NEURAL CONTROL OF MOVEMENT:

ENGINEERING THE RHYTHMS OF

THE BRAIN

Madeleine Lowery School of Electrical and Electronic Engineering

Centre for Biomedical Engineering

University College Dublin

2nd most common

neurological disorder

1-3 % of the population

over 65

7 million individuals

worldwide

Parkinson’s disease

Loss of dopamine in Parkinson’s disease

Imaging of dopaminergic activity in the Parkinsonian basal ganglia shown

by fluorodopa PET. Dunnett and Björklund, Nature 399, 1999.

Deep Brain Stimulation (DBS)

Stimulation

electrode

Subcutaneous

extension wire

Implanted

stimulator

0 V

Pulse

duration

Pulse

amptlitude

Charge-balancing pulse

Time

Stimulating

pulse

DBS

waveform

Deep Brain Stimulation (DBS)

DBS OFF DBS ON

Modelling deep brain stimulation

System models

Neural networks

Single neurons

Electric field

Macroscopic models

Skull

Scalp

Grey and

white matter

brain tissue

Cerebrospinal

fluid

Electrode

Electric field effects

Nerves

Volume of neural tissue activated

Grant and Lowery, IEEE Trans. Biomed. Eng., 2010.

Schmidt et al., IEEE Trans. Biomed. Eng., 2013.

0.975 quantile

0.025 quantile

mean

NETWORK EFFECTS

White matter fiber architecture of the brain. Measured from diffusion spectral imaging (DSI).

www.humanconnectomeproject.org

NETWORK EFFECTS

STN

GPe

Cortex

GPi

Thalamus

Striatum

GABA Glutamate

Collateral

Axon

AIS

SomaInterneurons

Pyramidal tract neurons

Collateral

Electrode

DBS LFP

Neural oscillations in Parkinson's disease

*Clinical data recorded by Dr Hayriye Cagnan and

Prof. Peter Brown, University of Oxford.

0 10 20 30 40 50

DBS off

DBS on

Frequency (Hz)Time (s)0 0.5 1.0 1.5 2.0

Recorded STN local field potential*

Simulated STN local field potential

DBS ON 0 10 20 30 40 50

DBS off

DBS on

Frequency (Hz)

DBS ON

Be

ta b

an

d p

ow

er

(no

rma

lise

d)

0 0.2 0.4 0.6 0.8 1 1.2

0

0.2

0.4

0.6

0.8

1.0

DBS amplitude (arbitrary units)

Simulated data Experimental data

Suppression of local field potential

oscillations with DBS

2040

6080

100120

0

1

2

3

4

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Amplitude (m

A)

Pulse duration (ms)

Oscill

atio

n a

mp

litu

de

100

150

200

250

0

20

40

60

80

100

120

0

0.5

1

1.5

Frequency (Hz)

Pulse duration (ms)

100

150

200

250

0

1

2

3

4

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Ampl

itude

(mA)

Frequency (Hz)

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Oscill

atio

n a

mp

litu

de

Oscill

atio

n a

mp

litu

de

Variation with DBS parameters (pulse amplitude, duration and frequency)

Suppression of low-frequency oscillations with high-frequency ‘dither’

Suppression of low-frequency oscillations with high-frequency ‘dither’

h

yarctan

2

)(2

)(

bs

kssG

a

α

T

T

u y

-0.2

-0.1

0

0.1

0.2

1 sec

DBS ‘on’

y

DBS

Comparison with Clinical Data

1Clinical patient data from Benabid et al., The Lancet, 337:403 406, 1991.

Clinical data1 (measured limb tremor)

Model predictions

Comparison with clinical data

Davidson, et al., IEEE Trans. Biomed. Eng. (2014; 2015)

Clinical LFP data (recorded at DBS electrode)

Model prediction

0 100 200 3000

5

10

0 100 200 3000

5

10

0 100 200 3000

5

10

0 100 200 3000

5

10

Frequency of Stimulation (Hz)

Patient A Patient B

Patient C Patient D

DB

S a

mp

litu

de

fo

r tr

em

or

su

pp

ressio

n (

mA

)

0 1 2 30

0.2

0.4

0.6

0.8

1

0 1 2 3 4 50

0.2

0.4

0.6

0.8

1

0 1 2 30

0.2

0.4

0.6

0.8

1

0 1 2 3 4 50

0.2

0.4

0.6

0.8

1

Amplitude of stimulation (V)

Patient #1

R2= 0.959

Patient #3

R2= 0.783

Patient #2

R2= 0.687

Patient #4

R2= 0.987

Be

ta (

15

-30

Hz)

LF

P p

ow

er

Adaptive deep brain stimulation

EMG signal

Muscle force

and tremor

Local field

potential

Adaptive deep brain stimulation

Local field

potential

EMG signal

Muscle force

and tremor

Controller

Adaptive deep brain stimulation

Conclusions Engineering the rhythms of the brain

Understand and modify neural activity

DBS suppresses neural oscillations

Variation with stimulation parameters

Adaptive DBS in near future

Acknowledgements

Collaborators

Dr Hayriye Cagnan and Prof. Peter Brown, University of Oxford.

Dr Christian Schmidt and Prof. Ursula Van Rienen, University of Rostock.

UCD Neuromuscular Systems and Neural Engineering Lab

Prof. Annraoi de Paor

Dr Peadar Grant

Dr Guiyeom Kang

Dr Clare Davidson

Eleanor Dunn

Closed-loop control of DBS Weakly-coupled network Strongly-coupled network

Increased synaptic gain to induce stronger oscillations

Grant and Lowery, IEEE Trans. Neural Syst & Rehab Eng, 2014

Neural oscillations in Parkinson's

Oscillatory activity recorded from GPi during and after bilateral STN DBS.

From Andrea A. Kühn et al. J. Neurosci. 2008;28:6165-6173

DBS ON DBS OFF

Suppression of local field potential

oscillations with DBS

Normalised beta

oscillation amplitude

Variation with DBS parameters (pulse amplitude, duration and frequency)