectrum

  12. The Time Series Models Weakly stationary time series (Brillinger, 1981):

  13. The Time Series Models Locally stationary time series (Dahlhaus, 2000):

  14. The Time Series Models Locally stationary time series with slowly evolving replicates:

  15. The Time Series Models • Replicates are uncorrelated. For each replicate, use existing methods to address nonstationarity over time. • Smooth the estimates over time and replicate-time.

  16. Performance

  17. Hippocampus Log Periodogram

  18. Nucleus Accumbens Log Periodogram

  19. A Relevant Scientific Question Is the power in a frequency band of interest the same between “familiar” and “novel” trials?

  20. Log Periodogram Models Weakly stationary data (Krafty et al, 2011):

  21. Log Periodogram Models Weakly stationary data (Krafty et al, 2011):where

  22. The Log Periodogram Models Locally stationary data (Krafty, 2007; Qin and Guo, 2009):

  23. The Log Periodogram Models Locally stationary data (Krafty et al, 2007):where

  24. The Proposed Log Periodogram Model

  25. The Proposed Log Periodogram Model

  26. The Proposed Log Periodogram Model

  27. Calling All Statisticians “Understanding how the brain works is arguably one of the greatest scientific challenges of our time.” - Alivisatos et al, 2013

  28. Calling All Statisticians • The BRAIN Initiative (USA) • The Human Brain Project (European Union) • 86 Institutions in Europe involved • €1 billion in funding / year

  29. Calling All Statisticians Very rich data sets • High temporal resolution (EEG, MEG, LFP) • High spatial resolution (PET, fMRI) • 300k spatial locations in fMRI • Imaging genetics Many open problems

  30. Calling All Statisticians Handbook of Modern Statistical Methods: Neuroimaging Data Analysis (eds: H. Ombao, M. Lindquist, W. Thompson, and J. Aston)

  31. Acknowledgments • Shaun Patel, Boston University • EmadEskandar, MGH

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