Chapter 3 Real-time stimulus artifact removal for closed- loop electrical brain stimulation

160 Pages·2017·5.72 MB·English

by  Anne Carter

Optimizing electrical brain stimulation for seizure
disorders

A Dissertation
SUBMITTED TO THE FACULTY OF
UNIVERSITY OF MINNESOTA
BY

Vivek Nagaraj

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

Theoden I. Netoff

March 2017
© Vivek Nagaraj 2017
ALL RIGHTS RESERVED
Acknowledgements
I would like to acknowledge the following people for training and support during my
PhD:
Dr. Theoden I Netoff – Thanks so much for all of the training. I have enjoyed the
opportunity to learn so many different subject areas in science and engineering under
your mentorship.
Netoff Lab – Thank you for all the training and great conversations about science and life
as a scientist! Dr. Oscar Miranda-Dominguez, Dr. Bryce Beverlin, Dr. Abbey Holt, Tyler
Stigen, Jennifer Zick, Dr. David Darrow, Ken Louie, Logan Grado, Pantea Moghimi
Nicholas Roberts, Benjamin Johnson, Tyler Nordmann, Pratik Parikh, Sarah West, and
David Molinari.
Family – Thanks so much to my amazing wife Kseniya Nagaraj for providing incredible
support during my PhD. I am also very thankful for all the support from my father
Kesavamurthy, mother Indira, brother Aaditya, sister-in-law Apoorva, uncle Krishna, and
cousin Nitin.
Friends – Thank you to my GPN class mates, John Paton, present and past Directors of
Graduate Studies, and most importantly the GPN program for giving me the opportunity
to be a student.
In addition, I would like to acknowledge my funding sources and my colleagues around
the world for the many engaging conversations at conferences and workshops about
Neuroscience, Epilepsy and Neuromodulation.
i
Dedication

This dissertation is dedicated to my family.
ii
Abstract
Approximately 1% of the world population is afflicted with Epilepsy. For many
patients, antiepileptic drugs do not fully control seizures. Electrical brain stimulation
therapies have been effective in reducing seizure rates in some patients. While current
neuromodulation devices provide a benefit to patients, efficacy can be improved by
optimizing brain stimulation so that the therapy is tuned on a patient by patient basis. One
optimization approach is to target deep brain regions that strongly modulate seizure prone
regions. I will present data on the effects of stimulation of two different anatomical
regions for seizure control, and establish my experimental platform for testing closed-
loop algorithms.
There are two general methods to implementing closed-loop algorithms to modulate
neural activity: 1) Model-free algorithms that require a learning period to establish an
optimal mapping between neural states and best therapeutic parameters, and 2) Model-
based algorithms that use forward predictions of the neural system to determine the
appropriate stimulation therapy to be administered. In this thesis, I will propose and test
two closed-loop control schemes to control the brain activity to prevent epileptogenic
activity while reducing stimulation energy. I will also present techniques to remove
stimulation artifacts so that neural biomarkers can be measured while simultaneously
applying stimulation. The methods I will present could potentially be implemented in
next generation electrical brain stimulation hardware for seizure disorders and other
neurological diseases.
iii
Table of Contents
OPTIMIZING ELECTRICAL BRAIN STIMULATION FOR SEIZURE DISORDERS ................................................ 1
ACKNOWLEDGEMENTS ............................................................................................................................. I
DEDICATION ............................................................................................................................................. II
ABSTRACT ............................................................................................................................................... III
TABLE OF CONTENTS ............................................................................................................................... IV
LIST OF TABLES ........................................................................................................................................ IX
LIST OF FIGURES ....................................................................................................................................... X
CHAPTER 1 INTRODUCTION ................................................................................................................. 1
1.1 INTRODUCTION .......................................................................................................................................... 2
1.2 NEED FOR NEW SEIZURE DETECTION AND PREDICTION DEVICES ............................................................................ 2
1.3 DEFINING SEIZURE PREDICTION ..................................................................................................................... 3
1.4 FUTURE OF SEIZURE THERAPY: CLOSING THE LOOP ............................................................................................ 3
1.5 DETECTION OF PRE-ICTAL AND ICTAL STATES.................................................................................................... 5
1.5.1 Signal Modalities ........................................................................................................................... 5
1.5.2 Features ........................................................................................................................................ 7
1.5.3 Linear vs Nonlinear features ......................................................................................................... 8
1.6 INTERVENTION .......................................................................................................................................... 9
1.6.1 Stimulation Target Selection ......................................................................................................... 9
1.6.2 Closed-loop intervention strategies ............................................................................................ 10
1.6.3 Optimization of Stimulation Parameters .................................................................................... 12
1.6.4 On-demand stimulation .............................................................................................................. 13
iv
1.6.5 Physiologically adapting closed-loop neuromodulation ............................................................. 14
1.6.6 Novel therapies ........................................................................................................................... 15
CHAPTER 2 DETERMINING OPTIMAL DEEP BRAIN TARGETS FOR SEIZURE CONTROL ......................... 17
2.1 INTRODUCTION ........................................................................................................................................ 18
2.1.1 Endopiriform Nucleus .................................................................................................................. 18
2.1.2 Ventral Hippocampal Commissure .............................................................................................. 21
2.2 METHODS .............................................................................................................................................. 23
2.2.1 Rodent Surgery ............................................................................................................................ 23
2.2.2 Seizure Model .............................................................................................................................. 24
2.2.3 Data Acquisition .......................................................................................................................... 25
2.2.4 Histology ..................................................................................................................................... 26
2.3 RESULTS ................................................................................................................................................. 26
2.3.1 Functional connection between the ENc and CA1 of the hippocampus. ..................................... 26
2.3.2 Open-loop stimulation of the ENc does not suppress seizures .................................................... 27
2.3.3 Effects of VHC stimulation on Hippocampal Dynamics ............................................................... 29
2.3.4 Low and high frequency stimulation of the Ventral Hippocampal Commissure suppresses
seizures ................................................................................................................................................ 31
2.4 DISCUSSION ............................................................................................................................................ 34
2.4.1 Chronic stimulation of the ENc .................................................................................................... 34
2.4.2 VHC-Hippocampus as a robust experimental preparation for closed-loop control experiments 35
CHAPTER 3 REAL-TIME STIMULUS ARTIFACT REMOVAL FOR CLOSED-LOOP ELECTRICAL BRAIN
STIMULATION 36
3.1 INTRODUCTION ........................................................................................................................................ 37
v
3.2 METHODS: DIGITAL IMPLEMENTATION ........................................................................................................ 40
3.2.1 Stimulation artifact removal methods ........................................................................................ 41
3.2.2 Computational model and simulations ....................................................................................... 47
3.2.3 Experimental methods ................................................................................................................ 48
3.3 RESULTS: DIGITAL IMPLEMENTATION ........................................................................................................... 49
3.3.1 Real time stimulation artifact removal in a computational model ............................................. 49
3.3.2 Offline implementation using In vivo data .................................................................................. 56
3.4 METHODS: ANALOG IMPLEMENTATION ........................................................................................................ 57
3.4.1 Bidirectional neural interface for real-time stimulation artifact removal ................................... 57
3.4.2 Experimental methods ................................................................................................................ 57
3.5 RESULTS: ANALOG IMPLEMENTATION .......................................................................................................... 58
3.6 DISCUSSION ............................................................................................................................................ 60
CHAPTER 4 OPTIMIZATION OF STIMULATION PARAMETERS FOR SEIZURE CONTROL USING MODEL-
FREE REINFORCEMENT LEARNING .......................................................................................................... 63
4.1 INTRODUCTION ........................................................................................................................................ 64
4.2 METHODS .............................................................................................................................................. 66
4.2.1 Epileptor Model ........................................................................................................................... 66
4.2.2 Determining minimum stimulation frequency to suppress seizures ........................................... 69
4.2.3 Temporal difference reinforcement learning algorithm implementation ................................... 72
4.2.4 Ictal and inter-ictal state estimation from LFP data ................................................................... 75
4.2.5 Reward ........................................................................................................................................ 77
4.3 RESULTS ................................................................................................................................................. 78
4.3.1 Open-loop stimulation controls stationary seizures ................................................................... 78
4.3.2 Closed-loop adaptive stimulation results in seizure control while minimizing energy ................ 80
vi
4.3.3 TD(0) converges to optimal solution when seizure dynamics are faster ..................................... 84
4.4 DISCUSSION ............................................................................................................................................ 86
4.4.1 State-space approximation ......................................................................................................... 86
4.4.2 Stimulation variables .................................................................................................................. 87
4.4.3 Action Selection ........................................................................................................................... 89
4.4.4 Algorithm robustness .................................................................................................................. 90
4.5 CONCLUSION .......................................................................................................................................... 90
CHAPTER 5 MODEL BASED CONTROL OF LOCAL FIELD POTENTIAL BIOMARKERS ............................... 91
5.1 INTRODUCTION ........................................................................................................................................ 92
5.2 METHODS .............................................................................................................................................. 97
5.2.1 Experimental Methods ................................................................................................................ 97
5.2.2 Black-box system identification .................................................................................................. 98
5.2.3 Model cross-validation .............................................................................................................. 100
5.2.4 Linear Quadratic Regulator Design ........................................................................................... 101
5.3 RESULTS ............................................................................................................................................... 104
5.3.1 Stimulation via LQG control leads to broad-band suppression of LFP activity .......................... 104
5.3.2 LQG performance: Responders vs Non-Responders .................................................................. 105
5.3.3 Model accuracy on out of sample data and controller efficacy ................................................ 107
5.3.4 Broadband power in the LFP prior to stimulation and controller efficacy ................................ 108
5.3.5 Robust models confer stronger LFP modulation ....................................................................... 109
5.4 DISCUSSION .......................................................................................................................................... 111
5.4.1 Model Design ............................................................................................................................ 111
5.4.2 Regulator Design ....................................................................................................................... 113
5.4.3 Control Policy ............................................................................................................................ 114
vii
CHAPTER 6 CONCLUSIONS ............................................................................................................... 116
6.1 OPTIMAL STIMULATION TARGETS .............................................................................................................. 117
6.2 STIMULATION ARTIFACT REMOVAL FOR CLOSED-LOOP ELECTRICAL BRAIN STIMULATION ........................................ 119
6.3 DETERMINING OPTIMAL STIMULATION PARAMETERS FOR SEIZURE CONTROL USING REINFORCEMENT LEARNING ........ 120
6.4 REJECTING SPONTANEOUS NEURAL ACTIVITY THROUGH MODEL BASED FEEDBACK CONTROL ................................... 122
REFERENCES ......................................................................................................................................... 125
APPENDIX A EPILEPTOR MODEL .................................................................................................... 140
A.1 STATE EQUATIONS ......................................................................................................................... 140
A.2 REDUCED MODEL .......................................................................................................................... 140
APPENDIX B REINFORCEMENT LEARNING ..................................................................................... 141
B.1 TD(0) ALGORITHM ........................................................................................................................ 141
APPENDIX C STATE SPACE CONTROL ............................................................................................. 142
C.1 HO-KALMAN ALGORITHM.................................................................................................................... 142
C.2 MODEL ACCURACY ACROSS PREDICTION HORIZONS ................................................................................... 143
viii