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Understanding the degenerate retina's response to electrical stimulation: an in vitro approach

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Understanding the degenerate retina's response to electrical stimulation: an in vitro approach

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Content UNDERSTANDING THE DEGENERATE RETINA’S RESPONSE
TO ELECTRICAL STIMULATION:
ANIN VITRO APPROACH
by
Alice K. Cho
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOMEDICAL ENGINEERING)
May 2014
Copyright 2014 Alice K. Cho
Epigraph
The only thing worse than being blind is having sight but no vision.
- Helen Keller
ii
Acknowledgments
I could not have completed my Ph.D. degree without the support and guidance from
a number of people. First, I would like to thank my adviser, Dr. James Weiland, for
providing me with the opportunity to pursue innovative research in an encouraging and
engaging environment. He allowed me to work independently when I needed to but was
always present to offer help and advice. He created an environment that encouraged us
to work hard but also to take the time to enjoy life outside of the lab, and I feel extremely
grateful to have had him as my adviser.
I would like to thank Dr. Alapakkam Sampath (Sam) for teaching me so many of the
technical skills I needed to complete this degree. He really made me feel like I was an
integral part of his lab. His enthusiasm for research and science was infectious and I am
extremely appreciative for everything I learned from him. I would also like to thank the
other members of my committee, Dr. Mark Humayun and Dr. Robert Chow, for all their
sage advice, conversations, and support throughout my years at USC.
I would like to thank my past labmates from the Weiland and Sampath labs who
helped me with numerous aspects of my research, including Aditi Ray, Leanne Chan,
Vivek Pradeep, Josh Miyagishima, Haru Okawa, Cyrus Arman, Devyani Nanduri, and
Neha Parikh. I would also like to thank the present members of the lab who made
iii
it enjoyable to go to work every day, including Andrew Weitz, Navya Davuluri, Dr.
Johan Pahlberg, Kate Fehlhaber, Dr. Artin Petrossians, Samantha Cunningham, Steven
Walston, Boshuo Wang, and Dr. Kiran Nimmagadda.
I am very grateful to my parents, Kwang Myung and Miri, and my brother, Michael.
They have always been, and continue to be, my biggest cheerleaders and I will forever be
thankful for their unconditional love and support. I would also like to thank Will for his
unwavering encouragement and commitment, and for always knowing how to make me
laugh.
iv
Table of Contents
Epigraph ii
Acknowledgments iii
List of Tables xii
List of Figures xiii
List of Abbreviations xvi
Abstract xviii
Chapter 1: Introduction 1
1.1 Overview of the Visual System . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.1 The Retina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Diseases of the Retina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.1 Age-related Macular Degeneration . . . . . . . . . . . . . . . . . . 5
1.2.2 Retinitis Pigmentosa . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Animal Models of Retinitis Pigmentosa . . . . . . . . . . . . . . . . . . . 7
1.3.1 Rd1 Mouse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
v
1.3.2 Rd10 Mouse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3.3 S334ter Rat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.3.4 P23H Rat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.4 Retinal Prosthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.4.1 Epiretinal Prosthesis (Second Sight) . . . . . . . . . . . . . . . . . 14
1.4.1.1 Description of the Device . . . . . . . . . . . . . . . . . . 14
1.4.1.2 Results from Clinical Trials . . . . . . . . . . . . . . . . . 16
1.4.1.3 Clinical Issues . . . . . . . . . . . . . . . . . . . . . . . . 17
1.5 Electrical Stimulation of the Retina . . . . . . . . . . . . . . . . . . . . . . 18
1.5.1 Retinal Ganglion Cell Response . . . . . . . . . . . . . . . . . . . . 18
1.5.1.1 Direct Ganglion Cell Activation . . . . . . . . . . . . . . 18
1.5.1.2 Indirect Ganglion Cell Activation . . . . . . . . . . . . . 22
1.6 Goal of this Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Chapter 2: Experimental Design and Methods 26
2.1 Single-cell Recordings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.1.1 Cell-attached Patch Clamp . . . . . . . . . . . . . . . . . . . . . . 27
2.1.2 Whole-cell Patch Clamp . . . . . . . . . . . . . . . . . . . . . . . . 28
2.1.3 Isolated Retina Preparation . . . . . . . . . . . . . . . . . . . . . . 29
2.1.4 Preliminary Data - Whole-cell vs. Cell-Attached . . . . . . . . . . 30
2.2 Extracellular Electrical Stimulation . . . . . . . . . . . . . . . . . . . . . . 32
2.2.1 Stimulating Electrode . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.2.2 Stimulus Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 34
vi
2.3 Recording Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.3.1 Electrophysiology Setup for Whole-cell Patch Clamp Recordings . 34
2.3.2 Custom Recording Chamber and Placement of Ground . . . . . . . 35
2.4 Rd10 Mouse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.4.1 Description of the Mutation . . . . . . . . . . . . . . . . . . . . . . 37
2.4.2 Genotyping the Rd10 Mutation . . . . . . . . . . . . . . . . . . . . 38
2.4.3 Histology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.4.4 Cross-breeding with YFP-expressing Mice . . . . . . . . . . . . . . 41
2.4.5 Genotyping Rd10/YFP Mice . . . . . . . . . . . . . . . . . . . . . 41
2.5 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.5.1 Threshold Calculation . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.5.2 Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.5.2.1 SAS (Statistical Analysis Software) . . . . . . . . . . . . 44
2.6 Neural Reconstruction and Modeling . . . . . . . . . . . . . . . . . . . . . 44
2.6.1 Fluorescence Imaging . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.6.2 Neurolucida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.6.3 Neuron Simulation Environment . . . . . . . . . . . . . . . . . . . 46
Chapter 3: Effects of degeneration on temporal response to electrical
stimulation 48
3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.1.1 Rheobase and Chronaxie . . . . . . . . . . . . . . . . . . . . . . . . 48
3.1.2 Response Latency - Direct vs. Indirect RGC Stimulation . . . . . 49
vii
3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.2.1 Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.2.2 Placement of Stimulating Electrode . . . . . . . . . . . . . . . . . 52
3.2.3 Strength-duration Curves . . . . . . . . . . . . . . . . . . . . . . . 52
3.2.4 Measurement of Response Onset Latency . . . . . . . . . . . . . . 53
3.2.5 Artifact Subtraction . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.3.1 Chronaxie Values are Comparable Between WT and Rd10 RGCs . 54
3.3.2 Response onset latencies to electrical stimulation are not different
between WT and rd10 . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Chapter 4: Influence of rd10 intrinsic properties on sensitivity to elec-
trical stimulation 59
4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.1.1 Retinal Degeneration - Sensitivity to Electrical Stimulation . . . . 59
4.1.2 Physiology of Degenerate Retina . . . . . . . . . . . . . . . . . . . 60
4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.2.1 Defining Threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.2.2 Measurement and Calculation of Intrinsic Properties . . . . . . . . 63
4.2.2.1 Resting Membrane Potential . . . . . . . . . . . . . . . . 63
4.2.2.2 Baseline Spontaneous Rate . . . . . . . . . . . . . . . . . 64
4.2.2.3 Membrane Periodicity . . . . . . . . . . . . . . . . . . . . 64
viii
4.2.2.4 Rebound Excitation . . . . . . . . . . . . . . . . . . . . . 64
4.2.2.5 Input Resistance and Membrane Time Constant . . . . . 65
4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.3.1 Stimulation Thresholds are Influenced by Soma Size in WT RGCs
(Pilot Data) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.3.2 Thresholds are Significantly Elevated in Rd10 RGCs . . . . . . . . 67
4.3.3 Analysis of Intrinsic Properties Reveals No Significant Differences
Between WT and Rd10 . . . . . . . . . . . . . . . . . . . . . . . . 68
4.3.4 Differences Arise Between WT and Rd10 RGCs When Categorized
by Spontaneous Rate . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.3.5 Rd10 RGCs Display Distinct Characteristics in Baseline Activity . 73
4.3.6 Periodicity and High Spontaneous Rate Contribute to Decreased
Thresholds in Rd10 RGCs . . . . . . . . . . . . . . . . . . . . . . . 75
4.3.7 Rebound Excitation and Functional Classification of Rd10 RGCs . 76
4.3.8 Analysis of Additional Intrinsic Parameters of Rd10 RGCs . . . . 79
4.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
Chapter 5: Modeling the response of RGCs to electrical stimulation 85
5.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.1.1 Biophysical and Anatomical Properties of the AIS . . . . . . . . . 85
5.1.2 Efficiency of Pulse Waveforms . . . . . . . . . . . . . . . . . . . . . 87
5.1.3 Waveforms for Selective Stimulation . . . . . . . . . . . . . . . . . 88
5.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
ix
5.2.1 Neuron Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . 89
5.2.1.1 Biophysical Mechanisms . . . . . . . . . . . . . . . . . . . 89
5.2.1.2 Na+ Channel Band . . . . . . . . . . . . . . . . . . . . . 93
5.2.1.3 Anatomical Features of the Axon . . . . . . . . . . . . . 93
5.2.2 Physiological Recordings . . . . . . . . . . . . . . . . . . . . . . . . 95
5.2.2.1 Placement of Stimulating Electrode . . . . . . . . . . . . 95
5.2.2.2 Fluorescence Imaging of the RGC Axon . . . . . . . . . . 96
5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.3.1 Realistic 3D Model of a Retinal Ganglion Cell . . . . . . . . . . . . 97
5.3.1.1 Repetitive Firing . . . . . . . . . . . . . . . . . . . . . . . 98
5.3.1.2 Family of Hyperpolarizing Current Steps . . . . . . . . . 99
5.3.2 Modeling the RGC Response to Electrical Stimulation . . . . . . . 100
5.3.2.1 Elicited Action Potentials . . . . . . . . . . . . . . . . . . 101
5.3.2.2 Strength-Duration Curve . . . . . . . . . . . . . . . . . . 102
5.3.3 Preliminary Work Toward a Predictive Model . . . . . . . . . . . . 102
5.3.3.1 Modeling the response to a novel stimulus: an asymmetric
charge-balanced waveform . . . . . . . . . . . . . . . . . . 103
5.3.3.2 2 ms Asymmetric and 500 us Aymmetric Pulses Selec-
tively Stimulate the Soma . . . . . . . . . . . . . . . . . . 107
5.3.3.3 Threshold Charge is Comparable for 2 ms Asymmetric
and 500 us Symmetric Pulses . . . . . . . . . . . . . . . . 108
5.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
x
Chapter 6: Summary 115
6.1 Key Findings and Future Work . . . . . . . . . . . . . . . . . . . . . . . . 115
References 120
xi
List of Tables
4.1 Mean, standard deviation, and Student’s t-test values for RGC parameters 70
4.2 Mean, standard deviation, and Student’s t-test values - rate group . . . . 71
4.3 Mean, standard deviation, and Student’s t-test values - periodicity . . . . 76
5.1 Table of dimensions (length, diameter) for all neural segments incorporated
into the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5.2 Distribution of channels and segment-specific conductances for all neural
segments of the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5.3 Comparisons of threshold charge for the model RGC at the soma and axon
for three test waveforms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.4 Physiological RGC response data applying three test waveforms at the
soma and axon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
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List of Figures
1.1 Cross-section of the human eye . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Cross-section of the human retina . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Fundus images and histologic sections of mouse retina . . . . . . . . . . . 9
1.4 Components of a retinal prosthesis . . . . . . . . . . . . . . . . . . . . . . 12
1.5 The Argus I and Argus II epiretinal prosthesis . . . . . . . . . . . . . . . 15
1.6 Direct activation of RGCs . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.7 Indirect activation of RGCs . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.1 Diagram of patch clamp technique . . . . . . . . . . . . . . . . . . . . . . 27
2.2 Patch clamp under IR illumination . . . . . . . . . . . . . . . . . . . . . . 29
2.3 Comparison of threshold values for two patch clamp configurations . . . . 31
2.4 Schematic of the stimulating electrode . . . . . . . . . . . . . . . . . . . . 33
2.5 Schematic cross-section of the recording setup . . . . . . . . . . . . . . . . 35
2.6 Custom recording chamber for physiological experiments . . . . . . . . . . 36
2.7 Gel genotyping the rd10 mutation . . . . . . . . . . . . . . . . . . . . . . 39
2.8 Histologic sections of normal and rd10 mouse retina . . . . . . . . . . . . 40
2.9 Gel genotyping YFP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.10 Dose response curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
xiii
2.11 Fluorescence image of a retinal ganglion cell . . . . . . . . . . . . . . . . . 45
2.12 The Neuron simulation environment . . . . . . . . . . . . . . . . . . . . . 47
3.1 Strength-duration curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.2 Direct and indirect activation of RGCs . . . . . . . . . . . . . . . . . . . . 50
3.3 Strength-duration curves for WT and rd10 ganglion cells . . . . . . . . . . 54
3.4 Response onset latency of elicited action potentials . . . . . . . . . . . . . 55
3.5 Response latency vs. current amplitude . . . . . . . . . . . . . . . . . . . 56
4.1 Dose response curves adjusted for spontaneous rate . . . . . . . . . . . . . 63
4.2 Threshold as a function of soma size in WT ganglion cells . . . . . . . . . 67
4.3 Relationship between threshold and soma diameter (WT vs rd10) . . . . . 68
4.4 Relationship between threshold and resting membrane potential (WT vs
rd10) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.5 Relationship between threshold and spontaneous rate (WT vs rd10) . . . 70
4.6 Comparisons of threshold, V
rest
, and spontaneous rate, categorized by rate
group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.7 Baseline activity and interspike interval (ISI) histograms . . . . . . . . . . 74
4.8 Comparisons between WT and high rate, periodic rd10 RGCs . . . . . . . 76
4.9 RGC response to a family of hyperpolarizing current steps . . . . . . . . . 77
4.10 Functional classification based on dendritic stratification depth . . . . . . 77
4.11 Comparisons between non-rebounding and rebounding rd10 RGCs . . . . 78
4.12 Relationships between Rn, soma size, and membrane time constant . . . . 80
4.13 Spontaneous rate as a function of age in degenerate animal models . . . . 83
5.1 Schematic of a neuron showing the axon initial segment . . . . . . . . . . 86
xiv
5.2 Schematic for a HH-type model of a retinal ganglion cell . . . . . . . . . . 89
5.3 Diagram of the anatomical features of a model axon . . . . . . . . . . . . 94
5.4 Position of stimulating electrode for physiological recordings . . . . . . . . 95
5.5 Fluorescent image of RGC showing dendrites and axon . . . . . . . . . . . 96
5.6 Fluorescence image and neuronal tracing of a RGC . . . . . . . . . . . . . 97
5.7 Repetitive firing of RGCs to a constant current step . . . . . . . . . . . . 98
5.8 RGC response to a family of hyperpolarizing current steps . . . . . . . . . 99
5.9 RGC response to an extracellular biphasic pulse . . . . . . . . . . . . . . . 101
5.10 Strength-duration curves for modeling and physiological data . . . . . . . 103
5.11 Test waveforms to determine selectivity at the soma . . . . . . . . . . . . 104
5.12 Simulation results for three test waveforms . . . . . . . . . . . . . . . . . 105
5.13 Dependence of threshold current and charge on pulse duration . . . . . . 106
5.14 Simulation testing selectivity at the soma for three test pulses . . . . . . . 107
5.15 Comparisons of modeling and experimental results using three test waveforms109
5.16 Experimental results showing threshold charge differences for three wave-
forms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5.17 Activating function for anodal and cathodal monophasic stimulation . . . 113
xv
List of Abbreviations
adRP autosomal dominant retinitis pigmentosa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
AIS axon initial segment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19
AMD age-related macular degeneration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
ANOVA analysis of variance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .44
arRP autosomal recessive retinitis pigmentosa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
bp base pairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
CNS central nervous system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
EtBr ethidium bromide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
EXAMD exudative age-related macular degeneration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5
GCL ganglion cell layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
GFP green fluorescent protein . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
GUI graphical user interface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .46
IHC immunohistochemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
ILM inner limiting membrane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
INL inner nuclear layer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3
IPG interphase gap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
IPL inner plexiform layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
IR infrared . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
ISI interspike interval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
LY Lucifer Yellow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45
NEAMD non-exudative age-related macular degeneration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
NS narrow segment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
xvi
ONL outer nuclear layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
OPL outer plexiform layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
P postnatal day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
PCR polymerase chain reaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
PDE phosphodiesterase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
PFA paraformaldehyde . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
rd1 retinal degenerate 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
rd10 retinal degenerate 10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
RGCs retinal ganglion cells. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8
RP retinitis pigmentosa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
RPE retinal pigment epithelium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5
SAS statistical analysis software. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .44
SC superior colliculus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
SSMP Second Sight Medical Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
V1 primary visual cortex. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3
VPU video processing unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
WT Wildtype. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29
YFP yellow fluorescent protein. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41
xvii
Abstract
Retinitis pigmentosa (RP) and age-related macular degeneration (AMD) are two common
outer retinal diseases for which there are currently no cures. These degenerative diseases
initiate the death of photoreceptor cells and can ultimately lead to complete blindness.
Despite the loss of these cells, however, much of the inner retina remains intact providing
a potential means for signal transmission to higher visual centers. Various approaches
to elicit visual percepts through retinal stimulation have demonstrated the feasibility of
engaging the remaining retina to convey visual information to the brain.
One strategy for inducing activity in residual retinal cells is to stimulate the retinal
ganglion ganglion cells (RGCs) electrically. RGCs receive information from inner retinal
cells and send visual information, encoded in trains of action potentials, along their
axons to higher visual centers. Significant rewiring and remodeling as well as individual
structural changes in inner retinal cells have been observed in human retina as well as
various animal models of degeneration. Therefore, direct stimulation of RGCs can bypass
the anomalous inner retina circuitry. However, RGC firing patterns, both spatial and
temporal, are critical to vision. To be able to best replicate natural vision, bioelectronic-
based vision restoration therapy must have exquisite control of RGC firing, which requires
a thorough understanding of how electrical stimulation elicits responses from RGCs.
xviii
The physiological behavior of RGCs in normal and degenerate mice was measured
using whole-cell patch clamp recordings. Results presented in this thesis demonstrate
that thresholds in degenerate ganglion cells are significantly elevated and highly variable
compared to normal controls, despite having comparable chronaxie values and response
latencies. Degenerate RGCs display increases in baseline spontaneous firing not com-
monly seen in wildtype cells. Furthermore, there is a significant correlation between
higher spontaneous rate and reduced stimulation thresholds in degenerate cells; sponta-
neous activity is not correlated with thresholds in normal RGCs. These findings suggest
that the physiology of RGCs is altered as degeneration progresses and may be due to
intrinsic changes in ganglion cells as well as synaptic contributions from bipolar and
amacrine cells.
The aim of the work presented in this thesis is to assess physiological properties of
retinal ganglion cells in a model of retinal degeneration, and to determine how degenerate
RGC physiology influences the response to electrical stimulation. This study is part of
an ongoing effort toward development of an epiretinal prosthesis for sight restoration.
xix
Chapter 1
Introduction
Electrical stimulation of the central nervous system to restore sight has been studied
for over two centuries (Lorach et al., 2012; Marg, 1991; Weiland et al., 2011). The
earliest attempts at visual restoration utilized gross application of electrical energy to the
forehead of a blind patient (LeRoy, 1755). In the 1960s, Giles Brindley developed the
first chronically-implanted visual prosthesis by placing electrodes in the patient’s visual
cortex (Brindley and Lewin, 1968). Although these methods seem quite primitive, the
use of electrical current to evoke visual responses is still currently applied in modern
prostheses. Loss of vision can severely impair a person’s ability to perform simple daily
activities. In the United States, approximately 1 in 28 Americans over the age of 40
suffers from blindness or low vision (Congdon et al., 2004). Interestingly, almost half of
the brain is exclusively involved in visual processing in primates, more than any other
sensory modality (Baker, 2012; Van Essen, 2004; Walker, 2009). Vision enables us to
perceive elements of our environment through sight and is arguably the most essential of
our senses. This introduction provides a review of our visual system, clinical diseases of
the retina, and a potential means for restoring vision in blind patients.
1
1.1 Overview of the Visual System
Our eyes allow us to perform many different visual functions that are essential to our daily
lives. The eye consists of multiple components that are integral to visual function (Fig.
1.1). Light enters the eye through the cornea and pupil; the pupil acts as an aperture by
regulating the amount of light entering the eye. The lens focuses the light onto a thin
layer of tissue located at the back of the eye – the retina.
Figure 1.1: Cross-section of the human eye. Light enters the eye through the cornea and is
focused onto the retina by the lens. Visual information is transmitted along the optic nerve to
higher visual centers in the brain. (Image courtesy of Webvision, http://webvision.med.utah.
edu/.)
1.1.1 The Retina
The retina has the ability to detect light and transmit visual information to the brain.
This structure lines the back of the eye and is approximately 300 m thick (Alamouti
and Funk, 2003; Demirkaya et al., 2013; Hitzenberger et al., 2003). In primate retina, an
additional structure known as the fovea is located in the central region of the macula,
2
and it is in this region where we have our highest visual acuity. The foveal region is
cone-dominated, where the retinal circuitry typically consists of one-to-one connections
between cones and inner retinal neurons, and more than half of the primary visual cortex
(V1) is devoted to central vision (Adams and Horton, 2003). Patients suffering from
central vision loss have difficulty with visual activities requiring fine resolution, such as
reading, and also exhibit poor fixation stability, orientation discrimination, and shape
discrimination (Bedell et al., 2009; Mangione et al., 1998; Neelam et al., 2009; Wang
et al., 2002).
Figure 1.2: Cross-section of the human retina. The photoreceptors detect photons of light and
convert this light energy into electrical signals which are then passed onto inner retinal neurons.
Bipolar and amacrine cells synapse onto ganglion cells which then convey this visual information,
encoded in trains of action potentials, along their axons to higher visual centers in the brain.
(Image courtesy of Webvision, http://webvision.med.utah.edu/.)
The retina consists of three distinct neural layers – the outer nuclear layer (ONL), in-
ner nuclear layer (INL), and ganglion cell layer (GCL) (Fig. 1.2). The ONL is the neural
layer located in the outermost region of the retina (relative to the center of the eye) and
is comprised of the nuclei of light-sensitive photoreceptors – the rods and cones. Rods are
tube-like structures that are primarily used for scotopic vision (low-light conditions) while
3
cones are conical in shape and are sensitive for photopic vision (well-lit conditions). The
nuclei of rods and cones are located in the ONL while their outer segments extend into
the subretinal space towards the pigment epithelium. The rods and cones make synaptic
connections onto bipolar and horizontal cells whose somata are located in the INL. Hor-
izontal cells are interneurons that make lateral connections in the outer plexiform layer
(OPL) while bipolar cells are oriented vertically and convey signals from photoreceptors
to amacrine and ganglion cells. Amacrine somata are also part of the INL, but displaced
amacrine cell somata can be found in the retinal ganglion cell layer; amacrine cells make
lateral connections in the inner plexiform layer (IPL). Bipolar and amacrine cells synapse
onto ganglion cells which are the output neurons of the retina. The ganglion cell axons
bundle together at the optic disk to form the optic nerve which carries visual information,
encoded in action potentials, to higher visual centers.
1.2 Diseases of the Retina
Age-related macular degeneration (AMD) and retinitis pigmentosa (RP) are two of the
most common retinal degenerative diseases that result in blindness due to aberrant al-
terations that occur in the pigment epithelium or photoreceptors. Although the onset of
photoreceptor death can be triggered by numerous factors, the physical manifestation of
these diseases is ultimately the same – loss of vision due to rod and cone degeneration –
and there is currently no known cure for AMD or RP. It is essential to understand how
these diseases progress in order to develop effective treatment strategies. A portion of
this work has been published in Retinal prostheses: current clinical results and future
4
needs,Weiland JD, Cho AK, Humayun MS, Ophthalmology (2011). Copyright courtesy
of the American Academy of Ophthalmology.
1.2.1 Age-related Macular Degeneration
The prevalence of age-related macular degeneration (AMD) is highest among patients
older than 55 years (Schuman et al., 2009). AMD primarily affects cells in the foveal
region of the retina and subsequently leads to loss of central vision. There are mul-
tiple causes for AMD including genetic mutations in photoreceptors and accumulation
of drusen (Medeiros and Curcio, 2001; Schuman et al., 2009). In clinical cases of AMD,
morphometric analysis has shown a pronounced decrease in the number of remaining pho-
toreceptors with less than 20% preservation of rods and cones (Kim et al., 2002; Medeiros
and Curcio, 2001). Despite the considerable loss of outer retinal cells, the inner retina
appears to remain largely intact based on histological assessment of remaining nuclei. In
areas where the retinal pigment epithelium (RPE) appeared normal, there was no sig-
nificant change in cell density of the inner retina of AMD patients compared to control
subjects (Kim et al., 2002). Additionally, approximately 70% of nuclei in the ganglion cell
layer remained in these patients. In cases where there was no RPE, there was a significant
difference in cell density for all retinal layers, including the inner retina. This suggests
that the RPE may play a role in preservation of the inner retina. In another study, mor-
phometric analysis of human patients with nonexudative (NEAMD) and exudative AMD
(EXAMD) showed a 50% loss of ganglion cells in EXAMD but no significant difference
between normal subjects in NEAMD cases (Medeiros and Curcio, 2001). This finding
suggests that patients with NEAMD might be more receptive to retinal rescue strategies.
5
1.2.2 Retinitis Pigmentosa
Retinitis pigmentosa (RP) encompasses a wide array of hereditary genetic mutations that
result in a significant loss of photoreceptors and has an incidence of 1 of every 4000 live
births (Bunker et al., 1984; Hartong et al., 2006; Milam et al., 1998; Weiland et al., 2005).
This subset of retinal degeneration initially affects the rods but can ultimately lead to
complete vision loss. Although the prevalence of AMD is higher than that of RP, the
genetic mutations underlying RP are better-documented. This allows for the creation of
animal models that closely represent the human progression of RP triggered by a specific
mutation. In humans, a large body of genes causing RP has been identified but there
are many genetic defects that are still unknown (Hartong et al., 2006). More than half
of all RP cases are autosomal recessive traits, a third are autosomal dominant, and the
remainder are X-linked.
In humans, the retina undergoes significant morphological and physiological changes
as photoreceptor degeneration progresses (Marc et al., 2003). Morphological studies in hu-
man RP patients have shown varying degrees of preservation of inner retinal and ganglion
cells (Humayun et al., 1999; Milam et al., 1998; Santos et al., 1997). However, despite
the moderate preservation of remaining cells following degeneration, there is evidence
showing significant changes that occur in the structural properties of retinal cell types as
well as the connections they make with other cells (Marc et al., 2003). In areas where the
retina had undergone severe photoreceptor loss, rods exhibited neurite sprouting with
some of the processes extending as far as the ganglion cell layer (Milam et al., 1998).
This study also showed extensive neurite sprouting in horizontal and amacrine cells that
6
were not present in age-matched control subjects. In addition to changes in individual
cell structure, the inner retina exhibits significant rewiring as degeneration progresses. In
later stages of degeneration, retinal cells can even migrate to other remaining layers in
the retina (Kim et al., 2002).
1.3 Animal Models of Retinitis Pigmentosa
Morphometric data analyzed thus far in patients with AMD and RP has been collected
post-mortem. However, information about the physiology and functionality of retinal
cells during degeneration is currently not feasible in human patients so the use of animal
models is paramount for gaining a complete understanding of how retinal degeneration
progresses. Numerous animal models exist that mirror the progression of RP in humans
and this section focuses on several commonly used models of RP.
1.3.1 Rd1 Mouse
One of the most widely studied animal models of RP is the rd1 mouse model . The rd1
mutation is caused by a nonsense mutation on the gene encoding the phosphodiesterase
(PDE)- subunit which is part of the phototransduction cascade. This particular model
represents an autosomal recessive form of RP (arRP) and is phenotypically similar to the
human homolog of the disease (McLaughlin et al., 1993, 1995). Rd1 mice experience an
early onset of photoreceptor death, usually around eye opening, with almost complete
loss of photoreceptors by P21 (Chang et al., 2002). Since the mutation originates in
the rods, the cones initially survive the onset of degeneration (Lin et al., 2009), though
the number of surviving cones is highly variable (LaVail et al., 1997). Strettoi et al.
7
found retraction of bipolar cell dendrites, reduced bipolar cell density, and abnormal
development of horizontal cell processes in the rd1 model (Strettoi and Pignatelli, 2000).
In retinal ganglion cells (RGCs), about ∼ 50% had undersized dendritic arbors which
suggests incomplete dendritic development likely due to the early onset of this mutation
(Damiani et al., 2012).
A frequently observed component of RGC activity during degeneration is the devel-
opment of hyperactivity and network oscillations (Borowska et al., 2011; Margolis et al.,
2008; Menzler and Zeck, 2011; Stasheff, 2008; Yee et al., 2012). Borowska et al. saw
oscillatory behavior (∼ 10 Hz frequency) in 30% of RGCs. Patch clamp recordings in
retinal slices also revealed membrane oscillations in bipolar cells (Cameron et al., 2013).
The exact source of increased activity is still unclear but pharmacological application
has shown that oscillatory behavior originates in the inner retinal cells in the rd1 mouse
and is not intrinsic to RGCs (Margolis et al., 2008; Menzler and Zeck, 2011). Margolis
et al. demonstrated that, despite degeneration induced changes, RGCs maintain their
physiological characteristics.
1.3.2 Rd10 Mouse
The rd10 mutation is a naturally occurring mutation that originates on the gene encoding
the subunit of the PDE in rods, similar to the rd1 mutation. However, the rd10
mutation is caused by a missense mutation. This mutation alters a single nucleotide which
subsequently changes the encoded protein and can render the protein nonfunctional or
induce intrinsic chemical changes within the protein. Mutations occurring in the gene
encoding the PDE- subunit in rods account for approximately 4% of all clinical cases of
8
Figure 1.3: Fundus images and histologic sections of wildtype, rd1, and rd10 mouse retina. WT
retina images at 3 months are shown on the left. The middle column shows pigment patches in
the fundus image of a rd1 mouse retina as well as a pronounced thinning of the outer nuclear layer
at P21. The rd10 mouse retina (right column) undergoes a slower degeneration compared to the
rd1 mutation. Fundus images show retinal patches similar to those seen in rd1 mice, though not
as severe; there is also thinning and loss in the outer nuclear layer (Chang et al., 2002).
autosomal recessive RP in the United States and are the most common identified cause
of arRP (McLaughlin et al., 1995).
Compared to the rd1 mutation, the rd10 model exhibits a slower progression of retinal
degeneration. Onset of photoreceptor degeneration occurs around P18, after the retina
undergoes normal development, with peak photoreceptor death around P25 and almost
complete loss of photoreceptors by the end of 2 months (Chang et al., 2007; Gargini
et al., 2007; Pennesi et al., 2012). Although the rd10 model displays a slower rate of
degeneration, there is evidence that the inner retinal cells undergo pronounced structural
changes. As degeneration progresses, bipolar cells begin to make ectopic connections with
9
the remaining cones (Puthussery et al., 2009, 2010); these abnormal connections may be
due to the sudden loss of rod synaptic input to bipolar cells. Additionally, horizontal
and bipolar cells exhibit neurite sprouting, retraction and loss of dendrites, and cell body
migration, and there is pronounced gliosis which is caused by the migration of M¨ uller
cell bodies to the outer retina (Phillips et al., 2010). Despite these alterations, bipolar
cells maintain glutamate sensitivity and the laminar organization of bipolar and amacrine
cells remains largely preserved (Barhoum et al., 2008; Phillips et al., 2010; Puthussery
et al., 2009). Mazzoni et al. analyzed several morphological characteristics of RGCs in
rd10 mice and found that there were no significant differences in ganglion cell dendritic
structure and density between rd10 mice and normal controls up to 9 months of age.
Similar to observations in rd1 mice, Stasheff et al. found increased spontaneous
activity in rd10 mice. There was a period of increasing hyperactivity that mirrored the
progression of peak photoreceptor death followed by a gradual decrease in spontaneous
activity for older mice. Although there is ample evidence of morphological preservation
in ganglion cells for this model of RP, there is still a lack of physiological analyses of
RGCs to conclusively suggest that these cells function normally.
1.3.3 S334ter Rat
The S334ter rat is a transgenic model of autosomal dominant RP (adRP). This mutation
originates in the rods and results in a truncated rhodopsin (Green et al., 2000). Defects of
the rhodopsin gene account for approximately 25% of all human RP cases (Colley et al.,
1995; Hartong et al., 2006). The S334ter line-3 rat displays an aggressive degeneration
with onset around P11; in this line, the retinal circuitry does not develop fully before
10
onset of degeneration. Similar to other models of degeneration, the S334ter retina exhibits
retraction of bipolar and horizontal cell dendrites and a thinning of the ONL (Ray et al.,
2010). Since this mutation originates in the rods, cone degeneration progresses much
slower with onset around P90.
1.3.4 P23H Rat
The P23H rat model carries a mutation that affects the rhodopsin gene caused by a
codon change, thus altering the resulting protein (Lewin et al., 1998). This particular
mutation accounts for approximately 12% of adRP clinical cases (Dryja et al., 1990). Like
other degeneration models, there is shortening of the inner and outer segments as well
as retraction of bipolar and horizontal cell dendrites and thickening of M¨ uller glia (Lu
et al., 2013). Several studies analyzing RGC morphology found decreasing ganglion cell
density with age starting around 6 months with significant loss of RGCs at the end of 2
years (Garcia-Ayuso et al., 2010; Kolomiets et al., 2010).
In P23H rats aged 12-16 months, Kolomiets et al. observed increased spontaneous
activity in approximately 11% of RGCs. Electrical stimulation of these degenerate RGCs
generated short-latency responses similar to those seen in normal retina (Kolomiets et al.,
2010). Sekirnjak et al. also observed increased hyperactivity in ganglion cells, but this
aberrant activity was predominantly seen in OFF-type RGCs. Additionally, receptive
field sizes were reduced as a function of age. Pharmacological application of synaptic
blockers only eliminated ∼ 50% of this hyperactivity, which suggests that the source of
increased spontaneous firing is not solely due to alterations in retinal circuitry (Sekirnjak
et al., 2011).
11
1.4 Retinal Prosthesis
RP and AMD are two of the most common outer retinal degenerative diseases for which
there are currently no known cures. Retinal prostheses aim to restore functional vision
to blind patients by stimulating electrically the remaining retinal cells. The surrounding
environment is captured through external means and this information is delivered to an
implant (consisting of multiple electrodes) which subsequently delivers current to the
retina (Fig. 1.4).
Figure 1.4: Components of a retinal prosthesis. A camera captures visual images from the user’s
environment. This information is further processed and then transmitted wirelessly to the retinal
implant. In this image, the implant is located on the epiretinal surface. Based on the captured
visual information, the implant delivers a stimulation pattern through the multielectrode array.
(Image courtesy of Annual Review of Biomedical Engineering.)
For prosthetic retinal stimulation, there are generally two approaches – subretinal
and epiretinal stimulation. Subretinal prostheses consist of an implant placed on the
photoreceptor side; this type of implant aims to utilize the remaining retinal circuitry
12
to evoke phosphenes. Morphological studies in various models of retinal degeneration
have shown the formation of a glial seal in the subretinal space as well as structural
changes in some inner retinal cells (retraction of bipolar and horizontal cell dendrites).
Engaging these inner retinal neurons relies on significant preservation of inner retinal
circuitry which has been shown to undergo pronounced reorganization as degeneration
progresses. Retina Implant (Reutlingen, Germany) has developed a subretinal prosthesis
system (Alpha IMS) that does not require an external camera. Instead, the implant
consists of a 9 mm
2
chip containing 1500 microphotodiode-amplifier-electrode elements.
An extracorporal cable (connected to a subdermal cable near the subject’s ear) plugs
into a power supply worn by the user. Clinical trials for the Alpha IMS prosthesis have
shown that patients are able to perceive light, resolve motion, distinguish stripe patterns,
and even read letters when using the system; however, the ability to perform these tasks
was largely dependent on surgical placement of the microphotodiode array (subfoveal vs.
parafoveal or non-foveal regions) (Stingl et al., 2013a,b).
Epiretinal prostheses aim to restore vision by stimulating RGCs directly, thus by-
passing the inner retinal circuitry. There are several groups working toward epiretinal
prostheses. Intelligent Medical Implants (IMI) (Bonn, Germany) has developed a device
consisting of three components: a retina stimulator, visual interface, and pocket pro-
cessor. The array consists of 49 electrodes and is secured to the epiretinal surface with
retinal tacks. The retina stimulator receives information wirelessly from the extraocu-
lar transmitter sutured to the sclera. Out of 20 implanted subjects, 15 had verifiable
threshold levels. Clinical testing in these subjects has shown that it is possible to elicit
13
visual percepts with this device and that subjects could recognize simple shapes such as
a horizontal bar (Hornig, 2007).
EpiRet (Giessen, Germany) has developed a completely intraocular retinal prosthesis
(Walter). Their EPIRET 3 system was implanted in 6 blind RP subjects in 2006 for
28 days and consisted of 25 iridium oxide electrodes. Similar to the IMI device, the
EPIRET array was secured to the retina using retinal tacks. Phosphenes were elicited
in all 6 subjects with average perceptual thresholds around 15 nC/cm
2
. Patients were
followed for 5 months after explantation and showed only minimal membrane proliferation
around the retinal tack regions (Roessler et al., 2009).
Second Sight Medical Products (Sylmar, CA) is the third group that has developed
a retinal prosthesis that has received both EU and FDA approval. From here on, we
focus on current clinical trial results and continued improvements for the Second Sight
epiretinal prosthesis.
1.4.1 Epiretinal Prosthesis (Second Sight)
1.4.1.1 Description of the Device
The epiretinal prosthesis by SSMP consists of a chronically implanted stimulator and an
external camera and video processing unit (VPU). There are two versions of this device,
both made by Second Sight. The Argus I system was the first version of this epiretinal
prosthesis. The Argus I device had a 16 electrode array (4x4 grid); the platinum disk
electrodes were either 260 or 520 m in diameter. The current device, the Argus II
system, has 60 electrodes (6x10 grid) that are all 200 m in diameter.
14
Figure 1.5: The Argus I and Argus II epiretinal prosthesis (Second Sight Medical Products,
Inc.). A: A camera, mounted on a pair of sunglasses, captures images and sends this information
wirelessly to a visual processing unit (VPU) worn by the user. The glasses also carry an external
coil for transmitting signals to the implant. Fundus images of the implanted Argus I (B) and
Argus II (C ) epiretinal prostheses. The Argus I implant consists of 16 electrodes, either 260 or
520m in diameter. The Argus II implant has 60 electrodes, each 200m in diameter (All images
courtesy of Second Sight Medical Products, Inc.)
The camera, mounted on a pair of glasses, captures video of the visual environment
and sends this information to the VPU, worn by the user. The captured images are
converted to electronic signals which are then sent to a transmitter coil mounted on the
glasses (Fig. 1.5). An electronics case containing a receiving and transmitting coil is fixed
to the sclera outside the eye. The electronic signals from the transmitter coil on the glasses
are wirelessly received by the receiving coil in the episcleral case. This information is then
sent to the implanted electrode array which subsequently delivers electrical stimulation
through the various electrodes. The implanted electrode array is secured to the retina
15
using a custom retinal tack and is positioned above the macula, covering a retinal area
corresponding to 20
∘ in visual angle (Humayun et al., 2012).
1.4.1.2 Results from Clinical Trials
Clinical trials for both the Argus I and Argus II system have demonstrated improvements
in visual performance for a majority of patients implanted with these devices. Patients
implanted with the Argus II device were asked to perform several visually-guided tasks
using the system and, as a control, using only their residual vision. One of the tasks
was to detect the motion of a high-contrast moving bar on a computer screen. In this
study, 15 out of 28 (∼ 54%) subjects performed this task better with the system on than
when using their residual vision (Dorn et al., 2013; Humayun et al., 2012). In an object
localization task where subjects were asked to identify the location of a high-contrast
square on a screen, 96% of subjects were able to perform this task better with the system
on (Ahuja et al., 2011; Humayun et al., 2012). Subjects were also able to identify letters
with varying degrees of success depending on the letters presented (da Cruz et al., 2013).
These findings suggest that electrical stimulation of the retina in blind patients can still
provide spatial information despite the effects of degeneration.
In one Argus I subject, Nanduri et al. compared the effects of frequency and amplitude
modulation. Increasing stimulation frequency was associated with an increase in perceived
brightness, not percept size. However, increases in stimulation amplitude increased both
the perceived brightness and size of the percept (Nanduri et al., 2012). These results
suggest the dynamic range for eliciting visual percepts may be greater when varying
stimulation frequency rather than altering stimulus amplitude.
16
1.4.1.3 Clinical Issues
Although a majority of subjects were able to perform various visually-guided tasks better
when using the Argus II system, only ∼ 55% of enabled electrodes across all subjects
were within electrochemical safety limits (1 mC/cm
2
). This may be due to differences
in electrode-retina distance, health of the retina under implant site, or current levels
that may have exceeded neural damage or electrochemical safety limits (McCreery et al.,
1990; Merrill et al., 2005; Shannon, 1992). Studies of perceptual thresholds in Argus I
patients found that the main factor influencing thresholds was the distance between the
electrodes and the retina (de Balthasar et al., 2008; Mahadevappa et al., 2005). Electrode
size, impedance, and retinal thickness did not significantly contribute to variations in
perceptual threshold.
Prolonged stimulation in Argus II patients resulted in fading of percepts that were
described as less bright and poorly localized (Perez Fornos et al., 2012). In this study,
patients reported a bright, well-localized percept at the onset of stimulation; however,
this brightness decreased rapidly with time which suggests desensitization of the retina.
These findings suggest that proximity of the electrode array to the retinal surface is
critical for lowering perceptual thresholds (and subsequently increasing the number of
available electrodes), and that percept fading may be diminished through improvements
to stimulation strategies.
17
1.5 Electrical Stimulation of the Retina
Clinical trials of retinal prostheses have provided valuable information about patients’
visual perceptions in response to electrical stimulation. Due to the nature of these tri-
als, the patients’ perceptions in response to stimuli (brightness, shape) can be assessed
qualitatively; however, an understanding of the physiology that generates these percep-
tions is not currently feasible. Analysis of the retina’s response to electrical stimulation
using both in vivo and in vitro methods can provide considerable information that could
not otherwise be ascertained. Understanding the physiology of degenerate retina and its
response to stimulation will enable continued improvements for future prostheses.
1.5.1 Retinal Ganglion Cell Response
The retinal ganglion cells are the output neurons of the retina, sending information,
encoded in trains of action potentials, to higher visual centers in the brain. Retinal
stimulation studies generally classify the response of RGCs based on origin of activation
– direct activation of RGCs or indirect activation through stimulation of second order
neurons. Various recording techniques – single-unit, multi-unit, fluorescence imaging –
can provide important information about the temporal and spatial characteristics of the
RGC response to electrical stimulation.
1.5.1.1 Direct Ganglion Cell Activation
Electrical stimulation of the retina typically activates a large region encompassing many
retinal cells, including RGCs, bipolar cells, amacrine cells, and even photoreceptors. In
vitro studies of isolated retina have used pharmacological techniques to distinguish direct
18
RGC responses from indirect responses. The most common feature of direct activation
of RGCs is the short latency of the elicited response. The range of direct RGC response
latencies varies between studies but is usually within 5 ms of stimulus onset and time-
locked to the stimulus (Freeman et al., 2011; Fried et al., 2006; Jensen and Rizzo, 2008;
Sekirnjak et al., 2006, 2008; Tsai et al., 2009). Most of these studies position the stimulat-
ing electrodes on the epiretinal side (ganglion cell layer); however, subretinal stimulation
also elicits short-latency responses in RGCs (Tsai et al., 2009).
Although direct activation of RGCs usually elicits a single spike (one-to-one), Sekirn-
jak et al. observed the presence of a RGC spike doublet in response to stimulation.
Extracellular recordings from rat, guinea pig, and monkey retina were made using a multi-
electrode array. Short-latency spikes were typically < 1 ms while long-latency spikes were
classified as responses occurring 5-10 ms after stimulus onset. This study found that long-
latency spikes always occurred in conjunction with a short-latency spike, suggesting that
these responses were actually part of a paired response to stimulation. Application of
pharmacological blockers did not abolish these long-latency responses, which implies di-
rect activation of RGCs for both short- and long-latency spikes. Similarly, multi-electrode
recordings from salamander retina revealed a series of ∼ 5 action potentials in response to
electrical stimulation; these spikes persisted after the application CdCl
2
, which suggests
these were a result of direct RGC stimulation.
The axon initial segment (AIS) is largely believed to be the site of action potential
initiation in RGCs (Carras et al., 1992; Rattay, 1999). Immunohistochemical analysis of
the retina has revealed the existence of a dense band of sodium channels near the AIS. The
distribution and specific types of voltage-dependent sodium channels vary between neural
19
segments (axon, soma, dendrites) but the presence of this sodium channel band at the
initial segment has been well-documented in many mammalian central neurons (Leterrier
et al., 2010, 2011; Vacher et al., 2008). Threshold mapping studies found that electrical
stimulation above this sodium channel band yielded the lowest thresholds (Fried et al.,
2009). Furthermore, modeling studies incorporating this sodium channel band found that
the length and distance of the band from the soma could influence sensitivity to electrical
stimulation. Contrary to these findings, Abramian et al. have suggested that the entirety
of the RGC axon is the site of action potential initiation. Displacement of the stimulating
electrode > 1 mm from the soma but along the axon path was able to elicit a response at
the same threshold as stimulation at the soma. However, it should be noted that these
experiments were performed in rabbit retina which exhibits intraretinal myelination of
RGC axons (Morcos and Chan-Ling, 2000; Reichenbach et al., 1988). Though these
findings do not conclusively establish the entire axon as the site of spike initiation, they
demonstrate that axonal stimulation thresholds may be similar to thresholds for direct
RGC soma activation.
Retinal ganglion cells are able to respond to high rates of stimulation that cannot
be followed when inner retinal neurons initiate synaptically-driven retinal ganglion cell
activity. (Sekirnjak et al., 2006; Tsai et al., 2009). The stimulation frequencies that
RGCs can reliably respond to can range from 200-600 Hz. Tsai et al. found that as
the stimulating frequency was increased, the number of long-latency spikes, indicative of
indirect RGC stimulation, decreased while short-latency spikes persisted; in this study,
RGCs were activated using subretinal stimulation. Ganglion cells may be able to respond
reliably to high rates of stimulation but the upper frequency limit varies for different
20
RGC types. Cai et al. found that brisk-transient ganglion cells could follow stimulation
frequencies of 600 Hz while other RGC classes could not follow even 200 Hz stimulation.
Figure 1.6: A: Ganglion cells are able to respond to high rates of electrical stimulation (0.2 ms
pulses) with single action potentials (asterisk) (Freeman et al., 2011). B: Elicitation of short-
latency RGC responses using subretinal stimulation. 10 overlaid traces of an OFF RGC to a
125 m, 0.1 ms biphasic pulse show elicited spikes are time-locked to stimulus onset (Tsai et al.,
2009).
Desensitization of the indirect response to electrical stimulation has been documented
in various studies (Cai et al., 2011; Sekirnjak et al., 2006; Tsai et al., 2009). Though RGCs
can respond to high frequency stimulation, they can also exhibit desensitization. Freeman
et al. saw a decrease in the total number of elicited spikes as the stimulating frequency
was increased from 2-16 Hz. The RGC response consisted of both short- and long-latency
spikes. As the stimulation frequency was increased, the total number of elicited spikes
also decreased; this persisted in the presence of synaptic blockers, which suggests that
the multiple spikes elicited by electrical stimulation originated in RGCs. A possible
mechanism for RGC desensitization may be the decline of voltage-gated sodium current.
Tsai et al. observed a decrease in the inward sodium current amplitude as stimulation
21
frequency was increased. The desensitization of both RGCs and inner retinal neurons
may mirror the perception of ‘fading’ experienced by prosthesis patients to prolonged
stimulation, although the origins and mechanisms for this phenomenon are still unclear.
1.5.1.2 Indirect Ganglion Cell Activation
Indirect activation of RGCs occurs through the stimulation of inner retinal neurons that
subsequently release synaptic neurotransmitter onto RGC dendrites (Jensen and Rizzo,
2008; Margalit et al., 2011; Sekirnjak et al., 2008). Although direct RGC activation
typically elicits a single spike per stimulus pulse, indirect activation usually generates
a burst of spikes. Unlike the response onset of RGCs to direct activation, responses
that engage the inner retina are not necessarily time-locked to the stimulus and exhibit
considerable ‘jitter’ in their response latencies (Jensen and Rizzo, 2008; Lee et al., 2013;
Tsai et al., 2009).
Response latencies for indirect activation of RGCs are longer than those for direct
activation. Definitions for long-latency spikes are variable between studies but generally
describe elicited responses that peak tens of milliseconds after stimulus onset (Freeman
et al., 2011). Though indirect responses occur through the activation of inner retinal
neurons (bipolar cells, amacrine cells, photoreceptors), they are typically paired with
a short-latency RGC response, which suggests that retinal stimulation simultaneously
activates ganglion cells and the inner retinal circuitry. Lee et al. investigated the effect
of pulse duration and stimulus amplitude on the RGC response to subretinal stimulation.
This study found that pulse duration can influence both the onset latency and total
number of spikes for indirect responses.
22
Figure 1.7: A: A 1 ms biphasic pulse elicited a single spike through direct activation of the
ganglion cell as well as a burst of spikes (asterisks) through indirect activation of the RGC. B:
Indirect activation of ganglion cells typically elicits a burst of spikes. Due to desensitization, the
number of spikes elicited per burst decreases substantially after the first pulse. The number of
elicited spikes is shown above the response (Freeman et al., 2011).
Studies have shown that RGCs can reliably respond to high rates of stimulation while
inner retinal neurons are limited to lower stimulation frequencies. This may be due
to differences in the biophysical mechanisms of inner retinal cells compared to RGCs.
Ganglion cells can exhibit desensitization to high frequency stimulation; the mechanism
for this may be a decrease in the voltage-gated sodium current (Tsai et al., 2009). Freeman
et al. used a model of a bipolar cell to evaluate the potential mechanisms that contribute
to stimulation frequency desensitization. The modeling results suggested that calcium
channel dynamics that mediate synaptic transmitter release were responsible for setting
the upper limit for stimulation frequencies in bipolar cells. Although inner retinal neurons
may not be able to follow high rates of stimulation, activation of these cells yields relatively
focal stimulation of the retina since bipolar cells make vertical connections onto ganglion
23
cells. Thus, indirect activation of RGCs would result in better spatial resolution while
direct activation of RGCs would yield better temporal resolution (Freeman et al., 2011).
Freeman et al. applied sinusoidal stimulation to purposefully engage distinct retinal cell
types. This study found that stimulating at different frequencies could selectively activate
photoreceptors (5 Hz), bipolar cells (25 Hz), and ganglion cells (100 Hz); furthermore,
frequencies < 25 Hz avoided axonal stimulation.
1.6 Goal of this Thesis
The aim of the work presented in this thesis is to assess physiological properties of retinal
ganglion cells in a model of retinal degeneration, and to determine how degenerate RGC
physiology influences the response to electrical stimulation. This study is part of an
ongoing effort toward development of an epiretinal prosthesis for sight restoration.
An increased understanding of the intrinsic behavior of degenerate retinal cells is
essential to improving current vision rescue strategies. Numerous studies using various
animal models of retinal degeneration have demonstrated that the physiology of retinal
cells is altered to varying degrees (Chen et al., 2005; Kolomiets et al., 2010; Margolis
and Detwiler, 2011; Margolis et al., 2008; Sekirnjak et al., 2011; Stasheff, 2008; Stasheff
et al., 2011). Despite aberrant physiological changes after photoreceptor death, there is
evidence showing relative morphological preservation of the remaining retinal cells (Lin
and Peng, 2013; Mazzoni et al., 2008; Medeiros and Curcio, 2001; Weiland et al., 2005),
providing a potential means for signal transmission to higher visual centers. The methods
for retinal stimulation and recording from individual RGCs are described in Chapter 2.
24
To determine the relative excitability of degenerate RGCs to electrical stimulation, the
temporal response properties in normal and degenerate retina were compared in Chapter
3. Chapter 4 investigates changes in intrinsic ganglion cell physiology in a mouse model
of degeneration, and evaluates the influence of certain intrinsic properties on stimulation
thresholds. In Chapter 5, a realistic model that reproduces various response properties
of a RGC is used predict the response to a novel stimulus. A summary of key findings
and future work are outlined in Chapter 6.
25
Chapter 2
Experimental Design and Methods
Retinal stimulation studies employ a variety of experimental configurations that typi-
cally require two elements – a means for delivering current to the retina and method
for recording neural activity. The specific stimulation and recording conditions largely
depend on the nature of the information one wishes to acquire through these studies.
This chapter details the experimental methods for recording the response from individual
retinal ganglion cells (RGCs) to external electrical stimulation.
2.1 Single-cell Recordings
There are various techniques for achieving single-unit recordings, including the commonly
used patch clamp technique. The patch clamp technique was first developed by Neher
and Sakmann (Neher and Sakmann, 1976); this recording method allowed them to study
the activity of individual acetylcholine-activated channels in frog. For stimulation studies
measuring the spiking behavior of retinal cells, single-cell recordings are made using either
cell-attached or whole-cell patch clamp techniques. Both of these recording modalities
allow the user to record activity from individual neurons. The patch pipette is advanced
26
toward the neuron until it comes in contact with the cell’s membrane and then gentle
suction is applied to form a high-resistance seal (gigaseal) with the membrane. The patch
of membrane under the pipette is ruptured to obtain whole-cell recordings while in cell-
attached recordings, the cell’s membrane remains intact; these two techniques are further
described in the following sections.
Figure 2.1: Diagram of different patch clamp configurations. Cell-attached patch clamp record-
ings leave the cell membrane intact while forming a gigaseal with the membrane. For whole-cell
recordings, a gigseal is formed and then suction is applied until the patch of membrane under the
pipette is ruptured, allowing access to the interior environment of the cell (Ogden, 1987).
2.1.1 Cell-attached Patch Clamp
One method of recording from individual neurons is using cell-attached patch clamping.
This type of recording is commonly used to study single channel currents; however, it
can also be used to study the activation of several channels, depending on the diameter
of the pipette tip. The approach is similar to the whole-cell recording method except the
27
cell membrane is not ruptured and thus remains intact, allowing the user to record extra-
cellular changes in current across the cell membrane. A disadvantage of this technique is
that it does not allow for direct measurement of the cell’s membrane potential or to hold
the cell at a given potential.
2.1.2 Whole-cell Patch Clamp
The majority of ganglion cell recordings were performed using whole-cell patch clamp.
Whole-cell recordings are achieved by forming a high-resistance seal with the cell’s mem-
brane and then rupturing the section of membrane under the pipette, thus allowing access
to the internal environment of the cell. This technique allows the user access to the in-
terior of the cell as well as the ability to manually hold the cell at various potentials.
Borosilicate glass (fire polished; OD 1.2 mm, ID 0.69 mm) was used for patch pipettes
with tip impedances ranging from 5-10 MΩ. A Sutter Instruments micropipette puller
was used to pull pipettes with impedances within this range. Signals were amplified us-
ing an Axopatch 200B amplifier and were acquired through an ITC-16 interface using
software written in Igor Pro (Wavemetrics) (Okawa et al., 2010). The pipette internal
solution contained (in mM): 125 K-Aspartate, 10 KCl, 10 HEPES, 5 NMG-HEDTA, 0.5
CaCl
2
, 1 ATP-Mg, 0.2 GTP-Mg with pH of ∼ 7.3 and osmolarity of ∼ 280 mOsm; mea-
sured liquid junction potential was approximately -10 mV. AgCl wire was used as the
active electrode in the pipette solution as well as the return electrode in the bath (using
a NaCl agar bridge). The patch electrode and return electrode were chloridized in NaCl
solution before each experiment. A glass pipette was used to carefully tear a section of
the inner limiting membrane (ILM) to expose several RGC bodies for electrophysiological
28
recordings. The ILM of rd10 retinas was more difficult to remove so a weighted horseshoe
was used to secure the retina during tearing. Prior to immersing the electrode in the ex-
ternal bath solution, positive pressure was applied to prevent any debris from occluding
the pipette tip. Once the pipette tip was in close contact with the cell membrane, the
pressure in the pipette was reversed to create a high resistance seal (> 1 GΩ, commonly
referred to as a gigaseal) with the membrane. Gentle suction was applied until the patch
of membrane under the pipette was ruptured. Since whole-cell recording brings the inter-
nal solution in contact with the interior contents of the cell, recordings were made 5-10
minutes after breaking into the cell to allow the internal solution of the cell to equilibrate
with the pipette solution.
Figure 2.2: Image of a whole-cell patch clamp recording from a ganglion cell in mouse retina
under infrared illumination. The stimulating electrode used in these studies is shown to the left
of the patch pipette. The inner limiting membrane was torn to expose several RGC bodies for
recording.
2.1.3 Isolated Retina Preparation
All experiments were conducted in an in vitro isolated mouse retina preparation. Wild-
type (WT) and rd10 mice were euthanized in accordance with protocols approved by the
29
IACUC of the University of Southern California. Mice were euthanized using cervical
dislocation, then both eyes were enucleated from the animal and a small slit was made in
the central region of the cornea. A cut perpendicular to the corneal slit was made to the
edge of the conjunctiva. The eye was rotated and then cut along the conjunctiva around
the perimeter of the eye resulting in two halves – one consisting of the cornea/lens and the
other consisting of the retina/sclera, commonly referred to as an eyecup. The two halves
were pulled apart and the residual vitreous humor was plucked from the eyecup with fine
forceps. The eyecup was hemisected close to the optic disk. Taking one eyecup half, the
frayed edges were trimmed, resulting in an approximately 2 mm square retinal section.
Using fine forceps, the retina was gently separated from the retinal pigment epithelium,
choroid, and sclera. The retina was then mounted onto a piece of Whatman Anodisc
membrane filter with the photoreceptor side facing down. The filter and retina were then
secured onto the bottom of the recording dish and placed in the recording chamber.
2.1.4 Preliminary Data - Whole-cell vs. Cell-Attached
Initially, recordings from RGCs were performed using the whole-cell method. However,
since the pipette internal solution dialyzes with the cell’s contents over time, we wanted to
investigate whether differences in recording configurations could affect the cell’s response
to electrical stimulation. We measured thresholds for a 500 s biphasic square pulse
in 5 WT ganglion cells using both cell-attached and whole-cell recording configurations.
A gigaseal was formed between the cell membrane and pipette tip, and stimuli were
delivered to determine threshold in the cell-attached configuration. Then, suction was
applied to that cell until the membrane was ruptured (whole-cell mode) and threshold
30
was subsequently measured. The comparisons between the two recording modalities are
shown in Fig. 2.3.
Figure 2.3: Comparison of threshold values for two patch clamp configurations. Representative
traces of elicited responses to electrical stimulation using a 500 s biphasic (cathodic-first) pulse
are shown for cell-attached (left) and whole-cell (right) recording modes. Elicited responses are
denoted by asterisks. The table of preliminary data shows stimulation thresholds for five cells in
both cell-attached and whole-cell modes; thresholds were not significantly different between these
two recording configurations (p = 0.1027).
Based on the preliminary data, there does not appear to be a significant difference in
stimulation threshold between the two recording configurations. Although threshold was
generally lower in cell-attached mode, these differences were not statistically significant
(p = 0.1027). An advantage of using the whole-cell recording technique is that it allows
us direct access to the interior of the cell so we can measure the cell’s response to a step
31
current/voltage. Additionally, using a fluorescent dye in the internal solution allows us to
fill the cell during recording so we can visualize the cell’s morphology; this would not be
feasible with cell-attached recordings. Since thresholds did not appear to be significantly
affected by the recording modality, all recordings from hereon were performed using whole-
cell patch clamp.
2.2 Extracellular Electrical Stimulation
Current retinal prostheses employ the use of electrical energy to elicit visual percepts.
Stimulation of the retina is achieved by delivering current through an array of electrodes
placed in close proximity to the retinal tissue. There are many parameters that can
influence the retinal response to stimulation, including electrode material and size, dis-
tance between electrode and tissue, and stimulus parameters. Here, we are interested
in understanding the response of RGCs to epiretinal electrical stimulation using a single
electrode.
2.2.1 Stimulating Electrode
The stimulating electrode used was a monopolar Platinum Iridium (Pt-Ir) disk electrode
with a 75 m diameter. The custom electrode was designed to accommodate the slope
of the recording chamber edges as well as the small working distance (∼ 2 mm) under the
40x water-immersion objective (Fig. 2.4).
Platinum iridium is the electrode material currently used in the Argus I and Argus II
retinal prostheses and is also widely used in a number of neural stimulation applications.
32
Figure 2.4: Schematic of the stimulating electrode. The custom electrode was designed so that
the electrode surface was parallel to the surface of the whole-mounted retina. The dimensions
were specified to fit within the working distance of the objective. The disk electrode material was
platinum iridium and was completely insulated except for the electrode tip (FHC, Inc., Bowdoin,
ME).
A good electrode material in an implantable device should be biocompatible, mechani-
cally stable, behave consistently during prolonged use, and sustain high current densities.
Platinum by itself is soft so creating an alloy with iridium bolsters its mechanical prop-
erties and improves hardness. However, the electrical properties are akin to platinum,
since the ratio is 90:10 Pt:Ir.
During electrical stimulation, charge injection from a metal electrode into physiolog-
ical media occurs via two mechanisms: charging/discharging of the double layer capac-
itance and Faradaic reactions occurring at the surface of the electrode (Merrill et al.,
2005). During Faradaic reactions, charge is transferred between the electrode and the
electrolyte by reduction and oxidation. An injection of negative current through the elec-
trode will cause reduction at the electrode-electrolyte interface (addition of an electron)
while a positive current will cause oxidation (removal of an electron). The double layer
33
capacitance is typically 10-20 F/cm
2
. Charge injection through hydrogen atom plating,
described by the following equation,
Pt + H+ + e-
Pt-H (2.1)
can increase the injectable charge of platinum electrodes (up to 10x more charge
injection capacity).
2.2.2 Stimulus Parameters
Studies have shown that some classes of RGCs can follow stimulation frequencies as
high as 300 Hz (Tsai et al., 2009). However, such high frequencies can also cause other
retinal cells to become desensitized to further stimuli. Unless noted otherwise, stimulation
frequency for experiments was maintained at 10 Hz (100 ms interpulse interval). Multi-
Channel Systems (Germany) stimulus software was used to deliver square pulses through
the external electrode. Charge-balanced biphasic square pulses (cathodic phase first) were
delivered for a range of pulse durations. Stimulus amplitudes were randomized within
pulsewidth groups to avoid conditioning of the RGC response.
2.3 Recording Configuration
2.3.1 Electrophysiology Setup for Whole-cell Patch Clamp Recordings
The retina was mounted in the recording chamber with the ganglion cell side facing
up. The tissue was superfused with heated (35− 37
∘ C ) and oxygenated (95% O
2
/5%
CO
2
) bicarbonate-buffered Ames’ medium (Sigma-Aldrich, Saint Louis, MO) at a rate
34
of 4-5 ml/min. The retina was visualized using two objectives – a Nikon 10x objective
(0.30 NA) and a Nikon water-immersion 40x objective (0.75 NA) – under infrared (IR)
illumination. Prior to recording, each cell was identified and the length of the soma was
measured along both its major and minor axes using Nikon NIS-Elements Microscope
Imaging Software. The position of the stimulating electrode relative to the targeted cell
was enabled by initializing the cell’s location as the origin using the micromanipulator
(Sutter Instruments, Novato, CA) holding the electrode; the electrode-cell distance in
any direction was then subsequently recorded from the micromanipulator.
Figure 2.5: Schematic cross-section of the recording setup. The retina was whole-mounted with
the external electrode positioned above the ganglion cell layer and the ground electrode on the
photoreceptor side; whole-cell recordings from RGCs were made using glass pipettes. (Image not
to scale.)
2.3.2 Custom Recording Chamber and Placement of Ground
A custom recording chamber was constructed to enable placement of the ground directly
behind the retina. This dish consists of two chambers connected by a 1 mm circular hole
in the center of the dish (Fig. 2.6). The upper chamber holds the mounted retina as
35
well as the recording electrode (pipette) and the stimulating electrode. An angled tunnel
drilled along the side of the dish allows for placement of the ground (and agar bridge)
underneath the retina, in the lower level of the custom chamber. A ring of vacuum grease
that was slightly larger than the entire mounted retina was made around the 1 mm hole,
and the Whatman filter with the whole-mounted retina was centered over this opening.
The grease was used to serve as an insulator to maximize the amount of current flowing
through the retina to ground (in the lower chamber). The placement of ground in the
lower chamber was chosen also to maximize the current flow through the retina; a ground
placed in the upper chamber would allow current to shunt through the solution since it
is the path of least resistance, and this in turn would subsequently raise thresholds. This
ground placement best simulates current clinical devices which have the return electrode
positioned outside the eye.
Figure 2.6: Recording chamber for physiological experiments. The custom recording dish is
shown on the left. A 1 mm hole in the center of the dish connects the upper and lower chambers.
The ground electrode was inserted through the tunnel connected to the lower chamber of the dish.
The placement of the custom dish and objective are shown on the right.
36
2.4 Rd10 Mouse
The work presented in this dissertation focused on investigating the physiological differ-
ences between normal and retinal degenerate mice. The model of retinal degeneration we
chose to study was the rd10 mouse. The rd10 mutation represents an autosomal recessive
form of RP (arRP) and has been shown to closely mirror the progression of the disease
in patients afflicted with the same mutation (Mazzoni et al., 2008). In this model of
degeneration, photoreceptor death peaks around P21 with almost complete loss of rods
and cones by the end of two months.
2.4.1 Description of the Mutation
The rd10 mutation is caused by a missense mutation on the gene encoding for the phos-
phodiesterase subunit (PDE- ), an integral component of the phototransduction cas-
cade (Chang et al., 2007). A missense mutation is a point mutation that alters a single
nucleotide. This codon will code for a different amino acid and subsequently, a different
protein. This can cause the protein to be nonfunctional or can induce slight changes to
the protein’s chemical properties. On the other hand, the rd1 mouse, which also has a
mutation encoding for the PDE- subunit, is an example of a nonsense mutation. This
is a point mutation that prematurely initiates a stop codon which ultimately results in a
truncated and non-functional protein. In the rd10 mutation, the source of the alteration
is in a base located on Chromosome 5, Exon 13, position 1678. This changes codon 560
from CGC (arginine) to TGC (cysteine) and results in the loss of the CfoI site. Specifi-
cally, this causes a change in the base at position 1678 from C to T, subsequently changing
37
the encoded amino acid. The CfoI site that is lost in the rd10 mutation is identified by
5’. . . GCGC. . . 3’ and 3’. . . CGCG. . . 5’ (Chang et al., 2007). The GCGC sequence is not
present in the rd10 mutation because the C base gets substituted by a T base, so the
altered sequence is GTGC (not the CfoI site).
2.4.2 Genotyping the Rd10 Mutation
To differentiate between mice that were heterozygous for rd10, homozygous for rd10, or
did not carry the rd10 mutation at all (WT), we used the following genotyping methods.
Tails of young mice (3-4 weeks old) were clipped (0.5 cm length) and digested with a
solution containing DirectPCR lysis reagent and Proteinase K. Tail tips were digested
overnight in a hybridization oven (55
∘ C). The primers used were: rd10 F (CTTTC-
TATTCTCTGTCAGCAAAGC) and rd10 R (CATGAGTAGGGTAAACATGGTCTG).
The PCR (polymerase chain reaction) amplification (adapted from Mazzoni2008) was
performed for 45 cycles by (1) denaturation at 94
∘ C for 3 min, (2) annealing at 94
∘ C,
60
∘ C, and 72
∘ C for 1 min, 30 sec, and 1 min, respectively, and (3) elongation at 72
∘ C for
7 minutes.
The product was purified and digested with theHhaI enzyme (New England Biolabs)
which codes for the restriction site sequence 5’. . . GCGC. . . 3’ and cleaves at the site
between the secondary G and C bases. This CfoI site (5’. . . GCGC. . . 3’) is present in
WT mice so this sequence will subsequently be cleaved when digested with the HhaI
enzyme; however, this restriction site is not present in the rd10 mutation. The amplified
PCR products were incubated in the HhaI enzyme for 2 hours at 37
∘ C. The resulting
product was run on a 3% agarose gel in order to separate the short DNA sequences. Mice
38
Figure 2.7: Image of a gel identifying wildtype, rd10+/-, and rd10+/+ mice. HhaI restriction
enzyme digests were run on a 3% agarose gel stained with ethidium bromide (EtBr. The DNA
ladder is a standard 1Kbp ladder. Rd10 mice homozygous for the mutation were identified by
a single band at 97 bp, WT mice had two bands at 54 and 43 bp, and rd10 heterozygous mice
(rd10+/-) had three bands at 43, 54, and 97 bp. The bands for 43 and 54 bp could not be
differentiated but were clearly distinct from the bands at 97 bp.
carrying the rd10 mutation (homozygous) were identified by a single band of 97 bp (base
pairs). WT mice had two bands at 54 bp and 43 bp. Rd10+/- (heterozygous) had bands
at 97 bp, 54 bp, and 43 bp (Fig. 2.7). A DNA HyLadder (1 Kbp) was used as a reference
for deposited bands (Denville Scientific).
2.4.3 Histology
The rd10 mutation exhibits peak photoreceptor death around P21 with almost complete
loss of photoreceptors by the end of two months. To ensure that the mice used were
photoreceptor degenerate, we performed hematoxylin and eosin (H&E) stains on retinae
of rd10 mice aged 1 month, 2 months, and 6 months. We also performed H&E staining
on WT mice aged 3 months for comparison (Fig. 2.8).
39
Figure 2.8: Histologic sections of normal and rd10 mouse retina. Top H&E staining of the
central retina for wildtype retina and rd10 retina at 3 ages (1 month, 2 months, 6 months).
Bottom Histologic sections for peripheral retina for wildtype, and rd10 retina (1 month, 2 months,
6 months).
Compared to normal retina, there is a clear thinning of the outer nuclear layer at
all three ages of rd10 mice. At one month, there appears to be a greater population of
surviving outer nuclei in the peripheral retina compared to the central retina. However,
as degeneration progresses into later stages, there is a clear loss of outer retinal neurons
in both the periphery and central retina. Additionally, the population of inner retinal
40
cells in the periphery appears to decrease with age while the central retina exhibits a
slower loss of these inner retinal cells.
2.4.4 Cross-breeding with YFP-expressing Mice
Rd10 mice were cross-bred with transgenic WT YFP-expressing mice (yellow fluorescent
protein) to document the paired physiology and morphology of degenerate RGCs. A line
of transgenic mice expressing spectral variants of GFP (green fluorescent protein) were
developed by Josh Sanes et. al. (Feng et al., 2000). We chose the Thy1-YFP-H line
since it sparsely labeled RGCs (< 10%) and would allow for visualization of an individual
cell’s full dendritic morphology. A higher percentage of labeling may lead to overlapping
dendritic fields of labeled cells.
2.4.5 Genotyping Rd10/YFP Mice
The procedure for digesting tail tips is similar to methods mentioned above. The YFP
primers used for PCR analysis were: yfp F (TCTGAGTGGCAAAGGACCTTAGG) and
yfp R (CGCTGAACTTGTGGCCGTTTACG) (Feng et al., 2000). The PCR amplifica-
tion was performed for 35 cycles by (1) denaturation at 94
∘ C for 3 min, (2) annealing
at 94
∘ C, 62
∘ C, and 72
∘ C for 30 sec, 1 min, and 1 min, respectively, and (3) elongation
at 72
∘ C for 2 minutes. The amplified product was run on a 1.5% agarose gel and mice
carrying the YFP gene were identified by a single band at 300 bp (Fig. 2.9). A DNA
HyLadder (1 Kbp) was used as a reference for deposited bands (Denville Scientific).
41
Figure 2.9: Image of a gel identifying mice expressing YFP in RGCs. PCR amplified products
were run on a 1.5% agarose gel stained with ethidium bromide. The DNA ladder is a standard
1Kbp ladder. Mice positive for YFP expression were identified by a single band at 300 bp.
2.5 Data Analysis
Various ganglion cell parameters were recorded, including stimulation threshold, resting
membrane potential, spontaneous rate, presence/absence of rebound excitation, and many
others. Since the measurement and calculation of several of these parameters was specific
to different aims in this thesis, the analyses for these parameters will be discussed in detail
in the appropriate subsequent chapters. This section will address threshold calculations
and statistical analyses used for all experiments, not specific to an individual aim.
2.5.1 Threshold Calculation
Threshold for a given cell was defined as the current level to elicit an action potential
in at least 50% of delivered pulses. The probability of eliciting a spike was defined as
the number of total elicited spikes measured divided by the total number of delivered
stimulus pulses. A dose-response curve (for a single pulse duration) was constructed
for each cell by plotting the elicited spike probability for the range of current amplitudes
delivered. Fig. 2.10 shows the dose-response curves for three different RGCs. The current
42
amplitude corresponding to an elicited spike probability of 50% (dashed line) was defined
as the cell’s threshold. Curves were fit using the equation
=

+
-xc
(2.2)
where p is the spike probability, x is the range of current amplitudes, and a, b, and c
are constants.
Figure 2.10: Dose response curves for three different RGCs. Spike probability was measured as
stimulation amplitude was varied. Threshold was defined as the current amplitude that elicited
a response in at least 50% of trials (dashed line).
2.5.2 Statistical Analysis
When comparing individual parameters between WT and degenerate RGCs, a Student’s
t-test was typically employed to determine whether there were statistically significant
group differences. However, for more complicated analyses involving the influence of
several parameters, an analysis software called SAS was used.
43
2.5.2.1 SAS (Statistical Analysis Software)
SAS is a statistical analysis program that allows the user to perform both basic statistical
analyses (t-test, correlation) as well as more complicated analyses (multiple regression,
ANOVA). In addition to the traditional Student’s t-test, SAS was used to construct
correlation matrices to identify any significant relationships between different parameters.
Multiple regression analysis was also used to quantify the overall significance of the effect
of multiple variables on our variable of interest (threshold). Data points were identified as
outliers using SAS residual analysis. The jackknife residuals follow a t-distribution with
n-k-2 degrees of freedom, where k represents the number of regressors and n is the sample
size. Since outlier analysis tested all n residuals, the significance level was adjusted
to a new significance level /n. The p-value for the potential outlier was determined by
applying a 2-sided t distribution probability test. If the p-value was less than the new
significance level, that data point was deemed a statistically significant outlier and was
excluded from the analysis.
2.6 Neural Reconstruction and Modeling
In addition to physiological recordings, RGC morphology was also recorded using sev-
eral imaging configurations. The physiology recording setup was equipped with the ap-
propriate light sources and filters for visualizing fluorescence. Additionally, a confocal
microscope was used to collect image stacks from fixed retina at the end of experiments.
44
2.6.1 Fluorescence Imaging
The physiology recording setup was equipped with an XCite 120Q excitation light source
(Lumen Dynamics, ON, Canada). RGCs were either transgenically labeled with YFP
(yellow fluorescent protein) or were filled with Lucifer Yellow during whole-cell recordings.
Lucifer Yellow was dissolved in the K-Aspartate mouse internal solution and dialyzed with
the contents of the cell over the course of a recording. Since both YFP and Lucifer Yellow
are in the same class of fluorescence (excitation peak 514 nm, emission peak 527 nm), they
were excited by blue excitation using a FITC Nikon filter (Nikon Instruments, Melville,
NY). The Nikon B-2E/C filter has excitation wavelengths 465-495 nm (peak 480 nm)
and emission wavelengths 515-555 nm (peak 535 nm). In some instances, the retina was
fixed in 4% paraformaldehyde (PFA) at the end of the experiment. The fixed tissue was
mounted on a glass slide and cover-slipped, and using the confocal microscope, z-stacks
were collected to provide 3D information for fluorescent cells (0.3m steps).
Figure 2.11: Fluorescence image of a RGC. Patch pipettes were filled with internal solution
containing Lucifer Yellow. During physiological recordings, the dye dialyzed with the internal
contents of the cell, allowing for visualization of the cell’s dendritic structure.
45
2.6.2 Neurolucida
Neurolucida is a neuron tracing software that allows the user to trace and reconstruct
3D images of individual neurons (MBF Bioscience, Williston, VT). Z-stacks from confo-
cal microscope images were imported into Neurolucida. RGCs were traced using their
AutoNeuron feature, which allows for automated and fully interactive neuron tracing.
Once the 3D reconstruction was complete, this information was exported as a .swc file, a
format readable by Neuron (described below).
2.6.3 Neuron Simulation Environment
Neuron is a simulation environment developed by Michael Hines and Ted Carnevale.
This program is well-suited for modeling individual neurons with complex anatomy and
biophysical properties. 3D traced images can be imported into the Neuron environment
where the structural properties of the traced cell are retained (such as dendritic length and
diameter). This environment was designed to address high-level neuroscience questions
without the mathematical/computational issues. Models can be built in two ways –
hoc or gui. Hoc syntax is similar to C programming whereas the gui (graphical user
interface) does not necessitate any programming. All sections of a cell are represented as
cylindrical compartments; each section has a length and diameter that must be specified.
Each section is divided into segments and the number of segments essentially provides
the resolution of the simulation.
In this thesis, Neuron was used to create a realistic model of a RGC; this model was
then modified to simulate extracellular stimulation of the cell. The aim was to create
a model that was both descriptive (based on empirical data) and predictive (capable of
46
Figure 2.12: The Neuron simulation environment. Top All sections of a neuron are repre-
sented as cylindrical compartments; each section must be specified by a length and diameter.
Bottom Example of Neuron’s GUI interface which typically consists of three major components -
a RunControl window to run the simulation, a CellBuilder file that specifies the biophysical and
topographical properties of the neuron, and a means for delivering stimuli to the cell.
predicting RGC response to a novel stimulus). These are described in further detail in
Chapter 5.
47
Chapter 3
Effects of degeneration on temporal response to electrical
stimulation
This chapter addresses the effects of retinal degeneration on the temporal properties of the
RGC response to electrical stimulation. Specifically, the purpose of the work presented in
this chapter is to investigate how retinal degeneration influences the membrane’s ability
to respond to stimulation.
3.1 Background
3.1.1 Rheobase and Chronaxie
Electrical stimulation of excitable tissue typically yields two parameters that describe the
relative excitability of a given membrane. A strength-duration curve is constructed by
plotting the stimulus strength to reach threshold for various pulse durations (Fig. 3.1).
Strength-duration curves are typically exponential with the stimulus strength decreasing
(as pulse width increases) to an asymptotic value, defined as rheobase. The pulse duration
corresponding to a stimulus level twice the rheobase is known as chronaxie; a pulse
48
duration equal to chronaxie also requires the lowest energy (Plonsey, 2000). Chronaxie
describes the minimum time required for a neuron to reach threshold and provides an
indication of its relative excitability. Thus, if any changes to membrane excitability occur
as a result of degeneration, the corresponding chronaxie values could be usefully compared
to chronaxies in normal mice.
Figure 3.1: Strength-duration curve. Rheobase is defined as the minimum current required for
a neuron to reach threshold for an infinitely long pulse. Chronaxie is defined as the minimum
time required to reach threshold at twice the rheobase (Plonsey, 2000).
Chronaxie values have been measured for various cell types and excitable tissue. Fac-
tors that can influence the measured chronaxie value include stimulus waveform, electrode
properties, temperature, and tissue inhomogeneity (Geddes, 2004). Strength-duration
curves in this thesis were determined with rectangular charge-balanced waveforms at a
constant temperature using the same diameter electrode. The only difference was the
condition of the tissue being stimulated (normal vs. degenerate retina).
3.1.2 Response Latency - Direct vs. Indirect RGC Stimulation
Retinal stimulation thresholds can vary significantly from study to study, and can depend
on a number of factors including stimulus parameters, the animal model used, recording
49
configuration, the neurons being activated, and definitions for the threshold response and
latency (single/multiple spikes, early/late response). To classify a response as a threshold
response, it is necessary to first define what ’threshold’ encompasses in the context of the
study. In retinal stimulation studies, threshold is typically characterized by the presence
of a response (elicited spike, evoked potential) within a specified time period after the
stimulus was presented; the number and latency of elicited spikes can vary depending on
which neurons are being activated (Fig. 3.2).
Figure 3.2: Direct and indirect activation of RGCs. A: Synaptically-mediated RGC responses
occur in discrete clusters. The top panel shows the spiking response to a 2 Hz pulse train; the
lower panel shows an expanded view of the early phase (gray box in top panel) (Freeman and
Fried, 2011). B: Response latencies of short and long latency responses. Sekirnjak et al. found
that long-latency spikes (asterisks) were always preceded by a short-latency spike; long-latency
spikes without short-latency spikes were not observed (Sekirnjak et al., 2006).
Generally, short latency responses (elicited action potentials) are typically defined as
those occurring within 2-3 ms of stimulus onset (Freeman et al., 2011; Sekirnjak et al.,
2006); however, there are cases where short latency covers any responses within 20 ms of
stimulus onset (Jensen and Rizzo, 2009). Patch clamp recordings in rabbit RGCs have
revealed differences in response latency based on cell-to-cell variability; also, responses
50
to subthreshold stimuli usually had longer latencies and fewer elicited spikes (Lee et al.,
2013). It is believed that response latency differs depending on whether RGCs are directly
activated or indirectly activated (by stimulation of second order neurons) (Freeman and
Fried, 2011). Direct activation usually results in one spike per stimulus pulse while
indirect activation can generate a burst of spikes. Chronaxie values for indirect activation
of RGCs can range from 14-18 ms (Freeman et al., 2011), which is indicative of the
time it takes for bipolar cells to synaptically release neurotransmitters onto ganglion cell
terminals; thus, indirect activation of RGCs would result in longer response latencies.
3.2 Methods
The techniques for stimulation of and recording from RGCs are described in detail in
Chapter 2. All recordings were performed using whole-cell current clamp to enable mea-
surement of membrane potential changes.
3.2.1 Animals
Wildtype (WT) and rd10 mice were purchased from Jackson Laboratories (Bar Harbor,
Maine) and bred into a common C57BL/6J background. The age of WT and rd10 mice
used for physiological recordings ranged from P39-80 (P68 ± 14, mean± SD) and P42-
P77 (P62± 18), respectively. The animals were housed in facilities on a 12 h light/dark
cycle, and were not dark-adapted prior to physiological recordings.
51
3.2.2 Placement of Stimulating Electrode
The electrode was positioned approximately ∼ 50 m above and ∼ 50 m laterally from
the targeted cell, which allowed for constant visualization of the RGC. The position of
the stimulating electrode relative to the targeted cell was measured using the microma-
nipulator holding the electrode. The reasons for positioning the electrode 50 m away
from the ganglion cell layer were twofold. In several cases, certain areas of the implanted
retinal prostheses are not in contact with the retina but are instead lifted off the surface,
so placing the electrode a given distance away from the retinal surface would simulate the
conditions where the electrode array was not flush against the retina. Another reason for
the electrode-retina distance was to eliminate the possibility of causing mechanical dam-
age to the retina which could potentially affect cellular physiology. Studies in retina have
shown that mechanical pressure can cause significant damage to the affected area, and
mechanical pressure coupled with electrical stimulation can further increase the extent of
this damage (Colodetti et al., 2007).
3.2.3 Strength-duration Curves
Charge-balanced biphasic current pulses (cathodic-phase first) were delivered at 10 Hz
frequency (interpulse period = 100 ms) for 4 different pulse durations – 100 s, 200 s,
500 s, and 1 ms, and stimulus amplitudes were randomized at each pulsewidth applied.
Responses to all delivered stimuli were fit with a dose-response curve. Threshold for
each cell was defined as the current level at which a spike was elicited in at least 50% of
delivered pulses. Strength-duration curves were fit using Lapicque’s equation (Lapicque,
1931):
52

th
=

rh
1−
-PD/ SD
(3.1)
where I
rh
represents the rheobase current, PD is the pulse duration, and SD
is the
strength-duration time constant.
3.2.4 Measurement of Response Onset Latency
An elicited action potential to extracellular stimulation was defined as a spike whose
peak occurred within 3 ms of the stimulus onset. The response onset latency of a cell
was defined as the time at which the peak of the elicited action potential occurred after
the stimulus was delivered. Any spikes that occurred outside of the 3 ms window did not
count toward the cell’s threshold response measurement.
3.2.5 Artifact Subtraction
The whole-cell measurements not only recorded elicited action potentials to stimulation
but also the stimulus artifact (biphasic square pulse). In most cases, the stimulus artifact
did not interfere with detection of the elicited spike; however, in some cells, the spike was
partially hidden in the stimulus artifact. To remove the stimulus artifact, raw traces where
a spike was not elicited were subtracted from traces that did elicit an action potential.
3.3 Results
The purpose of the work presented here was to investigate how retinal degeneration
influences the membrane’s ability to respond to electrical stimulation. Two parameters
53
were used to characterize and compare the relative excitability and temporal response
properties of WT and degenerate RGCs – chronaxie and response latency.
3.3.1 Chronaxie Values are Comparable Between WT and Rd10 RGCs
Strength-duration curves for WT and rd10 RGCs were constructed using charge-balanced
biphasic square pulses with durations ranging from 100 s to 1 ms (Fig. 3.3). Chronaxie
describes the nominal time period for a cell to reach threshold and is an indication of its
relative excitability. The loss of photoreceptors during degeneration causes gross struc-
tural changes in the rd10 retina in addition to physiological changes to inner retinal cells.
We compared chronaxie values between normal and rd10 RGCs to determine whether
electrical stimulation was directly activating ganglion cells in degenerate retina.
Figure 3.3: Strength-duration curves for (A) WT and (B) rd10 RGCs. Pulse duration ranged
from 100 s to 1 ms. Curves were fit using Lapicque’s equation and resulting chronaxie values
were 520 s and 550 s for WT and rd10 cells, respectively. Boxplots represent median, 1st
quartile, and 3rd quartile threshold values at each pulsewidth.
Rheobase currents extracted from the strength-duration data were 15 A and 18 A
for WT and rd10 RGCs, respectively. The corresponding chronaxie values were 520s for
WT and 550 s for rd10 ganglion cells. The chronaxie values were comparable between
54
normal and rd10 RGCs and were within the range of published chronaxie values for direct
ganglion cell activation (Freeman et al., 2011; Jensen et al., 2005; Sekirnjak et al., 2006).
These comparable chronaxie values suggest that, at this particular stage of degeneration,
it is feasible to directly activate retinal ganglion cells in the rd10 model, and the relative
excitabilities of rd10 RGCs compared to normal are not significantly affected. It should
be noted that even though two neurons have similar chronaxies, this does not imply that
the same current level will bring both neurons to threshold.
3.3.2 Response onset latencies to electrical stimulation are not different
between WT and rd10
For these experiments, an elicited action potential was defined as a spike whose peak
occurred within 3 ms of stimulus onset. The threshold response onset latencies to a
500 s biphasic pulse were compared between WT and rd10 RGCs. A spike occurring
outside the 3 ms window would not be considered a threshold response, regardless of the
consistency of the spike.
Figure 3.4: Response onset latency of elicited action potentials to extracellular biphasic stimu-
lus. (A,B) Overlaid traces showing response latencies for representative WT and rd10 RGCs at
threshold for each cell. The stimulus artifact was subtracted to reveal the elicited spikes. Gray
arrow denotes stimulus onset.
55
Fig. 3.4 shows overlaid traces of the threshold response for a representative WT and
rd10 ganglion cell. The gray arrow denotes the time of stimulus onset; the stimulus ar-
tifact was removed from all traces. The average spike onset latency for WT RGCs was
1.799± 0.309 ms (mean± SD, n = 19) and 1.778± 0.267 ms (n = 26) for rd10 RGCs.
An unpaired Student’s t-test determined that the difference between onset latencies for
normal and degenerate ganglion cells was not statistically significant ( P = 0.81). Sekirn-
jak et al. also found comparable response latencies between two degenerate rat models
(P23H, S334ter) and controls.
Figure 3.5: Elicited action potentials at various current levels for representative WT and rd10
RGCs. The respective threshold for each cell is denoted by the arrow; current amplitudes above
and below this threshold current elicited spikes (asterisks) within 3 ms of stimulus onset. Scale
bar – WT (50 mV, 2 ms); rd10 (100 mV, 2 ms).
Response latencies were also comparable for action potentials elicited at both sub-
threshold and superthreshold current levels. Fig. 3.5 shows raw traces of elicited action
potentials for representative WT and rd10 ganglion cells. The threshold amplitudes were
56
21 A and 42 A for the WT and rd10 cells, respectively. In normal RGCs, response
latencies and amplitude and duration of elicited action potentials were similar for sub-
threshold current amplitudes (18A, 20A) and superthreshold currents (23A, 25A);
this was also observed in rd10 RGCs. Based on these results, it appears that response
onset latency was not a function of stimulus amplitude.
3.4 Conclusions
Based on studies in various animal models of retinal degeneration, there is increasing
evidence that profound reorganization and remodeling of the retina takes place as degen-
eration progresses (Chen et al., 2005; Kolomiets et al., 2010; Margolis and Detwiler, 2011;
Margolis et al., 2008; Sekirnjak et al., 2011; Stasheff, 2008; Stasheff et al., 2011). These
studies have shown that the physiology of remaining retinal cells is altered to varying
degrees during degeneration. However, despite these aberrant changes initiated by pho-
toreceptor death, there is evidence demonstrating relative morphological preservation of
the remaining cells.
This chapter focused on the temporal properties of the RGC response to electrical
stimulation, and how action potential generation might be affected by retinal degenera-
tion. Puthussery et al. found that in the rd10 retina, bipolar cell dendrites formed ectopic
connections to cones during an intermediate stage of degeneration (Puthussery et al.,
2009); this was also observed in several other animal models of degeneration (Cuenca
et al., 2005; Peng et al., 2000). Also in the rd10 mouse, Gargini et al. observed dis-
placement of mGluR6 receptors to the soma and axon of bipolar cells following dendrite
57
retraction (Gargini et al., 2007). In light of these findings, we hypothesized that there
might be biophysical changes to the RGC membrane that could disrupt the mechanisms
facilitating action potential generation, even if ganglion cells remained structurally intact.
The various features of an action potential (timing, duration, amplitude) are typically
determined by the biophysical mechanisms (voltage-gated ion channels) present on the
cell membrane.
We constructed strength-duration curves for normal and rd10 RGCs and compared
the corresponding chronaxie values between genotypes. We found that rd10 RGC chron-
axie was comparable to WT and in agreement with previously reported values for direct
ganglion cell activation (Freeman et al., 2011; Jensen et al., 2005; Sekirnjak et al., 2006).
Response onset latencies were also not significantly different between normal and de-
generate RGCs. These results suggest that the biophysical mechanisms responsible for
spike generation are preserved at this stage of degeneration, and that direct activation of
ganglion cells is feasible in the rd10 retina.
58
Chapter 4
Influence of rd10 intrinsic properties on sensitivity to
electrical stimulation
There is well-documented evidence that the retina undergoes pronounced reorganization
and remodeling as degeneration progresses (Jones et al., 2012; Marc et al., 2007). The
extent of alterations in retinal circuitry and the potential impact on the physiology of
retinal ganglion cells with respect to retinal stimulation have not been fully explored.
The aim of this chapter was to assess the intrinsic RGC properties in a mouse model of
retinal degeneration (rd10) to determine how changes in ganglion cell physiology influence
excitability to electrical stimulation.
4.1 Background
4.1.1 Retinal Degeneration - Sensitivity to Electrical Stimulation
Despite the aberrant changes that take place in the inner retina during degeneration,
studies have shown relative morphological preservation of the remaining ganglion cells
(Mazzoni et al., 2008; Medeiros and Curcio, 2001). In vivo recordings from the superior
59
colliculus (SC) in a degenerate rat model (s334ter) found significantly elevated retinal
stimulation thresholds compared to normal rats; also, thresholds increased with age in
the degenerate rat (Chan et al., 2011). Numerous other studies using various in vitro
preparations have also observed similar increases in threshold in several animal models
of retinal degeneration (Jensen, 2012; O’Hearn et al., 2006). Sekirnjak et al. found
thresholds were not significantly different between degenerate (P23H) and normal rat
RGCs; in this preparation, the retina was flush against the electrode array and relatively
small electrodes were used (∼ 10m). In general, the existing literature suggests that RGC
sensitivity to electrical stimulation is reduced in degenerate retina in spite of structural
preservation of the remaining cells.
4.1.2 Physiology of Degenerate Retina
During degeneration, the synaptic input to second order retinal neurons (bipolar, hori-
zontal) decreases at the rate of photoreceptor death. The lack of input to these cells can
lead to structural and biophysical changes including retraction of dendrites and migration
of postsynaptic receptors (Jones and Marc, 2005; Strettoi et al., 2003). In turn, these
changes can ultimately affect the physiology of individual ganglion cells.
A notable observation across a multitude of degenerate animal models is the emergence
of spontaneous hyperactivity and in some cases, oscillatory spiking behavior, in RGCs
(Kolomiets et al., 2010; Margolis and Detwiler, 2011; Margolis et al., 2008; Menzler and
Zeck, 2011; Sekirnjak et al., 2009, 2011; Stasheff, 2008; Stasheff et al., 2011; Trenholm
et al., 2012). The source of increased spontaneous activity varies among animal models.
Margolis et al. showed that > 90% of the oscillations in rd1 mouse ganglion cells could
60
be abolished using pharmacological blockers (Margolis et al., 2008). However, Sekirnjak
found that only about 50% of hyperactivity in P23H rat RGCs was diminished through
pharmacological applications, which suggests that a portion of hyperactivity is intrinsic
to degenerate ganglion cells. In addition to increased RGC activity, Chen et al. found
that resting membrane potentials became depolarized as a function of age in RCS rat
(Chen et al., 2005). Thus, multiple studies indicate that physiological changes do occur
in degenerate RGCs; however, the impact on thresholds for visual prosthetic applications
has not been thoroughly explored.
4.2 Methods
The methods for recording RGC responses and stimulating the retina are described in
detail in Chapter 2. The aim of this chapter was to determine how certain intrinsic
properties are affected by degeneration in the rd10 model, and to evaluate how these
properties influence the response to electrical stimulation. The previous chapter focused
on the temporal properties of the RGC response. Here, we examine the potential effects
of degeneration on the intrinsic physiology of ganglion cells and the subsequent impact
on response thresholds in rd10 retina.
4.2.1 Defining Threshold
Threshold was defined as the current to elicit an action potential in at least 50% of
delivered pulses. The probability of eliciting a spike was calculated by dividing the
number of elicited spikes by the total number of delivered pulses. For each pulsewidth,
dose-response curves were constructed by plotting the elicited spike probability against
61
the stimulus current amplitudes delivered. Curves were fit using the logistic equation
p = a/(b+e-xc), where p is the spike probability, x is the range of current amplitudes,
and a, b, and c are constants. The current amplitude corresponding to an elicited spike
probability of 50% (gray dashed line in fig. 4.1) was defined as the cell’s threshold. Since
baseline spontaneous rates varied between cells, dose-response curves were adjusted to
include each cell’s spontaneous rate. For cells with spontaneous rate > 0, the dose-
response baseline was a non-zero value. If, for example, a cell had a spontaneous rate
of 20 Hz, then in a 100 ms time window, there would be 2 spontaneous spikes; the 100
ms window represents the stimulus interpulse interval in these experiments, i.e. the time
between the onset of the first stimulus and the next. For a spike to be considered a
threshold response, it would have to occur within 3 ms of stimulus onset which represents
3% of the 100 ms time window. The probability of a spontaneous spike occurring within
the 3 ms window was calculated as
number of spontaneous spikes
100
3% (4.1)
This probability represents the new baseline spontaneous rate; fitting the dose re-
sponse curve with a spontaneous rate of zero would be inaccurate for a spontaneous
active cell. Threshold was defined as the midpoint between [100% probability of eliciting
a spike – new baseline spontaneous rate %] (black traces in fig. 4.1).
62
Figure 4.1: Dose response curves adjusted for spontaneous rate. Cells with little to no sponta-
neous activity had dose response curves with a spike probability of 0 at rest (gray traces). The
adjusted spike probability for a spontaneous active cell was non-zero and incorporated the cell’s
spike rate at rest (black traces); threshold was defined as the midpoint between the resting spike
probability and the probability of eliciting a spike every time.
4.2.2 Measurement and Calculation of Intrinsic Properties
4.2.2.1 Resting Membrane Potential
The resting membrane potential, V
rest
, was measured passively through the patch pipette
in current clamp (zero current injection). After breaking in to the cell (whole-cell mode),
the cell was allowed to equilibrate for approximately 5-10 minutes before any recordings
were collected. Prior to stimulation, the resting membrane potential of the cell was
recorded for approximately 30 sec to 1 min. The resting potential was also recorded
between stimulus trials to monitor for any aberrant changes in the cell’s baseline behavior.
If the membrane potential deviated permanently from its initial value (> ± 3 mV), the
recording was stopped and no further data was collected from the cell.
63
4.2.2.2 Baseline Spontaneous Rate
The spontaneous firing rate of the cell at rest was calculated from the resting membrane
potential measurements. The spontaneous rate was defined as the total number of spon-
taneous spikes within a specified time period, i.e. the total number of spikes divided by
the duration of the passive recording.
4.2.2.3 Membrane Periodicity
Membrane periodicity was observed in a subset of rd10 RGCs and can be described
by a rhythmicity or oscillatory behavior in the spontaneous resting activity of the cell.
Interspike interval (ISI) histograms are useful for analyzing the resting electrical behavior
of a cell, and can be used to detect patterns in spike activity. The ISI histogram plots
the distribution of times between consecutive spikes. The spontaneous behavior of a cell
was classified as periodic based on the existence of a secondary peak in its corresponding
ISI histogram.
4.2.2.4 Rebound Excitation
Rebound excitation is characterized by a brief burst of action potentials following a period
of membrane hyperpolarization. The patch pipette (current clamp) was used to deliver
a family of hyperpolarizing currents steps. Each family consisted of 10 hyperpolarizing
steps (-10 pA/step), 300 ms in duration; the same series of steps was delivered five times
for each cell. A cell was classified as exhibiting rebound excitation if spikes were elicited
consistently for each trial and within 100 ms after the step was returned to baseline.
64
4.2.2.5 Input Resistance and Membrane Time Constant
Two additional intrinsic RGC properties - input resistance R
N
and membrane time con-
stant - were also measured to determine their influence on stimulation threshold. Input
resistance was calculated using Ohm’s law (V = IR). A hyperpolarizing 300 ms step
current was applied via the patch pipette; the low-amplitude current step resulted in a
membrane potential change of about ∼ 5-10 mV. Higher current steps were avoided since
the membrane dynamics of certain RGCs became nonlinear (O’Brien et al., 2002).
Input resistance is governed by the size of the cell and the density of open channels at
rest. The membrane time constant was also extracted by analyzing the change in mem-
brane potential to a hyperpolarizing current step. The exponential change in potential
was fit using the general equation
=
-t/ + (4.2)
where t represents time, is the membrane time constant, and a and c are constants.
All fits for membrane time constant that were included in the analyses had goodness of fit
R
2
values ≥ 99%. Membrane time constant is dependent on the membrane capacitance
and resistance ( = R

C

). Larger cells would have lower input resistance and higher
membrane capacitance.
65
4.3 Results
The aim of these experiments was to determine how ganglion cell physiology influences
threshold to electrical stimulation in both normal and degenerate retina. Numerous clin-
ical and animal studies have observed increased threshold in degenerate retina compared
to normal. Several RGC parameters were evaluated to determine the extent to which
these properties were affected in the rd10 mouse. Furthermore, we analyzed the indi-
vidual and combined effects of these parameters on excitation thresholds in normal and
degenerate cells.
4.3.1 Stimulation Thresholds are Influenced by Soma Size in WT RGCs
(Pilot Data)
Prior to evaluating the potential effects of retinal degeneration on the RGC response, it
was essential to understand the behavior of normal RGCs to stimulation and to evaluate
the effects of any structural or physiological properties on threshold. In a pilot study, we
investigated the effect of RGC soma diameter on stimulation thresholds in WT mice (Cho
et al., 2011); part of this section is adapted from this work. The aim was to determine
whether thresholds were dependent on structural properties of ganglion cells. In primate
and cat retina, a positive correlation between RGC soma size and axon diameter has
been identified (Sakai and Woody, 1988; Walsh et al., 1999). In peripheral nerve fiber
stimulation, studies have shown that larger diameter axons are recruited first to externally
applied current, possibly due to the decreased resistance of larger fibers (Stieglitz, 2005).
Additionally, the axon initial segment of RGCs is largely believed to be the site of action
66
potential initiation. Based on these findings, we expect that stimulation thresholds would
be lower for large diameter RGCs if threshold was predominantly dependent on these
structural properties (soma size/axon diameter).
Figure 4.2: Threshold as a function of soma diameter in WT ganglion cells. There was a
significant negative correlation between soma size and stimulation threshold ( P < 0.05), with
decreased thresholds for larger diameter RGCs.
Fig. 4.2 shows the relationship between threshold and soma size in WT RGCs. There
is a negative correlation between between threshold and diameter, where larger diameter
cells require less current to reach threshold; this correlation was statistically significant
(P = 0.0309).
4.3.2 Thresholds are Significantly Elevated in Rd10 RGCs
Since a significant correlation was found between threshold and soma diameter in WT
cells, the dependence of threshold on soma size was also examined in rd10 RGCs. Fig.
4.3 shows the relationship between stimulation thresholds and soma diameter for both
WT and rd10 ganglion cells.
There was no significant correlation found between threshold and soma size in degen-
erate cells. Furthermore, rd10 thresholds were highly variable and significantly elevated
67
Figure 4.3: Relationship between threshold and soma diameter in WT and rd10 RGCs. A: The
correlation between threshold and soma size was statistically significant in WT cells ( P = 0.0309)
but not in rd10 RGCs (P = 0.5871). B: Thresholds were more variable and significantly higher
in rd10 cells compared to WT retina. ** P < 0.001
(P < 0.001) compared to normal RGCs despite having comparable chronaxie values and
response latencies (Chapter 3).
4.3.3 Analysis of Intrinsic Properties Reveals No Significant Differences
Between WT and Rd10
The temporal properties of threshold responses in rd10 RGCs were comparable to WT
cells so we examined several intrinsic properties of these degenerate cells. The intrinsic
parameters measured included resting membrane potential V
rest
, baseline spontaneous
rate, input resistance R
N
, membrane time constant , and presence/absence of rebound
excitation. A pairwise correlation matrix of these intrinsic properties revealed a signifi-
cant correlation between threshold and resting potential, so we assessed the relationship
between threshold and V
rest
for WT and rd10 RGCs.
Fig. 4.4 shows the relationship between threshold and resting potentials for WT
and rd10 ganglion cells. Stimulation thresholds in both normal and degenerate RGCs
68
Figure 4.4: Relationship between stimulation threshold and resting membrane potential. A:
Both WT and rd10 RGCs displayed negative trends between threshold and resting membrane
potential, V
rest
; this correlation was statistically significant in rd10 cells ( P = 0.0114). B: The
difference in mean V
rest
values was not statistically significant between WT and rd10 RGCs.
appeared to be negatively related to resting potential. More depolarized cells had lower
thresholds than cells with more negative V
rest
values; this correlation was statistically
significant for rd10 cells ( P = 0.0114). This result might be expected since a cell with
a more depolarized resting potential would theoretically require less current to reach
threshold. When comparing group mean differences in resting potential between WT
and rd10 RGCs, there was no significant different in V
rest
values between genotypes.
Despite having significantly higher thresholds than WT cells, the group average resting
potentials for rd10 cells were comparable to normal.
Elevated spontaneous firing in degenerate RGCs has been observed in a number of
different animal models, so the relationship between threshold and baseline spontaneous
rate was also examined. Fig. 4.5 shows the relationship between threshold and sponta-
neous rate for WT and rd10 ganglion cells. There was no correlation between threshold
and spontaneous rate for either normal or degenerate RGCs, and mean differences in
spontaneous rate were not significantly different between the two groups. The mean,
69
Figure 4.5: Relationship between stimulation threshold and spontaneous rate. A: The corre-
lation between threshold and baseline spontaneous rate was not significant for both WT and
rd10 RGCs. B: There was no statistically significant difference between WT and rd10 mean
spontaneous rate values.
standard deviation, and Student’s t-test values for threshold, response latency, V
rest
, and
spontaneous rate for WT and rd10 RGCs are shown in table 4.1.
Table 4.1: Mean, standard deviation, and Student’s t-test values for RGC parameters
Mean, standard deviation, and Student’s t-test values for four RGC parameters (WT vs rd10) –
stimulation threshold, response onset latency, resting membrane potential, and baseline sponta-
neous rate.
A noteworthy observation was the range of spontaneous rates measured in both WT
and rd10 RGCs; this was also observed in P23H rat (Sekirnjak et al., 2009). This wide
variability in spontaneous activity demonstrates that there are certain cells that are very
quiet (essentially no spontaneous spiking) while others appear to be quite active at rest.
70
4.3.4 Differences Arise Between WT and Rd10 RGCs When Categorized
by Spontaneous Rate
Due to the appreciable variation in resting spontaneous rate, WT and rd10 RGCs were
categorized into two groups based on their level of spontaneous activity. Cells with
baseline rates greater than their respective group means were classified as ‘high rate’ cells
while those with rates below the mean were classified as ‘low rate’ cells. Differences in
mean threshold, V
rest
, and spontaneous rate between the two rate groups for WT and rd10
cells are shown in table 4.2. In WT retina, stimulation thresholds and resting membrane
potential values were comparable for low and high rate cells. However, rd10 thresholds
were significantly lower for cells displaying higher rates of spontaneous activity; these
high rate cells also had membrane potentials that were significantly more depolarized
than low rate cells.
Table 4.2: Mean, standard deviation, and Student’s t-test values - rate group
Mean, standard deviation, and Student’s t-test values for threshold, V
rest
, spontaneous rate,
categorized by rate group.
71
Figure 4.6: Comparisons of RGC threshold and intrinsic properties, categorized by rate group.
WT and rd10 cells were classified into two groups, low rate (left column) and high rate (right
column) cells. A: The difference in thresholds between WT and rd10 RGCs was statistically
significant only in cells with low spontaneous activity (a1). B: Mean V
rest
values were significantly
more depolarized in rd10 cells with high spontaneous activity (b2) (P = 0.0290). C : The range
of spontaneous rate values between WT and rd10 RGCs were comparable in both low rate and
high rate groups. ** P < 0.001, * P < 0.05
Comparisons of threshold, V
rest
, and spontaneous rate between low rate and high
rate WT and rd10 RGCs are shown in Fig. 4.6. The range of spontaneous rates between
WT and rd10 cells in both rate groups were comparable and average rate values were not
significantly different within groups (Fig. 4.6c). In low rate RGCs, stimulation thresholds
72
in rd10 cells were considerably higher than thresholds in normal retina, though resting
potentials were not different (Fig. 4.6a1,b1). However, in high rate cells, thresholds
were comparable between WT and rd10 cells even though rd10 resting potentials were
significantly more depolarized (Fig. 4.6a2,b2).
4.3.5 Rd10 RGCs Display Distinct Characteristics in Baseline Activity
Classification of cells by spontaneous rate group revealed differences in threshold and
V
rest
in both groups that were not apparent during a combined analysis of these same
parameters. We further examined the characteristics of each cell’s baseline spontaneous
behavior using their interspike interval (ISI) histograms. The resting spontaneous rate
and corresponding ISI histograms for four representative ganglion cells - one WT and
three rd10 - are shown in fig. 4.7. The high rate WT cell (Fig. 4.7a) did not appear
to exhibit any patterns in membrane potential fluctuations. Similarly, all WT RGCs
displaying high spontaneous rates had ISI histograms similar to that shown in fig. 4.7a.
Rd10 cells exhibited mainly three types of baseline activity: non-periodic spiking
cells, periodic spiking cells, and low rate cells displaying subthreshold periodic membrane
fluctuations (Fig. 4.7b-d). Non-periodic spiking RGCs were high rate cells that did not
have any apparent patterns in spontaneous activity and had ISI histograms similar to
high rate WT cells (Fig. 4.7b).
A subset of high rate RGCs displayed periodicity in their baseline spiking behavior
(Fig. 4.7c), resulting in a secondary peak in their ISI histograms around ∼ 100 ms which
corresponded to a baseline spiking frequency of about 10 Hz. There were also several
rd10 RGCs with periodic membrane fluctuations that did not necessarily generate action
73
Figure 4.7: Baseline activity and interspike interval (ISI) histograms. A: A representative
high rate WT RGC and its corresponding ISI histogram showed no obvious patterns in baseline
behavior. ISI histograms for all WT cells displaying high spontaneous rates were similar to the
histogram in a2. (B-D): Spontaneous baseline activity and corresponding ISI histograms for rd10
RGCs exhibiting primarily three types of spontaneous behavior. B: Rd10 cells with no apparent
patterns in spontaneous firing had ISI histograms (b2) similar to WT cells (a2). C : Several rd10
cells displayed periodicity in their baseline firing as evidenced by a secondary peak occurring at
∼ 100 ms in the ISI histogram (c2). D: A few rd10 RGCs had periodic fluctuations in membrane
potential that did not necessarily generate action potentials; the corresponding ISI histogram also
resulted in a secondary peak around ∼ 120 ms. Bin size = 5 ms.
74
potentials and were thus classified as low rate cells. Although the spontaneous rate values
for these cells were low since they did not always generate action potentials, there was
periodicity in their membrane fluctuations that resulted in secondary peaks similar to
those seen in periodic spiking RGCs (Fig. 4.7d). Secondary peaks were not present in
the ISI histograms of non-periodic high rate cells in both WT and degenerate retina.
4.3.6 Periodicity and High Spontaneous Rate Contribute to Decreased
Thresholds in Rd10 RGCs
An analysis of the spontaneous activity characteristics in rd10 retina yielded two distin-
guishing types of behavior - cells exhibiting periodicity in their resting spontaneous rates
and those with no apparent patterns in membrane fluctuations (non-periodic). To assess
whether periodicity in resting activity influenced intrinsic RGC properties, we compared
thresholds, V
rest
, and spontaneous rates between non-periodic and periodic rd10 ganglion
cells (table 4.3). Rd10 cells exhibiting periodicity at rest had significantly lower stimu-
lation thresholds, more depolarized membrane potentials, and higher spontaneous rates
than non-periodic RGCs.
The intrinsic parameters that resulted in the lowest stimulation thresholds in rd10
RGCs were higher spontaneous rate and baseline periodicity. When rd10 RGCs with
these properties were compared to all WT data, there was no significant difference in
stimulation thresholds (Fig. 4.8). However, despite having comparable thresholds to
normal retina, these high rate, periodic rd10 cells remained intrinsically different from WT
cells, with significantly more depolarized membrane potentials and elevated spontaneous
activity.
75
Table 4.3: Mean, standard deviation, and Student’s t-test values - periodicity
Mean, standard deviation, and Student’s t-test values for threshold, V
rest
, and spontaneous rate
between non-periodic and periodic rd10 RGCs.
Figure 4.8: Comparisons between WT and high rate, periodic rd10 RGCs. A: Stimulation
thresholds were comparable between the two groups (P = 0.3737). B: Membrane potential was
significantly more depolarized for high rate, periodic rd10 cells compared to normal ( P = 0.0005).
C : Spontaneous rates were significantly higher for this subset of rd10 cells ( P = 0.0023). ** P <
0.001, * P < 0.05
4.3.7 Rebound Excitation and Functional Classification of Rd10 RGCs
The analyses so far have been for rd10 RGCs selected at random and not specific to a
particular class of ganglion cells. Due to the loss of photoreceptors, it was not possible
to functionally classify degenerate ganglion cells as ON or OFF cells since they lacked a
light response. Previous work by Margolis et al. (Margolis and Detwiler, 2007; Margolis
et al., 2008) demonstrated that RGCs, in normal and rd1 mouse, could be classified as
ON or OFF based on their ability to fire rebound spikes; however, only cells with the
76
largest somas (∼ 20m) were targeted and were most likely alpha ganglion cells based on
their large size. This classification scheme defined cells with rebound excitation as being
OFF cells and cells exhibiting no rebound spikes as ON cells.
Figure 4.9: RGC response to a family of hyperpolarizing current steps. Rd10 ganglion cells
were classified based on the absence ( A) or presence (B) of rebound firing in response to a series
of hyperpolarizing steps.
Rd10 RGCs were filled with Lucifer Yellow during recordings which allowed for visual-
ization of the dendrites; additionally, the dendritic stratification depth was also measured.
Using the stratification sublayers by Ghosh et al. (Ghosh et al., 2004), rd10 RGCs were
classified as ON or OFF depending on where they stratified in the inner plexiform layer.
Figure 4.10: Functional classification for mouse bipolar cells based on dendritic stratification
depth. Ghosh et al. divided the IPL into five sublamina; cells stratifying in sublamina 1 and
2 were classified as OFF cells while those stratifying in sublamina 3-5 were defined as ON cells
(Ghosh et al., 2004).
77
Out of twelve cells, 8 were classified as ON, 2 were classified as OFF, and 2 were
bistratified (ON/OFF). Rd10 cells were also categorized based on the presence or absence
of rebound excitation; hyperpolarizing step family responses are shown in figure (Fig.
4.9) for representative rd10 RGCs exhibiting both no rebound spikes and robust rebound
firing.
In this subset of rd10 RGCs, functional classification based on the presence/absence
of rebound excitation did not correspond with classification based on dendritic stratifica-
tion. For cells with dendrities stratifying in the ON sublayer of the IPL, 3 out of 8 had
no rebound excitation while the rest exhibited rebound spikes. One of the two RGCs
stratifying in the OFF sublayer displayed rebound excitation, and both bistratified cells
had rebound spikes as well.
Figure 4.11: Comparisons between non-rebounding and rebounding rd10 RGCs. A: The dif-
ference in mean threshold was not significant between the two groups ( P = 0.24). B,C : Cells
exhibiting rebound spikes were significantly more depolarized ( P = 0.003) and had higher spon-
taneous activity (P = 0.0263) compared to cells with no rebound excitation. * P < 0.05
Since rd10 RGCs could not reliably be classified as ON or OFF based on rebound
excitation, these cells were categorized as ’no rebound’ or ’rebound’ rather than using the
functional classification of each cell. Fig. 4.11 shows the comparisons between threshold,
V
rest
, and spontaneous rate for non-rebounding and rebounding RGCs. Cells exhibiting
78
rebound spikes were more depolarized and had higher spontaneous activity than cells
with no rebound excitation; however, the mean threshold difference between these two
groups was not significant.
4.3.8 Analysis of Additional Intrinsic Parameters of Rd10 RGCs
Cell input resistance and membrane time constant values were not significantly correlated
with stimulation thresholds; however, further analyses revealed significant relationships
between certain intrinsic properties. Since R
N
is inversely related to the size of the cell,
we would expect a smaller cell to have a higher input resistance. The relationship between
R
N
and soma size for rd10 RGCs is shown in fig. 4.12. The correlation between these
two variables was not significant ( P = 0.0734), but there appears to be a negative trend
where larger diameter cells have lower resistance. Fig. 4.12 also shows the relationship
between R
N
and membrane time constant ; there was a significant positive correlation
between R
N
and (P < 0.001). depends on the membrane capacitance and resistance
(C
m
*R
m
) but is not dependent on cell size. A large cell would have a higher capacitance
(due its larger area) but a lower input resistance. The relationship between R
N
and suggests that a cell with a higher input resistance (and hence, a smaller diameter) would
take longer to respond to changes in input (higher ), yet a smaller cell would have a lower
membrane capacitance which in turn would lower the cell’s time constant. This reasoning
assumes that specific membrane resistance is constant across all cells. However, studies
in various CNS neurons have shown that R
m
can be influenced by synaptic signaling as
well as other conditions (Fan et al., 2005; Perreault, 2002; Speck, 1993), so differences in
specific membrane resistance may also contribute to the relationships seen in fig. 4.12.
79
Figure 4.12: Relationships between cell input resistance, soma size, and membrane time con-
stant. Left: There was a negative correlation between input resistance and soma diameter in
rd10 RGCs, where larger diameter cells had lower input resistance; however, this relationship was
not statistically significant. Right: There was a significant positive correlation between input
resistance and membrane time constant (P < 0.001).
4.4 Conclusions
The purpose of our study was to evaluate RGC physiology in a mouse model of retinal
degeneration and to understand how certain intrinsic properties influence the response
to electrical stimulation. Elevated stimulation thresholds in degenerate retina have been
observed both in clinical and animal studies (Chan et al., 2011; Jensen, 2012; Jensen and
Rizzo, 2009; Keseru et al., 2012; Mandel et al., 2013; O’Hearn et al., 2006; Perez Fornos
et al., 2012). Increased thresholds may be influenced by a number of factors including
the extent of retinal circuitry reorganization, changes in the population of functionally
viable cells, differences in stimulating/recording configurations, and possible cortical re-
organization.
In this study, we found that thresholds in rd10 ganglion cells were significantly ele-
vated and highly variable compared to normal RGCs. The rd10 mouse model was chosen
because studies have shown marked morphological preservation of RGCs up to 9 months
80
(Mazzoni et al., 2008) and this model exhibits a slower degeneration than the commonly
studied rd1 mouse (Chang et al., 2007). Rd10 RGC chronaxie was comparable to WT and
in agreement with previously reported chronaxie values for ganglion cell activation (Free-
man et al., 2011; Jensen et al., 2005; Sekirnjak et al., 2006). Although rd10 thresholds
were elevated, response onset latencies of elicited spikes at threshold were not signifi-
cantly different between WT and rd10 cells, which suggests the membrane mechanisms
responsible for action potential generation are preserved.
We investigated the dependence of threshold on two intrinsic properties, resting mem-
brane potential and baseline spontaneous rate. Threshold was negatively correlated with
V
rest
in both WT and rd10 cells. This result is not surprising since a cell with a more
depolarized resting potential would require less current to reach threshold than one with
a more negative V
rest
value. Although thresholds were consistently higher in rd10 RGCs,
the group averages for V
rest
and spontaneous rate values were comparable between WT
and rd10 cells.
A notable difference observed across multiple models of retinal degeneration is an
increase in spontaneous activity of RGCs (Margolis et al., 2008; Sekirnjak et al., 2011;
Stasheff, 2008; Stasheff et al., 2011; Toychiev et al., 2013). Although the source of aber-
rant activity in RGCs is unknown and may differ amongst the models, it is evident that
the loss of photoreceptors causes considerable changes to ganglion cell physiology. Signif-
icant differences in threshold and V
rest
were observed between WT and rd10 cells when
categorized into low and high rate groups. In cells with low rates of baseline activity,
rd10 cells had significantly higher thresholds than WT but had comparable V
rest
values.
Considering threshold was increased, we would have expected V
rest
to be more negative
81
for these rd10 cells since more current would be required for these cells to reach threshold.
In high rate cells, thresholds between WT and rd10 RGCs were comparable. However,
rd10 cells had significantly depolarized V
rest
values compared to WT; again, we expected
V
rest
to be comparable between WT and rd10 since thresholds were not significantly dif-
ferent. Though there were significant differences in threshold and V
rest
for rd10 ganglion
cells, WT cells appeared to be largely unaffected by differences in spontaneous activity
(table 4.1).
Various groups have reported RGC membrane oscillations as a prominent feature of
retinal degeneration (Margolis et al., 2008; Menzler and Zeck, 2011; Stasheff et al., 2011;
Trenholm et al., 2012; Ye and Goo, 2007; Yee et al., 2012). For rd10 RGCs in our study,
we observed membrane periodicity in ∼ 22% (13/59) of recorded cells, where 10/13 were
categorized as high rate periodic RGCs. Although periodicity in membrane fluctuations
was measured in a subset of high rate rd10 cells, certainly not every high rate RGC
displayed periodic behavior.
We found that two factors contributed to decreased stimulation thresholds in rd10
retina - higher spontaneous activity and membrane voltage periodicity. However, periodic
rd10 RGCs were also typically categorized as high rate cells so we were unable to isolate
the contributions from each parameter. Despite comparable thresholds to normal RGCs,
high rate periodic rd10 cells remained intrinsically different from WT (Fig. 4.8).
Based on a classification scheme demonstrated by Margolis et al., we attempted to
functionally classify cells as ON or OFF based on their ability to fire rebound spikes. We
filled cells with a fluorescent marker and classified cells based on the stratification of their
dendrites. Comparing the functional classification based on dendritic stratification with
82
rebounding and non-rebounding cells, we found that these two methods for classifying
RGCs did not agree, at least for this subset of rd10 cells. However, it should be noted that
the sample size was relatively small, but we could not definitively use this physiological
method to functionally classify this subset of rd10 cells.
Figure 4.13: Spontaneous rate as a function of age in degenerate animal models. A: Compared
to wildtype mice, rd1 and rd10 mice exhibited elevated spontaneous firing as degeneration pro-
gressed; spontaneous activity in rd1 mice continued to increase with age while rd10 spontaneous
rates slowly decreased in older animals (Stasheff et al., 2011). B: In P23H rat, OFF ganglion
cells exhibited an increase in spontaneous activity followed by a slow decline with increasing age
(Sekirnjak et al., 2011).
The age of rd10 mice used in this study and condition of the retina represent an
intermediate stage of retinal degeneration. From a clinical perspective, the decreased
thresholds observed in high rate rd10 RGCs are promising. However, it is unknown
whether this increased spontaneous activity is stable and persists through later stages of
degeneration. Reports of spontaneous flashes of light were documented in RP patients at
intermediate stages of degeneration which may be a clinical manifestation of the increased
activity observed across numerous degeneration models (Delbeke et al., 2001; Heckenlively
et al., 1988). If spontaneous activity eventually decreases, will thresholds for these cells
subsequently rise? When comparing spontaneous activity with age in rd10 mice, Stasheff
83
et al. observed an initial increase in baseline rate, peaking around P50, followed by a
slower decline in spontaneous activity in older animals (Fig. 4.13a). Similarly, in P23H
rat, Sekirnjak et al. observed an increase followed by slow decrease in spontaneous rate
with age in OFF RGCs (Fig. 4.13b). The periods of increased activity in both studies
coincided with intermediate stages of degeneration for the respective rodent models. Our
findings suggest that degenerate high rate RGCs in this intermediate stage of degeneration
will have decreased thresholds which are preferential from a clinical perspective. However,
increased spontaneous firing can potentially introduce noise that may affect the quality
of information sent to higher visual centers.
84
Chapter 5
Modeling the response of RGCs to electrical stimulation
In this chapter, we present preliminary work toward building a realistic model of a RGC
that can be useful as both a descriptive and predictive tool for retinal stimulation studies.
Neural models can provide insight into potential mechanisms for neural behavior that
may not be as feasible to determine experimentally. Although certain properties, such as
morphology, faithfully represent a real neuron, the majority of a model’s parameters are
specified by the user so experimental validation is necessary to improve the strength and
usefulness of the model. Here, we use empirical data to create a realistic ganglion cell
model that can potentially be used to predict the RGC response to novel stimuli.
5.1 Background
5.1.1 Biophysical and Anatomical Properties of the AIS
The axon initial segment (AIS) is largely believed to be the site of action potential
initiation across many types of neurons, including RGCs (Wimmer et al., 2010). The AIS
is the axonal segment located between the soma and the narrow segment of the axons (Fig.
85
5.1). Initially, the axon hillock, a specialized area of the soma connecting the cell body to
the axon, was thought to be the site of action potential initiation; however, physiological
recordings and modeling results from salamander and mudpuppy RGCs found that the
AIS was more excitable than the neighboring neural elements and was likely to be the
principal site of action potential initiation (Carras et al., 1992). In many cases, the axon
hillock is usually included as part of the AIS in neuronal models.
Figure 5.1: Schematic of a neuron showing the axon initial segment (AIS). The AIS (shown in
red) is located adjacent to the axon hillock which functions as a neuronal connection between the
soma and axon. The AIS is largely believed to be the site of action potential initiation (Wimmer
et al., 2010).
The thinning of the axon proximal to the AIS (narrow segment) has also been observed
in rabbit RGCs (Fried et al., 2009). Fried et al. found that stimulation thresholds (using
an extracellular electrode) were lowest at the AIS, consistent with findings in salamander
RGCs. This study used immunohistochemical methods to reveal the presence of a dense
band of sodium channels centered at the region of lowest thresholds, which suggests that
86
this sodium channel band is responsible for action potential initiation. The length of the
sodium band was variable between cells and was not limited to the AIS but could also
extend into the narrow segment (NS) of the axon.
5.1.2 Efficiency of Pulse Waveforms
Traditionally, retinal stimulation studies employ monophasic or symmetric biphasic square
pulses to elicit responses in RGCs. For in vitro and acute in vivo studies, monophasic
stimulation may be suitable; however, for chronic stimulation of tissue, symmetric bipha-
sic waveforms would be ideal to reverse the reactions occurring at the electrode-tissue
interface and thus preventing any undesirable electrochemical reactions. The majority
of strength-duration curves and chronaxie values from electrical stimulation of excitable
tissue have been determined using symmetric biphasic square pulses. However, chronaxie
is dependent on a number of factors, including stimulus waveform shape (Geddes, 2004),
so extracellular stimulation membrane parameters should be characterized with respect
to the applied pulse shape.
Stimulation thresholds are generally lower for cathodic-first biphasic pulses compared
to anodic-first pulses, though there are exceptions (Jensen and Rizzo, 2009; Koivuniemi
and Otto, 2011). Also, the use of an interphase gap (IPG) between the cathodic and an-
odic phases has been shown to decrease thresholds (Carlyon et al., 2005; John et al., 2013;
Weitz et al., 2011). The response of different excitable membranes can vary depending on
the stimulus pulse used, so experimental application of novel stimuli may help determine
the efficiency of different waveforms.
87
5.1.3 Waveforms for Selective Stimulation
Optimization of neural stimulation parameters should not only consider the most energy-
efficient waveforms but also waveforms that may be used to selectively stimulate specific
neural segments. Ideally, electrodes for neural stimulation would be small and in close
proximity to the targeted neural region to activate a focal region. In practice, the place-
ment and proximity of implantable electrodes to the targeted region is variable. In these
cases, it would be beneficial to apply stimuli that selectively activate a specific neural
segment or cell type. Several studies have investigated the efficiency of unconventional
stimulus waveforms for selective activation of various neural areas. In the retina, Free-
man et al. found that sinusoidal stimulation may selectively target inner retinal neurons
(bipolar cells, photoreceptors) due to the differences in chronaxie for these cell types.
McIntyre et al. and Koivuniemi et al. found that cathodic-first asymmetric waveforms
could reduce thresholds and may selectively stimulate cell bodies for cortical stimulation
applications. Also, models for nerve fiber stimulation have suggested that asymmetric
and non-rectangular waveforms may also be effective for diameter-selective activation
of fibers. Although symmetric square pulses have conventionally been used for neural
stimulation, application of novel stimuli may also be effective for selective activation of
excitable tissue.
88
5.2 Methods
The parameters for neuron simulations as well as the electrical stimulation parameters
are described in this chapter. Descriptions of methods used throughout all studies are
described in detail in Chapter 2.
5.2.1 Neuron Simulations
The morphology of fluorescent RGCs (Lucifer Yellow fluorescence marker or transgenic
YFP) was traced using Neurolucida and imported into the Neuron simulation environ-
ment. The approximate structural properties of neural elements were maintained so the
diameters and lengths of RGC dendrites were not altered in any simulations.
5.2.1.1 Biophysical Mechanisms
The model ganglion cell consisted of several Hodgkin-Huxley-type voltage-gated ion chan-
nels; the representative schematic for the various ion channels incorporated throughout
the cell membrane are shown below (Fig. 5.2).
Figure 5.2: Schematic for a Hodgkin-Huxley-type model of a retinal ganglion cell. Components
of this RGC model include Na
+
, Ca
2+
, delayed rectifier K
+
, A-type K
+
, T-type Ca
2+
, and mixed
cation H channels with a leak channel and membrane capacitance.
The total ionic current based on the above schematic is as follows:
89
where m and h represent the activation and inactivation states, respectively, of the
Na
+
channel, c represents the activation state for the voltage-gated Ca
2+
channel, n
is the activation state for delayed rectifier K
+
channel, a and h
a
describe the activation
and inactivation states, respectively, of the A-type K
+
channel, l represents the activation
state of the hyperpolarization-induced mixed cation H channel, and m
t
and h
t
describe the
activation and inactivation states, respectively, of the T-type Ca
2+
channel. The opening
probability of activation gates increases with depolarization while the reverse is true for
inactivation gates. Simulation parameters are listed below; the channel conductance
values for each segment of the model neuron are specified in the following section.
The gating variables m, h, c, n, a, ha, l, and mt for the voltage-gated ionic cur-
rents were governed by the following first-order kinetic equations ( x represents the gating
variable):
90
The inactivation gate for the T-type Ca
2+
channel has two closed states so the kinetics
for this ionic current are described by the second-order equations:
The voltage-dependent rate constants for each ion channel are shown below.
91
92
5.2.1.2 Na+ Channel Band
Fried et al. used immunohistochemical methods to reveal the presence of a dense band
of sodium channels near the axon initial segment. The length and relative distance of
this sodium band from the soma was variable between cells and was not restricted to the
initial segment. Although the location and length of the bands could be ascertained from
the staining methods, they did not provide quantitative measurements about the density
of sodium channels in this region relative to the surrounding neural segments. A higher
density of sodium channels was distributed in the initial segment and part of the narrow
segment for the model cell’s axon; specific densities used for simulations are detailed in
the following section.
5.2.1.3 Anatomical Features of the Axon
Axons were modeled with either a uniform axon diameter from the soma to the distal axon
or by incorporating a narrow segment (NS) immediately following the axon initial segment
(AIS). The schematic for an axon with a narrow segment is shown in Fig. 5.3. Rather
than abruptly change the diameter between the AIS and NS, a taper was incorporated to
reflect the gradual transition between adjacent neural elements. There were two tapering
regions – a narrowing taper between the AIS and NS and a broadening taper between
the NS and the distal axon.
The length and diameter of each neural segment are shown in table 5.1.
The distribution of channels and their segment-specific densities are listed in table
5.2.
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Figure 5.3: Diagram of the anatomical features of a model axon. The model incorporated
tapering segments between the AIS, narrow segment, and distal axon.
Table 5.1: Table of dimensions (length, diameter) for all neural segments incorporated into the
model
Table 5.2: Distribution of channels and segment-specific conductances for all neural segments of
the model
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5.2.2 Physiological Recordings
Responses of individual RGCs were measured using whole cell current clamp recordings.
Detailed methods for whole cell recording and measuring thresholds are discussed in
Chapter 2.
5.2.2.1 Placement of Stimulating Electrode
The purpose of the experiments in this chapter was to compare stimulation thresholds
at the soma and the distal axon using an asymmetrical waveform. Instead of positioning
the electrode 50 m laterally and above the ganglion cell layer (GCL) as in previous
experiments, the electrode was instead positioned 50 m directly above the edge of the
targeted RGC (Fig. 5.4).
Figure 5.4: Position of stimulating electrode for physiological recordings. Left: The stimulating
electrode was a 75 m diameter platinum iridium disk electrode positioned approximately 50 m
directly above the edge of the targeted RGC for recording (right).
When stimulating at the axon, the electrode was again 50 m above the GCL. The
distance between the electrode and RGC varied depending on where the cell was located
relative to the optic disk. The axon bundle, including the axon of the targeted RGC, was
followed to the farthest distance that could be reliably visualized under the objective.
95
Nikon NIS Elements software (version 4.1) was used to measure the distance between the
electrode and the soma when stimulating at the axon (Fig. 5.4).
5.2.2.2 Fluorescence Imaging of the RGC Axon
Threshold comparisons at the soma and distal axon necessitated confirmation that the
axon of the targeted RGC was intact. All cells were filled with Lucifer Yellow and
imaged at the end of the recording using Nikon NIS Elements software. Figure 5.5 shows
an example of a filled RGC with attached axon.
Figure 5.5: Fluorescent image of RGC showing dendrites and axon. (A-F): Images of a ganglion
cell (pipette filled with Lucifer Yellow) at varying focal depths showing dendritic structure, soma,
and axon. (G,H ): Dendritic structure and intact axon after removal of the pipette.
96
5.3 Results
Based on empirical data, a realistic RGC model was constructed to replicate certain
properties of ganglion cells such as rebound excitation and the ability to spike repetitively
to a constant current input. Additionally, the response of the model RGC to extracellular
stimulation was also analyzed. Our novel stimulus was an asymmetric charge-balanced
biphasic (cathodic-first) rectangular pulse. The model was used to predict the expected
response to this novel stimulus. Physiological recordings from WT and rd10 RGCs were
measured to test the validity of the modeled results.
5.3.1 Realistic 3D Model of a Retinal Ganglion Cell
The simulations in this section were performed using the morphological properties from
the cell shown in Fig. 5.6. Unless specified otherwise, the model incorporated a narrow
axon segment as well as the biophysical mechanisms detailed in Section 5.2.1.3; the axon
did not incorporate any curvature or bends.
Figure 5.6: Fluorescence image and neuronal tracing of a RGC. A z-stack provided 3D informa-
tion about the cell’s morphology which was subsequently imported into a neural reconstruction
program (Neurolucida). The exported 3D traced cell was then imported into the Neuron sim-
ulation environment where anatomical properties of the axon and biophysical mechanisms were
modified.
97
5.3.1.1 Repetitive Firing
The model was used to simulate the repetitive firing of a ganglion cell to a step current.
The Neuron IClamp function was used to deliver a constant step current to the cell for
400 ms; step onset was at 100 ms. Fig. 5.7 shows simulations for two step amplitudes
and corresponding physiological results for two different RGCs.
Figure 5.7: Repetitive firing of RGCs to a constant current step. Simulated ( a1,b1) and phys-
iological (a2,b2) current clamp recordings of the repetitive firing of RGCs to a 400 ms constant
current step. Step onset at t = 100 ms.
The RGC model was able to simulate the repetitive firing of ganglion cells to a constant
current step. The amplitudes of action potentials and frequency of firing were comparable
between the simulated results and experimental recordings.
98
5.3.1.2 Family of Hyperpolarizing Current Steps
The Neuron IClamp function was used to deliver a family of hyperpolarizing current steps
(300 ms in duration) to the RGC model. Each step family had amplitudes ranging from
0 to -100 pA (10 pA increments). Fig. 5.8 shows the hyperpolarizing step families for
two types of cells – those without any rebound excitation and those exhibiting rebound
firing.
Figure 5.8: RGC response to a family of hyperpolarizing current steps. Top: Simulation results
for a cell exhibiting no rebound excitation (A1) and a cell firing rebound spikes ( A2). Bottom:
Physiological RGC current clamp recordings for non-rebounding (B1) and rebounding (B2) gan-
glion cells. A total of 10 current steps (0 to -100 pA; increments of -10 pA) were delivered for
both simulated and physiological results.
To simulate a ganglion cell without rebound excitation, the T-type Ca
2+
and I
h
chan-
nels were not included in the cell’s biophysical mechanisms. Based on extensive in vitro
99
work, it is believed that the mechanisms driving rebound responses are primarily gener-
ated by these two channels (Engbers et al., 2011). The h channel, a hyperpolarization-
activated mixed cation channel that is normally closed at rest, is activated when the
membrane potential is hyperpolarized; after termination of a hyperpolarizing step, the I
h
channel produces an immediate depolarization. The T-type Ca
2+
channel is generally in
an inactivated state at rest; hyperpolarization removes this inactivation and subsequent
depolarization (by bringing the membrane potential back to baseline) strongly activates
this conductance, which then produces a regenerative low threshold calcium spike (Mitra
and Miller, 2007). After the hyperpolarizing influence is terminated, the combined influ-
ence of these two channel conductances results in a net depolarization which subsequently
boosts the generation of conventional fast Na
+
currents resulting in rebound excitation.
Without these channels inserted into the model, the cell does not display any rebound
activity after the negative step is terminated. To model a RGC exhibiting rebound ex-
citation, the T-type Ca
2+
channels were inserted into all neural segments (axon, soma,
dendrites). Similar to physiological results, the number of rebound spikes and rebound
firing frequency was dependent on the level of hyperpolarization prior to termination of
the current step; also, the onset of rebound excitation occurred sooner as the membrane
potential was incrementally hyperpolarized (larger negative step amplitude).
5.3.2 Modeling the RGC Response to Electrical Stimulation
The model for simulating the response to electrical stimulation used a point source elec-
trode which could be positioned anywhere in the 3D space surrounding the model cell. To
100
maintain consistency with physiological experiments, the model electrode was positioned
50 m above the ganglion cell soma.
5.3.2.1 Elicited Action Potentials
A 500s biphasic (cathodic-first) symmetric square pulse was delivered through the model
electrode to simulate both subthreshold and threshold responses (Fig. 5.9). Overlaid
traces of subthreshold and threshold responses for a representative WT ganglion cell are
also shown in fig. 5.9a2,b2.
Figure 5.9: RGC response to an extracellular biphasic pulse. Top: The response of a model RGC
(a1) and representative ganglion cell (a2) to a subthreshold stimulus level. Bottom: Threshold
responses for the same model and physiological RGCs in a1 and a2. Physiological recordings
consists of 10 overlaid traces; elicited action potentials are denoted by an asterisk (b2).
The size of the stimulus artifact is much larger for physiological recordings which
may be attributed to differences in the modeling and experimental recording configura-
tions. The model consists of a single RGC in an infinite volume of solution modeled
101
with a constant resistivity ( = 70 Ω-cm) while physiological recordings of RGCs are
measured in intact retina; additionally, the external stimulator and recording amplifier
may also contribute to the differences in stimulus artifact. Despite these differences, the
response latency and amplitude of elicited spikes are comparable between the modeling
and experimental results.
5.3.2.2 Strength-Duration Curve
Thresholds for pulsewidths ranging from 100 s to 10 ms were determined using the
model and the corresponding strength-duration curve was constructed (Fig. 5.10A). The
strength-duration curve for rd10 RGCs is also shown for comparison in figure (Fig. 5.10B).
Lapicque’s equation (Lapicque, 1931) was used to extract rheobase (7 A) and chron-
axie (400 s) for the model cell. The model chronaxie was lower than experimentally-
measured chronaxie values for WT and rd10 RGCs (520 s and 550 s, respectively).
The discrepancy in physiological and simulated chronaxie values may arise because the
strength-duration curve for the model was constructed with a single RGC simulation
whereas the chronaxie value from physiological recordings was averaged for a range of
different RGCs; though lower, the modeled chronaxie value was very close and within the
range of reported experimental values.
5.3.3 Preliminary Work Toward a Predictive Model
In previous sections, the model was used to simulate the responses of real physiological
RGC recordings. Various parameters within the model were tuned to reliably reproduce
different aspects of the RGC response. Here, we used the model to predict the RGC
102
Figure 5.10: Strength-duration curves for modeling and physiological data. A: Chronaxie for
the model RGC was approximately 400 s; similar to electrophysiological recordings, the model
data was fit using Lapicque’s equation to determine chronaxie. B: Strength-duration data for
physiological recordings yielded chronaxie values of 520 s and 550 s for WT and rd10 cells,
respectively (Chapter 3).
response to a novel stimulus, and then performed the corresponding experiments in WT
and rd10 retina to test the validity of modeled results.
5.3.3.1 Modeling the response to a novel stimulus: an asymmetric charge-
balanced waveform
In this section, we used the model to predict the response to an asymmetric charge-
balanced (cathodic-first) rectangular waveform. Asymmetric waveforms are traditionally
not used for neural stimulation; rather, monophasic or symmetric biphasic square pulses
are the conventional stimuli used in clinical and pre-clinical applications. Several ex-
perimental and modeling studies have suggested that cathodic-first waveforms decrease
stimulation thresholds, and that asymmetric cathodic-first waveforms may selectively
stimulate cell bodies (as opposed to axons/nerve fibers) (Jensen et al., 2003; McIntyre
103
and Grill, 2000, 2002). A majority of these studies focused on different neural applica-
tions, such as cortical, cochlear, and neuromuscular stimulation since studies utilizing
novel waveforms for retinal stimulation have been fairly limited.
Three different biphasic charge-balanced stimulus waveforms were applied to the
model: a 500 s symmetric, a 2 ms asymmetric, and a 2 ms symmetric pulse (Fig.
5.11). The asymmetric pulse had a 2 ms duration cathodic phase and a 500 s duration
anodic phase. Since the waveforms were not all symmetric, threshold was calculated in
terms of charge (nC) rather than current (A), where charge (Q) is the product of pulse
amplitude (I) and pulse duration (d): (Q = I*d).
Figure 5.11: Three test waveforms applied to the model investigating selectivity at the soma
for different pulses. A: A 500 s symmetric biphasic (cathodic-first) square pulse (used in physi-
ological recordings) was applied to the model RGC. B: An asymmetric, charge-balanced biphasic
square pulse with a 2 ms cathodic phase and 500 s anodic phase was applied as the novel wave-
form for selectivity at the soma. C : A 2 ms symmetric biphasic square pulse was also tested to
determine the difference in charge requirements for a longer duration stimulus.
The modeled threshold responses to all three waveforms are shown in fig. 5.12. The
definition of an elicited action potential was dependent on the stimulus pulse. For the
500s pulse, an elicited spike was defined as one occurring within 3 ms of stimulus onset
(similar to previous definitions (Chapter 3)). An elicited spike for the 2 ms asymmetric
pulse was one occurring within 4 ms of stimulus onset, while for the 2 ms symmetric
pulse, the action potential had to be elicited within 5 ms of stimulus onset.
104
Figure 5.12: Simulation results for three test waveforms. Response of the model RGC at
threshold to a (A) 500 s symmetric pulse, (B) 2 ms asymmetric pulse, and (C ) 2 ms symmetric
pulse (test waveforms shown in fig. 5.11).
When stimulating at the soma, the external electrode was positioned 50 m above
the ganglion cell body. For axonal stimulation, the electrode was placed 50 m above
the center of the axon and 500 m laterally from the soma. The values for threshold
charge at the soma and axon for all three pulses are shown in table 5.3. Charge when
stimulating at the axon was higher for all waveforms than stimulation at the soma, which
suggests that these waveforms may selectively stimulate the soma. For this simulation,
the relative increase in threshold charge for the 500 s pulse was less pronounced than
the 2 ms duration pulses.
Table 5.3: Comparisons of threshold charge for the model RGC at the soma and axon for three
test waveforms.
Fig. 5.13 shows the dependence of both threshold current and charge on the pulse
duration. For very short pulses, threshold currents are very high; as pulse duration
increases to an infinitely long pulse, the current to elicit a response plateaus to a baseline
105
value, commonly referred to as the rheobase current. However, the threshold charge
increases linearly with duration so longer pulses will result in higher charge.
Figure 5.13: Dependence of threshold current and charge on pulse duration. As pulse duration
increases, the current (I) to elicit a threshold response reaches a minimum value (rheobase); an
infinitely long pulse would still require a current level equal to the rheobase current to achieve
neuronal activation. On the other hand, the threshold charge (Q) increases linearly with pulse
duration. These relationships were obtained using rectangular, constant-current pulses (Geddes,
2004).
Based on the relationship between charge and pulse duration, we would expect the
2 ms asymmetric and 2 ms symmetric waveforms to have higher threshold charge values
than the 500 s pulse. Threshold charge for the three waveforms at the soma and axon
are shown in fig. 5.14.
As expected, the required charge for longer pulses is higher than for the 500 s pulse.
Although the 2 ms asymmetric and 2 ms symmetric waveforms were selective for the soma
(i.e. higher threshold charge at the axon), both waveforms would likely still stimulate
axons since their charge values were higher than the threshold charge at the axon for the
500 s pulse.
106
Figure 5.14: Simulation results for threshold charge at the soma and axon using three test wave-
forms. All three test pulses were selective for the soma though selectivity was more pronounced
for the 2 ms asymmetric and 2 ms symmetric waveforms.
5.3.3.2 2 ms Asymmetric and 500 us Aymmetric Pulses Selectively Stimulate
the Soma
Experiments in WT and rd10 mice were performed using methods similar to those detailed
in Chapter 2.
The values for threshold charge at the soma and axon as well as the electrode-RGC
distance for each cell are shown in table 5.4. The physiological response to the same
three test waveforms was recorded for stimulation at the soma and axon. Comparisons
of the simulated responses and electrophysiological responses to the three test waveforms
is shown in fig. 5.15.
The distance between the targeted RGC and optic disk was variable which subse-
quently affected the distance between the RGC and stimulating electrode. The response
latencies for all three waveforms were comparable to simulation results. Both the 500 s
symmetric and 2 ms asymmetric pulses required significantly more charge at the axon,
which suggests that these waveforms selectively stimulate the soma (Fig. 5.15).
107
Table 5.4: Physiological RGC response data applying three test waveforms at the soma and
axon.
5.3.3.3 Threshold Charge is Comparable for 2 ms Asymmetric and 500 us
Symmetric Pulses
Threshold charge values at the soma and at the axon were compared for the three wave-
forms. Fig. 5.16 shows the charge comparisons at both locations for all three pulses.
The charge was significantly higher for the 2 ms symmetric waveform than the 500s
symmetric and 2 ms asymmetric waveforms, regardless of stimulus location. However,
108
Figure 5.15: Differences in threshold charge for three test waveforms applied at the soma and
axon. A: three test waveforms used for modeling and physiological recordings (500 s symmetric,
2 ms asymmetric, and 2 ms symmetric pulses). B: Threshold responses of a model RGC for
each pulse. C : Physiological recordings of threshold responses for a representative RGC using the
same stimulus waveforms. Threshold responses consists of 10 overlaid traces with elicited action
potentials denoted by asterisks. D: The increases in charge at the axon for the 500 s symmetric
and 2 ms asymmetric pulses were statistically significant (*P < 0.05); there was no statistically
significant difference in charge between the soma and axon for the 2 ms symmetric pulse.
the difference in charge between the 500 s symmetric pulse and the 2 ms asymmetric
pulse was not significantly different at both the soma and axon.
109
Figure 5.16: Experimental results showing threshold charge differences for three waveforms
applied at the soma and axon. The 2 ms symmetric pulse required significantly more charge
to activate RGCs compared to both the 500 s symmetric and 2 ms asymmetric waveforms.
Threshold charge differences between the 500 s symmetric and 2 ms asymmetric pulses were not
statistically significant. **P < 0.001, * P < 0.05
5.4 Conclusions
Parameters derived from strength-duration data, including chronaxie and rheobase, pro-
vide quantitative measurements of the relative excitability of various membranes. How-
ever, a majority of experimentally-derived values have been determined using limited
pulse waveforms, i.e. monophasic or symmetric biphasic square pulses. In this chapter,
we used a realistic model of a retinal ganglion cell to predict the response to a novel
waveform. The model was tuned to reproduce certain RGC properties, such as repetitive
firing, rebound excitation, and eliciting a single spike to an extracellular stimulus pulse.
An asymmetric charge-balanced (2 ms cathodic/500s anodic) rectangular pulse was
selected as the novel stimulus. Although long duration pulses (25-50 ms) may be used
to selectively activate inner retinal cells and thus localize the area of excitation, these
pulses also require significantly more charge and limit the stimulation frequency (Lee
et al., 2013). Studies have demonstrated that very short duration pulses can primarily
elicit responses from RGCs (Sekirnjak et al., 2006, 2009). In these studies, the retina was
110
directly in contact with the stimulating electrode array. Analyses in retinal prosthesis
patients have shown variability in electrode/retinal surface distance as well as an increase
in perceptual thresholds as this distance increases (de Balthasar et al., 2008; Mahade-
vappa et al., 2005). Very short duration pulses would require much higher threshold cur-
rents which may potentially exceed the compliance voltage of an implantable stimulator.
Findings from various modeling and experimental studies for cortical and cochlear stim-
ulation found that asymmetric waveforms may decrease stimulation thresholds compared
to thresholds using symmetric pulses (Koivuniemi and Otto, 2011; Macherey et al., 2006;
Shepherd and Javel, 1999; van Wieringen et al., 2008), and furthermore, that asymmet-
ric cathodic-first pulses may selectively stimulate cell bodies (as opposed to axons/nerve
fibers) (McIntyre and Grill, 2000, 2002).
The model showed selectivity at the soma for all three modeled waveforms, although
the relative increase in charge was less pronounced for the 500 s pulse. Also, the thresh-
old charge values for the 2 ms pulses were higher than the 500 s pulse, which was not
entirely surprising since charge has been shown to increase linearly with pulse duration
(Geddes, 2004). Based on the simulations, we expected all three waveforms to selectively
stimulate the RGC soma. However, since the relative difference in charge for the 500
s symmetric pulse was much lower than both 2 ms duration pulses, we would expect
the dynamic range for threshold charge at the soma to be greater for the longer-duration
pulses. Since thresholds are variable from cell to cell, if applying a 500 s symmetric
stimulus, a slight increase in charge (2-3 nC) would begin to activate passing axons.
111
Physiological recordings from WT and rd10 RGCs showed comparable threshold
charge levels for the 500 s symmetric and 2 ms asymmetric pulses while the 2 ms sym-
metric pulse required significantly more charge than the other two waveforms. Geddes
showed that charge increases linearly with pulse duration; however, this relationship was
derived using symmetric square pulses. For asymmetric pulses, it is possible that thresh-
old charge is not linearly related to pulse duration or simply may rise very slowly as pulse
duration is increased, which may be one reason the threshold charges were comparable
between the 500 s symmetric and 2 ms asymmetric pulses.
Similar to the modeling results, the threshold charge at the RGC soma for the 2 ms
asymmetric pulse was significantly lower than axonal threshold, which suggests selectivity
at the soma. The 2 ms symmetric pulse had comparable charge at both the RGC soma
and axon, suggesting that this waveform would not differentiate between cell bodies and
passing axons. The comparable threshold charge between 500 s symmetric and 2 ms
asymmetric pulses was unexpected (assuming a linear charge/pulse duration relationship)
since the duration of the asymmetric pulse was 4 times longer than the 500 s pulse. A
stimulation study of rat auditory cortex by Koivuniemi et al. applied both symmetric and
asymmetric square waveforms and found that the primary determinant of threshold level
was cathode phase duration; in this study, asymmetry did not contribute to significant
reductions in threshold (Koivuniemi and Otto, 2011). Contrary to our results, the study
by Koivuniemi et al. showed that stimuli with shorter pulse durations (cathodic-first)
required less charge to elicit a threshold response.
112
Figure 5.17: Activating function for anodal and cathodal monophasic stimulation. The activat-
ing function is responsible for activating a neural element by electrical stimulation. The shaded
areas represent regions where the activating function is positive and would thus activate a cell or
neural fiber at those locations (Rattay, 1989).
The activating function, as described by Rattay, is responsible for activating a fiber
by extracellular stimulation (Rattay, 1989). The activating function for monophasic an-
odal and cathodal stimulation is shown in Fig. 5.17. The areas that are shaded (positive
values for the activation function) represent regions along a cell or nerve fiber that would
be depolarized while un-shaded regions represent areas of hyperpolarization. Both catho-
dal and anodal stimuli are capable of eliciting threshold responses, though the areas of
activation differ (unimodal region for cathodal stimulation, bimodal for anodal stimula-
tion) and thresholds for anodic-phase first stimulation are generally higher compared to
cathodic-first pulses (Jensen and Rizzo, 2006; Jensen et al., 2005). Our asymmetric wave-
form consists of a 2 ms cathodic phase immediately followed by a 500 s anodic phase.
The cell membrane is depolarized by the 2 ms duration, lower amplitude pulse, though
this depolarization may not necessarily elicit an action potential on its own. When the
higher amplitude, shorter duration 500 s anodic phase is applied, the cell membrane
is already in a state of depolarization (due to the preceding cathodic phase) and thus,
theoretically closer to threshold. If applying the anodic phase from the cell’s resting
113
state, this 500s anodic pulse may not elicit an action potential (subthreshold response).
However, if the cell membrane is moderately depolarized prior to this anodic pulse, the
areas of depolarization (shown by the anodal activation function in Fig. 5.17 may be
sufficient to generate an action potential. In this, it is possible that the asymmetry of the
waveform, specifically this particular combination of cathodic/anodic phase durations,
may contribute to charge thresholds comparable to shorter pulses. On the other hand,
asymmetric waveforms with longer cathodic phases (5-10 ms) may show an increase in
thresholds; increasing duration of a near-threshold stimulus will result in a decreased
sodium conductance and increase potassium conductance since the neuron will being
to repolarize, effectively elevating threshold (Noble, 1966). Although symmetric square
pulses have conventionally been used for neural stimulation, these results suggest that
asymmetric pulses may also be efficient for stimulation of RGCs.
114
Chapter 6
Summary
The work presented in this thesis has investigated the effects of retinal degeneration on
the temporal response properties and sensitivity of retinal ganglion cells to electrical
stimulation.
6.1 Key Findings and Future Work
There are numerous studies investigating the retina’s response to electrical stimulation;
however, a majority of these studies have been conducted in normal retina. The rd10
mouse model was chosen since it exhibits a slower rate of degeneration, allowing the retinal
circuitry to fully develop before onset of degeneration. A more aggressive degeneration
model would not enable us to distinguish the effects of incomplete retinal development
from those of retinal degeneration.
The first aim in this thesis was to determine whether retinal degeneration influences
the temporal response properties of RGCs. This investigation revealed that the ability
of degenerate ganglion cells to generate action potentials in a manner similar to normal
115
controls did not appear to be affected in the rd10 mouse model. Chronaxie values and re-
sponse latencies were comparable between normal and degenerate cells, which suggest the
membrane mechanisms responsible for action potential generation are preserved in degen-
erate RGCs. Since the temporal properties of elicited spikes were generally preserved in
the rd10 model, this implies that a retinal prosthesis would be able to effectively generate
RGC responses at threshold in blind patients.
Although the temporal response properties of degenerate RGCs appeared to be largely
uninfluenced by degeneration, stimulation thresholds were significantly elevated and highly
variable compared to normal retina. Thresholds for prostheses are dependent on several
factors, one being the proximity of the implant to the retina. Higher thresholds could
potentially render inactive a number of electrodes on a prosthesis to avoid electrochemical
safety limit and hardware compliance issues).
Investigation of several RGC intrinsic properties revealed that resting membrane po-
tential and baseline spontaneous activity both contributed to reduced stimulation thresh-
olds in degenerate retina; these two properties did not influence thresholds in normal mice.
These findings suggest that loss of photoreceptors initiates changes that ultimately alter
the intrinsic physiology of ganglion cells.
A notable observation in rd10 retina was an increase in the spontaneous activity of
RGCs, consistent with findings of elevated firing in other models of degeneration. In
some cases, these membrane fluctuations were periodic or oscillatory in nature. Analysis
of stimulation thresholds revealed that RGCs with these properties (increased sponta-
neous firing and membrane periodicity) had low thresholds comparable to those in normal
116
retina. Even still, the intrinsic behavior of this particular subset of RGCs was significantly
different from normal controls despite reduced thresholds.
From a clinical perspective, the reduction in thresholds for spontaneous active RGCs
is desirable with respect to prosthetic applications. However, it is unknown whether
this increased spontaneous activity will persist or even remain stable as degeneration
progresses. These findings warrant further examination of the intrinsic RGC physiology
at various stages of degeneration to understand how these changes may impact thresholds
for stimulation.
Our findings suggest that degenerate high rate RGCs in an intermediate stage of
degeneration will have decreased thresholds which are preferential from a clinical per-
spective. If spontaneous activity and thresholds are indeed correlated, treatments to
preserve inner retinal function and thus maintain intrinsic excitability would be highly
beneficial for prosthesis patients. Animal studies using various retinal neuroprotective
therapies, including neurotrophic factors and pharmacological targeting of ion channels,
have shown varying degrees of success in preserving ganglion cells and inner retinal neu-
rons. These neuroprotective therapies might not necessarily be treating the source of
degeneration but may help prolong retinal health, possibly enough to preserve vision in
a patient’s lifetime.
On the other hand, increased spontaneous firing can potentially introduce noise that
may affect the quality of information sent to higher visual centers. It would be interesting
to examine spontaneous retinal activity in RP patients as well as prosthesis subjects to
understand the role and potential implications of elevated firing with respect to prosthetic
applications. It is possible that increased spontaneous activity may not significantly
117
impact visual information generated by a prosthesis, in which case, preservation of the
inner retina would be an important strategy for improving outcomes in retinal prosthesis
patients.
Using empirical data, a realistic model was constructed to investigate the RGC re-
sponse to a novel waveform, an asymmetric (2 ms cathodic/500 s anodic) pulse hy-
pothesized to selectively stimulate the soma. Physiological recordings revealed the novel
stimulus selectively activated the soma, in line with the simulated results. However, the
difference in threshold was variable from cell to cell and fell within the standard devia-
tions of thresholds at the soma and axon, which suggests that a threshold charge level
for soma stimulation of one cell could potentially also be the threshold charge level for
axonal stimulation of a different cell. Interestingly, the asymmetric waveform had com-
parable threshold charge to a conventional symmetric pulse (500 s/phase), even though
the asymmetric cathodic phase was 4x longer in duration. Although symmetric square
pulses have conventionally been used for neural stimulation, these results suggest that
asymmetric pulses may also be efficient for stimulation of RGCs.
Desensitization to prolonged stimulation has been reported with prosthesis patients
describing this phenomenon as a ‘fading’ of the evoked percept. Conventional stimulation
strategies deliver high frequency trains of symmetric pulses, which can contribute to this
desensitization. In light of these clinical observations, it would be worthwhile to examine
the effects of alternating (symmetric/asymmetric) waveforms in prosthesis subjects to
determine if different stimulus trains can mitigate the effects of desensitization.
The work presented in this thesis takes a careful examination of the changes in RGC
physiology in a mouse model of retinal degeneration, and how these alterations potentially
118
affect the ganglion cell response to electrical stimulation. Despite certain physiological
differences, degenerate ganglion cells maintain the ability to consistently generate action
potentials and reliably respond to threshold stimulation levels in a manner comparable
to the normal RGC response, a promising finding in the quest to restore vision through
prosthetic stimulation.
119
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Abstract (if available)

Abstract Retinitis pigmentosa (RP) and age‐related macular degeneration (AMD) are two common outer retinal diseases for which there are currently no cures. These degenerative diseases initiate the death of photoreceptor cells and can ultimately lead to complete blindness. Despite the loss of these cells, however, much of the inner retina remains intact providing a potential means for signal transmission to higher visual centers. Various approaches to elicit visual percepts through retinal stimulation have demonstrated the feasibility of engaging the remaining retina to convey visual information to the brain. ❧ One strategy for inducing activity in residual retinal cells is to stimulate the retinal ganglion ganglion cells (RGCs) electrically. RGCs receive information from inner retinal cells and send visual information, encoded in trains of action potentials, along their axons to higher visual centers. Significant rewiring and remodeling as well as individual structural changes in inner retinal cells have been observed in human retina as well as various animal models of degeneration. Therefore, direct stimulation of RGCs can bypass the anomalous inner retina circuitry. However, RGC firing patterns, both spatial and temporal, are critical to vision. To be able to best replicate natural vision, bioelectronic‐based vision restoration therapy must have exquisite control of RGC firing, which requires a thorough understanding of how electrical stimulation elicits responses from RGCs. ❧ The physiological behavior of RGCs in normal and degenerate mice was measured using whole‐cell patch clamp recordings. Results presented in this thesis demonstrate that thresholds in degenerate ganglion cells are significantly elevated and highly variable compared to normal controls, despite having comparable chronaxie values and response latencies. Degenerate RGCs display increases in baseline spontaneous firing not commonly seen in wildtype cells. Furthermore, there is a significant correlation between higher spontaneous rate and reduced stimulation thresholds in degenerate cells

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Asset Metadata

Creator Cho, Alice K. (author)

Core Title Understanding the degenerate retina's response to electrical stimulation: an in vitro approach

School Viterbi School of Engineering

Degree Doctor of Philosophy

Degree Program Biomedical Engineering

Publication Date 12/06/2012

Defense Date 12/13/2013

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag electrophysiology,OAI-PMH Harvest,Retinal degeneration,retinal prosthesis

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Weiland, James D. (committee chair), Chow, Robert H. (committee member), Humayun, Mark S. (committee member), Sampath, Alapakkam P. (committee member)

Creator Email alicecho84@gmail.com,uscalice@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-364286

Unique identifier UC11296547

Identifier etd-ChoAliceK-2259.pdf (filename),usctheses-c3-364286 (legacy record id)

Legacy Identifier etd-ChoAliceK-2259.pdf

Dmrecord 364286

Document Type Dissertation

Format application/pdf (imt)

Rights Cho, Alice K.

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA