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Oscillations in Mammalian Sensorimotor
Processing
Diane WhitmerDissertation Defense
Division of Biological SciencesSeptember 29, 2008
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Overview
I. Oscillations in the Rat Vibrissa SystemA. How can rats use their whiskers to locate objects?B. Does hippocampal theta drive whisking?
II. Visually cued Finger Movements in Human Epilepsy PatientsA. What is the neural signature of movements?B. (How) Should intracranial signals be un-mixed? **ICA**
III. Conclusions and Next Steps
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Overview
I. Oscillations in the Rat Vibrissa SystemA. How can rats use their whiskers to locate objects?
B. Does hippocampal theta drive whisking?
II. Visually cued Finger Movements in Human Epilepsy PatientsA. What is the neural signature of movements?B. (How) Should intracranial signals be un-mixed?
III. Conclusions and Next Steps
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Rats engage in exploratory
“whisking”
Berg & Kleinfeld, 2003
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The Rat Vibrissae Pathway
Kolb and Tees, 1990
Deschenes et al., 2001
Brecht et al., 1997
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Mehta, Whitmer et al., 2007
Coding strategies for object localization
Small or no movements
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Mehta, Whitmer et al., 2007
Coding strategies for object localization
Small or no movements
Whisker movements
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Behavioral testing of whisking
Lever
Water Fountain
Position Sensor
Reward Outlet Vacuum
Reward Inlet Valve
Nose Sensor
Restraint Bar
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Mehta, Whitmer et al., 2007
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Mehta, Whitmer et al., 2007
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Responses from a Testing Session
Mehta, Whitmer et al., 2007
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Mehta, Whitmer et al., 2007
Responses Latencies
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Mehta, Whitmer et al., 2007
Coding strategies for object localization
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Significance for the Rat Vibrissa System
• Vibrissa process sensory information about What and Where
• Results from discrimination of location in rostral-caudal plane suggests overall scheme for position in 3-d space:
Ahissar & Knutsen, 2008
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Overview
I. Oscillations in the Rat Vibrissa SystemA. How can rats use their whiskers to locate objects? Information about the location of the whisker is combined with contact information.B. Does hippocampal theta drive whisking?
II. Visually cued Finger Movements in Human Epilepsy PatientsA. What is the neural signature of movements?B. (How) Should intracranial signals be un-mixed?
III. Conclusions and Next Steps
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Overview
I. Oscillations in the Rat Vibrissa SystemA. What is the significance of phase in the whisking cycle?Information about the location of the whisker is combined with contact information.B. Does hippocampal theta drive whisking?
II. Visually cued Finger Movements in Human Epilepsy PatientsA. What is the neural signature of movements?B. (How) Should intracranial signals be un-mixed?
III. Conclusions and Next Steps
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Hippocampal theta rhythm is associated with voluntary movement in the rat
• running
• jumping
• exploratory head movements
• swimming
Vanderwolf, 1969
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Berg, Whitmer, Kleinfeld, 2006
Are these two signals phase-locked?
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Coherence quantifies phase-locking
Berg, Whitmer, Kleinfeld, 2006
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Coherence quantifies phase-locking
Berg, Whitmer, Kleinfeld, 2006
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Trial to trial variability of coherence between whisking and hippocampal theta
Berg, Whitmer, Kleinfeld, 2006
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Berg, Whitmer, Kleinfeld, 2006
Trial to trial variability of coherence between whisking and hippocampal theta
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Berg, Whitmer, Kleinfeld, 2006
Coherence between whisking and hippocampal theta is not significant
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Overview
I. Oscillations in the Rat Vibrissa System A. What is the significance of phase in the whisking cycle?Information about the location of the whisker is combined with contact information. B. Does hippocampal theta drive whisking? NO
II. Visually cued Finger Movements in Human Epilepsy PatientsA. What is the neural signature of movements?B. (How) Should intracranial signals be un-mixed?
III. Conclusions and Next Steps
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Overview
I. Oscillations in the Rat Vibrissa SystemA. What is the significance of phase in the whisking cycle?Information about the location of the whisker is combined with contact information.B. Does hippocampal theta drive whisking? NO.
II. Visually cued Finger Movements in Human Epilepsy PatientsA. What is the neural signature of movements?B. (How) Should intracranial signals be un-mixed?
III. Conclusions and Next Steps
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Scales of measurement of
electrophysiological brain signals
Churchland & Sejnowski, 1992
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Electroencephalography (EEG) recordings
Jasper & Penfield, 1949
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Cocktail Party
Independent Component Analysis of EEG Data
Makeig, Bell, Jung & Sejnowski, 1996
CSF
EEG
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Assume that sources are:
1. Statistically independent
2. Volume conduction instantaneous (no time delays)
3. Sources mix linearly to produce channel data
4. Spatially stationary
Independent Component Analysis
x = A s (theory) x: recorded channel data
s: actual underlying sources
A: “mixing matrix”
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Onton & Makeig, 2006
1. ICA separates EEG data into different brain rhythms that are modulated during this working memory task.
Voltage
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Onton & Makeig, 2006
2. The maps from projecting independent components onto the electrodes produce biologically plausible patterns (dipoles)
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CSFEEG
??
Standard EEG ICA of EEG Intracranial EEG (iEEG)
ICA of iEEG
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Is ICA useful for the interpretation of intracranial data?
Three Ways to Assess:
1. Are the time series of intracranial channels statistically independent? (Control)
2. Do independent component maps appear consistent with anatomically and/or functionally linked brain regions?
3. Does ICA separate functionally distinct brain processes?a. Pathological signals?
b. Event-related dynamics?
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Patient Electrode Locations
Intracranial Montage:
Right Lateral hemisphere
6x8 Grid
Two 8-contact strip
Right mesial surface
Three 4-contact strips
Two 4-contact strips
One 4-contact strip
Inter-hemispheric fissure
Mesial temporal lobe
Orbital Frontal Surface
Right frontal & lateral lobe
Lateral temporal lobe
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right ring
Time (msec)
Stimulus Key-press Beep
0
Next Stimulus
ISI: 1.570 sec
Visually Cued Finger Movement Task
Task Design
10 Trials Per Finger Per Condition (N = 400)
Block Design:L pic - R pic - L word - R wordL pic - R pic - L word - R word
Finger presentation randomized within a block
epoch: 2 sec
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Grid 24
Grid 25
Grid 26
Grid 27
Grid 28
Grid 29
IC1
IC2
IC3
IC4
IC5
IC6
Example Channel (black) and Component (blue) Time Series
ICA of Intracranial Data
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ICA of Intracranial Data
Reduction in pairwise mutual information from channels to components
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ICA of Intracranial Data
Reduction in pairwise mutual information from channels to components
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Independent component map are consistent with anatomically and/or functionally
linked brain regions
Focal
Diffuse
Complex
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Right lateral frontal Grid
Lateral Temporal Strips Mesial Temporal Strips
Orbital Frontal Surface strip
Strips in anterior frontal interhemispheric fissure
= FIRDA: frontal intermittent rhythmic delta, reportedly synchronous
ICA separates pathological “FIRDA” acivity
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Epileptic “Frontal Intermittent Rhythmic Delta Activity” (FIRDA)
IC3
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Cortical signatures of movement
Jasper & Penfield, 1949
Jasper & Andrews, 1936
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Cortical signatures of movement
Jasper & Penfield, 1949
Miller et al, 2007
Jasper & Andrews, 1936
Alpha/beta power
decrease
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Cortical signatures of movement
Jasper & Penfield, 1949
Miller et al, 2007
Gamma power increase
Alpha/beta power
decrease
Jasper & Andrews, 1936
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Finger movementFinger movement
Finger movement
Mu blocking on Grid24
Grid24 log spectral power
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Finger movement
IC18 captures classic event-related spectral changes and mu blocking associated with finger movement
IC18, 89% of Grid24
Finger movement
Finger movement
ICA finds components with classic movement-related dynamics
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Independent components identify components in overlapping brain areas with different dynamics
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ICA is useful for the interpretation of intracranial data
Three Ways to Assess:
1. ICA finds a set of time series that are more statistically independent than the sensor data (sanity check)
2. Independent component maps appear consistent with anatomically linked brain regions
3. ICA separates functionally distinct brain processes:a. Pathological signals
b. Event-related dynamics
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Next Steps for ICA of Intracranial Data
1. ICA for the interpretation of cognitive task data for which the dynamics are not known in advance
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Next Steps for ICA of Intracranial Data
1. ICA for the interpretation of cognitive task data for which the dynamics are not known in advance
2. Advanced ICA methods- Complex/convolutive ICA- Multiple mixtures ICA
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Next Steps for ICA of Intracranial Data
1. ICA for the interpretation of cognitive task data for which the dynamics are not known in advance
2. Advanced ICA methods- Complex/convolutive ICA- Multiple mixtures ICA
3. Source localization: patient-specific “forward model” that accounts for craniotomy
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Oscillations
1. Whisker movements are oscillatory, and can be used to locate objects in space.
2. Hippocampal theta is not the rhythm that drives vibrissa movements.
3. Coherence can be used to determine whether oscillations are phase-locked.
4. Alpha, beta, and gamma oscillations correspond to voluntary movements in the human.
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Soloman & Hartmann, 2006
Robotic Whiskers
Prosthetic Arm
Tetraplegic patient controls computer cursor with brain signals
photo from Donoghue Lab
Applications to Brain-Computer Interfaces
Caplan et al., 2006
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Acknowledgements
Thesis Committee
Terry Sejnowski
David Kleinfeld
Scott Makeig
Greg Worrell
Eric Halgren
Pam Reinagel
Gert Cauwenberghs
Woods Hole
Neuroinformatics
Partha Mitra
Hemant Bokil
Ifije Ohiorhenuan
Jean Verrette
Swartz Center
Zeynep Akalin-Acar
Bob Buffington
Arno Delorme
JR Duann
Toby Fernsler
Klaus Gramann
T-P Jung
Il Keun Lee
Ryan Low
Julie Onton
Jason Palmer
Nima Bigdely Shamlo
Elke Van Erp
Andrey Vankov
Ying Wu
Kleinfeld Lab
Rune Berg
Omar Clay
Dan Hill
Rodolfo Figueroa
Samar Mehta
Quoc Nguyen
Nozomi Nishimura
Chris Schaffer
Lee Schroeder
Phil Tsai
Mayo Clinic
Matt Stead
Karla Crockett
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Acknowledgements
Thesis Committee
Terry Sejnowski
David Kleinfeld
Scott Makeig
Greg Worrell
Eric Halgren
Pam Reinagel
Gert Cauwenberghs
Woods Hole
Neuroinformatics
Partha Mitra
Hemant Bokil
Ifije Ohiorhenuan
Jean Verrette
Swartz Center
Zeynep Akalin-Acar
Bob Buffington
Arno Delorme
JR Duann
Toby Fernsler
Klaus Gramann
T-P Jung
Il Keun Lee
Ryan Low
Julie Onton
Jason Palmer
Nima Bigdely Shamlo
Elke Van Erp
Andrey Vankov
Ying Wu
Kleinfeld Lab
Rune Berg
Omar Clay
Dan Hill
Rodolfo Figueroa
Samar Mehta
Quoc Nguyen
Nozomi Nishimura
Chris Schaffer
Lee Schroeder
Phil Tsai
Mayo Clinic
Matt Stead
Karla Crockett
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Acknowledgements
Thesis Committee
Terry Sejnowski
David Kleinfeld
Scott Makeig
Greg Worrell
Eric Halgren
Pam Reinagel
Gert Cauwenberghs
Woods Hole
Neuroinformatics
Partha Mitra
Hemant Bokil
Ifije Ohiorhenuan
Jean Verrette
Swartz Center
Zeynep Akalin-Acar
Bob Buffington
Arno Delorme
JR Duann
Toby Fernsler
Klaus Gramann
T-P Jung
Il Keun Lee
Ryan Low
Julie Onton
Jason Palmer
Nima Bigdely Shamlo
Elke Van Erp
Andrey Vankov
Ying Wu
Kleinfeld Lab
Rune Berg
Omar Clay
Dan Hill
Rodolfo Figueroa
Samar Mehta
Quoc Nguyen
Nozomi Nishimura
Chris Schaffer
Lee Schroeder
Phil Tsai
Mayo Clinic
Matt Stead
Karla Crockett
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Acknowledgements
Thesis Committee
Terry Sejnowski
David Kleinfeld
Scott Makeig
Greg Worrell
Eric Halgren
Pam Reinagel
Gert Cauwenberghs
Woods Hole
Neuroinformatics
Partha Mitra
Hemant Bokil
Ifije Ohiorhenuan
Jean Verrette
Swartz Center
Zeynep Akalin-Acar
Bob Buffington
Arno Delorme
JR Duann
Toby Fernsler
Klaus Gramann
T-P Jung
Il Keun Lee
Ryan Low
Julie Onton
Jason Palmer
Nima Bigdely Shamlo
Elke Van Erp
Andrey Vankov
Ying Wu
Kleinfeld Lab
Rune Berg
Omar Clay
Dan Hill
Rodolfo Figueroa
Samar Mehta
Quoc Nguyen
Nozomi Nishimura
Chris Schaffer
Lee Schroeder
Phil Tsai
Mayo Clinic
Matt Stead
Karla Crockett
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Acknowledgements
Family
Priscilla, Ellen, Roger, Rachel, Ralf, Julian, Jonas, Jim, Lisa
Friends
Zoe Argento, Alicia Bicknell, Rael Cahn, Kim Ditomasso, Gloriana
Gallegos, Andra Ghent, Jody Harrell, Kaori Hirata, Dan Keller, Mina
Kinukawa, Jessica Kleiss, Debra Knight, Zoe Langsten, Oanh Nguyen,
Fij Ohiorhenuan, Steve Oldenburg, Jeff Slattery, Ben Sullivan, Emilija
Simic, Corinne Teeter, Elke Van Erp, Jean Verrette, Shane Walker,
Amaya Becvar Weddle
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Acknowledgements
Family
Priscilla, Ellen, Roger, Rachel, Ralf, Julian, Jonas, Jim, Lisa
Friends
Zoe Argento, Alicia Bicknell, Rael Cahn, Kim Ditomasso, Gloriana
Gallegos, Andra Ghent, Jody Harrell, Kaori Hirata, Dan Keller, Mina
Kinukawa, Jessica Kleiss, Debra Knight, Zoe Langsten, Oanh Nguyen,
Fij Ohiorhenuan, Steve Oldenburg, Jeff Slattery, Ben Sullivan, Emilija
Simic, Corinne Teeter, Elke Van Erp, Jean Verrette, Shane Walker,
Amaya Becvar Weddle
My dissertation is dedicated to the lovingmemory of my grandfather Martin Littman,who wanted to celebrate this day but passedaway on March 16, 2006.
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Additional Slides
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Traveling waves in cortex
Ermentrout & Kleinfeld, 2001
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Traveling waves in cortex could appear synchronous from a distance when averaged
over sizable cortical patch
1.5 cm
Example
10 Hz wave
velocity ~= 200 cm/sec
2pi * 10 Hz * 1.5 cm / 200 cm/sec
= 0.15pi = 27 degrees
27 degree phase difference between center and edge
3 cm cortical patch
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Traveling waves in primate motor cortex
Example
40 Hz wave
velocity = 28 cm/sec
2pi * 40 Hz * 0.2 cm / 28 cm/sec
= 0.57pi = ~102 degrees
Sizable phase difference between center and edge of the electrode array.
Do the waves travel the entire distance of an area of cortex over which the activity would be averaged by iEEG?
0.4 cm width of electrode array
Rubino et al., 2006
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Principle Component Analysis (PCA) versus Independent Component Analysis (ICA)
PCA ICA
Based on variance Based on statistical independence (stronger requirement)
Groups together the “sources” of the data
Separates the data sources
Typically used for dimensionality reduction
Requires separate method for dimensionality reduction (e.g. PCA, then ICA)
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Infomax ICA Algorithm
Define: x(t) = A*s(t)
Goal: find u and W such that W*x(t) = u(t)
and u is independent: p(u) = p1(u1)*p2(u2)*...pN(uN)
1. Sphere the data: diagonalize the covariance matrix of x: <xxT> = I
2. Maximize the joint entropy of Y = g(u), where g is sigmoid
3. Find a matrix W such that max{H(g(Wx))}
4. Define a surface H(g(Wx))
5. Find the gradient d/dW H(...) and ascend it
6. When the gradient is zero, a maximum is reached
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Applications of ICA
• Acoustics: cancellation of acoustic reverberations
• Geophysics: seismic deconvolution
• Image processing: restoration of images
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Theta phase for encoding spatial location
Buzsaki, 2004
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Latency Distribution
Mehta, Whitmer et al., 2007
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Mehta, Whitmer et al., 2007
ROC curve
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Mehta, Whitmer et al., 2007
Controls
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Berg, Whitmer, Kleinfeld, 2006
Peak amplitudes are not correlated
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|C |= P1/(NK −1)
Confidence limits on coherence estimates
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Reduction in pairwise mutual information from channels to components
ICA of Intracranial Data
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Role of beta oscillations in motor system
• Preparatory motor activity (Sanes & Donoghue)
• Maintain steady contractions of contralateral muscles
• Bind sensory and motor areas during motor maintenance behavior (Brovelli et al., 2003)
• Priming of motor movement for receiving sensory input
• Clock for coordinating timing of movements
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The Scientific Method
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