oscillations in mammalian sensorimotor processing diane whitmer dissertation defense division of...

Oscillations in Mammalian Sensorimotor

Processing

Diane WhitmerDissertation Defense

Division of Biological SciencesSeptember 29, 2008

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

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

Rats engage in exploratory

“whisking”

Berg & Kleinfeld, 2003

The Rat Vibrissae Pathway

Kolb and Tees, 1990

Deschenes et al., 2001

Brecht et al., 1997

Mehta, Whitmer et al., 2007

Coding strategies for object localization

Small or no movements

Mehta, Whitmer et al., 2007

Coding strategies for object localization

Small or no movements

Whisker movements

Behavioral testing of whisking

Lever

Water Fountain

Position Sensor

Reward Outlet Vacuum

Reward Inlet Valve

Nose Sensor

Restraint Bar

Mehta, Whitmer et al., 2007

Mehta, Whitmer et al., 2007

Responses from a Testing Session

Mehta, Whitmer et al., 2007

Mehta, Whitmer et al., 2007

Responses Latencies

Mehta, Whitmer et al., 2007

Coding strategies for object localization

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

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

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

Hippocampal theta rhythm is associated with voluntary movement in the rat

• running

• jumping

• exploratory head movements

• swimming

Vanderwolf, 1969

Berg, Whitmer, Kleinfeld, 2006

Are these two signals phase-locked?

Coherence quantifies phase-locking

Berg, Whitmer, Kleinfeld, 2006

Coherence quantifies phase-locking

Berg, Whitmer, Kleinfeld, 2006

Trial to trial variability of coherence between whisking and hippocampal theta

Berg, Whitmer, Kleinfeld, 2006

Berg, Whitmer, Kleinfeld, 2006

Trial to trial variability of coherence between whisking and hippocampal theta

Berg, Whitmer, Kleinfeld, 2006

Coherence between whisking and hippocampal theta is not significant

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

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

Scales of measurement of

electrophysiological brain signals

Churchland & Sejnowski, 1992

Electroencephalography (EEG) recordings

Jasper & Penfield, 1949

Cocktail Party

Independent Component Analysis of EEG Data

Makeig, Bell, Jung & Sejnowski, 1996

CSF

EEG

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”

Onton & Makeig, 2006

1. ICA separates EEG data into different brain rhythms that are modulated during this working memory task.

Voltage

Onton & Makeig, 2006

2. The maps from projecting independent components onto the electrodes produce biologically plausible patterns (dipoles)

CSFEEG

??

Standard EEG ICA of EEG Intracranial EEG (iEEG)

ICA of iEEG

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?

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

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

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

ICA of Intracranial Data

Reduction in pairwise mutual information from channels to components

ICA of Intracranial Data

Reduction in pairwise mutual information from channels to components

Independent component map are consistent with anatomically and/or functionally

linked brain regions

Focal

Diffuse

Complex

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

Epileptic “Frontal Intermittent Rhythmic Delta Activity” (FIRDA)

IC3

Cortical signatures of movement

Jasper & Penfield, 1949

Jasper & Andrews, 1936

Cortical signatures of movement

Jasper & Penfield, 1949

Miller et al, 2007

Jasper & Andrews, 1936

Alpha/beta power

decrease

Cortical signatures of movement

Jasper & Penfield, 1949

Miller et al, 2007

Gamma power increase

Alpha/beta power

decrease

Jasper & Andrews, 1936

Finger movementFinger movement

Finger movement

Mu blocking on Grid24

Grid24 log spectral power

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

Independent components identify components in overlapping brain areas with different dynamics

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

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

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

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

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.

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

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

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

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

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

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

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.

Additional Slides

Traveling waves in cortex

Ermentrout & Kleinfeld, 2001

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

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

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)

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

Applications of ICA

• Acoustics: cancellation of acoustic reverberations

• Geophysics: seismic deconvolution

• Image processing: restoration of images

Theta phase for encoding spatial location

Buzsaki, 2004

Latency Distribution

Mehta, Whitmer et al., 2007

Mehta, Whitmer et al., 2007

ROC curve

Mehta, Whitmer et al., 2007

Controls

Berg, Whitmer, Kleinfeld, 2006

Peak amplitudes are not correlated

|C |= P1/(NK −1)

Confidence limits on coherence estimates

Reduction in pairwise mutual information from channels to components

ICA of Intracranial Data

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

The Scientific Method