lfps 1: spectral analysis kenneth d. harris 11/2/15
TRANSCRIPT
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LFPs 1: Spectral analysis
Kenneth D. Harris11/2/15
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Local field potentials
• Slow component of intracranial electrical signal • Physical basis for scalp EEG
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Today we will talk about
• Physical basis of the LFP
• Current-source density analysis
• Some math (signal processing theory, Gaussian processes)
• Spectral analysis
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Physical basis of the LFP signal
Synaptic input
• Kirchoff’s current law:• Current flowing into any location balances
current flowing out of it.
• Extracellular space is resistive
• Ohm’s law applied to return current:
• Assumes uniformity across x and y
Intracellular current
Charging current (capacitive)plus leak current (resistive)
Return current
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Linear probe recordings• Record , spacing
• Extracellular current
• Intracellular current
• Current source density (CSD)
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Spatial interpolation• To make nicer figures, interpolate before
taking second derivative.
• Which interpolation method?• Linear?• Quadratic?
• Cubic spline method fits 3rd-order polynomials between each “knot”, 1st and 2nd derivative continuous at knots.
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Current source density
• Laminar LFP recorded in V1
• Triggered average on spikes of simultaneously recorded thalamic neuron
• Getting the sign right• Remember current flows from V+
to V–• Local minimum of V(z) = Current
sink =second derivative positive Jin et al, Nature Neurosci 2011
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Current source density: potential problems• Assumption of (x,y) homogeneity
• Gain mismatch• The CSD is orders of magnitude smaller than the raw voltage• If the gain of channels are not precisely equal, raw signal bleeds through
• Sink does not always mean synaptic input• Could be active conductance
• Can’t distinguish sink coming on from source going off• Because LFP data is almost always high-pass filtered in hardware
• Plot the current too! (i.e. 1st derivative). This is easier to interpret, and less susceptible to artefacts.
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Signal processing theory
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Typical electrophysiology recording system
• Filter has two components • High-pass (usually around 1Hz). Without this, A/D converter would saturate• Low-pass (anti-aliasing filter, half the sample rate).
Amplifier Filter A/D converter
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Sampling theorem
• Nyquist frequency is half the sampling rate
• If a signal has no power above the Nyquist frequency, the whole continuous signal can be reconstructed uniquely from the samples
• If there is power above the Nyquist frequency, you have aliasing
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Power spectrum and Fourier transform• They are not the same!
• Power spectrum estimates how much energy a signal has at each frequency.
• You use the Fourier transform to estimate the power spectrum.
• But the raw Fourier transform is a bad estimate.
• Fourier transform is deterministic, a way of re-representing a signal
• Power spectrum is a statistical estimator used when you have limited data
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Discrete Fourier transform
• Represents a signal as a sum of sine/cosine waves
• is real, but is complex. • Magnitude of is wave amplitude• Argument of is phase• Still only degrees of freedom: .
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Using the Fourier transform to estimate power• Noisy!
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Power spectra are statistical estimates• Recorded signal is just one of many that could have been observed in the
same experiment
• We want to learn something about the population this signal came from
• Fourier transform is a faithful representation of this particular recording
• Not what we want
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Continuous processes
• A continuous process defines a probability distribution over the space of possible signals
Sample space =all possible LFP signals
Probability density 0.000343534976
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Stationary Gaussian process
• Time series
• Multivariate Gaussian distribution:
• Stationary Gaussian process• . • is autocovariance function• is a constant, usually 0.
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Autocovariance
• Autocovariance
• It is a 2nd order statistic of
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Power spectrum estimation error
• Power spectrum is Fourier transform of • Also a second order statistic
• For a Gaussian process, is proportional to a distribution.• Std Dev = Mean, however much data you have
• That’s why estimating power spectrum as is so noisy
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Power spectrum estimation
• Need to average to reduce estimation error
• If you observe multiple instantiations of the data, average over them• E.g. multiple trials
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Tapering
• Fourier transform assumes a periodic signal
• Periodic signal is discontinuous => too much high-frequency power
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Welch’s method
• Average the squared FFT over multiple windows
• Simplest method, use when you have a long signal
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Welch’s method results (100 windows)
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Averaging in time and frequency
• Shorter windows => more windows • Less noisy• Less frequency resolution
• Averaging over multiple windows is equivalent to averaging over neighboring frequencies
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Multi-taper method
• Only one window, but average over different taper shapes• Use when you have short signals• Taper shapes chosen to have fixed
bandwidth
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Multitaper method (1 window)
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http://www.chronux.org/
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Hippocampus LFP power spectra
• Typical “1/f” shape
• Oscillations seen as modulations around this
• Usually small, broad peaks
CA1 pyramidal layerBuzsaki et al, Neuroscience 2003
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Connexin-36 knockout
Buhl et al, J Neurosci 2003
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Stimulus changes power spectrum in V1
• High-frequency broadband power usually correlates with firing rate• Is this a gamma oscillation?
Henrie and Shapley J Neurophys 2005
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Attention changes power spectrum in V1
Chalk et al, Neuron 2010