Searching for Nested Oscillations in Frequency and Sensor Space. Will Penny. Wellcome Trust Centre for Neuroimaging. University College London.

Similar documents
Consider the following spike trains from two different neurons N1 and N2:

A Multivariate Time-Frequency Based Phase Synchrony Measure for Quantifying Functional Connectivity in the Brain

Statistical inference for MEG

Dynamic Causal Modelling for EEG and MEG. Stefan Kiebel

New Machine Learning Methods for Neuroimaging

Recipes for the Linear Analysis of EEG and applications

Exercises. Chapter 1. of τ approx that produces the most accurate estimate for this firing pattern.

Dynamic Causal Modelling for EEG and MEG

A Canonical Circuit for Generating Phase-Amplitude Coupling

Principles of DCM. Will Penny. 26th May Principles of DCM. Will Penny. Introduction. Differential Equations. Bayesian Estimation.

Event-related fmri. Christian Ruff. Laboratory for Social and Neural Systems Research Department of Economics University of Zurich

NeuroImage 54 (2011) Contents lists available at ScienceDirect. NeuroImage. journal homepage:

The General Linear Model. Guillaume Flandin Wellcome Trust Centre for Neuroimaging University College London

Techniques to Estimate Brain Connectivity from Measurements with Low Spatial Resolution

Modelling temporal structure (in noise and signal)

Frequency of gamma oscillations routes flow of information in the hippocampus

Dynamic Causal Modelling for fmri

Synchrony in Neural Systems: a very brief, biased, basic view

Bayesian inference J. Daunizeau

Data analysis methods in the neuroscience Zoltán Somogyvári

Model Comparison. Course on Bayesian Inference, WTCN, UCL, February Model Comparison. Bayes rule for models. Linear Models. AIC and BIC.

The General Linear Model (GLM)

Marr's Theory of the Hippocampus: Part I

Wellcome Trust Centre for Neuroimaging, UCL, UK.

Bayesian inference J. Daunizeau

HST 583 FUNCTIONAL MAGNETIC RESONANCE IMAGING DATA ANALYSIS AND ACQUISITION A REVIEW OF STATISTICS FOR FMRI DATA ANALYSIS

PERFORMANCE STUDY OF CAUSALITY MEASURES

Patterns, Memory and Periodicity in Two-Neuron Delayed Recurrent Inhibitory Loops

Effective Connectivity & Dynamic Causal Modelling

Rhythms in the gamma range (30 80 Hz) and the beta range

Causality and communities in neural networks

Causal modeling of fmri: temporal precedence and spatial exploration

Analyzing Anatomical and Functional Brain Connectivity. - M/EEG Functional and Resting-State Connectivity Maren Grigutsch

Experimental design of fmri studies & Resting-State fmri

Experimental design of fmri studies

Statistical Inference

Experimental design of fmri studies

Will Penny. 21st April The Macroscopic Brain. Will Penny. Cortical Unit. Spectral Responses. Macroscopic Models. Steady-State Responses

Beyond Univariate Analyses: Multivariate Modeling of Functional Neuroimaging Data

Approximate, not perfect synchrony maximizes the downstream effectiveness of excitatory neuronal ensembles

Collecting the data. A.- F. Miller 2012 DQF- COSY Demo 1

arxiv: v4 [stat.me] 27 Nov 2017

Electroencephalogram Based Causality Graph Analysis in Behavior Tasks of Parkinson s Disease Patients

Spatial Cells in the Hippocampal Formation

The ASL signal. Parenchy mal signal. Venous signal. Arterial signal. Input Function (Label) Dispersion: (t e -kt ) Relaxation: (e -t/t1a )

The Mixed States of Associative Memories Realize Unimodal Distribution of Dominance Durations in Multistable Perception

Experimental design of fmri studies

Gamma and Theta Rhythms in Biophysical Models of Hippocampal Circuits

Part 2: Multivariate fmri analysis using a sparsifying spatio-temporal prior

Decision-making and Weber s law: a neurophysiological model

Morphometry. John Ashburner. Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London, UK.

arxiv:physics/ v1 [physics.bio-ph] 19 Feb 1999

Dynamic Causal Modelling for EEG/MEG: principles J. Daunizeau

Detection of spike patterns using pattern ltering, with applications to sleep replay in birdsong

Neural Nets in PR. Pattern Recognition XII. Michal Haindl. Outline. Neural Nets in PR 2

Morphometrics with SPM12

2.3 Oscillation. The harmonic oscillator equation is the differential equation. d 2 y dt 2 r y (r > 0). Its solutions have the form

Synaptic dynamics. John D. Murray. Synaptic currents. Simple model of the synaptic gating variable. First-order kinetics

Songting Li. Applied Mathematics, Mathematical and Computational Neuroscience, Biophysics

A. Motivation To motivate the analysis of variance framework, we consider the following example.

Morphometrics with SPM12

Figure 1-figure supplement 1

Detecting event-related changes of multivariate phase coupling in dynamic brain networks

Mutual Information in Frequency and its Application to Measure Cross-Frequency Coupling in Epilepsy

!) + log(t) # n i. The last two terms on the right hand side (RHS) are clearly independent of θ and can be

LINEAR SYSTEMS. J. Elder PSYC 6256 Principles of Neural Coding

A MULTIVARIATE TIME-FREQUENCY BASED PHASE SYNCHRONY MEASURE AND APPLICATIONS TO DYNAMIC BRAIN NETWORK ANALYSIS. Ali Yener Mutlu

How do biological neurons learn? Insights from computational modelling of

The Multivariate Gaussian Distribution

Conductance-Based Integrate-and-Fire Models

Will Penny. SPM short course for M/EEG, London 2013

Nature Neuroscience: doi: /nn Supplementary Figure 1. Localization of responses

Estimation of Propagating Phase Transients in EEG Data - Application of Dynamic Logic Neural Modeling Approach

Old and New Methods in the Age of Big Data

The Damped Pendulum. Physics 211 Lab 3 3/18/2016

Temporal Neuronal Oscillations can Produce Spatial Phase Codes

Fundamentals of Computational Neuroscience 2e

Neuronal Shot Noise and Brownian 1/f 2 Behavior in the Local Field Potential

Learning from Data: Regression

Tracking whole-brain connectivity dynamics in the resting-state

An Introductory Course in Computational Neuroscience

Supporting Online Material for

M/EEG source analysis

CORRELATION TRANSFER FROM BASAL GANGLIA TO THALAMUS IN PARKINSON S DISEASE. by Pamela Reitsma. B.S., University of Maine, 2007

How to read a burst duration code

A realistic neocortical axonal plexus model has implications for neocortical processing and temporal lobe epilepsy

Nature Neuroscience: doi: /nn.2283

How Spike Generation Mechanisms Determine the Neuronal Response to Fluctuating Inputs

Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting

Jean-Baptiste Poline

ICA [6] ICA) [7, 8] ICA ICA ICA [9, 10] J-F. Cardoso. [13] Matlab ICA. Comon[3], Amari & Cardoso[4] ICA ICA

COMP 546. Lecture 21. Cochlea to brain, Source Localization. Tues. April 3, 2018

Dynamical systems in neuroscience. Pacific Northwest Computational Neuroscience Connection October 1-2, 2010

Data Analysis I: Single Subject

Gamma-band synchronization in the neocortex: novel analysis methods and their application to sensory and motivational systems Vinck, M.A.

RECOGNITION ALGORITHM FOR DEVELOPING A BRAIN- COMPUTER INTERFACE USING FUNCTIONAL NEAR INFRARED SPECTROSCOPY

Dynamic Modeling of Brain Activity

A Model for Real-Time Computation in Generic Neural Microcircuits

Learning Cycle Linear Hybrid Automata for Excitable Cells

Decoding conceptual representations

Transcription:

in Frequency and Sensor Space Oscillation Wellcome Trust Centre for Neuroimaging. University College London. Non- Workshop on Non-Invasive Imaging of Nonlinear Interactions. 20th Annual Computational Neuroscience (CNS) Meeting. 28th July 2011, Stockholm

Oscillation Oscillation Non- Phase Amplitude Coupling (PAC).

Canolty et al (2006) define the modulation index as 1 N M = z[n] N where n=1 z[n] = a γ [n] exp (iφ θ [n]) The significance of M is then assessed using a surrogate data approach. Oscillation Non-

Vanhatalo et al. (2004) and Mormann et al. (2005) use the (PLV) between the phase of the lower frequency oscillation and the phase of the amplitude envelope of the higher frequency oscillation 1 PLV = N N exp ( i(φ θ [n] φ aγ [n]) ) n=1 The significance of PLV is then assessed using a surrogate data approach. Oscillation Non-

Oscillation Bruns and Eckhorn (2004) define the as ESC = Corr(x θ [n], a γ [n]) The significance of ESC is assessed using t distributions. Non-

Penny et al. (2008) use a (GLM) approach based on the multiple regression model a γ = Xβ + e where β are regression coefficients, e is additive Gaussian noise and the design matrix X contains three columns: cos(φ θ [n]) sin(φ θ [n]) A column of 1 s Oscillation Non- Significance is assessed using F-tests over the first two regression coefficients. More generally, X could be a Fourier series.

ECoG Data Data from Kai Miller and Jeff Ojemann at Washington State. They collected from subjects performing a one-back visual working memory task. Each item (picture of a house) was presented twice. On the second presentation of the item subjects press a button. On the second presentation the item therefore does nt need to be remembered. On the first presentation it does. We computed PAC measures for each trial between 6Hz theta and high gamma (76 to 200Hz). Oscillation Non- The measures were then Gaussianised for each trial, and we tested for between condition (remember vs not) differences using two sample t-tests at each electrode.

ESC and GLM detect nested oscillations that the other measures don t. ECoG Data ESC (top left), GLM (top right), PLV (bottom left), (bottom right). Oscillation Non-

ECoG Data Oscillation Non- Current item does not need to be remembered.

ECoG Data Oscillation Non- Current item needs to be remembered.

A population of Slow GABA-A cells inhibits a population of Fast GABA-A cells. Oscillation Non- Each cell is a single compartment Hodgkin-Huxley model (White et al, 1998).

Populations of GABA-B (top,slow) and GABA-A (bottom,fast) cells. Oscillation Non- Fast cells had synaptic rise times of 1ms and fall times of 9ms. For the slow cells they are 5ms and 150ms.

Comparison of PAC measures. Oscillation Non- GLM (green), PLV (black), ESC (red), (blue). See Penny et al. (2008) for many further tests.

Experimental Paradigm Oscillation Non- of Visual Working Memory (Fuentemilla et al. 2010).

Multivariate Analysis at Encoding Oscillation Non- Multivariate classification based on sensor space spectra using features from 13 to 80 Hz.

Multivariate Classification of Maintenance Oscillation Non- Greater replay during memory conditions.

Replay is Phase-Locked to Theta Theta activity was then projected to source space, and for each source, Poch et al. (2011) computed the phases at which patterns were replayed. To see if these phases were non-uniformly distributed, a PLV measure was computed for each source. Poch et al. (2011) then tested to see which sources had PLVs that predicted of memory performance. This identified a right hippocampal and a right inferior frontal region. Oscillation Non-

Theta Sources Oscillation Non-

Theta Sources Oscillation Non-

Processing Stream Extract phase of theta activity in source region. Extract time-frequency maps at each sensor, v, from frequencies f = 16 : 4 : 128 Hz during delay period. For each trial compute GLM PAC measure. Record fitted regression coefficients s fv and c fv. The sine and cosine terms for each frequency and sensor Create a NIFTI format image for each measure. There are 3 conditions and 40 trials per condition, with 2 measures per trial. This gives 240 data points per subject Set up design matrix in SPM and implement a GLM analysis in space-frequency Litvak et al, 2010) Use Random Field Theory to correct for multiple comparisons Oscillation Non-

Images are entered in the following order Sine coefficients for Sine coefficients for Non-Config Sine coefficients for Config Cos coefficients for Cos coefficients for Non-Config Cos coefficients for Config Oscillation Non-

Oscillation Non-

The statistical signifiance of phase amplitude coupling is corrected for the multiple comparisons over space and frequency using Random Field Theory. Oscillation Non- We can use the standard threshold eg FWE=0.05.

Non- Oscillation Non-

Non- Oscillation Non-

Non- Oscillation Non-

Oscillation Non-

Oscillation Non-

Oscillation Non-

G. Buzsaki (2006) Rhythms of the Brain. Oxford University Press. R. Canolty et al (2006) High gamma power is phase-locked to theta oscillations in neocortex. Science 313, 1626-8. L. Fuentemilla et al (2010) Theta-coupled periodic replay in working memory. Current Biology 20, 1-7. V. Litvak et al. (2011) EEG and MEG data analysis in SPM8. Comput Intell Neurosci. Article ID:852961. K. Miller et al. (2009) Power-Law Scaling in the Brain Surface Electric Potential. PLoS CB, 5(12):e1000609. F. Mormann et al. (2005) Phase/amplitude reset and theta-gamma interaction in the human medial temporal lobe. Hippocampus 15:890-900. W. Penny et al (2008) Testing for Oscillation. Journal of Neuroscience Methods, 174, 50-61. C. Poch et al (2011) theta-phase modulation of replay correlates with configural-relational short-term memory performance. Journal of Neuroscience, 31(19):7038-7042. S. Vanhatalo et al. (2004) Infraslow oscillations modulate excitability and interictal activity in the human cortex during sleep. PNAS 101(14):5053-7. Oscillation Non- J. White et al. (2000) s of interneurons with fast and slow GABA-A kinetics. Proc Natl Acad Sci USA, 97(14):8128-33.