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Oscillations in Mammalian Sensorimotor Processing. Diane Whitmer Dissertation Defense Division of Biological Sciences September 29, 2008. Overview. Oscillations in the Rat Vibrissa System A. How can rats use their whiskers to locate objects? B. Does hippocampal theta drive whisking?

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Oscillations in mammalian sensorimotor processing

Oscillations in Mammalian

Sensorimotor

Processing

Diane Whitmer

Dissertation Defense

Division of Biological Sciences

September 29, 2008


Overview

Overview

  • Oscillations in the Rat Vibrissa System

    A. How can rats use their whiskers to locate objects?

    B. Does hippocampal theta drive whisking?

  • Visually cued Finger Movements in Human Epilepsy Patients

    A. What is the neural signature of movements?

    B. (How) Should intracranial signals be un-mixed? **ICA**

    III. Conclusions and Next Steps


Overview1

Overview

  • Oscillations in the Rat Vibrissa System

    A. How can rats use their whiskers to locate objects?

    B. Does hippocampal theta drive whisking?

  • Visually cued Finger Movements in Human Epilepsy Patients

    A. What is the neural signature of movements?

    B. (How) Should intracranial signals be un-mixed?

    III. Conclusions and Next Steps


Oscillations in mammalian sensorimotor processing

Rats engage in exploratory “whisking”

Berg & Kleinfeld, 2003


The rat vibrissae pathway

The Rat Vibrissae Pathway

Kolb and Tees, 1990

Brecht et al., 1997

Deschenes et al., 2001


Oscillations in mammalian sensorimotor processing

Coding strategies for object localization

Small or no movements

Mehta, Whitmer et al., 2007


Oscillations in mammalian sensorimotor processing

Coding strategies for object localization

Small or no movements

Whisker movements

Mehta, Whitmer et al., 2007


Oscillations in mammalian sensorimotor processing

Behavioral testing of whisking

Restraint Bar

Nose Sensor

Reward Inlet Valve

Position Sensor

Lever

Water Fountain

Reward Outlet Vacuum


Oscillations in mammalian sensorimotor processing

Mehta, Whitmer et al., 2007


Oscillations in mammalian sensorimotor processing

Mehta, Whitmer et al., 2007


Oscillations in mammalian sensorimotor processing

Responses from a Testing Session

Mehta, Whitmer et al., 2007


Oscillations in mammalian sensorimotor processing

Responses Latencies

Mehta, Whitmer et al., 2007


Oscillations in mammalian sensorimotor processing

Coding strategies for object localization

Mehta, Whitmer et al., 2007


Significance for the rat vibrissa system

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


Overview2

Overview

  • Oscillations in the Rat Vibrissa System

    A. 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?

  • Visually cued Finger Movements in Human Epilepsy Patients

    A. What is the neural signature of movements?

    B. (How) Should intracranial signals be un-mixed?

    III. Conclusions and Next Steps


Overview3

Overview

  • 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?

  • Visually cued Finger Movements in Human Epilepsy Patients

    A. What is the neural signature of movements?

    B. (How) Should intracranial signals be un-mixed?

    III. Conclusions and Next Steps


Oscillations in mammalian sensorimotor processing

Hippocampal theta rhythm is associated with voluntary movement in the rat

  • running

  • jumping

  • exploratory head movements

  • swimming

Vanderwolf, 1969


Oscillations in mammalian sensorimotor processing

Are these two signals phase-locked?

Berg, Whitmer, Kleinfeld, 2006


Oscillations in mammalian sensorimotor processing

Coherence quantifies phase-locking

Berg, Whitmer, Kleinfeld, 2006


Oscillations in mammalian sensorimotor processing

Coherence quantifies phase-locking

Berg, Whitmer, Kleinfeld, 2006


Oscillations in mammalian sensorimotor processing

Trial to trial variability of coherence between whisking and hippocampal theta

Berg, Whitmer, Kleinfeld, 2006


Oscillations in mammalian sensorimotor processing

Trial to trial variability of coherence between whisking and hippocampal theta

Berg, Whitmer, Kleinfeld, 2006


Oscillations in mammalian sensorimotor processing

Coherence between whisking and hippocampal theta is not significant

Berg, Whitmer, Kleinfeld, 2006


Overview4

Overview

  • 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

  • Visually cued Finger Movements in Human Epilepsy Patients

    A. What is the neural signature of movements?

    B. (How) Should intracranial signals be un-mixed?

    III. Conclusions and Next Steps


Overview5

Overview

  • 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.

  • Visually cued Finger Movements in Human Epilepsy Patients

    A. What is the neural signature of movements?

    B. (How) Should intracranial signals be un-mixed?

    III. Conclusions and Next Steps


Oscillations in mammalian sensorimotor processing

Scales of measurement of electrophysiological brain signals

Churchland & Sejnowski, 1992


Electroencephalography eeg recordings

Jasper & Penfield, 1949

Electroencephalography (EEG) recordings


Oscillations in mammalian sensorimotor processing

CSF

Cocktail Party

EEG

Independent Component Analysis of EEG Data

Makeig, Bell, Jung & Sejnowski, 1996


Oscillations in mammalian sensorimotor processing

Independent Component Analysis

x = A s

(theory)

x: recorded channel data

s: actual underlying sources

A: “mixing matrix”

Assume that sources are:

  • Statistically independent

  • Volume conduction instantaneous (no time delays)

  • Sources mix linearly to produce channel data

  • Spatially stationary


Oscillations in mammalian sensorimotor processing

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

Voltage

Onton & Makeig, 2006


Oscillations in mammalian sensorimotor processing

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

Onton & Makeig, 2006


Oscillations in mammalian sensorimotor processing

CSF

EEG

Intracranial EEG (iEEG)

ICA of iEEG

Standard EEG

ICA of EEG

??


Is ica useful for the interpretation of intracranial data

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?


Oscillations in mammalian sensorimotor processing

Patient Electrode Locations

Inter-hemispheric fissure

Right frontal & lateral lobe

Orbital Frontal Surface

Mesial temporal lobe

Lateral temporal lobe


Oscillations in mammalian sensorimotor processing

Visually Cued Finger Movement Task

Stimulus

Key-press

Beep

Next Stimulus

Time (msec)

0

epoch: 2 sec

ISI: 1.570 sec

Task Design

10 Trials Per Finger Per Condition (N = 400)

Block Design:

L pic - R pic - L word - R word

L pic - R pic - L word - R word

Finger presentation randomized within a block

right ring


Oscillations in mammalian sensorimotor processing

ICA of Intracranial Data

Example Channel (black) and Component (blue) Time Series

Grid 24

Grid 25

Grid 26

Grid 27

Grid 28

Grid 29

IC1

IC2

IC3

IC4

IC5

IC6


Oscillations in mammalian sensorimotor processing

ICA of Intracranial Data

Reduction in pairwise mutual information from channels to components


Oscillations in mammalian sensorimotor processing

ICA of Intracranial Data

Reduction in pairwise mutual information from channels to components


Oscillations in mammalian sensorimotor processing

Independent component map are consistent with anatomically and/or functionally linked brain regions

Focal

Diffuse

Complex


Oscillations in mammalian sensorimotor processing

= FIRDA: frontal intermittent rhythmic delta, reportedly synchronous

Right lateral frontal Grid

Lateral Temporal Strips

ICA separates pathological “FIRDA” acivity

Strips in anterior frontal interhemispheric fissure

Orbital Frontal Surface strip

Mesial Temporal Strips


Oscillations in mammalian sensorimotor processing

IC3

Epileptic “Frontal Intermittent Rhythmic Delta Activity” (FIRDA)


Cortical signatures of movement

Jasper & Penfield, 1949

Cortical signatures of movement

Jasper & Andrews, 1936


Cortical signatures of movement1

Jasper & Penfield, 1949

Miller et al, 2007

Cortical signatures of movement

Alpha/beta power decrease

Jasper & Andrews, 1936


Cortical signatures of movement2

Jasper & Penfield, 1949

Miller et al, 2007

Cortical signatures of movement

Gamma power increase

Alpha/beta power decrease

Jasper & Andrews, 1936


Oscillations in mammalian sensorimotor processing

Mu blocking on Grid24

Finger movement

Finger movement

Grid24 log spectral power

Finger movement


Oscillations in mammalian sensorimotor processing

ICA finds components with classic movement-related dynamics

Finger movement

Finger movement

IC18, 89% of Grid24

Finger movement

IC18 captures classic event-related spectral changes and mu blocking associated with finger movement


Oscillations in mammalian sensorimotor processing

Independent components identify components in overlapping brain areas with different dynamics


Ica is useful for the interpretation of intracranial data

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

Next Steps for ICA of Intracranial Data

  • ICA for the interpretation of cognitive task data for which the dynamics are not known in advance


Next steps for ica of intracranial data1

Next Steps for ICA of Intracranial Data

  • 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 data2

Next Steps for ICA of Intracranial Data

  • 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

Oscillations

1.Whisker movements are oscillatory, and can be used to locate objects in space.

  • 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.


Oscillations in mammalian sensorimotor processing

Applications to Brain-Computer Interfaces

Robotic Whiskers

Soloman & Hartmann, 2006

Tetraplegic patient controls computer cursor with brain signals

Caplan et al., 2006

Prosthetic Arm

photo from Donoghue Lab


Acknowledgements

Acknowledgements

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

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

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


Acknowledgements1

Acknowledgements

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

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

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


Acknowledgements2

Acknowledgements

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

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

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


Acknowledgements3

Acknowledgements

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

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

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


Acknowledgements4

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


Acknowledgements5

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 loving

memory of my grandfather Martin Littman,

who wanted to celebrate this day but passed

away on March 16, 2006.


Oscillations in mammalian sensorimotor processing

Additional Slides


Oscillations in mammalian sensorimotor processing

Traveling waves in cortex

Ermentrout & Kleinfeld, 2001


Oscillations in mammalian sensorimotor processing

Traveling waves in cortex could appear synchronous from a distance when averaged over sizable cortical patch

3 cm cortical patch

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

1.5 cm


Oscillations in mammalian sensorimotor processing

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


Oscillations in mammalian sensorimotor processing

Principle Component Analysis (PCA) versus Independent Component Analysis (ICA)


Oscillations in mammalian sensorimotor processing

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)

  • Sphere the data: diagonalize the covariance matrix of x: <xxT> = I

  • Maximize the joint entropy of Y = g(u), where g is sigmoid

  • Find a matrix W such that max{H(g(Wx))}

4. Define a surface H(g(Wx))

  • Find the gradient d/dW H(...) and ascend it

    6.When the gradient is zero, a maximum is reached


Oscillations in mammalian sensorimotor processing

Applications of ICA

  • Acoustics: cancellation of acoustic reverberations

  • Geophysics: seismic deconvolution

  • Image processing: restoration of images


Oscillations in mammalian sensorimotor processing

Theta phase for encoding spatial location

Buzsaki, 2004


Oscillations in mammalian sensorimotor processing

Latency Distribution

Mehta, Whitmer et al., 2007


Oscillations in mammalian sensorimotor processing

ROC curve

Mehta, Whitmer et al., 2007


Oscillations in mammalian sensorimotor processing

Controls

Mehta, Whitmer et al., 2007


Oscillations in mammalian sensorimotor processing

Peak amplitudes are not correlated

Berg, Whitmer, Kleinfeld, 2006


Oscillations in mammalian sensorimotor processing

Confidence limits on coherence estimates


Oscillations in mammalian sensorimotor processing

ICA of Intracranial Data

Reduction in pairwise mutual information from channels to components


Oscillations in mammalian sensorimotor processing

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


Oscillations in mammalian sensorimotor processing

The Scientific Method


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