modeling the evolution of neurophysiological signals
DESCRIPTION
Modeling the Evolution of Neurophysiological Signals. Mark Fiecas Hernando Ombao. Data Characteristics. Small signal-to-noise ratios. Data Characteristics. Nonstationary time series data. Data Characteristics. Evolving over time within a replicate - PowerPoint PPT PresentationTRANSCRIPT
Modeling the Evolution of Neurophysiological Signals
Mark FiecasHernando Ombao
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Data Characteristics
Small signal-to-noise ratios
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Data Characteristics
Nonstationary time series data
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Data Characteristics
Evolving over time within a replicate
Nonidentical replicates across the experiment
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Example
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A Learning Association Experiment
Time
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A Learning Association Experiment
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Evolving Evolutionary Coherence
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Evolving Evolutionary Coherence
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Evolving Evolutionary Spectrum
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Evolving Evolutionary Spectrum
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The Time Series Models
Weakly stationary time series (Brillinger, 1981):
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The Time Series Models
Locally stationary time series (Dahlhaus, 2000):
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The Time Series Models
Locally stationary time series with slowly evolving replicates:
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The Time Series Models
1. Replicates are uncorrelated. For each replicate, use existing methods to address nonstationarity over time.
2. Smooth the estimates over time and replicate-time.
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Performance
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Hippocampus Log Periodogram
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Nucleus Accumbens Log Periodogram
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A Relevant Scientific Question
Is the power in a frequency band of interest the same between “familiar” and “novel” trials?
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Log Periodogram Models
Weakly stationary data (Krafty et al, 2011):
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Log Periodogram Models
Weakly stationary data (Krafty et al, 2011):
where
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The Log Periodogram Models
Locally stationary data (Krafty, 2007; Qin and Guo, 2009):
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The Log Periodogram Models
Locally stationary data (Krafty et al, 2007):
where
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The Proposed Log Periodogram Model
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The Proposed Log Periodogram Model
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The Proposed Log Periodogram Model
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Calling All Statisticians
“Understanding how the brain works is arguably one of the greatest scientific challenges of our time.”
- Alivisatos et al, 2013
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Calling All Statisticians
• The BRAIN Initiative (USA)• The Human Brain Project (European
Union)– 86 Institutions in Europe involved– €1 billion in funding / year
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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
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Calling All Statisticians
Handbook of Modern Statistical Methods: Neuroimaging Data Analysis (eds: H. Ombao, M. Lindquist, W. Thompson, and J. Aston)
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Acknowledgments
• Shaun Patel, Boston University• Emad Eskandar, MGH