PHARMACOLOGICAL AND SENSORY STIMULATION OF by Vikram ...

PHARMACOLOGICAL AND SENSORY STIMULATION OF

AUDITORY CORTEX PLASTICITY IN ADULT RATS

by

Vikram Jakkamsetti

APPROVED BY SUPERVISORY COMMITTEE:

___________________________________________

Dr. Michael P. Kilgard, Chair

___________________________________________

Dr. Lawrence J. Cauller

___________________________________________

Dr. Marco Atzori

____________________________________________

Dr. Christa K. McIntyre

Copyright 2008

Vikram Jakkamsetti

All Rights Reserved

Dedicated to Dad and Mom



by

VIKRAM JAKKAMSETTI, M.B.B.S., M.D., M.S.

DISSERTATION

Presented to the Faculty of

The University of Texas at Dallas

in Partial Fulfillment

of the Requirements

for the Degree of

DOCTOR OF PHILOSOPHY IN COGNITION AND NEUROSCIENCE

THE UNIVERSITY OF TEXAS AT DALLAS

December, 2008

3340479

3340479 2009

v

ACKNOWLEDGEMENTS

Working on a PhD has been like learning how to swim. I conquered my fear of drowning

only recently and the adrenaline surges during my first attempts at swimming last year have

definitely seared the experience deep into my memory. I vividly remember the burning lungs,

the panic, the flailing and thrashing of limbs as I groped for the edge of the pool. My first

few months in the PhD program were no less painful. I was overwhelmed, totally out of my

element, and often saw my sense of self-worth sinking toward dangerous lows. And just like

swimming, all I had to do was reach out and hold onto a firm support as I breathed sweet,

soothing fresh air each time. My support took many forms-mentor, colleague, mentee,

sibling, parent, friend, official, the memory of an inspiring grandparent. Without the buoyant

effect created collectively by all my supports, I would have definitely drowned.

The people who have supported me the most are my Dad and Mom. This awareness

surprised me a little, since Dad and I have rarely had detailed discussions regarding the rigors

of PhD. Yet, I hold him in the highest stead, probably because his example was good enough.

He grew up poor, the eldest son amongst fifteen brothers and sisters, with the responsibility

of being the primary provider for this family. His determination in continuing his PhD at a

highly respected and demanding institute in India, progress into working for respected

research institutes in Europe, eventual success in becoming an entrepreneur with his own

factory- a lot of this being done while being the primary provider and soul for his Mom,

siblings, wife and two children- inspires me tremendously. His example of using scientific

acumen and logical thought-and the successful consequences of the application of such

vi

acumen and thought in daily living- is a strong buoy holding me up as I traverse the choppy

waters of scientific research. My Mom instilled in me a love for literature and creative

writing and the beautiful art of generous diplomacy. As part of a struggling family with

relatively few resources to be shared amongst uncles, aunts and a sibling all under the same

roof, I learnt from her that sharing and negotiating gracefully helps everyone: an attribute

that helped my scientific research in a busy lab and collaborations with adjoining labs. In the

words of the respected late Randy Pausch PhD. : I believe I won the parents lottery.

I have no dearth of superlative words to describe my experience in having Dr.Kilgard

as my mentor-fantastic, super cool, awesome, very very very enriching, neuro guru- these are

some words that come to my mind. He coached me on learning how to think. “Experiments

will happen. You are here to learn to think”, he said in similar words. His example of picking

up a sub-field, mastering it, then moving onto another sub-field is an inspiration for two

useful attitudes: a) that one should master one thing at a time and b) having a good breadth of

knowledge across sub-fields helps instruct each sub-field. He approaches neuroscience with

fascinating enthusiasm that is extremely contagious. His style of management is my first

exposure to a fluid corporate style of complete delegation and complete accountability which

I aspire to emulate in the future. With his interaction with fresh PhD candidates I have begun

to appreciate a neat and subtle method adopted by him. He gauges a student’s growing

abilities and accordingly adjusts his level of expectations. This flexibility was extremely

helpful in my initial years as I floundered repeatedly in learning basic scientific writing

skills.

I have been lucky to have the opportunity to learn from my Dissertation Committee

members- Dr. Lawrence Cauller, Dr. Marco Atzori and Dr. Christa McIntyre. Specific

vii

incidents that come to my mind: a) Dr. Cauller suggesting that much of learning involves

forming our very own personal construct of a given phenomena, and seeing if new

information fits into that construct. If it does not, this leads to two possibilities-either the

construct is incomplete/wrong, or the new information is incongruent with a basic

neuroscientific principle-which in itself can be a fascinating issue to explore. b) Dr. Atzori’s

habit of committing to a deadline and respecting it regardless of circumstances. I’ve

promised myself that I shall one day succeed in inculcating this habit and practicing to do

that has been of immense benefit in managing small personal writing deadlines.

Kevin Chang has been my colleague and collaborator for most of my PhD education.

His assistance in my experiments was of immense help. As an undergraduate researcher, he

helped me train animals, troubleshoot broken equipment, record neuronal responses and

manage people. He was humble and methodical in something as simple as cleaning

enrichment cages, which allowed me to feel comfortable in delegating a modest proportion of

experiment work to him.

I am grateful for the multiple discussions and collaborations I have had with graduate

student colleagues Jai Shetake, Justin Nichols, Amanda Puckett, Crystal Engineer, Claudia

Perez, Rafael Carrasco, Roshini Jain, Mitali Bose, Ben Porter and Dave Pena, Helen. Jai

helped me tremendously in my experiments and patiently listened when I had an idea to

share and discuss. Justin and I collaborated on a paper and I learnt patience and staying calm

from him. I consider him a self-made engineer/scientist-the best kind-, a quality that I hope to

emulate. Amanda is a natural “smartie”. Having her insights was invaluable. I especially

cherish the holistic discussions we had of “how science is done”. Crystal is to me the epitome

of quiet elegant hard work. She is an inspiration and a trend setter. My standards have

viii

dramatically changed for the better after she published her Nature Neuroscience paper.

Working with Claudia was my first experience with being involved in a project where I did

not have all the answers. Her questions during inferior colliculus access surgery and mapping

stimulated me to be more aware of the details of experimental protocols. Rafael and

Roshini’s expertise and coaching in my first project helped keep me afloat during my tough

initial months. Mitali included me in her project and it gave me a lot of satisfaction being a

part of a project where I could be involved and enjoy the benefits of learning without having

to do the hard work required by the first author. Ben-bless his heart- has successfully taken

on the mantle of organizing regular lab activities like managing birthday cakes/organizing

lunches/setting up lab meeting times etc. Efficient organization saves a lot of time and I am

grateful for it. Dave stepped in to help me handle a molecular biology phase of an experiment

during “crunch” time and I am very thankful for that. Helen provided me with speech sounds

for my enrichment project.

Scores of undergraduates and four high school students helped me in my work and I

could not have completed my experiments without them. They include Juliann Record, Mona

Noorizadeh, Hamid Shah, Rachelen Samuel, Rachel Nance, Trishna Sharma, Jai Gandhi,

Caleb Dunham, Rolan Torres, Jamie Kalangara, Kamini Krishnan, Scott Nietfeld, Stuart

Michnick, Matt Ditzler , Joseanne Howard, Siby Spurgeon, Chris Heydrick, Sneha Idiculla,

Gabriel Mettlach , Theresa Lii, Linda Yang, Jessica Moore, Farwa Ali, Larry Nentwig,

Maulie Happawana, Blake Farha, Jennifer Grisiel, Kinsey Ram, Swati Chanini, Ann Nguyen,

Deepthi Vupalla, Laura Thibodeaux, Sana Khan and Miwa Murray. Rolan Torres, Kamini

Krishnan, Scott Nietfeld, Matt Ditzler, Kevin Jordan, Jamie Kalangara and Gabriel Mettlach

were especially helpful in recording neuronal responses. Rolan’s neat and methodically

ix

mapping skills, Kamini’s mapping expertise and Scott’s programming skills were of great

help when I had a deadline to meet.

I am grateful for Dr.Sandra Chapman’s time and effort during my first year in the

university. I was convinced at that time that I could do basic science research as well as

clinical research at the same time. Dr.Chapman arranged for me and attended a meeting with

a respected pediatrician. If it were not for her efforts (and Dr.Kilgard’s), I might have

followed up on my university applications to leave the University of Texas at Dallas for a

university with clinical facilities on a misconceived quest to do clinical and basic science

research at the same time.

I am thankful for the help provided by the staff in our department. Abbie Bailey

helped me tremendously in procuring equipment for the lab. Bonnie Dougherty, Mary Felipe,

Susie Milligan and Jo Valcik helped me breeze through any dealings with the department or

university. Nuvala Nguket helped in providing me with software when I needed it.

I have a special thank you for former graduates from the Kilgard Lab. Navzer

Engineer, Pritesh Pandya, Raluca Moucha and Cherie Percaccio laid the foundation for

students like me to build on. Their papers were immensely useful in giving me a ready

framework to build a paper on.

My loving non-university friends suffered through my prolonged absences in their

lives and I will never forget that. My sister Deepa Jakkamsetti and Molly Anderson have

been my closest and strongest of supports and I love them for that.

It is said that once you learn to swim, you can never forget that skill. I hope that is

true. I am only too aware that swimming in a pool while learning is far simpler than

swimming in the difficult waters of today’s research seas. I sincerely hope that I can

x

successfully navigate through these seas and validate the trust, effort and time given by all

the people who supported me.

July 2008

xi



Publication No. ___________________

Vikram Jakkamsetti, Ph.D.

The University of Texas at Dallas, 2008

Supervising Professor: Dr. Michael Kilgard

The adult brain has an amazing capacity to change in response to environmental experience.

Understanding the principles of experience-dependent plasticity will help us design effective

treatments for neuronal processing disorders. Environmental enrichment has been

successfully used in treating multiple neuronal processing disorders. The underlying

physiological changes consequent to environmental enrichment has been studied in the

primary sensory cortex. However, non-primary sensory cortices occupy a greater proportion

of the cortex involved in sensory processing. In the first part of the dissertation I explored the

physiological consequences of environmental enrichment in the posterior auditory field

(PAF)-a distinct non-primary auditory field. It was seen that enrichment induced PAF

neurons to become selective, fire faster to stimuli, and respond better to rapidly successive

stimuli. In the second part of my dissertation I explored the induction of experience-

dependent plasticity using modulation of developmental mechanisms. During development,

continuous sensory input prior to maturation of cortex increases the representation of the

xii

experienced sensory input in the cortex. Such experience-dependent plasticity depends on the

presence of high levels of cyclic adenosine monophosphate (cAMP) in the cortex prior to

maturation. In an adult rat, we paired acoustic input with injections of Rolipram-a drug that

increases cortical cAMP levels and observed that Rolipram increased the length of the cortex

activated by the paired tone and induced primary cortex neurons to become more selective to

the paired tone. In the third part of the dissertation I explored induction of experience-

dependent plasticity using modulation of attentional mechanisms. It has been previously

demonstrated that paying attention to a tone for a tone discrimination task stimulates the

nucleus basalis to release cortical acetylcholine which activates muscarinic M1 receptors to

increase the representation of that tone in the primary auditory cortex. We paired acoustic

input with injections of M1 agonist Cevemiline and observed an increase in the length of the

cortex corresponding to the acoustic input. The experiments in this dissertation attempt to

understand experience dependent brain changes and use current understanding of the

mechanisms of experience dependent plasticity to research drugs that could help improve

neuronal processing for neuronal disorders.

xiii

TABLE OF CONTENTS

Acknowledgments ...............................................................................................................v

Abstract .............................................................................................................................. xi

List of Illustrations ........................................................................................................... xiv

Chapter 1: Introduction ...................................................................................................... 1

Chapter 2: Plasticity of temporal and spectral information processing in the rat

posterior auditory field induced by environmental enrichment .......................................... 5

Appendix: Chapter 2 ..........................................................................................................22

Chapter 3: Rolipram induces frequency specific cortical plasticity in rat A1 .................. 38


Chapter 4: M1 agonist Cevimeline (AF102B) induces input specific frequency

map plasticity in rat primary auditory cortex .................................................................... 6o


Chapter 5: Summary and Conclusions ............................................................................. 87

Vita

xiv

LIST OF ILLUSTRATIONS

CHAPTER TWO

Figure 1. Schematic figure of standard and enriched environment. ................................. 22

Figure 2. Representative A1-PAF map from control (A) and enriched (B) group. .......... 23

Figure 3. Population PSTH of responses to pure ones recorded from enriched

(N=127) and control (N=127) rat PAF. ............................................................................ 24

Figure 4. Latency of response to pure tones in PAF. ....................................................... 25

Figure 5. Mean PSTHs for 9.6 Hz noise burst train in rat PAF neurons. ....................... 26

Figure 6. Normalized mean Repetition Rate Transfer Function (RRTF) in

PAF neuron. ...................................................................................................................... 27

Figure 7. Vector strength of PAF neurons in response to noise burst trains

in PAF. .............................................................................................................................. 28

Figure 8. Rayleigh Statistic of PAF neurons in response to noise burst trains. .............. 29

Figure 9. Nearest neighbor classifier performance in recognizing neural

responses to noise burst trains in PAF .............................................................................. 30

Figure 10. Examples of speech spectrograms. .................................................................. 31

Figure 11. Population PSTHs of response to /DAD/ & /TAD/ in PAF. ......................... 32

Figure 12.Population PSTHs of response to /RAD/ & /LAD/ in PAF. ............................ 33

Table 1. Response Properties of Posterior Auditory Neurons from rats housed

in enriched and standard environments ............................................................................. 34

CHAPTER THREE

Figure 1.Illustration of experimental protocol. ................................................................. 51

xv

Figure 2.Example of a tuning curve and frequency A1 map recorded from

a naïve rat. ......................................................................................................................... 52

Figure 3. Example of cortical length measurement for Rolipram injected group. .......... 53

Figure 4. Example of cortical length measurement for vehicle injected group. ............. 54

Figure 5. Cortical length comparisons. ............................................................................. 55

Figure 6. Bandwidth Plasticity. ......................................................................................... 56

CHAPTER FOUR

Figure 1. . Illustration of experimental protocol. .............................................................. 73

Figure 2 Example of a tuning curve and frequency A1 map recorded from

a naïve rat. ......................................................................................................................... 74

Figure 3. Example of cortical length measurement for low tone exposure group. .......... 75

Figure 4. Example of cortical length measurement for high tone exposure group. ......... 76

Figure 5. Cortical length comparisons (M1 agonist). ....................................................... 77

Figure 6.Cortical length comparisons (Amphetamine). .................................................... 78

1

CHAPTER ONE

INTRODUCTION

Millions of adults suffer from mental health disorders. In the United States alone, 26.2 % of

adults have a diagnosable mental disorder for a given year . Based on the 2004 Census, this

results in 57.7 million people requiring help for mental health disorders. The magnitude of

the problem is huge and the cost to society in billions of dollars. Many of these disorders do

not have definitive treatments, and with a continuing addition of freshly diagnosed cases

being added to a pool of pre-existing ones, the issue is destined to become graver with time.

A lack of treatment for mental disorders could be attributed to a major extent to a lack of

definitive knowledge regarding the underlying disorder and its potential therapy. Instances

where a working knowledge of the nature of the pathology and mechanisms of potential

therapies have been discerned, have led to breakthrough treatments for brain disorders (Tallal

et al., 1998). This optimism, that understanding neuronal mechanisms will make a

contribution-however small-to finding treatments, was a strong motivation behind the

research in this dissertation.

An effective therapeutic intervention that spans across many neuronal disorders is

that of exposure to an enriched environment (Will et al., 1977; Kolb and Gibb, 1991;

Hannigan et al., 1993; Hockly et al., 2002; Morley-Fletcher et al., 2003). Along with genetic

influences, an organism’s survival is hugely influenced by cues from the environment. For

example, a deer seeking to escape from a leopard is genetically endowed with an ability to

outrun the leopard over long distances. However, learning to detect miniscule variations in

2

acoustic input is essential for survival, since this will allow the deer to discern the movement

of a leopard through the grass and avoid being ambushed. The ability of the deer’s brain to

learn to detect behaviorally relevant acoustic input allows its survival. Such brain plasticity in

response to environmental cues has been studied in the laboratory. Enriching animals with

behaviorally environmental cues leads to significant changes in gene expression (Staiger et

al., 2002) morphology(Volkmar and Greenough, 1972), physiology (Engineer et al., 2004)

and behavior (Churs et al., 1996). Such an ability to induce brain plasticity has been used in

treating multiple neuronal processing disorders. Understanding the physiological

underpinnings of environmental enrichment in the cortex will be helpful in furthering our

knowledge of neuronal mechanisms involved in effective treatments.

The first chapter involves understanding the physiological response of a non-primary

auditory cortex to environmental enrichment. Multiple studies have explored experience

dependent plasticity in primary sensory cortices. However non-primary cortices comprise a

bigger portion of the cortex devoted to sensory processing, and to the best of our knowledge,

physiological changes induced by enrichment in non-primary cortices have yet to be

investigated. We exposed rats to an environment that was rich in behaviorally relevant

acoustic cues and then investigated physiological responses of a distinct non-primary

auditory field – the posterior auditory field(PAF). Previous experiments exploring

environmental enrichment induced plasticity in primary sensory cortices demonstrated that

receptive fields get sharper, onset latencies get faster and the strength of neuronal responses

get stronger. An increase in response strength to a single tone lead to a decreased ability to

recover quickly and fire again to a successive tone. This is a quandary, since clinical

literature suggests that experiential plasticity induced by training leads to a better ability to

3

respond to rapidly successive auditory stimuli (Hayes et al., 2003a). However, clinically seen

evoked potentials are a summation of action potentials seen across different auditory fields.

Investigating non-primary auditory cortex for temporal processing plasticity induced by

enrichment could help resolve this quandary.

The impressive changes in physiological processing of sounds after enrichment seen in

our lab (Engineer et al., 2004; Percaccio et al., 2005; Percaccio et al., 2007) induced a keen

curiosity to understand the synaptic mechanisms involved. This resulted in my collaboration

with Dr.Marco Atzori’s lab members, wherein it was revealed that glutamate release at

synapses increases significantly after environmental enrichment (Nichols et al., 2007). The

involvement of neurotransmitter modulation in plasticity is exciting, since it offers the

possibility of clinical manipulation. While systemic administration of agents that directly

increase glutamate release may not be clinically viable due to adverse effects, the seed of the

possibility of inducing plasticity through neuromodulation had taken root.

The second chapter deals with induction of experience dependent plasticity through

neuromodulation of critical period mechanisms. The period between establishment of

connections from peripheral sensory organs to sensory cortex and final maturation of the

sensory cortex is referred to as a critical period. During the critical period, passive exposure

to sensory stimuli can drive stimuli specific changes in the cortex. Experiments have proved

that such activity dependent cortical reorganization is mediated by an increase in basal cAMP

(cyclic adenosine mono phosphate) levels in the cortex. We hypothesized that raising cAMP

levels in adult rat cortex will mimic experience dependent plasticity seen during the critical

period. Rolipram increases cortical cAMP levels by inhibiting its breakdown. This would

suggest that rolipram can induce experience dependent plasticity by harnessing mechanisms

4

involved in critical period plasticity. Training to increase cortical representation of an

environmental cue is suggested to be of benefit in cortical disorders (Xerri et al., 1998).

Providing evidence that Rolipram increases cortical representations of cues that are paired

with rolipram injections could be of great use in inducing cortical plasticity to correct cortical

processing disorders.

The third chapter provides evidence for cholinergic neuromodulation of cortical

plasticity. Multiple studies demonstrate the involvement of acetylcholine in generating

cortical plasticity (Kilgard and Merzenich, 1998b; Penschuck et al., 2002; Zhang et al.,

2006). Experience dependent plasticity is mediated by cholinergic activation of muscarinic

receptors (Kilgard and Merzenich, 1998a) , specifically M1 receptors (Zhang et al., 2006).

We hypothesized that systemic administration of an M1-agonist (Cevemiline Hydrocholride)

immediately before exposure to multiple repetitions of a single tone would induce tone

specific plasticity in the primary auditory cortex.

Researching neuronal mechanisms that could lead to treatments for cortical

processing disorders might seem daunting at first glance. After all, the brain is a complex

system, and research involves examination of a relatively narrow region. However, each

study adds a significant piece to the brain puzzle, taking us one step closer to helping the

millions of our fellow human beings who suffer from cortical disorders. I hope you enjoy

reading the following chapters as much as I did in conducting the research for them.

5

CHAPTER TWO

PLASTICITY OF TEMPORAL AND SPECTRAL INFORMATION PROCESSING IN

THE RAT POSTERIOR AUDITORY FIELD INDUCED BY

ENVIRONMENTAL ENRICHMENT

Vikram Jakkamsetti, Kevin Q. Chang, and Michael P. Kilgard

School of Behavioral and Brain Sciences, GR41


800 W. Campbell Road

Richardson, Texas 75080-3021

Running Title: Plasticity of Temporal and Spectral Information Processing in the rat

Posterior Auditory Field induced by Environmental Enrichment

Key words: : non-primary auditory cortex, paired-pulse facilitation, repetition rate transfer

function, spike synchronization, speech coding

Corresponding author: Vikram Jakkamsetti

Email: [email protected]

6

ABSTRACT

Sensory non-primary cortex constitutes an important component of the cortex involved in

sensory information processing. Little is known regarding their representation of neuronal

plasticity. In our previous studies in primary auditory cortex (A1) we observed that

environmental enrichment is a powerful tool that induced significant changes in neuronal

onset latencies, receptive field bandwidths, increased response strength and increased paired

pulse depression to successive stimuli. Here we examine neurophysiological plasticity in the

posterior auditory field (PAF) of rats after environmental enrichment. Enrichment caused a

significant decrease in onset latencies and response durations to pure tones by more than

30%. Short response durations increased the ability to respond to rapidly successive stimuli,

leading to paired pulse facilitation and increased phase locking of PAF responses to noise

burst trains. Finally, enriched enhanced the response to speech sounds with rapid onsets. Our

results support earlier observations that non-primary fields are more plastic than primary

fields.

INTRODUCTION

Environmental enrichment significantly improves recovery from stroke and traumatic brain

injury (Will et al., 1977; Kolb and Gibb, 1991; Hannigan et al., 1993; Rampon et al., 2000;

Hockly et al., 2002; Morley-Fletcher et al., 2003; Jankowsky et al., 2005) and has been

proposed as a treatment for neuronal disorders like Alzheimer’s disease, dyslexia and autism

(Hayes et al., 2003a; Percaccio et al., 2005). However, its mechanism of action is far from

clear. Molecular and morphological analyses of the cerebral cortex in enriched animals

reveal that enrichment increases glutamate release (Nichols et al., 2007), gene expression

7

(Staiger et al., 2002), dendritic spines (Globus et al., 1973) , dendritic branching (Volkmar

and Greenough, 1972; Greenough et al., 1973) and synapses per neuron (Sirevaag and

Greenough, 1987). Neurophysiological studies document that enrichment causes primary

cortical neurons to respond more strongly and more selectively to sensory stimuli (Beaulieu

and Cynader, 1990; Coq and Xerri, 1998; Engineer et al., 2004). Enrichment also modulates

temporal response properties in these neurons, inducing neurons to fire earlier (Engineer et

al., 2004) and increasing paired pulse depression (Percaccio et al., 2005). While these studies

have advanced our knowledge of primary cortex physiology after enrichment, primary

regions occupy but a small proportion of cortex, and little is known regarding enrichment

induced plasticity in non-primary sensory cortex. Having a more comprehensive idea of

cortical changes as a consequence of sensory enrichment would be helpful.

Previous plasticity studies have suggested that non-primary sensory cortex is more

susceptible to experience-dependent changes. For example, discriminating orientations of

bars induces greater plasticity in V4 than V1 neurons (Raiguel et al., 2006). Fear

conditioning generates greater receptive field plasticity in non-primary auditory cortex

compared to primary auditory cortex (A1) (Diamond and Weinberger, 1984). Nucleus

basalis stimulation paired with a tone causes changes in receptive fields and neuronal firing

rates in posterior auditory field (PAF) that is not observed in A1 (Puckett et al., 2007). These

studies led us to hypothesize that enrichment would induce changes in non-primary auditory

cortex that were greater in magnitude than A1.

We chose to conduct our study in the well characterized posterior auditory field (PAF).

Compared to A1, PAF neurons have broader frequency bandwidths, slower onset responses

and a lesser ability to fire to each stimuli in a train of rapidly successive stimuli (Doron et al.,

8

2002; Pandya et al., 2007). PAF has been implicated in processing of slow varying complex

spectro-temporal stimuli(Phillips et al., 1995; Heil and Irvine, 1998; Tian and Rauschecker,

1998; Loftus and Sutter, 2001; Pandya et al., 2007) and plays a critical role in sound

localization (Malhotra et al., 2004).

In this study, we tested the hypotheses that enrichment would 1) increase PAF’s selectivity

and strength of response for tones 2) increase the cortical following rate of PAF for noise

burst trains, and 3) increase the onset response to speech.

METHODS

Thirteen female Sprague-Dawley rats aged 25 days post partum were placed in either

enriched or standard environments for 8 weeks. The enriched and standard housing

conditions were identical to earlier reports published from our lab (Engineer et al., 2004;

Percaccio et al., 2005). Rats in both environments received food and water ad libitum and

were on a reverse 12-h light/dark cycle.

Environmental Exposure

Enriched Environment: Six rats were housed in a large cage (45 X 76 X 90 cm) located in a

room separate from the main rat colony. The cage had four levels linked by ramps and rats

entering a level elicited a unique sound due to hanging chains and wind chimes hung over the

entrance of each level. Stepping on two of the three ramps triggered delivery of two different

tones (2.1 or 4 kHz). Motion near the water source set off a motion detector that emitted an

electronic chime. Rats on an exercise wheel evoked a tone (3 kHz Piezo speaker) and

activated a small green light emitting diode with each rotation. Each movement-activated

9

sound had unique spectral and temporal features that provided behaviorally meaningful

information about the location and activity of other rats in the cage.

The rats were exposed to 74 randomly selected sounds every 2-60 s from a CD player, seven

of which triggered a pellet dispenser (Med Associates, St. Albans, VT, USA) to release a

sugar pellet to encourage attention to the sounds. The sounds included simple tones,

amplitude-modulated and frequency-modulated tones, noise burst, and other complex sounds

(rat vocalizations, classical music, rustling leaves, etc.). The rewarded tracks included

interleaved tones of different carrier frequencies (25-ms long, 4-,5-,9-,12-,14-, and 19-kHz

tones with interstimulus intervals ranging from 50 ms to 2 s) and frequency modulated

sweeps (1 octave up or down in a 140- or 300-ms sweep with interstimulus intervals ranging

from 80 to 800 ms). The rats in the enriched environment were exposed to these sounds

spanning their entire hearing range (1-45 kHz), 24h/day. After four weeks in the enriched

environment, rats reached sexual maturity and a vasectomized male rat was added to the cage

to encourage natural social interactions appropriate for the age.

Standard Environment: Six age matched control rats were housed 2 per cage (26 X 18 X 18

cm). These rats heard sounds related to typical room traffic and vocalizations from 30-40

other similarly housed rats.

All methods and procedures were in accordance with guidelines set by NIH for

Ethical Treatment of Animals and received the approval of the University Committee on

Animal research at the University of Texas at Dallas.

Acute Surgery

Physiological experiments were conducted after eight weeks of differential housing.

Anesthesia for surgery was induced by pentobarbital sodium (50 mg/kg ip) to achieve a state

10

of areflexia and maintained with supplemental dilute pentobarbital (8 mg/kg ip). The rat’s

level of anesthesia was monitored by heart rate, breath rate, and toe pinch and cardiovascular

status monitored by the presence of urine during regular bladder voiding. A heating pad and

rectal probe was used to hold the core body temperature at 37oC. Fluid balance was

maintained with a 1:1 mixture of 5% dextrose and Ringer lactate (~0.5 ml/h). The trachea

was cannulated to administer humidified air and ensure adequate ventilation and to minimize

breathing sounds. The cisterna magna was drained to prevent cerebral edema. The right

auditory cortex was then exposed, the dura resected and viscous silicon oil added to the brain

surface to prevent desiccation. Electrode penetration points were referenced using vascular

landmarks and marked on a digitized photograph of the auditory cortex surface. Care was

taken to avoid penetration of visible vasculature.

Experimenters were blind to the housing condition of the rat during surgery and

recordings, though in some cases unkempt fur made it clear that the rats were housed in the

enriched environment.

Stimulus Presentation

Acoustic stimuli were presented in a double-walled sound attenuating chamber from a

speaker (Motorola model No. 40-1221) at 90 degrees azimuth and 0 degrees elevation from

the base of the contralateral ear. Frequency and intensity calibrations were done using

Tucker-Davis (Alachua, FL) SigCal software and an ACO Pacific (Belmont, CA)

microphone (PS9200-7016).

Tones: 1296 randomly interleaved pure tones (25 ms duration, 3 ms ramps, every 500 ms)

were generated using Brainware (Tucker-Davis Technologies). The tones included 81

11

logarithmically spaced frequencies from 1-32 kHz, each at 16 different intensities spaced 5

dB SPL apart from 0-75 dB SPL.

Noise burst Trains: 12 repetitions of 14 noise burst trains (3-20 Hz interstimulus intervals)

with each noise burst 25ms in duration, having ramps of 3ms and a bandwidth of 1-32 kHz.

Each train was presented 2 s after the termination of the last train.

Speech: Four natural speech sounds, /dad/, /tad/, /rad/ and /lad/, were recorded from a single

native English speaker in a sound booth with a sampling rate of 44,100 Hz. As in our earlier

study, the sounds were frequency shifted one octave higher with a vocoder without altering

the amplitude envelope to better match the rat hearing range (Engineer et al., 2007). The

intensity of each speech sound was adjusted so that the loudest 100 ms was 65 dB SPL.

Speech sounds was presented in random order, each speech sound repeated 20 times, with a

sampling rate of 100 kHz and a silent interval of 2 sec separating each stimuli presentation.

Recording

Two pairs of Parylene-coated tungsten microelectrodes (FHC, Bowdoin, ME) with 250 μm

separation, 1.5 ± 0.5 MΩ) were lowered 600-680 μm below the pial surface (layer IV/V) of

the auditory cortex to record multi-unit activity from 35 – 60 penetration sites. The neural

signals were filtered using a high pass filter and amplified (10,000 times). Action potentials

waveforms were recorded whenever a set threshold (600 mv) was surpassed. A1 was defined

based on tonotopy and latency. The abrupt lack of tonotopicity at A1’s posterior most border

was taken as the A1-PAF border. A site was considered non responsive if the action

potentials at that site were less than two standard deviations above the mean of spontaneous

firing rate. Criteria for identifying PAF sites were applied by a well-trained observer blind to

the housing status of each rat.

12

Data Analysis

Tuning Curve Analysis: All data analysis was done offline to avoid a potential bias in site

selection and by an observer blind to the housing condition of the rat for the data being

analyzed. Tuning curve parameters were defined by a program written in MATLAB. The

spontaneous firing rate was calculated as the spike rate in the first 8 ms recorded after

presentation of tone and before onset of a neural response in the cortex. Onset latency was

the time from the onset of the stimulus to the earliest reliable neural response reaching two

standard deviations above spontaneous firing rate. End latency was defined as the time when

the PSTH (post stimulus time histogram-created by summing responses to all the tones

within a site’s tuning curve) returned back to baseline. The characteristic frequency (CF) was

defined as the frequency that evoked a reliable response at the lowest intensity (response

threshold). Frequency bandwidth (BW) was the range of frequencies that a site responded to

at 10, 20, 30 and 40 dB above threshold. Voronoii tessellation using MATLAB was done to

determine polygons corresponding to each penetration site (Figure 1). In essence, each point

within a polygon is closest to the recording point enclosed within that polygon.

Noise Burst Train Analysis: Noise burst repetition rate transfer functions (RRTF) were

calculated for each site by quantifying action potentials per stimulus. A normalized RRTF

was calculated for comparison across recording sites since the number of neurons involved in

a multi-unit cluster could vary. Normalized RRTFs were estimated by finding the mean

evoked response of neurons to each of the last 5 noise bursts in a train and dividing that by

the evoked response to the first noise burst. Hence, values greater than 1 indicate facilitation

and values less than 1 indicate adaptation. Neuronal responses during the first 5 ms after

noise burst train presentation were considered as spontaneous firing rate. Action potentials

13

within a time window of 14-85 ms after the onset of a noise burst in a noise burst train were

considered as having occurred in response to the noise burst. For noise burst trains presented

at rates greater than 10 Hz, the time windows associated with each noise burst in a train

would overlap. Hence for rates >10Hz the time window began with 14ms after the onset of

the second noise burst and ended 85 ms after the onset of the fifth noise burst. To examine

the synchronization between neuronal firing and repeated noise bursts, we estimated vector

strength using the following formula:

where n= total number of action potentials, ti is the time of occurrence of the ith action

potential and T is the interstimulus interval. A value of 1 depicts perfect synchronization

while a value of 0 indicates none. The Rayleigh Statistic (2n x Vector Strength2, where n is

the total number of action potentials) is a circular statistic that combines vector strength and

number of action potentials to indicate the statistical significance of vector strength. A value

greater than 13.8 is considered statistically significant.

Speech Analysis: The average spike rate for each speech sound was estimated in 1 ms bins

from the Post Stimulus Time Histogram (PSTH). The average spike rate in the first 14 ms

after presentation of the speech sound was taken as the spontaneous discharge rate and

subtracted from the mean firing rate in all analysis. Driven spike rates for speech onsets were

estimated from 14 to 140 ms after presentation of speech sound. Neuronal responses from a

site were considered in the analysis if that site had a driven response (i.e. greater than thrice

the standard deviation above the mean of spontaneous discharge) to speech onset.

14

RESULTS

General Observations

A total of 792 extracellular multi-unit cortical sites (control=432 , enriched=360)

from 13 rats (control=7, enriched=6) were recorded in the auditory cortex across A1

(control=110 sites, enriched=69sites), PAF (control=156 sites, enriched=127 sites) and

ventral auditory field (control= 45 sites, enriched=34 sites) . A1 low frequency neurons were

recognized by their antero-posterior frequency gradient, short onset latencies and narrow

receptive fields. Consistent with a previous study from our lab (Engineer et al., 2004) this

subset of A1 neurons demonstrated a trend towards enrichment induced decrease in onset

latency (control=12.7 ±.45ms, enriched = 12.04 ± 0.29ms, p= 0.28) and significantly

sharpened receptive fields (Bandwith 10dB above threshold: control=1.66± 0.16 octaves,

enriched=1.19±0.22 octaves, p<.05) . A1 neurons progressively decrease in CF with

posterior extent and a sharp interruption of this frequency gradient at A1’s posterior border

signifies the A1-PAF border (Doron et al., 2002; Pandya et al., 2007). 283 recording sites

posterior to the A1-PAF border were classified as being in PAF (control=156,

enriched=127). As seen in earlier studies greater onset latencies and wider bandwidths of

PAF neurons contrasted sharply with A1 recording sites at the A1-PAF border (figure 2). The

antero-posterior (AP) length and total area of PAF were indistinguishable in enriched and

control rats (AP length: control 1.07±0.15, enriched 1.35±0.18 mm, p>0.05; Area : control

0.71± 0.15 mm2, enriched 1.07±0.18 mm

2, p>0.05).

15

Plasticity in processing temporal information

Enrichment decreases latency of neuronal responses

As auditory information from the cochlea progresses through each neuronal

processing station, the time of onset of neuronal response and the total duration of driven

response progressively increases . PAF is classified as non-primary cortex and is

characterized by neurons that are slow to respond and have a long duration of driven

response. Consistent with previous reports , control PAF neurons had a rise in firing rate that

was more gradual, with a slower return to baseline and a longer duration of driven response

when compared to A1 (figure 3). Exposing rats to an enriched environment caused their PAF

neurons to respond faster. In enriched rats, PAF neurons responded significantly quicker-

responses beginning in almost half the time, and reaching peak firing rate 42% earlier than

control PAF neurons. The return of neuronal firing rate to spontaneous levels took 37% less

time than taken by control rats (figure 4) (see Table 1). The rapid rise to peak firing rate and

quick return to baseline lead to 30% shorter response durations when compared to control

rats (control 46.07± 3.29 ms, enriched 32.2 ± 1.91 ms, p<0.01).

Enrichment enhances synchronization to rapidly successive stimuli

PAF contains neurons with varying onset latencies and varying response durations (Doron et

al., 2002). As a population, different PAF neurons respond at different times to the same

acoustic input. This results in poor synchronization between acoustic stimuli and neuronal

response when compared to A1 (Pandya et al., 2007), especially for rapidly incoming input.

On using vector strength (VS) as a measure of synchronization, we observed that our control

rats had values similar to an earlier published study from our lab (figure 7) (Pandya et al.,

16

2007)- vector strength decreased with increased speed of stimuli occurrence. Enrichment

induced neurons to fire more in phase with each iteration of a noise burst in a rapidly

modulated noise burst train. The average maximum vector strength (VS) for control rats was

0.52 ± 0.02 and for enriched rats was 0.69 ± 0.02 (p< 10-7

). As a population, control PAF

neurons had their highest vector strength at 5.12 Hz while enrichment increased this measure

to 7.6 Hz. The Rayleigh statistic, a measure of the statistical significance of vector strength

was significantly higher for the enriched group (figure 8) (average maximum Rayleigh

Statistic : control rats 128 ± 16.9 ,enriched rats 182.9 ± 17.9 , p< 0.05).

Enrichment induces paired pulse facilitation

PAF behaves as a low pass filter for temporally modulated sounds. For repeated acoustic

stimuli with long interstimulus intervals, PAF neurons respond almost as well to successive

stimuli as they do to the first stimulus. However, for more rapidly incoming acoustic input,

neurons respond more weakly to successive stimuli, evidencing paired pulse depression at

rapid rates of stimuli iterations (Pandya et al., 2007). In agreement with these prior studies,

for modulation rates upto 7.6 Hz , our control PAF response to a following noise burst was

almost as strong as the first noise burst. With more rapid modulation rates, the neuronal

response to repeated stimuli progressively decreased, depicting paired-pulse depression

(figure 5 & 6).

Latencies are correlated with better cortical following rates (Kilgard and Merzenich,

1999). Enrichment induced shorter latencies should likely lead to better cortical following

rates in the enriched neurons. In sharp contrast to control neurons, enrichment induced

paired-pulse facilitation at slow modulation rates and decreased paired pulse depression at

17

rapid modulation rates (figure 6). For example, at a modulation rate of 8.4 Hz, control rats

had a normalized response significantly less than 1, indicating paired pulse depression

(numbers p<.05), whereas enriched rats responded to the second noise burst with almost

twice the number of spikes evoked for the first noise burst (numbers, p<.05). The average

best modulation rate for enriched rats was significantly higher than that for control rats

(control=4.13 ± 0.27 Hz , enriched=5.29 ± 0. 37 Hz, p<0.05). After responding to the first

noise burst in a train, enriched neurons recovered faster to fire at 50% of the first response for

the second noise burst. The average limiting repetition rate (the fastest modulation rate that

evoked a response at least 50% of the best modulation rate) was significantly greater after

enrichment (control= 8.49 ± 0.44 Hz, enriched=10.93 ± 0.42 Hz ,p<10-3

).

An ability to respond quickly and for a briefer period to successive temporal cues suggests

that enriched PAF neurons respond differentially to noise burst trains that have minor

differences in their inter noise burst intervals. In other words, enriched PAF neurons might

find it easier than control rat neurons to differentiate a rapid noise burst train from noise burst

trains with almost similar speeds of presentation. To test this, we used a near-neighbor

classifier- this classifier compares the PSTH of the presented noise burst train with the

average PSTHs of each of the 14 noise burst train groups (see Methods) and finds the best

match. The presence of neuronal activation to noise burst trains enabled control PAF neurons

to correctly predict the rate of the noise burst train amongst the 14 choices (correct prediction

= 24.24 %, chance= 7.2 %, p<.01). Enrichment significantly increased the prediction above

control rats to 33% (control = .2424 ± .024 enriched= .3372 ± .024, p<.01), more than 4

times greater than a chance prediction.

18

Enrichment enhances sensitivity to temporal cues in cortical processing of speech

A speech sound is encoded in A1 in the precise timing of action potentials generated in

response to that speech sound (Engineer et al., 2007). Only a millisecond-by-millisecond

observation of the temporal sequence of neuronal activation after presentation of a speech

sound correlates best with behavioral recognition of that speech sound. Current literature

suggests that PAF neurons respond best to temporal cues that change slowly over time, over

tens of milliseconds. This suggests that PAF neurons would respond to speech with poor

temporal resolution. However, enrichment quickens response latencies in PAF. This lead us

to hypothesize that enrichment would increase the sensitivity to temporal cues in speech. To

test this hypothesis we examined the response of enriched PAF neurons to speech sounds.

/DAD/ and /TAD/ have a quick onset of spectral energy while /RAD/ and /LAD/ has a

slower onset of spectral energy (figure 10). Enrichment caused PAF neurons in enriched rats

to respond to speech stimuli in a more phasic manner. Speech stimuli with rapid onset power

spectrograms like /DAD/ elicited stronger instantaneous peak firing rates (see figure for

values with significant changes between groups), while those with slower onset power

spectrograms like /LAD/ induced a weaker PAF response after enrichment.

Plasticity in processing spectral information

The enriched environment contains multiple spectral cues experienced by rats as behaviorally

relevant. An enriched experience induced a sharpening of receptive fields (bandwidths) in A1

(Engineer et al., 2004). This finding was confirmed in our subset of control A1 neurons

(Bandwith 10dB above threshold: control=1.66± 0.16 octaves, enriched=1.19±0.22 octaves,

p<.05). PAF receives input from multiple frequency areas in A1 and is characterized by

19

wider bandwidths . In agreement with earlier studies, our control rats had bandwidths in

PAF that were wider than in A1 (see Table 1 for control data). Enrichment significantly

increased the selectivity of frequency tuning in PAF. Enriched rats had 25% narrower

bandwidths than control rats 10 dB above threshold (Table 1). The 1st and 3

rd quartiles for 10

dB above threshold for enriched rats were 0.13 and 1.75 octaves respectively whereas for

control rats the 10th

and 90th

percentiles were 0.75 and 2.43 octaves respectively.

Greater response strength in enriched rats

Pure Tone Stimuli: Since earlier studies demonstrate an increase in action potentials induced

by the presentation of a tone after environmental enrichment in the primary auditory cortex,

we predicted that PAF in enriched rats would respond to tones with more spikes. enriched

rats had a 53% greater response in instantaneous firing rate at the time of population PSTH

peak when compared to control rats (control 24.80 ± 3.79 spikes/sec, enriched 37.97 ± 3.91

spikes/ sec p<0.05). (mention findings reflected in single units) . Similarly, enrichment

caused a significant increase in the instantaneous peak firing rate in response to a single noise

burst (Table 1) without a change in the total number of spikes/noise burst. Spontaneous firing

rate and total number of spikes/tone did not change after enrichment (see Table1).

DISCUSSION

Synopsis of findings

We provide evidence that enriched induces PAF neurons to fire more selectively, fire faster

to acoustic stimuli and return faster to baseline. This makes it possible for the cortex to keep

up with rapidly incoming stimuli.

20

Narrowing of Bandwidths

The brain responds to behaviorally relevant stimuli by reorganizing its neurons to respond

more selectively to it. Neurons that responded to a wider range of sensory stimuli get

reorganized to respond to a selective subset of stimuli. Such a narrowing of receptive fields is

seen in primary cortices across sensory systems. Our finding of sharper receptive fields in

secondary cortex confirms previous such findings of plasticity in studies of non-primary

cortex (Diamond and Weinberger, 1986; Bao et al., 2001; Puckett et al., 2007)). While

Diamond and Weinberger (1984) pointed out that a greater proportion of secondary cortex

neurons undergo bandwidth narrowing when compared to primary cortex, our study is the

first to show the magnitude of narrowing.

Greater magnitude of changes in PAF compared to A1

Our current results indicate that environmental enrichment causes more plasticity in

PAF compared to our previous study in A1 (Engineer et al., 2004). Engineer et al showed

that enrichment decreases frequency bandwidth and peak latency in A1 by 8% and 5%,

respectively. Our new results reveal a 42% and 25% reduction in PAF bandwidth and peak

latency, respectively, which is a 3-fold greater reduction in bandwidth and an 8-fold greater

decrease in latency compared to A1. This finding is in agreement with earlier observations

that non primary auditory cortex evidences greater plasticity compared to A1 (Diamond and

Weinberger, 1984; Puckett et al., 2007)

Along with receiving auditory input from ventral region of thalamus via A1, PAF

receives direct auditory connections from medio-dorsal thalamus as part of the non-classical

pathway (winer Current Opin Neurobio). Similar to PAF, medial and dorsal divisions of the

thalamus have longer latencies and response durations (refs). They evidence frequency

21

specific changes in firing rate after classical conditioning (Edeline behav neruosc,

1991,1992) . Stronger non-classical pathway connections would lead to faster neural

transmission which would shorten PAF response latencies. Since shorter latencies are

associated with better phase-locking and a better cortical following rate (Kilgard Hearing

research and Brosch and Schreiner Table 1), the changes in response latency may be

responsible for the observed changes in PAF processing of temporally complex sounds.

Clinical Relevance

Sensory enrichment has been useful as a therapeutic intervention for neuronal disorders .

Sensory enrichment through training improves behavioral measures and neural phase-locking

to stimuli for dyslexia (Tremblay et al., 2001; Hayes et al., 2003). Without treatment,

dyslexics have difficulty using temporal information in speech (Tallal and Piercy, 1973;

Tallal et al., 1985; Reed, 1989; Hari and Kiesilä, 1996; Wright et al., 1997) paralleled

physiologically by a weaker response to the second tone in a pair (Nagarajan et al., 1999) and

a response less in phase with acoustic stimuli (McAnally and Stein, 1996). While our

previous observation that enrichment impaired A1 responses to rapidly presented stimuli

seemed to be at odds with clinical observations our new observation that environmental

enrichment enhances PAF responses to rapidly presented stimuli suggests the possibility that

plasticity in non-primary auditory cortex could contribute to clinically observed

neurophysiological and behavioral improvements after training.

22

APPENDIX

CHAPTER TWO

Figure 1. Schematic figure of standard and enriched environments. A) The standard

environment consisted of 1-2 rats housed in a hanging cage in the animal colony. B) The

enriched environment contained meaningful acoustic cues: a rat’s movement near the water

source, on a ramp, on the running wheel or through the chimes hanging at a level’s entrance

triggered different sounds. Seven of the 74 sounds randomly played by the CD player were

accompanied by the dispensation of a sugary reward.

23

-0.5 0 0.5 1 1.5 2 2.5

0

0.5

1

1.5

2

2.5

516

816

62

88

78 10

5

oo

11

48

712

914

97

24

22

23

10

22

6

78

89

82

78

41

27

55

29

o26

1313

oo

2323

oo

o17

oo

o

11

oo

33

A1 PAF

Best Freq (kHz)

1

2

4

8

16

32

64

CONTROL ENRICHED

-0.5 0 0.5 1 1.5 2

0

0.5

1

1.5

2

13

12

6

3

2

o

1

1

o

o

2

2

17

19

o

o

5

2

o

12

9

o6

11

21

13

2

1620

5

18

132

2

13

911

17

2

1

1

21

8

21

o 10

10

7

8

24

20

28

12

20

8

2

17

21

10

10

A1 PAF

Best Freq (kHz)

1

2

4

8

16

32

64

A B

Figure 2. Representative A1-PAF map from control (A) and enriched (B) group. Each

polygon represents one recording site . Values within each polygon reflect the parameter

being mapped. The dark line indicates the A1-PAF border. The empty circles denote sites

unresponsive to tones.

24

0 50 100 150 200 250 300 350-10

0

10

20

30

40

Time after tone onset (ms)

Re

sp

on

se

Str

en

gth

(S

pik

es

/se

c)

PSTH of PAF Response to Tones

Standard

Enriched

Figure 3

Figure 3. Population PSTH of responses to pure ones recorded from enriched (N=127) and

control (N=127) rat PAF. Gray shading indicates standard error of mean. Note the increase in

instantaneous firing rate and shorter response duration following enrichment.

25

-0.5 0 0.5 1 1.5 2 2.5

0

0.5

1

1.5

2

2.5

1210

311

2913

109

1310

109

3114

00

1011

912

1011

1612

1010

1111

1011

1010

1111

1111

1312

1211

2820

3017

4131

2017

6572

1956

4233

9523

075

7558

00

1618

00

074

00

062

00

3066

Control

A1 PAF

Latency

10

20

30

40

50

60

70

-0.5 0 0.5 1 1.5 2

0

0.5

1

1.5

2

21

13

12

13

23

0

12

13

0

0

13

14

15

16

14

0

0

14

11

0

1629

015 26

13

14

19

1615

14

1314

1

18

1314

141712

12

14

15

13

14

1819

234

0 165

15

11

12

15

1611

19

15

18

14

15

17

19

Enriched Rat

A1 PAF

Latency

10

20

30

40

50

60

70

Onset Peak End of Peak0

10

20

30

40

50

60

70

80

90

100

110

*

*

*

Late

ncy(m

s)

Latency Measures

Standard

Enriched

A

B C

Figure 4. Latency of response to pure tones in PAF A) Bars plot the mean latency to onset,

peak and end of neuronal response. Enriched rat neurons (N=127) initiate, reach peak and

terminate their responses faster than control rat neurons (N=156). B) Control rat A1-PAF

map illustrating varied onset latencies in PAF which are greater than that in A1. C) Enriched

rat A1-PAF map depicting a quicker onset latency in PAF neurons compared to control rats

26

100 200 300 400 500 600 700 800 9000

10

20

30

40

50

60

70

80

90

Time(ms)

Sp

ikes/s

ec

PSTH response to noise burst

Enriched

Control

Figure 5. Mean PSTHs for 9.6 Hz noise burst train in rat PAF neurons. For the second noise

burst, note facilitation of response in neurons after enrichment (N=115) and depression of

response in the control neurons (N=132). The black dots above the PSTHs indicate

significant differences (p<.05) in instantaneous firing rate at that millisecond for the two

housing conditions .

27

0 2 4 6 8 10 12 14 16 18 200

0.5

1

1.5

2

******

Repetition Rate Transfer Function

Acti

on

Po

ten

tials

/Sti

mu

lus

Repetition Rate(Hz)

Standard

Enriched

No

rma

lize

d s

pik

es

/sti

mu

lus

Figure 6. Normalized mean Repetition Rate Transfer Function (RRTF) in PAF neurons. The

mean RRTF at each repetition rate is plotted with standard error of mean. Enriched neurons

(N=115) demonstrate a greater ability than control neurons (N=132) in keeping up with fast

incoming acoustic input. Asterisks (*) denote p<.05.

28

2 4 6 8 10 12 14 16 18 20

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

Rates(Hz)

Vecto

r S

tren

gth

Vector Strength

Standard

Enriched

Figure 7. Vector strength of PAF neurons in response to noise burst trains in PAF. Vector

strength, an indication of phase-locking of stimuli is plotted for each rate of noise burst train

presentation with standard error of mean. Enriched neurons (N=115) had a greater

synchronization than control neurons (N=132) between acoustic stimulus and action

potentials for faster rates of noise burst trains. Asterisks (*) denote p<.05.

29

2 4 6 8 10 12 14 16 18 200

50

100

150

200

250

300

Rates(Hz)

Rale

igh

Sta

tisti

c v

alu

e

Rayleigh Statistic

Enriched

Standard

Figure 8. Rayleigh Statistic of PAF neurons in response to noise burst trains. Rayleigh

Statistic combines the degree of synchronization with action potentials evoked and is a

measure of the statistical significance of vector strength. Asterisks (*) denote p<.05.

30

CONTROL(n=156) ENRICHED(n=127)0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

chance

PE

RC

EN

T C

OR

RE

CT

NOISE BURST CLASSIFIER

Figure 9. Nearest neighbor classifier performance in recognizing neural responses to noise

burst trains in PAF. The classifier is presented with a single sweep of neural activity to a

noise burst train and predicts the rate of the noise burst train. Enriched neurons had more

distinct responses to each of the 14 noise burst trains and a higher performance on the

classifier.. Asterisk (*) denotes p<.0001.

31

Time (ms)

Fre

quency (

kH

z)

0 100 200 300 4000

1

2

3

4

x 104

Time (ms)

Fre

quency (

kH

z)

0 200 4000

1

2

3

4

x 104

Time (ms)

Fre

quency (

kH

z)

0 200 4000

1

2

3

4

x 104

Time (ms)

Fre

quency (

kH

z)

0 200 4000

1

2

3

4

x 104 /tad/ /lad/

/rad//dad/

Sto

p C

on

so

na

nts

Liq

uid

C

on

so

na

nts

Figure 10. Examples of speech spectrograms. Spectrograms of 4 speech sounds used to

determine PAF neuron responses. Note the slow onset of spectral energy in liquid consonant

sounds /lad/ and /rad/ (right panel) as compared to stop consonants /tad/ and /dad/ (left

panel).

32

50 100 150 200 250 300 350 400 450

0

20

40

60

80

100

Sp

ikes/s

ec

Time(ms)

PSTH response to /TAD/ in PAF

Control

Enriched

50 100 150 200 250 300 350 400 450

0

20

40

60

80

100

Sp

ikes/s

ec

Time(ms)

PSTH response to /DAD/ in PAF

Control

Enriched

A B

Figure 11. Population PSTHs of response to /DAD/ & /TAD/ in PAF. The black dots above

the PSTHs indicate significant differences (p<.05) in instantaneous firing rate at that

millisecond for the two housing conditions . Note an increase in instantaneous firing rate for

the onset response to /DAD/ & /TAD/ in enriched neurons (N=124) compared to control

neurons (N=135).

33

50 100 150 200 250 300 350 400 450

0

20

40

60

80

100

Sp

ikes/s

ec

Time(ms)

PSTH response to /LAD/ in PAF

Control

Enriched

50 100 150 200 250 300 350 400 450

0

20

40

60

80

100

Sp

ikes/s

ec

Time(ms)

PSTH response to /RAD/ in PAF

Control

Enriched

A B

Figure 12.Population PSTHs of response to /RAD/ & /LAD/ in PAF. The black dots above

the PSTHs indicate significant differences (p<.05) in instantaneous firing rate at that

millisecond for the two housing conditions . Note a lack of an increase in instantaneous firing

rate for the onset response to /RAD/ & /LAD/ in enriched neurons (N=124) compared to

control neurons (N=135) when contrasted with responses to /DAD/ & /TAD/ (figure 10).

Table 1. Response Properties of Posterior Auditory Neurons from rats housed in enriched and standard environments

Parameter Standard (N=153 PAF sites)

Enriched (N=126 PAF sites)

Significant change

after EE

P value

Receptive Field - BW10 (octaves) 1.83 + 0.12 1.37 + 0.15 25% <0.05

Onset Latency (ms) 43.83+ 5.8 24.23+ 3.72 45% <0.05

Peak Latency (ms) 57.48+ 5.64 33.33+ 3.64 42% <0.01

End Latency (ms) 89.53 + 5.67 56.43+ 3.9 37% <0.0001

Neural response threshold (dB)

Tonal peak firing rate(spikes/sec)

25.16 + 1.46

24.8 + 3.79

22.47 +1.29

37.97 + 3.91

NA

53%

0.19

<0.05

Tonal response strength(spikes/tone)

Noise burst peak firing rate(spikes/sec)

Noise burst response strength(spikes/noise)

1.44 + 0.1

43.88 + 1.12

1.14 + 0.11

1.12 + 0.07

56.08 + 1.63

1.17 + 0.11

22%

28%

NA

<.05

<10-5

0.71

Spontaneous firing (spikes/20ms) 11.68 + 0.77 13.27 + 1.09 NA 0.22

Values indicate mean + standard error. Statistical significance was assessed using Student’s t-tests. or indicates a decrease or

increase in value

34

35

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38

CHAPTER THREE

ROLIPRAM INDUCES FREQUENCY SPECIFIC CORTICAL PLASTICITY

IN RAT A1

Vikram Jakkamsetti, Kevin Q. Chang, Jai A. Shetake and Michael P. Kilgard

School of Behavioral and Brain Sciences, GR41




Running Title: Rolipram Induces Frequency Specific Cortical Plasticity in rat A1

Key words: : phosphodiesterase IV, critical period, experience dependent plasticity,

neuromodulation



39

ABSTRACT

The period between establishment of connections from peripheral sensory organs to

sensory cortex and final maturation of the sensory cortex is referred to as a critical period.

During the critical period, passive exposure to sensory stimuli can drive stimuli specific

changes in the cortex. Experiments have proved that such activity dependent cortical

reorganization is mediated by an increase in basal cAMP (cyclic adenosine mono phosphate)

levels in the cortex. We hypothesized that raising cAMP levels in adult rat cortex will mimic

experience dependent plasticity seen during the critical period. To test this we injected rats

with 0.5mg rolipram, a phosphodiesterase IV inhibitor, and immediately exposed them to

tones ~every 1 sec for 2 hours every day for 20 days. We observed that the length of the

cortex corresponding to half an octave above and below input frequency had increased by

30%. Rolipram significantly narrowed the receptive fields for tones in an input specific

manner. Reactivation of input specific critical period plasticity in adults could contribute to

researching tools for effecting targeted plasticity in neurorehabilitation.

INTRODUCTION

The cerebral cortex is highly plastic during development. Passive exposure to sensory stimuli

in a developing animal causes stimuli specific reorganization of the sensory cortex. Examples

of such experience dependent plasticity can be seen across sensory systems. Suturing the

eyelid shut during development increases the proportion of the primary visual cortex

responding to the contralateral eye (Hubel et al., 1977). Plucking all but one row of whiskers

in a mouse induces an increase in barrel cortex column size for that row (Schlaggar et al.,

1993). In the auditory cortex, passive exposure to a tone during development increases the

40

brain extent responding to that tone (Zhang et al., 2001a; de Villers-Sidani et al., 2007).

However, after a critical period, such passive sensory exposure fails to cause experience

dependent plasticity in the sensory cortex (Recanzone et al., 1993; de Villers-Sidani et al.,

2007). Reactivation of activity dependent plasticity in the adult cortex could be of immense

value in providing a tool to research treatments for cortical processing disorders in adults.

Experience dependent plasticity in the developing sensory cortex requires synaptic

reorganization and related protein synthesis (Antonini and Stryker, 1993; Lendvai et al.,

2000; Trachtenberg et al., 2002). An event implicated in such synaptic reorganization is the

activation of cortical Cyclic Adenosine Monophosphate (cAMP) Response Element Binding

(CREB) protein (Mower et al., 2002). An increase in cortical cAMP levels during the critical

period activates PKA to stimulate CREB mediated activity dependent plasticity (Reid et al.,

1996) (Fischer et al., 2004) (Muller, 2000).. In fact, local infusion of cAMP analogues in

adult animals resulted in reactivation of ocular dominance plasticity in adult visual

cortex(Imamura et al., 1999). We hypothesized that elevation of cortical cAMP levels with a

systemically administered agent would induce experience dependent plasticity too.

Systemically administered Rolipram inhibits phosphodiesterase-IV to elevate cAMP

in brain structures, including the dorsal cochlear nucleus, medial geniculate body and all six

layers of the auditory cortex (Perez-Torres et al., 2000). The presence of evidence

suggesting that rolipram can cause input specific cortical plasticity could be of immense

benefit in stimulating the research of clinically applicable tools for inducing input specific

cortical plasticity.

41

To test our hypothesis, we administered rolipram to rats daily, followed immediately

by exposure to multiple iterations of a pure tone. After twenty days we evaluated

electrophysiological properties in A1 for cortical plasticity.

METHODS

To test rolipram’s ability to cause frequency specific map expansion, we involved 34

Sprague-Dawley rats (Charles Rivers Laboratories, Wilmington, MA). Of these fourteen

were injected with rolipram and exposed to either 4 or 19 kHz tones. Twelve more underwent

the same sound exposure preceded by injections with a vehicle instead of rolipram. Eight rats

underwent electrophysiological protocols without exposure to either tone or drug (figure 1).

Drug treatments and acoustic stimuli administration during chronic sound exposure::

Rolipram (Sigma,St.Louis,MO) dissolved in 1% DMSO and 0.9% saline at a dilution of

0.5mg/ml and was injected subcutaneously at a dose of 0.5mg/kg immediately before

exposing animals to pure tones in a double walled sound shielded chamber over a period of

two hours, once every day for 20 days. The period of exposure was based on the fact that

rolipram reached peak serum levels at 30mins after systemic administration and had a half

life of 1-3 hours for rats (Krause and Kuhne, 1988). DMSO control animals received 1ml/kg

of 1% DMSO dissolved in saline with the same acoustic exposure as the experimental

animals. Naïve control animals did not receive drug or acoustic exposure. A lack of weight

loss and untoward behavior of rats on daily observation indicated that the experiment

protocol was not stressful.

42

Acoustic stimuli-Tones: Pure tones, 4 or 19 kHz, 250ms duration, 25 ms onset and offset

ramps, at ~ 1 sec inter stimulus intervals with randomly varying amplitudes (20-60 dB). The

tones were generated by a MATLAB (Mathworks Inc., Natick, MA) program using Real

Time Processors (RP2) manufactured by TDT Technologies Inc. (Alachua, FL) and played

by a speaker calibrated for using an ACO Pacific (Belmont, CA) microphone (PS9200-7016)

and programs written in MATLAB for calibration of tone and intensity.

Surgery and Electrophysiological recording:

Acute Surgery

24 hours after the last day of drug and tone exposure, the rats underwent acute surgery for

electrophysiology. Anesthesia for surgery was induced by pentobarbital sodium (50 mg/kg

ip) and maintained to achieve a state of areflexia with supplemental dilute pentobarbital (8

mg/kg ip). The rat’s level of anesthesia was monitored by heart rate, breath rate, and toe

pinch. The animal’s cardiovascular status was further monitored by presence of urine during

hourly bladder voiding. Body temperature which was kept at 37oC by a heating pad each time

the anal temperature fell below 37 oC. Fluid balance was maintained with a 1:1 mixture of

5% dextrose and Ringer lactate (~0.5 ml/h). The trachea was cannulated to administer

humidified air and minimize oropharyngeal breath sounds. The cisterna magna was drained

to prevent cerebral edema. The right auditory cortex was then exposed, the dura resected and

viscous silicon oil added to the brain surface to prevent desiccation. Electrode penetration

points were referenced using vascular landmarks and marked on a digitized photograph of

the auditory cortex surface. Care was taken to avoid penetration of visible vasculature.

43

Stimulus Presentation and data collection:

Acoustic stimuli-Tones: were presented in a double-walled sound attenuating chamber from

a speaker (Motorola model No. 40-1221) 10 cm away from the contralateral ear. Frequency

and intensity calibrations were done using Tucker-Davis SigCal software and an ACO

Pacific (Belmont, CA) microphone (PS9200-7016). 1296 randomly interleaved pure tones

(25 ms duration, 3 ms ramps, every 500 ms) were generated using Brainware (Tucker-Davis

Technologies). The tones included 81 logarithmically spaced frequencies from 1-32 kHz,

each at 16 different intensities spaced 5 dB SPL apart from 0-75 dB SPL. Parylene-Tungsten

electrodes, 50μm in diameter, were used to collect multi-unit data from cortical layer IV-V

(600-650 μm intracortical depth).

Data Analysis

Tuning Curve Analysis: All data analysis was done offline. Tuning curve parameters were

defined by a program written in MATLAB. The spontaneous firing rate was calculated as the

spike rate in the first 9 ms recorded after presentation of tone and before onset of a neural

response in the cortex. Onset latency was the time from the onset of the stimulus to the

earliest reliable neural response reaching two standard deviations above spontaneous firing

rate. End latency was defined as the time when the PSTH (post stimulus time histogram)

returned to spontaneous levels. The neuronal responses between onset latency and end

latency were plotted with frequency of tone presentation as abscissa and intensity of tone

presentation as ordinate to derive tuning curves (figure 2). The characteristic frequency (CF)

was defined as the frequency that evoked a reliable response at the lowest intensity (response


44


determine frequency polygons corresponding to each penetration site (Figure 1). In essence,

each point within a polygon is closest to the recording point enclosed within that polygon.

Classification of the primary cortical field:The auditory cortex in rats consists of at least 4

distinct fields-primary auditory cortex(A1), posterior auditory field(PAF), anterior auditory

field(AAF) and ventral auditory field(VAF). We recognized A1 sites by their antero-

posterior gradient, shorter latencies and narrower bandwidths (figure 2). The border of A1

with neighboring auditory fields was decided in the following manner: a) the A1-Posterior

auditory field(PAF) border was demarcated by an abrupt termination of A1’s frequency

gradient and confirmed by PAF sites having wider bandwidths and slower onset times

(Kalatsky et al., 2005; Pandya et al., 2007; Polley et al., 2007). b) the A1-Anterior auditory

field(AAF) border was demarcated by the reversal of the tonotopic gradient between the two

fields (Kalatsky et al., 2005; Polley et al., 2007) c) the A1-Ventral auditory field (VAF) was

decided by the presence of VAF sites having longer onset latencies, wider bandwidths and an

increased incidence of non-monotonic sites (Kalatsky et al., 2005).

Calculation of normalized antero-posterior extent: There is a significant correlation between

CFs in A1 and anteroposterior distance. Since the slope of this frequency gradient varied

with each rat (give standard deviations for both groups), for analysis of antero-posterior

extents of A1 across rats, we rotated the recording points along the frequency gradient axis

until the best correlation between CFs and antero-posterior distance was attained. The antero-

posterior total length of A1 was calculated from the anterior most point in A1 to the posterior

most point in A1 and assigned the value of 1. All intra A1 lengths were compared in relation

to the total length and assigned the proportional value. We compared normalized antero-

45

posterior extents across groups to see for an effect in map expansion since % area could be

affected by a variability in A1 recording site distribution.

RESULTS

Exposing rats to a repeated tone after injecting them with rolipram increased the cortical A1

representation of the tones in a frequency specific manner. A1 neurons activated by the

presented tone became more selective with rolipram.

The auditory cortex in rats consists of at least 4 distinct fields-primary auditory

cortex(A1), posterior auditory field(PAF), anterior auditory field(AAF) and ventral auditory

field(VAF). A total of 2117 extracellular multi-unit cortical sites from 34 rats were recorded

in the auditory cortex across different fields including A1, PAF, AAF, and VAF. We

recognized A1 sites by their antero-posterior gradient, shorter latencies and narrower

bandwidths. Recordings from non-A1 sites were used to ascertain the borders of A1 (see

methods).

Rolipram induces frequency specific plasticity in A1

During development, the cortical plasticity seen is dependent on the sensory input

experienced. Exposure to a low frequency tone causes a low frequency A1 map expansion

and exposure to a high tone causes a high frequency A1 map expansion (Zhang et al.,

2001b). To test our hypothesis that rolipram would induce experience dependent plasticity

similar to that seen during development, we compared rats injected with rolipram that

received either a low frequency (4 kHz) or a high frequency (19 kHz) tone. Figure 3 depicts

data from an example A1 map from each group. Rats injected with rolipram and exposed to

46

a low frequency tone evidenced a higher proportion of A1 responding to low frequency

tones. For the rat exposed to 4kHz, 32 % area of A1 had CFs ranging from 2.8 to 5.6 kHz

(half an octave below and above 4 kHz respectively). In stark contrast, rats exposed to 19

kHz had a contraction in representation of low frequencies. Only 19 % area of A1 had CFs

ranging from 2.8-5.6 kHz. However, the rat exposed to 19 kHz had an expansion in A1 area

(A1 area= 39 %) that had CFs ranging from 13.4 kHz to 26.9 kHz (half an octave below and

above 19 kHz respectively) while the rat exposed to 4 kHz had a contraction in A1 area (A1

area= 20 %) representing higher frequencies. Correspondingly, the antero-posterior extent of

the frequency region being activated by chronic tone exposure increased, while the non-

activated cortical region showed a contraction in length. Rolipram significantly increased the

cortical extent corresponding to 4kHz to 29% above vehicle controls (mean normalized

length of A1 for 4kHz : vehicle = 0.17 ± 0.03, rolipram=0.22 ± 0.02 , p<.05) (figure 5). A

similar rolipram induced increase was seen for rats exposed to 19 kHz after rolipram

injections (mean normalized length of A1 for 19kHz : vehicle = 0.28 ± 0.03, rolipram=0.36

± 0.04 , p<.05). As has been demonstrated in previous reports, an experience dependent map

expansion was accompanied by a significant decrease in the proportion of cortex not

activated by the tone paired with rolipram (Kilgard and Merzenich, 1998a).

Rolipram causes an input specific change in receptive fields

Neurons in the primary sensory cortex respond to a specific range of sensory stimuli in the

somatosensory, visual or auditory space. For example, an A1 neuron might fire action

potentials for any tone in a 2 to 8 kHz range, which is referred to as the receptive field for

that neuron. Experience dependent plasticity can modify receptive fields. Training to

47

discriminate a low frequency tone for example, narrows receptive fields (Recanzone et al.,

1993). To examine if rolipram modifies bandwidths (receptive fields) of activated neurons,

we compared the bandwidths of low and high frequency neurons (figure 6). Being exposed to

a high frequency tone while under the effect of rolipram significantly sharpened the receptive

fields of high frequency neurons (5.6 – 32 kHz) (BW @ 40 dB for 19kHz exposed groups :

vehicle = 2.15 ± 0.06, rolipram=1.7 ± 0.05 , p<.05). The same low frequency neurons had

wider receptive fields if the rat was exposed to a 19 kHz tone. Similarly, a 19 kHz tone

caused a narrowing of bandwidths in high frequency neurons and a widening of bandwidths

for low frequency neurons. In effect, rolipram induced neurons to become more selective to

the tone that activated them.

Changes associated with passive tone exposure

Passive sensory input exposure has shown mixed results in previous studies. While

habituation is seen to occur to response strength in A1 (Condon and Weinberger, 1991) and

hyper stimulation of a whisker decreases cortical area in somatosensory cortex (Feldman and

Brecht, 2005), studies in the auditory cortex have noted a lack of similar map contraction to

non behaviorally relevant stimuli. Exposure to ~300 repetitions of a tone daily that did not

have behavioral relevance or that which was not paired with nucleus basalis stimulation did

not change the representation of that tone in the cortex (Recanzone et al., 1993; Kilgard and

Merzenich, 1998a). Possibly due to a 20 fold greater daily sound exposure (exposure to

~6400 tone repetitions daily) in our study, we observed a trend towards a decreased

representation of a tone after passive exposure. It appears that rolipram prevents this

contraction and further increases cortical representation of tone above naïve control rats by

48

9% (see figure). Passive exposure showed a trend towards affecting receptive fields too.

Neurons activated by a repeated tone became less selective for that tone, which was

significant for high frequency neurons (figure 6).

We failed to see a tone specific change in response strength, latencies or spontaneous firing

rates.

DISCUSSION

We provide evidence of inducing frequency specific plasticity in adult rats by

modulation of critical period mechanisms. A previous study has provided evidence for

pharmacological reactivation of critical period plasticity by modulating cortical inhibitory

systems in adult animals receiving continuous drug and sensory stimulation for multiple

weeks (Vetencourt et al., 2008).We suggest that our study is unique in contributing to the

issue since : a) We propose and modulate a different system -cAMP modulation for

reactivation of critical period rather than modulation of inhibitory systems. b) The precision

of input specific plasticity is greater in our study c) Our study simulates a therapy session in a

rehabilitative clinic- short drug and tone exposure times decreasing the possibility that post

therapy environment would affect map plasticity.

Concerns arise regarding the ubiquitous presence of cAMP and hence a wide region

of action of rolipram in the body. Studies indicate sub-cortical sites for rolipram action

(Perez-Torres et al., 2000) , suggesting the plausibility that our study’s A1 findings could be

attributed to a primarily sub-cortical re-organization. In fact, studies of cortical map

reorganizations also noticed massive sub-cortical receptive field re-mapping (Pons et al.,

49

1991; Jones, 2000). It remains to be shown if such sub-cortical plasticity cause or are caused

by cortical reorganization.

The anesthesia used in this study, sodium pentobarbital, activates GABAA (γ-amino

butyric acid) receptors at multiple sites involved in auditory signal processing (Richter and

Holtman Jr, 1982), potentially influencing our plasticity study. However, the following

points suggest the validity of using sodium pentobarbital in our study : a) a lack of effect of

sodium pentobarbital on CFs (Gaese and Ostwald, 2001), b) the fact that both the

experimental and the control rats received the same anesthesia and any change in neuronal

response would be due to a factor that is not common to both groups, c) the lack of an

opportunity for rolipram to interact with anesthesia since all anesthetic recordings took place

at least 24 hours after the last rolipram injection.

Rolipram could be useful in a clinical context. Rolipram can penetrate the blood brain

barrier and improve memory for hippocampal tasks (Barad et al., 1998; Zhang and

O'Donnell, 2000), achieve beneficial effects in models of Alzheimer’s disease (Gong et al.,

2004), depression (Wachtel, 1983), psychosis (Siuciak et al., 2007) and inflammatory

disorders in the Central Nervous System (CNS) (Sommer et al., 1995) . Rolipram is

prescribed as an oral anti-depressant in Europe and Japan and has shown proof of beneficial

CNS effects (Smith, 1996). Our presence of evidence suggesting that rolipram can cause

input specific cortical plasticity could be of immense benefit in a) stimulating the research of

clinically applicable tools for inducing input specific cortical plasticity and b) appropriate

usage of the drug when used as an anti-depressant.

Administration of other agents did succeed in achieving sensory input specific

receptive field plasticity in adults (Manunta and Edeline, 1997; Imamura et al., 1999;

50

Penschuck et al., 2002). However, their route of drug administration was either local or short

lasting (lasting only minutes) or both. Our preliminary results are the first to show

development of input specific cortical plasticity as a consequence of systemic administration

of an agent. Besides, since our cortical recordings are done at least 24 hours after the last

rolipram injection, our results are the first to show long lasting input specific cortical

plasticity induced by systemically administered agents. Developing systemically applicable

agents that cause long term targeted cortical reorganization could be of great value in guiding

neuronal rehabilitation research.

51

APPENDIX

CHAPTER THREE

Habituation Injection + Tone Exposure

Period after

last

injection

Rolipram + 4 kHz (N=8)

Rolipram + 19kHz (N=6)

Vehicle + 4 kHz(N=6)

1-2 days 20 days 1 day

Ne

uro

ph

ysio

log

ica

l

rec

ord

ing

Vehicle +19 kHz(N=6)

Figure 1. . Illustration of experimental protocol. Habituation involved making a rat

comfortable with the immobilization technique used for subcutaneous injections. A waiting

period of 24 hours after the last injection was undertaken to avoid high serum levels of

injected agent during physiology.

52

-0.5 0 0.5 1 1.5 2 2.5 3 3.5

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

16x3x

o

o

9xo

10x

o 16x26x

24

31

6

14x

24

32

3x5x

19

27

2x2x

23

o

6x6x

13

24

2x2x

o

5x

2

2

11

17

2

4

10

16

811

23

26

22

10

15

4

4

12

12

1

2

5

12

2

2

6

13

1

2

2

8

2x1

2x

11x

3x1x

5

12

1515

6x

10x

1513

14

25

1324

25

32x

7x1

11

23

Best Freq

10

20

30

40

50

60

Naïve Control

A

Naïve Control

B

Figure 2.A) Example of a tuning curve recorded from a naïve rat. CF=Characteristic

Frequency. BW=Bandwidth. B) Example of a cortical primary auditory cortex (A1) map

from a naïve rat. Note the frequency gradient from high to low frequencies progressing from

anterior to posterior. “x” indicates sites in non-primary auditory cortex and “o” indicates

sites non-responsive to tones.

53

5 12

1515

12 16

2022

12 212121

3 21718

21413

1512

2826

152

31

5

24

22

6

1717

5

1513 10

0

22

3

5

17

225

5

25

18

1.8

26

26

1.4

1.9

1.9

9

Normalized antero-posterior distance

r196final

Best Freq (kHz)

1

2

4

8

16

32

60

0 0.2 0.4 0.6 0.8 11

2

4

8

16

32

Chara

cte

ristic F

requency


2125 66

1.8

2

66

5

6

1520

1.6

2627

35

610

6

62426

2

6

6222

5

26

1723

1.6

1.8

515

2531

1.7

1722

7

567

6


r3final

Best Freq (kHz)

1

2

4

8

16

32

60

0 0.2 0.4 0.6 0.8 11

2

4

8

16

32

1

Chara

cte

ristic F

requency


A B

C D

Figure 3. Example of cortical length measurement for Rolipram injected group . A) &B)

Example A1 map and illustration of frequency gradient of a rat injected with Rolipram and

exposed to 4 kHz tones. The vertical dark blue solid and dotted vertical lines bracket the

regions half an octave above and below 4 kHz and 19 kHz. C) & D) Similar example plot of

rat injected with Rolipram and exposed to 19 kHz tones. Note the increase in antero-posterior

length around 4 kHz for the rat exposed to 4 kHz and around 19 kHz for the rat exposed to

19 kHz. Note also the A1 map contraction around the unexposed frequency.

54

29

2

4

1525

1.7

4

13

22

15

16

16

1.9

815

16

9

11

2624

1.8

1.84

2

4

2228

4

1724

1315 51415

11

13

1625

3

42

2

15

237

27


rolipa4kdms09final

Best Freq (kHz)

1

2

4

8

16

32

60

0 0.2 0.4 0.6 0.8 11

2

4

8

16

32

Chara

cte

ristic F

requency


7

12

2

6

6 2

43

7 2

7

2 1.5

13

225

1.9

1.9

19

6

20 1.7

8

73

28

1.8

25

2624

30

86

14

2415

13

15

15

7

8

189

18

9

23

7


rolipa19kdm21final

Best Freq (kHz)

1

2

4

8

16

32

60

0 0.2 0.4 0.6 0.8 11

2

4

8

16

32

Chara

cte

ristic F

requency


A B

C D

Figure 4. Example of cortical length measurement for vehicle injected group . A) &B)

Example A1 map and illustration of frequency gradient of a rat injected with vehicle and

exposed to 4 kHz tones. C) & D) Similar example plot of rat injected with vehicle and

exposed to 19 kHz tones. Note the decrease in antero-posterior length around 4 kHz for the

rat exposed to 4 kHz and around 19 kHz for the rat exposed to 19 kHz.

55

Nav Veh+4k Veh+19k Roli+4k Roli+19k0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

CORTICAL LENGTH OF LOW FREQ. NEURONS

NO

RM

AL

IZE

D C

OR

TIC

AL

LE

NG

TH

Nav Veh+4k Veh+19k Roli+4k Roli+19k0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

CORTICAL LENGTH OF HIGH FREQ. NEURONS

NO

RM

AL

IZE

D C

OR

TIC

AL

LE

NG

TH

A B

Figure 5. Cortical length comparisons. A) Cortical length corresponding to half an octave

above and below 4 kHz. B) Cortical length corresponding to half an octave above and

below19 kHz. (*) = significant changes between the two rolipram injected groups. (***)=

significant changes between the rolipram group and the vehicle group exposed to the same

tone. (.) = significant changes between the two vehicle injected groups All significant

comparisons characterized by p< .05..

56

LOW CF NEURONS HGH CF NEURONS1.7

1.8

1.9

2

2.1

2.2

2.3

2.4

2.5

BANDWIDTH PLASTICITY

BA

ND

WID

TH

@ 4

0 d

B(O

CT

AV

ES

)

Roli+4k

Roli+19k

LOW CF NEURONS HGH CF NEURONS1.7

1.8

1.9

2

2.1

2.2

2.3

2.4

2.5

BANDWIDTH PLASTICITY

BA

ND

WID

TH

@ 4

0 d

B(O

CT

AV

ES

)

Veh+4k

Veh+19k

A B

Figure 6. Bandwidth Plasticity. A) Average bandwidths 40 dB above threshold for the two

Rolipram injected groups along with standard error of means. The solid line between the low

and high frequency neuron groups illustrates a double dissociation effect induced by

rolipram. Low frequency neurons underwent narrowing of receptive fields if activated by low

frequency tone and broadened receptive fields if not activated ( by a 19 kHz tone). A

corresponding effect is seen for high frequency neurons. B) Average bandwidths 40 dB

above threshold for the two Rolipram injected groups along with standard error of means.

Note that repeated passive exposure to a tone causes changes opposite to that seen with tones

+ Rolipram.

57

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60

CHAPTER FOUR

M1 AGONIST CEVIMELINE (AF102B) INDUCES INPUT SPECIFIC FREQUENCY

MAP PLASTICITY IN RAT PRIMARY AUDITORY CORTEX

Vikram Jakkamsetti, Jai A. Shetake, Kevin Q. Chang, Rolan O. Torres, Kamini Krishnan,

and Michael P. Kilgard

School of Behavioral and Brain Sciences GR41




Running Title: M1 agonist Cevimeline (AF102B) induces input specific

frequency map plasticity in rat primary auditory cortex

Key words: : nucleus basalis stimulation, critical period, amphetamine, neuromodulation



61

ABSTRACT

Nucleus Basalis stimulation paired with sensory input releases cortical acetylcholine in adult

mammals to induce input specific cortical plasticity. Acetylcholine mediates this effect

through muscarinic M1 receptors. We hypothesized that M1 agonist administration paired

with sensory stimuli would induce stimuli specific cortical plasticity. To test this hypothesis,

rats were given subcutaneous injections of an M1 agonist Cevimeline (AF102B) and exposed

to a 250ms pure tone ~1/ sec for two hours every day for 20 days. One day after the last

injection, multi-unit recordings were conducted under anesthesia to construct primary

auditory cortex frequency maps. For rats exposed to a 4 kHz tone, the proportion of cortex

containing characteristic frequencies 4 kHz ± 0.25 octaves was significantly increased by

30%. Similarly, pairing a 19 kHz tone with M1 agonist injections increased the extent of

cortex containing characteristic frequencies 19 kHz ± 0.25 by 25%. Having a

pharmacological tool to reopen the critical period and induce input specific cortical plasticity

could be useful in better understanding and treating cortical disorders.

INTRODUCTION

The adult brain has a remarkable potential to rewire itself. Significant cortical reorganization

is induced in response to behaviorally relevant sensory cues in the environment. Training to

detect a sensory stimulus leads to an enhanced representation of that stimulus in the cortex .

For example, in the auditory cortex, training to discriminate a tone leads to an increased

representation of that tone in the auditory cortex (Recanzone et al., 1993). Fear conditioning

to a tone induces identical receptive field plasticity in the cortical map (Bakin and

Weinberger, 1990). The nucleus basalis has been implicated in this experience dependent

62

plasticity. On presentation of a behaviorally relevant cue, connections from the forebrain and

the amygdala to the nucleus basalis stimulate the nucleus basalis to release acetylcholine in

the cortex (Mesulam and Geula, 1988). The released acetylcholine plays a crucial role in

nucleus basalis mediated plasticity. Cortical acetylcholine modifies cortical receptive field

properties to increase the representation of the cue (Kilgard and Merzenich, 1998a). In fact,

lesions of the cholinergic neurons in the nucleus basalis prevent the development of cortical

plasticity (Kilgard and Merzenich, 1998a; Miasnikov et al., 2001) and blocking the effect of

acetylcholine on muscarinic receptors causes a lack of cortical reorganization (Miasnikov et

al., 2001). Acetylcholine’s induction of activity dependent plasticity is mediated by M1

receptors . In M1-knockout mice, there is about 70% reduction of nucleus basalis mediated

tone-specific changes in receptive fields, suggesting that nucleus basalis mediated plasticity

requires M1 receptor activation (Zhang et al., 2006). We hypothesized that activation of the

M1 receptor by an M1-agonist along with presentation of a tone will induce activity

dependent reorganization of the auditory cortex. To test this hypothesis, we evaluated the

cortical responses of rats after 20 days of daily exposure to tones after subcutaneous

injections of M1-agonist Cevemiline.

Since about 30% of the cortical plasticity achieved by nucleus basalis stimulation

occurs through non-M1 muscarinic receptors it seems likely that administration of an agent

that induces cortical acetylcholine release by directly stimulating NB would induce cortical

plasticity that is greater in magnitude than administration of an M1-agonist. Systemic

Amphetamine has been demonstrated to stimulate the nucleus basalis to release cortical

acetylcholine (Casamenti et al., 1986; Arnold et al., 2001). We hypothesized that

63

administration of amphetamine along with tones would lead to activity dependent plasticity

that is greater than that seen by M1-agonist alone.

METHODS

Forty five Sprague-Dawley rats (Charles Rivers Laboratories, Wilmington, MA) underwent

neurophysiological analysis in this study. Of these twelve were injected with Cevemiline and

exposed to either 4 or 19 kHz tones. Twelve more rats underwent the same sound exposure

preceded by injections with saline instead of Cevemiline. Nine were injected with

amphetamine and exposed to either 7 or 24 kHz tones. Three rats were injected with saline

and exposed to 7 kHz. Eight rats underwent electrophysiological protocols without exposure

to either tone or drug (figure 1).

Drug treatments and acoustic stimuli administration during chronic sound exposure::

Cevimiline (Evoxac®

, Daiichi Sanyo Inc., Parsippany, NJ, USA) or D-amphetamine (Sigma-

Aldrich Corp , St.Louis, MO, USA) dissolved 0.5mg/ml in 0.9% saline was injected

subcutaneously at a dose of 0.5mg/kg immediately before exposing animals to pure tones in a

double walled sound shielded chamber over a period of two hours, once every day for 20

days. The period of exposure was based on the fact that Cevimeline reached peak serum

levels at one and a half hours after systemic administration (product brochure from Evoxac)

and that D-amphetamine induced acetylcholine release in the cortex almost reached baseline

levels 110 minutes after i.p. injection (Casamenti et al., 1986). Saline vehicle control animals

received 1ml/kg of 0.9% saline with the same acoustic exposure as the experimental animals.

Naïve control animals did not receive drug or acoustic exposure. A lack of weight loss and

64

excessive salivation on daily observation indicated that the experiment protocol did not result

in observable adverse effects.

Acoustic stimuli-Tones: Pure tones, 4 or 19 kHz, 250ms duration, 25 ms onset and offset

ramps, at ~ 1 sec inter stimulus intervals with randomly varying amplitudes (20-60 dB). The

tones were generated by a MATLAB (Mathworks Inc., Natick, MA) program using Real

Time Processors (RP2) manufactured by TDT Technologies Inc. (Alachua, FL) and played

by a speaker calibrated for using an ACO Pacific (Belmont, CA) microphone (PS9200-7016)

and programs written in MATLAB for calibration of tone and intensity.

Surgery and Electrophysiological recording:

Acute Surgery

24 hours after the last day of drug and tone exposure, the rats underwent acute surgery for

electrophysiology. Anesthesia for surgery was induced by pentobarbital sodium (50 mg/kg

ip) and maintained to achieve a state of areflexia with supplemental dilute pentobarbital (8

mg/kg ip). The rat’s level of anesthesia was monitored by heart rate, breath rate, and toe

pinch. The animal’s cardiovascular status was further monitored by presence of urine during

hourly bladder voiding. Body temperature which was kept at 37oC by a heating pad each time

the anal temperature fell below 37 oC. Fluid balance was maintained with a 1:1 mixture of

5% dextrose and Ringer lactate (~0.5 ml/h). The trachea was cannulated to administer

humidified air and minimize oropharyngeal breath sounds. The cisterna magna was drained

to prevent cerebral edema. The right auditory cortex was then exposed, the dura resected and

viscous silicon oil added to the brain surface to prevent desiccation. Electrode penetration

65

points were referenced using vascular landmarks and marked on a digitized photograph of

the auditory cortex surface. Care was taken to avoid penetration of visible vasculature.

Stimulus Presentation and data collection:

Acoustic stimuli-Tones: were presented in a double-walled sound attenuating chamber from

a speaker (Motorola model No. 40-1221) 10 cm away from the contralateral ear. Frequency

and intensity calibrations were done using Tucker-Davis SigCal software and an ACO

Pacific (Belmont, CA) microphone (PS9200-7016). 1296 randomly interleaved pure tones

(25 ms duration, 3 ms ramps, every 500 ms) were generated using Brainware (Tucker-Davis

Technologies). The tones included 81 logarithmically spaced frequencies from 1-32 kHz,

each at 16 different intensities spaced 5 dB SPL apart from 0-75 dB SPL. Parylene-Tungsten

electrodes, 50μm in diameter, were used to collect multi-unit data from cortical layer IV-V

(600-650 μm intracortical depth).

Data Analysis

Tuning Curve Analysis: All data analysis was done offline. Tuning curve parameters were

defined by a program written in MATLAB. The spontaneous firing rate was calculated as the

spike rate in the first 9 ms recorded after presentation of tone and before onset of a neural

response in the cortex. Onset latency was the time from the onset of the stimulus to the

earliest reliable neural response reaching two standard deviations above spontaneous firing

rate. End latency was defined as the time when the PSTH (post stimulus time histogram)

returned to spontaneous levels. The neuronal responses between onset latency and end

latency were plotted with frequency of tone presentation as abscissa and intensity of tone

presentation as ordinate to derive tuning curves (figure 2). The characteristic frequency (CF)

66

was defined as the frequency that evoked a reliable response at the lowest intensity (response



determine frequency polygons corresponding to each penetration site . In essence, each point

within a polygon is closest to the recording point enclosed within that polygon.

Classification of the primary cortical field:The auditory cortex in rats consists of at least 4

distinct fields-primary auditory cortex(A1), posterior auditory field(PAF), anterior auditory

field(AAF) and ventral auditory field(VAF). We recognized A1 sites by their antero-

posterior gradient, shorter latencies and narrower bandwidths (figure). The border of A1 with

neighboring auditory fields was decided in the following manner: a) the A1-Posterior

auditory field(PAF) border was demarcated by an abrupt termination of A1’s frequency

gradient and confirmed by PAF sites having wider bandwidths and slower onset times

(pandya, doron,polley). b) the A1-Anterior auditory field(AAF) border was demarcated by

the reversal of the tonotopic gradient between the two fields (polley,kalatsky) c) the A1-

Ventral auditory field (VAF) was decided by the presence of VAF sites having longer onset

latencies, wider bandwidths and an increased incidence of non-monotonic sites (kalatsky).

Calculation of normalized antero-posterior extent: There is a significant correlation between

CFs in A1 and anteroposterior distance. Since the slope of this frequency gradient varied

with each rat (give standard deviations for both groups), for analysis of antero-posterior

extents of A1 across rats, we rotated the recording points along the frequency gradient axis

until the best correlation between CFs and antero-posterior distance was attained. The antero-

posterior total length of A1 was calculated from the anterior most point in A1 to the posterior

most point in A1 and assigned the value of 1. All intra A1 lengths were compared in relation

67

to the total length and assigned the proportional value. We compared normalized antero-

posterior extents across groups to see for an effect in map expansion since % area could be

affected by a variability in A1 recording site distribution.

RESULTS

General Observations

A total of 3441 extracellular multi-unit cortical sites from 45 rats were recorded in the

auditory cortex from different fields including A1 , PAF, and ventral auditory field (VAF)

.CFs and classification of sites as belonging to A1 were done as in chapter 3. Briefly, A1

sites were identified by their tonotopcity, narrow bandwidths and shorter latencies and non-

A1 sites were used to determine the border of A1 (see methods).

Cevimeline + 4 kHz increases low frequency cortical extent

. Exposure of a rat to a tone during nucleus basalis stimulation increases the proportion of the

cortex that represents that tone. To test our hypothesis that Cevimeline would induce

plasticity similar to that seen during nucleus basalis stimulation we estimated the area of the

cortex that corresponded to CFs half an octave above and below 4kHz . Consistent with

nucleus basalis stimulation studies, Cevimeline demonstrated a 33% increase in the

percentage of A1 area with CFs associated with the input frequency. Inspite of an impressive

difference in means between the rat groups, a variability in the narrow region of area being

compared likely prevented this result from being statistically significant (%area of A1 for

4kHz : vehicle = 27± 4.8 , Cevimeline= 33 ± 5.3 , p>.05). The anteroposterior extent of a

frequency region is a one dimensional measure and less variable in A1. This measure is less

68

likely to show a change without an actual shift in the border of a frequency region along the

tonotopic axis. We quantified the cortical extent corresponding to a given tone by

determining the antero-posterior distance covered by CFs that ranged half an octave below

and above the given tone (figure 3). Cevimeline significantly increased the cortical extent

corresponding to 4kHz to almost 30% above controls (normalized length of A1 for 4kHz :

0.17± 0.03 vehicle = , Cevimeline=0.22 ± 0.01 , p<.05) (figure 5 A).

Cevimeline + 19 kHz increases high frequency cortical extent

To observe if Cevimeline causes effects that are input specific, we examined cortical extents

of rats injected with Cevimeline and exposed to 19 kHz. Compared to vehicle rats ,

Cevimeline injected rats had a 30 % increase in the cortical length corresponding to half an

octave above and below 19 kHz controls (normalized length of A1 for 4kHz : 0.27± 0.03

vehicle = , Cevimeline=0.33 ± 0.04 , p<.05) (figure 5 B). Expansion to the input frequency

was accompanied by a contraction of cortical extent corresponding to non input frequencies.

For example, the frequency region corresponding to 4kHz in a rat injected with Cevimeline

and exposed to 19 kHz was significantly smaller than for a rat injected with Cevimeline and

exposed to 4 kHz.

Amphetamine + tone fails to induce input specific plasticity

Animals injected with amphetamine and exposed to 7 kHz had the same frequency gradients

as the saline control and the naïve control group. A clear difference in tonotopicity between

the different groups was not obvious. In order to objectively check for changes in

tonotopicity, we examined the antero-posterior distance covered by CFs that ranged from 0.5

69

octaves below to 0.5 octaves above 7 kHz. Administration of amphetamine at 7 kHz did not

significantly increase the relevant cortical length when compared to both the naïve control

and the saline control animals (figure 6A) . Administration of 24 kHz along with

Amphetamine did not cause an input specific change in the cortical map either (figure 6B).

DISCUSSION

We provide evidence that activation of M1-receptors in adults with 0.5mg of Cevemiline

induces frequency specific plasticity in the cortex. Amphetamine, which causes a non-

specific activation of multiple cholinergic receptors, does not induce input specific plasticity.

Many studies, both clinical and animal based, (Feeney and Hovda, 1985; Walker-Batson et

al., 1995; Buetefisch et al., 2002; Dinse et al., 2003; Tobey et al., 2005) suggest a strong role

of amphetamine in facilitating activity dependent . However, pairing amphetamine with

exposure to pure tones failed to cause input specific changes in the tonotopicity of A1. There

are two possibilities: A) Amphetamine did elicit input specific plasticity but the results were

not discernable or B) Amphetamine did not produce input specific frequency map plasticity

A)Amphetamine generated input specific plasticity in non-primary auditory cortex: Tobey et

al, 2005 used functional brain imaging techniques and found an enhancement of the signal in

regions of the auditory cortex responsible for processing the stimuli. Functional brain

imaging has a relatively lower spatial resolution (over millimeters) as compared to multiunit

electrode recording (fractions of a millimeter). The changes seen in functional brain imaging

could be the result of plasticity in a local network that extends over the primary auditory

cortex, possibly even including some secondary auditory cortical areas. Our study evaluated

70

changes in the primary auditory cortex and could have missed any plasticity occurring

outside this field.

II) Amphetamine failed to produce input specific frequency map expansion:

A failure to see significant findings could be due to the following factors: a) action on

multiple neurotransmitters: Along with increasing cortical acetylcholine, amphetamine also

increases nor-epinephrine, dopamine and serotonin levels in the cortex. With the resultant

non-specific activation of multiple cortical sites with multiple neurotransmitters, experience

dependent activation of specific cortical neurons could have had less of an effect in driving

plasticity. b) Chronic administration of drug instead of acute: Neurorehabilitation studies

that show activity or use-dependent enhancement of function achieved by amphetamine were

done using acute administration of amphetamine (Buetefisch et al., 2002; Dinse et al.,

2003)Those studies employing long term amphetamine administration did so with intervals

of 3-4 days between sessions (Feeney and Hovda, 1985; Walker-Batson et al., 1995). Acute

or interrupted administration of amphetamine could potentially prevent phenomena like

down-regulation of receptors associated with long-term continuous application of

amphetamine. Daily administration of drugs that prevent re-uptake of norepinephrine or

serotonin – the same neurotransmitters that amphetamine elevates – prevents development of

cortical plasticity (Gerdelat-Mas et al., 2005; Lange et al., 2007) . We used daily

amphetamine doses in our current study, and this protocol of administration could have

contributed to a lack of significant findings.

Recordings in this study were done under the effect of anesthesia. The current study

is based on the hypothesis that M1 receptors activated by acetylcholine contribute to cortical

plasticity. A number of plasticity studies examining the function of nucleus basalis mediated

71

acetylcholine release in the auditory cortex suggest that the use of general anesthesia does not

obscure significant findings. These studies include the use of a different anesthetic agent

urethane (Edeline et al., 1994b; Bakin and Weinberger, 1996; Bjordahl et al., 1998), under

anesthesia during training and recording(Bakin and Weinberger, 1996) , awake during

training but recording under anesthesia(Bjordahl et al., 1998) and unanesthetized during

training and recording (Edeline et al., 1994a) . Significant changes are noticed in acute

(Edeline et al., 1994a; Edeline et al., 1994b; Bakin and Weinberger, 1996; Bjordahl et al.,

1998) as well as chronic training protocols (Kilgard and Merzenich, 1998a) and have

reliably shown plasticity findings whether investigated immediately(Edeline et al., 1994b;

Bakin and Weinberger, 1996; Dimyan and Weinberger, 1999) or a day after cessation of

training (Bjordahl et al., 1998; Kilgard and Merzenich, 1998a) . The above given evidence

suggests that recording under anesthesia would not have obscured findings of cortical

plasticity in our study.

Systemic administration of Cevemiline (AF102B) and similar M1 agonists have been

used successfully to enhance memory in adult rats (ref), Alzheimer models (ref), and to

induce an increase in tone evoked responses in the auditory cortex (O’Neill). Cevemiline is

currently being used in the United States for Sjogren’s syndrome (refs). The presence of

evidence suggesting that Cevemiline can cause sensory input specific brain changes could be

of immense benefit in a) stimulating the research of clinically applicable tools for inducing

input specific cortical plasticity and b) appropriate usage of the drug today when used for

Sjogren’s syndrome.

Millions of people suffer from deficits in cortical neuronal processing as a

consequence of stroke, trauma, developmental anomalies and tumors. Recovery from and

72

rehabilitation of cortical neuronal processing disorders has been largely unsuccessful.

Expansion of primary cortical maps has been associated with improvements in behavioral

outcomes after cortical lesions in animal models (Xerri et al., 1998). Basic science

experiments have demonstrated that the frequency map of A1 is capable of massive

frequency specific map expansion as a consequence of long term operant training in a tone

discrimination task (Recanzone et al., 1993), fear conditioning (Bakin and Weinberger, 1990)

and nucleus basalis stimulation (Kilgard and Merzenich, 1998a). However, translation of

these map expansion methods into clinical practice as a therapy for cortical disorders has

proved to be very challenging. We suggest that researching M1 agonists to stimulate input

specific map plasticity in a clinical setting may aid the development of more effective

therapies for cortical processing disorders.

73

APPENDIX

CHAPTER FOUR

Habituation Injection + Tone ExposurePeriod after

last injection

Cevimeline + low(N=6)

Cevimeline+ high (N=7)

Vehicle + low (N=9)

1-2 days 20 days 1 day

Ne

uro

ph

ysio

log

ica

l

rec

ord

ing

Vehicle + high (N=6)

Amphetamine + low (N=5)

Amphetamine + high (N=4)

Figure 1. Illustration of experimental protocol. Habituation involved making a rat

comfortable with the immobilization technique used for subcutaneous injections. A waiting

period of 24 hours after the last injection was undertaken to avoid high serum levels of

injected agent during physiology. The vehicle + low group included vehicle + 4 kHz (N=6)

and vehicle + 7 kHz (N=3).

74

-0.5 0 0.5 1 1.5 2 2.5 3 3.5

-0.2

0

0.2

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Naïve Control

Figure 2.A) Example of a tuning curve recorded from a naïve rat. CF=Characteristic

Frequency. BW=Bandwidth. B) Example of a cortical primary auditory cortex (A1) map

from a naïve rat. Note the frequency gradient from high to low frequencies progressing from

anterior to posterior. “x” indicates sites in non-primary auditory cortex and “o” indicates

sites non-responsive to tones.

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C D

Figure 3. Example of cortical length measurement for low tone exposure group . A) &B)

Example A1 map and illustration of frequency gradient of a control rat injected with vehicle

and exposed to 4 kHz tones. The vertical dark blue solid and dotted vertical lines bracket the

regions half an octave above and below 4 kHz and 19 kHz. C) & D) Similar example plot of

rat injected with M1 agonist Cevemiline and exposed to 4 kHz tones. Note the increase in

antero-posterior length around 4 kHz for the Cevemiline injected rat .

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Figure 4. Example of cortical length measurement for high tone exposure group . Example

A1 map and illustration of frequency gradient of a control rat injected with vehicle (A & B)

and Cevemiline (C&D) and exposed to 19 kHz tones. Note the increase in antero-posterior

length around 19 kHz for the Cevemiline injected rat .

77

Nav Veh+4k Veh+19k M1+4k M1+19k0

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Figure 5. Cortical length comparisons. A) Cortical length corresponding to half an octave

above and below 4 kHz. B) Cortical length corresponding to half an octave above and

below19 kHz. (***)= significant changes between the rolipram group and the vehicle group

exposed to the same tone. (.) = significant changes between the two vehicle injected groups.

All significant comparisons characterized by p< .05.

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Nav Veh+7k Amph+7k Amph+240

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Figure 6.Cortical length comparisons. A) Cortical length corresponding to half an octave

above and below 7 kHz. B) Cortical length corresponding to half an octave above and below

24 kHz. Note lack of significant changes in cortical length induced by amphetamine

79

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CHAPTER FIVE

SUMMARY AND CONCLUSIONS

The adult brain samples the environment through the sensory system and undergoes

dramatic changes for behaviorally relevant environmental features. This ability to adapt helps

an organism benefit from experience to increase its chances for survival. Understand the

mechanisms of experience dependent plasticity will help us harness this amazing ability of

the brain to change to relevant sensory input, which could help us clinically drive brain

changes that counteract those seen in brain disorders. This brings us to two main questions

that the dissertation aims to address: First, how does the sensory representation of the

environment in the brain change with meaningful experience? Second, how could we induce

experience dependent changes that could be explored in a clinical context?

The first chapter attempts to answer the first question by examining experience

dependent changes induced by environmental enrichment in a non-primary cortical auditory

field. Environmental enrichment has long been used for treating cortical processing disorders

and this chapter aims in understanding the cortical substrate of auditory enrichment. We

observed that enrichment induces PAF neurons to fire faster, more in phase with acoustic

input and with a better ability to keep up with rapid incoming input. An ability to respond

quickly allows neurons to more easily detect small changes in speed of incoming input.

Speech sounds that have a fast onset of sound energy induce stronger responses after

enrichment. An ability to fire more easily to rapidly incoming input by PAF neurons is

congruent with clinical findings of a betterment in cortical processing speed of dyslexics

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after auditory training (Hayes et al., 2003b). Since enrichment induces paired pulse

depression to rapid inputs in A1 (Percaccio et al., 2005), we suggest that the clinically seen

findings might be attributable to non-primary cortical processing.

The second and third chapter attempt to bridge the gap between powerful basic

science insights and clinically applicable treatments for brain disorders. Multiple studies have

induced experience dependent plasticity in an adult lab animal. These studies predict the

immense clinical possibilities based on their research. For example, nucleus basalis

stimulation or local cortical application of cholera toxin (a cAMP elevating agent) paired

with a sensory stimuli induce input specific cortical reorganization (Kilgard and Merzenich,

1998a; Imamura et al., 1999). However the expense of deep brain stimulation and potential

adverse effects of deep brain electrode implantation or systemic administration of cholera

toxin decrease the feasibility of directly using these insights in a clinical context. We aimed

to bypass this bottleneck in translational research by using pharmacological agents that

induce plasticity through systemic administration. We observed that exposing rats to multiple

repetitions of a single tone after an increase of cAMP via injections of rolipram significantly

increased the extent of the cortex that responded to that tone. Rolipram also induced an input

specific narrowing of receptive fields within 20 days, a finding normally only seen after

auditory training for multiple months (Recanzone et al., 1993). M1-agonist, Cevemiline

increased the length of the cortex responding to the exposed tone. In contrast, an agent that

caused a non specific activation of all cholinergic receptors (amphetamine) failed to cause

tone specific cortical plasticity.

While we understand that much work has to be done to take our understanding of

experiential plasticity to clinically applicable treatments, we hope that the studies offered in

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this dissertation will make a contribution-albeit small- to building a bridge from basic science

to clinical therapy.

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REFERENCES

Churs L, Spengler F, Jürgens M, Dinse HR (1996) Environmental enrichment counteracts

decline of sensorimotor performance and deterioration of cortical organization in

aged rats. Soc Neurosci Abstr 22:102.






Hayes EA, Warrier CM, Nicol TG, Zecker SG, Kraus N (2003) Neural plasticity following


114:673-684.




Kilgard MP, Merzenich MM (1998a) Cortical Map Reorganization Enabled by Nucleus

Basalis Activity. In, p 1714: AAAS.

Kilgard MP, Merzenich MM (1998b) Cortical map reorganization enabled by nucleus basalis







Nichols JA, Jakkamsetti VP, Salgado H, Dinh L, Kilgard MP, Atzori M (2007)

Environmental enrichment selectively increases glutamatergic responses in layer II/III

of the auditory cortex of the rat. In, pp 832-840: Elsevier.



In, pp 38-50.

91

Percaccio CR, Pruette AL, Mistry ST, Chen YH, Kilgard MP (2007) Sensory experience

determines enrichment-induced plasticity in rat auditory cortex. Brain Research

1174:76-91.




Staiger JF, Masanneck C, Bisler S, Schleicher A, Zuschratter W, Zilles K (2002) Excitatory

and inhibitory neurons express c-Fos in barrel-related columns after exploration of a

novel environment. In, pp 687-699.

Tallal P, Merzenich MM, Miller S, Jenkins W (1998) Language learning impairments:

integrating basic science, technology, and remediation. Exp Brain Res 123:210-219.

Volkmar FR, Greenough WT (1972) Rearing Complexity Affects Branching of Dendrites in

the Visual Cortex of the Rat. In, p 1445.




Xerri C, Merzenich MM, Peterson BE, Jenkins W (1998) Plasticity of Primary

Somatosensory Cortex Paralleling Sensorimotor Skill Recovery From Stroke in Adult

Monkeys. Journal of Neurophysiology 79:2119-2148.

Zhang Y, Hamilton S, Nathanson N, Yan J (2006) Decreased input-specific plasticity of the

auditory cortex in mice lacking M1 muscarinic acetylcholine receptors. Cerebral

cortex 16:1258-1265.

VITA

Vikram Jakkamsetti was born in Baroda, India in 1973. After completing his high school

education in Rosary High School, Baroda in 1992, he entered the medical school program at

Government Medical College, Surat, India. He was awarded his M.B.B.S. degree in 1998 and

subsequently went on to receive an M.D. in Internal Medicine from the same institute in

2002. His residency dissertation demonstrated correlative patterns between adenosine

deaminase levels in the cerebrospinal fluid (CSF) and CSF total protein, CSF white blood

corpuscle counts and mortality in tuberculosis meningitis patients. He elected to pursue a

fourth year as senior resident to complete and publish a study of cardiovascular

manifestations of leptospirosis in the Journal of Association of Physicians in India. He

subsequently entered the University of Texas at Dallas to begin his doctoral studies under the

guidance of Dr.Michael Kilgard. His long-term objective is to work to translate basic science

research into clinically viable therapeutic interventions for brain disorders.

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