Optical Interrogation of the Spontaneous Dynamics of Prefrontal Cortical

Networks

by

Andrew Blaeser

M.Sc., Brown University, Providence, RI, 2009

B.A., Boston University, Boston, MA, 2007

A dissertation submitted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy in the Department of Physics at Brown University

PROVIDENCE, RHODE ISLAND

May 2015

© Copyright 2015 by Andrew Blaeser

iii

This dissertation by Andrew Blaeser is accepted in its present form by the Department

of Physics as satisfying the dissertation requirement for the degree of Doctor of

Philosophy.

Date: ___________ ___________________________________________

Arto V. Nurmikko, Ph.D., Advisor

Recommended to the Graduate Council

Date: ___________ ___________________________________________

Derek Stein, Ph.D., Reader

Date: ___________ ___________________________________________

Barry W. Connors, Ph.D., Reader

Approved by the Graduate Council

Date: ___________ ___________________________________________

Peter Weber, Dean of the Graduate School

iv

CURRICULUM VITAE

ANDREW BLAESER

142 Elton St, Providence, RI 02906 | (631) 848-4728 | Andrew_Blaeser@brown.edu

EDUCATION

Brown University, Providence, RI

Ph.D. in Physics Fall 2014

Dissertation: Dynamics of spontaneous neural activity in medial prefrontal cortex.

M.Sc. in Physics 2009

Boston University, Boston, MA

B.A. in Physics 2007

B.A. in Psychology

Minor: Mathematics

Graduate of the Boston University Honors Program

RESEARCH EXPERIENCE

Nurmikko Lab, Brown University 2008-2014

Ph.D. Candidate

Optical and electrophysiological investigation of population dynamics of medial prefrontal cortex.

Development of a nanoparticle for enhancement of photoactive protein (GFP, ChR2) performance.

DISSERTATION SUMMARY

I studied the activity of populations of prefrontal cortical neurons in acute brain slices using calcium

imaging and patch-clamp electrophysiology. I also developed a general framework for analyzing such

data, including image segmentation, signal processing, event detection, calcium indicator calibration,

dimensionality reduction and detection of activity patterns. Network activity in PFC depends strongly

on the balance of excitation and inhibition. Using various neuromodulatory and neuropharmacological

perturbations, I characterized the transition between different regimes of excitability at the level of

individual neurons and local ensembles. Whereas the PFC tended to be relatively quiescent under

baseline conditions, increasing excitability produced a variety of interesting behaviors, such as

enhanced synchrony, persistent firing, and, in the most extreme case, epileptiform discharges. I also

examined the role of rhythmic bursting on the larger network, and the interactions between bursting

neurons in particular. The detailed effects of enhanced excitability were found to be highly dependent

on the molecular mechanism used to achieve it. For example, blockade of GABAA receptors or

agonization of NMDA receptors both enhanced excitability, but resulted in distinct activity patterns.

This work represents the most comprehensive study yet of the cellular and molecular mechanisms

underlying spontaneous network dynamics of PFC at the local spatial scale enabled by calcium

imaging.

PUBLICATIONS

Blaeser, A.S., Connors, B.W., and Nurmikko, A.V. Spontaneous dynamics of deep-layer prefrontal

cortical networks. In preparation.

RESEARCH INTERESTS

Neurobiological mechanisms underlying cognition

Optical approaches to neuroscience

v

Advanced mathematical and statistical methods for data analysis

Development of novel therapies for brain disorders

SKILLS AND TECHNIQUES

Calcium imaging

Electrophysiology

Brain slice preparation

Rodent stereotaxic surgical procedures (virus injection, cranial window)

Rodent colony maintenance, breeding

Optics and photonics

Histology

Computer programming (MATLAB, C++)

Science writing and presentation (Microsoft Office, LaTeX, Adobe Illustrator)

Image processing

Optogenetics

CONFERENCE POSTERS

Blaeser, A.S. and Nurmikko, A.V. 2014. Spontaneous dynamics of deep-layer prefrontal cortical

populations. Society for Neuroscience Annual Meeting. Washington, D.C. Submitted.

Blaeser, A.S., Connors, B.W., and Nurmikko, A.V. 2012. In vitro calcium imaging reveals diverse

spontaneous activity patterns. Society for Neuroscience Annual Meeting. New Orleans, LA.

Blaeser, A.S., Ho, D., Sun, S., and Nurmikko, A.V. 2011. Nanoparticle-based enhancement of

photoactive proteins. National Science Foundation’s Emerging Frontiers in Research and Innovation

Conference. Arlington, VA.

TEACHING EXPERIENCE

Brown University, Providence, RI

Neuroengineering 2012

Guest lecturer on patch-clamp electrophysiology

OTHER RESEARCH EXPERIENCE

Narain Lab, Brown University (at Fermi National Accelerator Laboratory)

Graduate student 2007 - 2008

Development of an algorithm for correction of b-jet energy

measurements

Bansil Lab, Boston University

Research assistant 2005 – 2007

Polymer physics

Smith Lab, Boston University (at Brookhaven National Laboratory)

Research assistant 2006

Ultraviolet and x-ray spectroscopy of novel materials

MEMBERSHIPS

Society for Neuroscience 2010 – 2014

vi

Acknowledgements

This thesis is the culmination of a long intellectual journey that has taken many

twists and turns. Through the years, I have been helped by a great many people, and I

am eternally grateful to them all. In particular I thank my advisor, Arto Nurmikko, for

taking me on as a student. Arto has supported me through many long years of research.

He has shown enormous patience as I undertook a project largely outside of my

background training, and provided me with the resources and advice to develop as a

scientist. I also thank the many students and staff of the Nurmikko lab, past and present,

who have helped me in innumerable ways. Thanks to Ilker Ozden, Heng Xu, Fabien

Wagner, Jiayi Zhang, Jing Wang, Dave Borton, Hayato Urabe, Travis May, Yao Lu, Cuong

Dang, Ben Brush, Jacob Komar, Chris Heelan, Sunmee Park, Emre Sari, Songtao Chen,

Zeyang Yu, Joonhee Lee, Kwangdong Roh, Farah Laiwalla, Naubahar Agha, Juan Aceros,

Ming Yin, Yanqiu Li , Nicki Driscoll, Tasha Nagamine, Rizwan Huq, and Melissa Tseng.

I also thank Barry Connors for the enormous amount of support that he has

given through the years. I have become a de facto member of his group, and even

worked in his lab for several months. Meeting regularly with Barry, and attending his

group meetings, has given me an amazing, informal education on neuroscience that I

am very grateful to have received. I also thank Scott Cruikshank, Shane Crandall, Liz Hur,

Garrett Neske, Arthur Sugden, Tanya Stevens, Saundy Patrick, Chris Deister, Nicolai

Konow, Carlos Aizenman, Wilson Truccolo and Rebecca Burwell for providing advice and

support on all aspects of experimental science.

vii

Thanks to Derek Stein for serving on my committee, advising me on my research,

and for being a great teacher. I thank Meenakshi Narain and Greg Landsberg for taking

me on as a graduate student in my first year at Brown. Although I decided to leave

particle physics, I am grateful for all I learned and experienced while working with them.

Likewise, I thank Gena Kukartsev, Paul Huwe, Patrick Tsang and Suvadeep Bose for all

their help during that time.

Looking beyond the academic aspects of the thesis, I have been very fortunate to

have the support of my family. Thanks to Larry and Susan Blaeser for helping me in

every possible way to get to this point. Thanks to Jim Blaeser and Alycia Blaeser for your

support and inspiration. Thanks especially to my amazing fiancée, Elizabeth Mermel, for

having the patience to stick with me even when graduation was perpetually two years

away. Thanks to Leonard and Debra Mermel for welcoming me into your family.

Finally, I thank the many friends who have stuck with me through graduate

school and helped me to stay (mostly) sane throughout. Thanks to Alex Metaj, Ben Sher,

Alex Geringer-Sameth, Sean McDonald, Dave Malling, Fabien Wagner, Steve Palefsky,

Juliette Alimena, Mike Antosh, Helen Hanson, Mike Luk, Scott Field, Ryan Michney,

Marius Osmeni, Igli Doci, Basela Metaj, Apryl Holder, Maya Porath, Anuj Girdhar, Jule

Moskowitz and Annie Wray.

viii

Table of Contents

Chapter 1 - Introduction ..................................................................................................... 1

1.1 Prefrontal cortex: Functional and Clinical Perspectives ............................................ 2

1.1 Prefrontal cortex: Anatomy and Physiology ............................................................ 4

1.3 Excitation and Inhibition .......................................................................................... 9

1.4 Microcircuits ........................................................................................................... 13

1.5 Cortical Rhythms and Synchrony ............................................................................ 16

1.6 Calcium imaging ...................................................................................................... 20

1.7 Goals ........................................................................................................................ 25

Chapter 2 – Methods ....................................................................................................... 27

2.1 Experimental Methods ............................................................................................ 27

2.2 Analysis ................................................................................................................... 34

Chapter 3 – Results .......................................................................................................... 50

3.1 GCaMP Expression and Calibration ........................................................................ 50

3.2 Spontaneous Activity .............................................................................................. 58

3.3 Effects of NMDA ..................................................................................................... 70

3.4 Effects of Picrotoxin ................................................................................................ 78

Chapter 4 – Discussion .................................................................................................... 85

4.1 Technical Innovations ............................................................................................. 86

4.2 Spontaneous Dynamics .......................................................................................... 94

4.3 NMDA ................................................................................................................... 102

4.4 Picrotoxin .............................................................................................................. 106

4.5 Summary and Future Directions ........................................................................... 107

References ..................................................................................................................... 111

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List of Tables

Table 1. Viral vectors used for expression of genetically encoded calcium indicators ... 28

Table 2. Chemical compositions of artificial cerebrospinal fluids used for this thesis .... 29

Table 3. Summary of all data sets included in this thesis ................................................ 50

x

List of Illustrations

Figure 1.1 Evolution of the primate prefrontal cortex ...................................................... 4

Figure 1.2 Developmental timelines of PFC in humans and rodents. ............................... 5

Figure 1.3 Anatomy of rodent mPFC ................................................................................. 6

Figure 1.4 Subtypes of pyramidal cells within mPFC ......................................................... 9

Figure 1.5 Putative microcircuitry underlying working memory ..................................... 13

Figure 1.6 Diverse roles of calcium in neural physiology ................................................ 22

Figure 2.1 Relationship between spiking, fluorescent events and subevents ................ 37

Figure 2.2 Example of semi-automated segmentation results ....................................... 39

Figure 2.3 Example of subevent validation ..................................................................... 41

Figure 2.4 Detection of synchronous events ................................................................... 44

Figure 3.1 Expression of GCaMP in mPFC ........................................................................ 51

Figure 3.2 Detection of single action potentials with GCaMP6f ..................................... 54

Figure 3.3 Calibration of GCaMP6f. .................................................................................. 55

Figure 3.4 Error rates. ...................................................................................................... 56

Figure 3.5 Basic characterization of spontaneous activity. .............................................. 59

Figure 3.6 Properties of spontaneous subevents. ............................................................ 60

Figure 3.7 Rhythmic activity in baseline ACSF. ................................................................. 62

Figure 3.8 Analysis of coherence in baseline ACSF .......................................................... 64

Figure 3.9 Pairwise correlation between neurons in baseline ACSF ............................... 66

Figure 3.10 Synchronous events in baseline ACSF .......................................................... 68

Figure 3.11 Example of a massive synchronous event. .................................................. 69

Figure 3.12 Effects of NMDA on spontaneous activity. .................................................. 71

Figure 3.13 Effects of NMDA on rhythmic activity. ........................................................ 73

Figure 3.14 Effects of NMDA on coherence .................................................................... 74

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Figure 3.15 Effects of NMDA on pairwise correlations ................................................... 76

Figure 3.16 Effects of NMDA on synchrony ..................................................................... 78

Figure 3.17 Effects of picrotoxin on spontaneous activity .............................................. 80

Figure 3.18 Effects of picrotoxin on rhythmic activity...................................................... 81

Figure 3.19 Effects of picrotoxin on coherence ............................................................... 82

Figure 3.20 Effects of picrotoxin on pairwise correlation ............................................... 83

Figure 3.21 Effects of picrotoxin on synchrony ............................................................... 84

xii

List of Abbreviations

AAV adeno-associated virus

AP action potential

CI confidence interval

DAP depolarizing afterpotential

EPSP excitatory post-synaptic potential

GABA γ-aminobutyric acid

GC genome copies

GCaMPx GFP calcium modulated protein, version x

GECI genetically encoded calcium indicator

GFP green fluorescent protein

IL infralimbic area

mPFC medial prefrontal cortex

NMDA N-methyl-D-aspartate

NMDAR NMDA receptor

NMDG N-methyl-D-glucamine

PFC prefrontal cortex

PL prelimbic area

Ptx picrotoxin

RE relative error

ROI region of interest

ROB repetitive oscillatory bursting

RS regular spiking

SD standard deviation

xiii

SEM standard error of the mean

1

CHAPTER 1: INTRODUCTION

The human brain contains around 89 billion neurons (Azevedo, et al. 2009), and

a typical neurons receives 5,000 – 50,000 synaptic inputs (Brewer, et al. 2009; Alonso-

Nanclares, et al. 2008). From these numbers alone, it is apparent that the brain is an

extraordinarily complicated physical system. And yet, brains are organized in highly

stereotyped patterns at many spatial scales, ranging from sub-micron (for instance, the

distribution of ion channels on the cell membrane of an individual neuron) up to the

diameter of the entire brain. Moreover, brains are highly similar from person to person,

and there is considerable homology between the human brain and that of lesser

animals. Evolution, the great tinkerer, has somehow found a way to tame this

complexity and exploit it to perform a remarkable variety of functions.

Unraveling the physical mechanisms exploited by evolution to mediate

seemingly magical phenomena such as consciousness and memory is a fundamental

goal of brain science. Even partial solutions might offer information of great academic

and clinical relevance. Fortunately, the convergence of centuries of progress in biology,

chemistry, physics, mathematics, engineering, psychology, statistics, computer science

and medicine has enabled researchers to start to scratch the surface. With this context

in mind, this thesis represents an infinitesimal piece of an enormous puzzle that will

likely take another century to solve (if it is solvable at all). By observing the activity of

populations of neurons within the prefrontal cortex (PFC), and studying their

interactions, we sought to provide new insights about the principles through which the

2

PFC implements its many functions and dysfunctions. Towards this goal, we also

developed a novel method of image segmentation for calcium imaging data, and used

the product of this algorithm to help decontaminate our data. Thus, the results of this

thesis should be of interest both to scientists interested in prefrontal cortex, and more

generally to any researcher using calcium imaging.

1.1 Prefrontal Cortex: Functional and Clinical Perspectives

In 1890, the pioneering Scottish neurologist David Ferrier noted, “we find that

what is generally termed the prefrontal lobe… gives no or very doubtful, response to

electrical stimulation.” (Ferrier, 1890). To Ferrier and his contemporaries, the lack of an

observable sensory or motor response to PFC stimulation presented a mystery that was

largely outside of their ability to study rigorously. For decades, the frontal lobe was

largely relegated to the status of “silent lobe.” This situation began to change decades

later, as World War I provided an abundance of case studies demonstrating that insults

to the frontal lobes precipitated dramatic changes in personality and/or cognitive ability

(Brower and Price, 2001). In 1936, Egas Moniz began experimenting with leucotomy

(lesion of the PFC) in humans, and by mid-century the practice gained widespread

acceptance as a treatment for mental illnesses such as depression, schizophrenia,

manic-depression and criminality.

While the medical practice of leucotomy has been discontinued, the recognition

of the PFC as a critical component for various cognitive functions has only grown since

3

then, and the PFC remains a major target for therapies aimed at relieving various brain

disorders. Within the intact, neurotypical brain, the PFC is associated with several

distinct psychological concepts. Working memory, the ability to maintain and

manipulate information in mind temporarily to guide behavior, is associated with

elevated activity in the PFC (Courtney, et al. 1998). PFC also plays a key role in selective

attention, enabling the switching of focus between behaviorally relevant stimuli, and

suppressing irrelevant stimuli (Zanto, et al. 2011). PFC is also implicated in decision

making, and is known to play an important role in self-control and delaying gratification

(Krawcyzk, 2002).

Commensurate with these functions, disorders and lesions of the prefrontal

typically result in cognitive deficits. For example, schizophrenia is characterized by

hypofrontality (diminished activity in the PFC) and schizophrenic patients typically

present with (among many symptoms) deficits in working memory, attention and/or

decision making (Berman and Weinberger, 1990). Similarly, dysfunction of the

neuromodulatory systems within the PFC has been implicated as a key factor in

attention-deficit/hyperactivity disorder (Arnsten and Li, 2004).

These lines of evidence illustrate that, although the PFC may be “silent” in the

sense of not directly evoking any sensory or motor response, it plays a major role in

many fundamental aspects of the human experience. Disorders that affect the PFC

therefore often have devastating consequences. Despite dramatic advances in medicine

since the era of lobotomies, such as the advent of antipsychotic drugs, development of

PFC-targeted therapies remains a major unmet need in psychiatry and neurology. A

4

deeper knowledge of the dynamics within PFC at the level of neurons and networks of

neurons has the potential to help point the way towards such advances.

1.2 Prefrontal Cortex: Anatomy and Physiology

The brain is generally organized in chronological order, with the most

evolutionarily ancient structures occupying the most anterior portion of the brain, while

newer structures tend to occupy more posterior positions. In light of this schematic, it is

notable that the prefrontal cortex encompasses, as its name implies, the most anterior

aspect of the frontal lobes. Comparative anatomy and examination of fossil records

(Jerison, 2006) have confirmed that, from an evolutionary perspective, the neocortex is

among the newest structures in the brain. While all vertebrates have neocortex, the size

of PFC in particular closely tracks the human evolution (Figure 1.1). This increase in PFC

size played a direct role in the evolution of human intelligence. In a sense, the human

PFC represents the pinnacle of brain evolution so far.

5

Figure 1.1. Scaling of prefrontal cortex volume with overall brain size through the course

of primate evolution. ‘Orang’ = orangutan. Reproduced from Jerison, 2006.

This evolutionary recency echoes in the developmental program of the brain.

Just as the PFC was among the last brain regions to evolve, the PFC is among the latest

brain structures to fully mature. In humans, structural changes in the frontal lobes are

observed between adolescent and adult brains, whereas other regions are relatively

unchanged (Sowell, et al. 1999). The development of rodent PFC exhibits a similar

pattern, but on a dramatically different time scale (Figure 1.2).

Figure 1.2. Developmental timelines of prefrontal cortex for rat and human brain. Reproduced from Kolb, et al. 2012.

Dramatic differences between primate and rodent PFC have led to some

controversy about whether rodents contain a homologous brain structure at all.

Detailed anatomical comparisons have concluded that rodents do in fact have a PFC

(Uylings, et al., 2003). The most widely accepted criterion for defining rodent PFC

evokes connectivity: the rodent PFC is defined by strong reciprocal connectivity to the

medial dorsal thalamic nuclei (Diavec, et al. 1993). Broadly, the rodent PFC is

6

anatomically divided into lateral, ventral/orbital, and medial regions (Figure 1.3A). The

medial PFC (mPFC), which is the focus of this thesis, is further divided into prelimbic,

infralimbic and anterior cingulate areas (Figure 3b). (Van De Werd, et al. 2010).

Figure 1.3: Anatomical divisions of A) mouse frontal cortex and B) mPFC in particular. Fr2 = frontal area 2, ACd = anterior cingulated (dorsal), PL = prelimbic, IL = infralimbic, MO = medial orbital. Roman numerals I-VI indicate cortical layers. Modified from Van de Werd, et al., 2010.

Like all other neocortical areas, the mPFC is organized as a series of layers that

extend parallel to the pial surface (Figure 1.3A). The distinction between these layers is

determined by the morphology of the neurons within, as well as their connectivity and

cell-type. Layer 1, the most superficial layer, is sparsely populated by somata, but

consists primarily of an intricate mesh of axons from distant brain regions, as well as the

dendritic tufts of pyramidal cells from deeper layers of the cortical column. Layer 2/3

(layers 2 and 3 are commonly grouped together) is identified by small and medium-sized

pyramidal cells, whose projections are primarily within the local cortical column and the

7

nearest neighboring columns. Layer 4 is populated by small granular neurons, and acts

as the primary recipient of inputs from the thalamus. It is notable that rodent PFC lacks

layer 4, and thalamocortical axons primarily synapse on layers 5 and 3 instead (Kuroda,

et al., 1995). Layer 5 is populated by relatively large pyramidal neurons that send

projections to other cortical areas, and to subcortical structures such as the amygdala

and striatum. Layer 6 neurons are of similar size, but send projections to the thalamus

and/or the thalamoreticular nucleus. It must be noted that, while the layers are defined

by the properties of their constituent pyramidal neurons, all layers are also permeated

by other types of neurons (such as interneurons), and non-neurons (such as glia and

vasculature).

This thesis focuses entirely on the activity of deeper layers of mPFC: layers 5 and

6. Thus, the results of this study describe the behavior of those neurons principally

responsible for transmitting information out of the mPFC to other cortical and

subcortical regions. Layer 5 has also been shown to have a causal role in driving network

activity within the local cortical column (Beltramo, et al., 2013). Even within a given

layer, pyramidal neurons are not a completely homogenous population. Pyramidal

neurons have been functionally divided between regular-spiking (RS) and intrinsic

bursting (IB) subtypes (Connors and Gutnick, 1990) based on the temporal pattern of

action potentials (AP) that results from intracellular injection of a suprathreshold

depolarizing current. RS neurons respond by firing orderly sequences of APs that exhibit

a pronounced afterhyperpolarization (AHP) and modest adaptation in firing rate. IB

neurons respond by initially firing a brief, high-frequency sequence of APs (a burst)

8

followed by a series of regularly-spaced APs. The burstiness of IB neurons is explained

by the presence of a depolarizing afterpotential (DAP), which lasts tens of milliseconds

after an AP and transiently increases the cell’s probability of firing another AP.

Electrophysiological studies have described at least 4 distinct subtypes of

pyramidal neurons in layers 5 and 6 of rodent mPFC (Yang, Seamans and Gorelova,

1996). In addition to the aforementioned RS and IB subtypes, they identify repetitive

oscillatory bursting (ROB) cells, which respond to depolarizing current injection by

emitting multiple bursts at regular intervals. Yang, et al. also identified a rare,

intermediate (IM) pyramidal cell subtype. IM cells have similar spiking patterns to RS

cells, but exhibit a complex sequence of AHP and DAPs. Whereas RS, IB and ROB cells all

contain apical dendrites (prominent dendrites extending along the cortical columnar

axis from the peak of each pyramidal cell soma towards the upper layers) that extend to

layer 1, IM cells were also distinguished by relatively short apical dendrites that did not

reach the superficial layers. The electrophysiological and morphological characteristics

of each subtype are summarized in Figure 1.4.

9

Figure 1.4: Morphological and electrophysiological properties of pyramidal cell subtypes in rat PFC. From Yang, Seamans and Gorelova, 1996.

1.3 Excitation and Inhibition

1.3.1 NMDA Receptors

Excitatory neurotransmission in the brain is primarily mediated by release of

glutamate into the synaptic cleft. Glutamate molecules subsequently bind to receptors

on the postsynaptic membrane, opening ion channels and allowing depolarizing

currents into the postsynaptic neuron. Several classes of glutamate receptors have been

identified, among which NMDA receptors (NMDARs) are of particular interest with

respect to the goals of this thesis.

10

NMDA receptors are so named due to their specific activation by N-methyl-D-

aspartate (NMDA), an amino acid derivative that does not occur naturally in the brain

(Watkins, 1981). Compared to other classes of glutamate receptors, NMDARs have

several notable properties. First, the NMDAR is both ligand-gated and voltage-gated: at

the typical neuronal resting membrane potential of around -70 mV, the pore of its ion

channel is blocked by a magnesium ion and will not transmit significant current even

upon binding of glutamate. This magnesium blockade is relieved at around -50 mV, and

will open in response to binding of glutamate (Ruppersberg, Kitzing and Schoepfer,

1994). As a result of this double-gating, NMDARs act as a coincidence detector that is

selective to events wherein the neuron is sufficiently depolarized (usually by other types

of glutamate receptors), and the NMDAR has bound a glutamate molecule. Under these

conditions, the NMDAR participates in synaptic transmission. Second, NMDAR ion

channels conduct several species of cations, including sodium, potassium and calcium

(Flatman, et al., 1986). The entry of calcium into the cytosol is of particular importance,

as ionic calcium plays diverse roles in cell biology (see Figure 1.6). Third, once activated,

the NMDAR-mediated current lasts for tens or hundreds of milliseconds, substantially

longer than other glutamate receptors (Dingledine, et al., 1999). Therefore, synaptic

events that elicit sufficient depolarization to activate NMDAR-mediated current persist

far longer than weaker inputs, extending the window over which a neuron can

effectively integrate inputs from other neurons (Wang, 1999).

Because of these unique properties, NMDARs are central to understanding many

brain functions. For the purpose of this thesis, we focus on the role of NMDARs in the

11

prefrontal cortex. Antagonizing NMDARs in PFC helps to reveal their contribution to

performance of various cognitive tasks. Studies taking this approach have shown

NMDARs to have a major role in visuospatial attention (Murphy, Dalley and Robbins,

2005), set-shifting (Stefani, Groth and Moghaddam, 2003; Krystal, et al. 2000), impulse

control (Murphy, et al. 2011) and working memory (Wang, et al., 2013). Prefrontal

NMDAR hypofunction has been implicated as a major contributor to the symptoms of

schizophrenia (Olney, Newcomer and Farber, 1999; Jackson, Homayoun and

Moghaddam, 2004). Surprisingly, NMDA antagonists have been reported to produce

increases in PFC activation, as characterized by metabolic activity (Breier, et al., 1997)

and neuronal firing (Suzuki, et al., 2002; Jackson, Homayoun and Moghaddam, 2004).

This seemingly paradoxical effect (blocking excitatory transmission leads to an overall

increase in activity) is partially explained by the particular sensitivity of fast-spiking

interneurons to NMDA hypofunction: reduced excitatory drive to interneurons leads to

disinhibition of their target pyramidal cells, overcompensating their reduction of

NMDAR-mediated excitation (Homayoun and Moghaddam, 2007).

NMDA is known to exert an excitatory influence on brain tissue, inducing many

interesting activity motifs, such as repetitive firing, rhythmic oscillations in membrane

potentials and synchronous activation of network ensembles of neurons (Flatman, et al.

1983; Carillo-Reid, et al. 2008). For this reason, NMDA remains a popular tool for

generating network activity in slice preparations. However, because NMDARs are

expressed broadly throughout the brain, it is difficult to distinguish direct effects of

12

NMDA on NMDARs from indirect effects produced by the general increase in synaptic

activity (Aranov and Wang, 1997).

1.3.2 GABAergic Inhibition

In opposition to the excitatory effects of glutamate, γ-aminobutyric acid (GABA)

is the primary neurotransmitter mediating inhibition in the brain. Diverse, local

interneurons represent the primary source of GABAergic synapses within neocortex.

Binding of GABA to receptors on the postsynaptic membrane elicits hyperpolarizing

currents that reduce a neuron’s probability of firing an AP. For the purposes of this

thesis, we focus on the ubiquitous GABAA receptor subtype, which represents the

predominant mediator of inhibition in neocortex (Connors, Malenka and Silva, 1988).

GABAA receptors open chloride-selective ion channels in direct response to binding of

GABA.

GABAergic inhibition plays a role in virtually every facet of normal brain function.

By opposing excitation, inhibition maintains balance within neural systems. Failure of

inhibition leads to unchecked growth of excitation and, ultimately, epilieptiform activity

(Chagnac-Amitai and Connors, 1989; Cammarota, et al., 2013). Inhibition is also

implicated in rhythmogenesis of gamma oscillations, which are associated with

attention and cognition (Traub, et al., 1996; Cardin, et al., 2009). Inhibition is proposed

to play an important role in spatial working memory, suppressing the activity of neurons

that are irrelevant to the memory formation (Figure 1.5), thus guarding against

distraction (Goldman-Rakic, 1995; Arnsten, 2009).

13

In this thesis, we examined the effects of reduced inhibition on network activity

in mPFC using picrotoxin, a non-competitive antagonist of the GABAA receptor.

Figure 1.5. Schematic depiction of the PFC microcircuit proposed to subserve spatial working memory. Networks of pyramidal cells (triangles) and interneurons (circles) are tuned to respond preferentially to specific orientations (90° or 270°, in this example) of a visual stimulus. Pyramidal cells of similar tuning form recurrent, excitatory synapses on each other, leading to self-sustained activity even after the stimulus has been removed. Interneurons form inhibitory synapses on oppositely-tuned pyramidal cells, suppressing their activity during the memory. Adapted from Arnsten, 2003.

1.4 Cortical Microcircuits

A primary function of any cortical area is to process signals from other brain

regions, and relay a modified signal to other regions to perform some useful function,

such as motor output, perception or memory. In this sense, the cortex represents a sort

14

of local signal processor, and the transfer function from input to output depends on

several factors. The biophysical properties of the constituent cells, such as those

overviewed in section 1.2, certainly play a major role in shaping the cortical response to

input. Neuronal resting membrane potential, input resistance, burstiness, firing rate

adaptation and action potential shape are just a few examples of cell-specific properties

that have clear implications for constraining the outputs that a cortical network is

capable of emitting.

Perhaps equally important are the local interactions between neurons. In a

completely decoupled network, output would depend entirely on the details of each

individual neuron’s response to the input. Such a situation is problematic in terms of

tolerance of noise, as similar incoming stimuli would need to repeatedly activate the

same neurons in the same way to generate similar outputs, and in terms of robustness

to cell death. Reversely, a network of very strongly coupled neurons, where the activity

of a single neuron activated (or silenced) the entire network would be severely limited

in its repertoire of outputs, effectively destroying most of the information encoded in

the input. Considering these limiting cases, it is clear that some intermediate case,

where at least some neurons are weakly coupled, is theoretically more useful from an

information theory perspective. Experimentally, synaptic coupling between cortical

neurons has been reported to follow a lognormal distribution, where the majority of

pairs exhibit weak or no coupling, but relatively few pairs are strongly coupled (Song, et

al. 2005; Mizuseki and Buzsáki, 2013).

15

Crucially, the synaptic coupling between neurons is not completely random. On

the contrary, neocortex appears to be organized into stereotyped connectivity patterns

based on factors such as cell-type and cortical layer (Douglas and Martin, 1991; Földy,

Dyhrfjeld-Johnsen and Soltesz, 2005). By organizing excitatory and inhibitory

connections among neurons with diverse intrinsic properties, ensembles of neurons can

tune themselves to manifest a wide range of transfer functions. These subnetworks are

commonly referred to as cortical microcircuits (for example, Figure 1.5). Analogous to

electronic circuits in a microchip, cortical microcircuits control the flow of information

from some input layer to output, presumably performing some useful operation(s) on

the information along the way. For instance, networks of interneurons in visual cortex

have been implicated in signal division and subtraction respectively (Wilson, et al. 2012).

Identifying microcircuits in the brain, deriving basic principles governing their

development, and understanding their functional contributions to higher-level

processes, have emerged as important undertakings in modern brain science.

The analogy between electronic circuits and cortical microcircuits is

conceptually useful, but there are several major differences that must be noted. First,

whereas the wiring of an electronic circuit is fixed once the circuit is manufactured, a

cortical microcircuit self-organizes throughout development, and may continuously re-

wire itself through mechanisms such as cortical plasticity and synaptic pruning in

response to the inputs it receives. This flexibility provides a substrate through which

some forms of learning, memory and addiction are realized. Second, whereas the

elements of electronic circuits are usually deterministic, neurons are stochastic. Hence,

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identical inputs will not yield identical outputs in general, though microcircuits can be

made reliable with sufficient redundancy and fine-tuning.

1.5 Cortical Rhythms and Synchrony

1.5.1 Cortical Rhythms

Every action potential is an electrical event: the transient inversion of the

electrochemical gradient between the inside and outside of a neuron. Maxwell’s

equations demand that each such event be accompanied by some fluctuations in the

electromagnetic field around the neuron. Any significant volume of brain tissue will

consist of somata, dendrites and/or axons from thousands or millions of neurons, and

the electromagnetic fluctuations of each AP from each neuron combine through the

superposition principle. Naively, one might predict that these fluctuations will cancel on

average, and the electromagnetic field outside the brain would be of little or no interest,

but this is not the case. Due to the geometry of the brain (for example, neighboring

pyramidal cells in neocortex have very similar orientation and morphology) and

correlations in the activity of nearby neurons, the electromagnetic field outside the

brain, and even outside the skull, still contains a surprising amount of information.

Many techniques exist for measuring and interpreting these signals and a full

review is beyond the scope of this thesis. Electroencephalography (EEG), the most

common technique, involves placement of electrodes on the scalp. Spectral analysis of

the resulting signals provides a window into the overall activity of large populations of

cortical neurons on the other side of the skull. Neurophysiologists have divided the

17

spectrum into several frequency bands: delta (< 4 Hz), theta (4-8 Hz), alpha (8-13 Hz),

beta (14-30 Hz), and gamma (> 30 Hz) (Niedermeyer, 1999). These bands are thought to

reflect different activity modes, and their prevalence within a particular cortical area is

generally highly dependent on the subject’s mental and behavioral state. There is a

major, ongoing effort in brain science to make direct connections between the empirical

observation of each band in a given region and the underlying neuronal sources.

For the purpose of this thesis we focus on delta waves. Classically, this frequency

band is closely associated with non-rapid eye movement stages of sleep, and some

forms of anesthesia (Achermann and Borbély, 1997). Intracellular recordings in vivo

have shown that the large-amplitude deflections in EEG reflect the synchronized cycling

of cortical neurons between periods of hyperpolarization and quiescence (DOWN states)

and relative depolarization and spiking (UP states) (Steriade, Nunez and Amzica, 1993).

Thalamocortical interactions have been identified as a major contributor to this

oscillation (Steriade, et al., 1993; David, et al., 2013), but delta-waves have also been

observed in vivo after thalamic lesions (Steriade, Nunez and Amzica, 1993), and in slice

preparations in which thalamocortical connections have been completely severed

(Sanchez-Vives and McCormick, 2000). UP-DOWN states are also synchronized with

oscillations in the ventral tegmental area (VTA), a major source of dopamine to the

cortex (Gao, et al., 2007). Intriguingly, a putative source of delta rhythms has been

identified in the medial frontal cortex through EEG analysis (Michel, et al., 1992; Alper,

et al., 2006), but to our knowledge the neuronal basis for this source has not been

described.

18

Functionally, delta rhythms are implicated in memory consolidation (Lee and

Wilson, 2002). Interactions between neocortex and hippocampus in the delta band are

thought to stimulate the replay of firing sequences associated with memories, causing

synaptic plasticity to rewire cortical connections to store information over long time-

scales (Born, 2010). Delta activity has also been correlated with performance on various

cognitive tasks, including some measures of working memory (Harmony, 2013) and

decision making (Nácher, et al, 2013). Frontal intermittent rhythmic delta patterns have

been associated with diverse disease states, but their significance is not clear (Accolla,

et al., 2011; Wasler and Isler, 2005).

1.5.2 Synchrony

The concept of synchrony is of fundamental importance in neuroscience. In

prefrontal cortex, synaptic connections between pyramidal neurons are typically so

weak that a single spike from an individual neuron evokes a transient (~10 ms)

excitatory post-synaptic potential (EPSP) on the order of 1 mV (Povysheva, et al., 2006),

far less than the 20-30 mV depolarization usually required to reach the threshold for

action potential firing. However, if many neurons fire simultaneously (that is, within a

time window shorter than the time course of individual EPSPs) their collective EPSPs can

sum to exceed threshold and evoke a post-synaptic spike. Synchronous activity in

pyramidal cells is therefore of particular interest, as it represents the form of activity

that is most likely to successfully recruit downstream neurons.

19

Furthermore, the release of neurotransmitter into the synapse in response to an

AP is probabilistic and may fail more than 50% of the time in some systems (Allen and

Stevens, 1994). Consider a toy-model system in which all neurons have the same

probability of failure, Pfail. If N neurons fire together, assuming independence of synaptic

failure from neuron to neuron, the probability that they will all fail is PfailN, and the

probability distribution of the fraction of neurons that fail is given by the binomial

distribution. Clearly, synchronous firing of many neurons is one way to mitigate the

unreliability of individual synapses.

Synchrony manifests in many different contexts within the brain. Perhaps the

most extreme form of synchrony is seen during epileptic seizures, when large numbers

of neurons fire with synchrony on the order of 100 ms or less (Truccolo, et al., 2014).

Populations spikes in the hippocampus reflect synchronous firing of many neighboring

neurons (Andresen, Bliss and Skrede, 1971). Fast-spiking interneurons often form

electrical synapses with one another (Gibson, Beierlein and Connors, 1999), helping

them to synchronize and act as a potent source of inhibition within neocortex (Deans, et

al. 2001). In the prefrontal cortex, Sakurai, et al. (2013) have identified diverse forms of

synchronous firing associated with performance of working memory tasks. In light of

these examples (among countless others) it is clear that characterization of synchrony

within any neural system is of great interest for understanding the function of the

system.

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1.6 Calcium Imaging

Taking the view that each neuron represents a quasi-independent unit, it is

readily apparent that studying the workings of the brain at a granular level requires the

ability to measure the activity of many or all neurons simultaneously. This problem has

been identified as a defining challenge for modern brain science (Alivisatos, et al. 2013).

Currently, electrophysiological techniques (measuring the activity of neurons by

detecting the electrical currents or voltages from electrodes placed in or very near the

neurons of interest) offer the ability to interrogate as many as hundreds of neurons with

unmatched temporal resolution. Researchers and physicians in the BrainGate project

have already used arrays of 96 electrodes to record populations of neurons within

motor cortex of tetraplegic patients, read out the spiking of hundreds of neurons, and

decode this activity in real-time to enable these patients to control robotic arms

(Hochberg, et al. 2012). This is a remarkable demonstration of the power of

interrogating large populations of neurons simultaneously.

Calcium imaging presents an alternative method of measuring neuronal activity

that is capable of studying even larger populations. For example, Ziv et al. (2013)

recently reported imaging more than 1,000 neurons simultaneously. The basic

technique of calcium imaging is to fill neurons with some calcium indicator, a

fluorophore whose fluorescence depends on the intracellular concentration of calcium

ions, [Ca2+]. By recording movies of the fluorescence of the imaged neurons, the time-

cours, [Ca2+](t), for each neuron can be inferred under a linear-Gaussian model:

21

F(t) = α[Ca2+](t) + β + ε(t), (1)

where F(t) represents fluorescence intensity at frame t, α is a constant model parameter

relating calcium concentration to fluorescence, β is a constant term covering

background fluorescence, and ε(t) is a Gaussian noise-model term (Vogelstein, et al,

2009).

Ionic calcium plays remarkably diverse roles in various areas of neural physiology

(Figure 1.6). Thus, it is not surprising that neurons exert exquisite control over [Ca2+],

typically maintaining a baseline concentration of around 100 nM. During the course of

spiking, this concentration can increase ten-fold or more (Berridge, et al, 2001). The

tight regulation of calcium during rest, combined with a dramatic increase during

spiking, enables calcium imaging to function as an indirect indicator of neuronal spiking.

Treating each action potential as a discrete, instantaneous influx, one can model

intracellular calcium concentration as:

[Ca2+](t+dt) = (1-dt/τ) [Ca2+](t) + (dt/τ) [Ca2+]base + An(t) (2)

where dt is the time between frames of the calcium movie, τ is the time constant of

calcium extrusion, [Ca2+]base represents baseline calcium concentration, n(t) is the

number of spikes during frame t, and A represents the magnitude of the increase in

[Ca2+] due to a single AP (Vogelstein, et al, 2009). The basic framework of Equations 1

and 2 has been used to develop sophisticated analytical tools for estimating n(t) from

calcium imaging data. However, this approach requires knowledge of several

22

parameters (A, [Ca]base and τ), assumptions about the constant nature of calcium influx

due to action potentials, and noise modeling.

Figure 1.6: Cartoon depiction of the diverse roles of ionic calcium in neuronal physiology. PMCA = plasma membrane calcium ATPase. NCX = sodium-calcium exchanger. nAChR = nicotinic acetylcholine receptor. AMPA-R = α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor. mGluR = metabatropic glutamate receptor. VGCC = voltage-gated calcium channel. SERCA = sarco-/endoplasmic reticulum calcium ATPase. RyR = ryanodine receptor. IP3R = inositol trisphosphate receptor. TRPC = transient receptor potential channel (canonical). Reproduced from Grienberger and Konnerth, 2012.

Due to the indirectness of this approach, it is important to understand the

limitations of calcium imaging. First, the underlying spiking and calcium dynamics play

out at a time scale of milliseconds, substantially faster than the time-resolution of most

imaging systems. Even for imaging systems capable of millisecond-scale imaging, there

is a well-known tradeoff between frame-rate and signal-to-noise ratio due to shot noise.

In practice, calcium imaging is almost always conducted at frame rates between 1 – 100

23

Hz, reducing the technique’s time-resolution. Additionally, calcium imaging necessitates

that calcium ions bind to indicator molecules. The physical chemistry governing the

binding and release of calcium has major implications for the performance of the

indicator. An indicator that binds calcium very strongly will provide a brighter signal and

higher sensitivity to single spikes, but strongly bound calcium will take longer to

dissociate from the indicator molecule, thereby decreasing the time-resolution of the

indicator and causing signal saturation at higher calcium levels associated with high-

frequency spike trains. These considerations imply that selection of indicator is a key

determinant of what activity regime is observable with calcium imaging.

Despite these limitations, calcium imaging has several advantages over

electrophysiology that explain its popularity in neuroscience. For the purposes of this

work, the primary advantage was that imaging enables the interrogation of large

numbers of neurons in a very local (<250 m diameter) neighborhood with excellent

spatial resolution. There is currently no electrophysiological method capable of

simultaneously measuring the activity of so many neurons with such precise spatial

information about each neuron. Furthermore, insertion of electrodes into brain tissue is

inherently destructive, whereas imaging usually leaves the tissue unperturbed. Finally,

although electrophysiology provides unparalleled temporal resolution, for many

purposes the temporal resolution of imaging is already sufficient for the experimental

question at hand. For example, Clancy et al recently reported a proof-of-principle brain-

machine interface based solely on calcium imaging (Clancy, et al. 2014). Such a system

24

might someday become competitive with the aforementioned BrainGate system,

without necessitating the implantation of electrode-arrays.

In this work, we used two variants of the genetically-encoded calcium indicator

(GECI) green fluorescent calcium modulated protein (GCaMP): GCaMP5 and GCaMP6f.

Both indicators are based on the same basic GCaMP structure: a green fluorescent

protein (GFP) molecule fused to the calcium sensing domain of calmodulin. Upon

binding calcium, conformational changes in the protein near the fluorophore lead to

increased fluorescence, thereby revealing the cell’s calcium dynamics (Akerboom, et al.

2012; Chen, et al. 2013). By using genetically encoded indicators, we avoided several

technical challenges involved with dye-based calcium imaging, such as the difficulty of

bulk-loading dye into neurons in acute slices without compromising slice health or only

labeling the most superficial neurons. Using a GECI also enables control over which cells

express the indicator, by expression of an appropriate promoter, whereas bulk-loading

fluorescent dye leads to nonspecific labeling of any and all cells within a region,

including non-neurons.

We chose a brain slice model for imaging the mPFC out of necessity: the

prefrontal region is inaccessible to conventional in vivo calcium imaging methods due to

its location. In vivo calcium imaging is usually performed using 2-photon microscopy

with an objective positioned just above the brain surface, but infrared scattering limits

the depth range of this approach to a few hundred microns, far short of the ~2 mm

distance from the pial surface to PFC. By cutting coronal slices, we gained optical,

25

electrophysiological and pharmacological access to the mPFC that is unparalleled by any

extant technique for in vivo models. The major tradeoff of this approach is a loss of

physiological realism, as the slice preparation severs distant inputs to PFC, and the

process of slicing is inherently traumatic to the tissue.

1.7 Goals

The prefrontal cortex has been studied at many different scales, ranging from

the molecular level up to the level of human subjects in psychological experiments. The

fundamental goal of this thesis was to fill a gap in this continuum. Interactions between

local recurrent networks of neurons within the PFC are held to be the physical substrate

through which working memory is sustained, and other cognitive functions are realized.

Combining brain slice methods, calcium imaging and patch-clamp techniques enabled us

to observe the spontaneous activity of mPFC neurons at a mesoscopic scale (~200x200

μm) that, to our knowledge, has been largely unexplored. By applying various

neuropharmalogical perturbations, we were also able to investigate the effects of these

drugs on PFC network dynamics at this scale.

Specifically, we identified several goals:

1) to develop analytical methods for overcoming some methodological challenges of

epifluorescence-based calcium imaging

2) to characterize the expression and performance of GCaMP calcium indicators in mPFC

26

3) to establish spontaneous activity in mPFC-containing as a model system for studying

prefrontal network activity

4) to study rhythms within mPFC networks, and the possible interactions between

rhythmic neurons

5) to investigate synchrony within mPFC networks, and to infer functional connectivity

between neurons

6) to explore the effects of excitatory and disinhibitory pharmacological perturbations

on network dynamics

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CHAPTER 2: METHODS

2.1 Experimental Methods

2.1.1 Viral Injections

Expression of the GECIs was achieved through stereotaxic injection of adeno-

associated virus (AAV) into the brain. All viruses were obtained from the University of

Pennsylvania Vector Core facility. The details of the viral constructs are summarized in

Table 1. All injections were performed on young mice, usually at post-natal day 12. Each

subject was first deeply anesthetized by intraperitoneal injection of a cocktail of the

dissociative anesthetic ketamine and meditomidine (Dormitor) dissolved in saline. Upon

the onset of deep anesthesis (as determined by failure to react to a pinch of a hind

limb), the head was shaved and swabbed with isopropyl alcohol. The mouse was head-

fixed in a stereotactic apparatus, and a small incision was made in the scalp along the

midline, revealing the skull. A small craniotomy was drilled in the skull at the following

stereotaxic coordinates (in mm) relative to the bregma point:

(medial-lateral, anterior-posterior) = (0.6, 2.2).

After waiting five minutes, a tapered glass pipette loaded with viral fluid was slowly

lowered to the target depth 2 mm below the brain surface. The injection was performed

by applying periodic (2 Hz) pulses of compressed air onto the back end of the pipette,

with the pressure tuned such that the injection rate was roughly 6 μL/hour. The total

volume injected per mouse was about 1 μL. After the injection was completed, the

28

pipette was left in the brain an additional 10 minutes to allow the virus to diffuse away

from the injection site. The pipette was subsequently withdrawn, and the skin was

closed. The subject was awakened by intraperitoneal injection of atipamezole

(Antisedan) and returned to its home cage. Each mouse was allowed at least 4 days

post-injection for recovery and gene expression before experimentation.

Viral Construct Promoter Fluorophore Titer (GC/ml)

AAV1.hSyn.GCaMP5G.WPRE.SV40 hSynapsin GCaMP5G 2.7 x 1013

AAV1.hSynap.flex.GCaMP5G.WPRE.SV40 hSynapsin GCaMP5G 1.47 x 1013

AAV1.Syn.GCaMP6f.WPRE.SV40 Synapsin GCaMP6f 3.58 x 1013

Table 1. Summary of viral constructs used to express GECIs. GC stands for “genome copies.”

2.1.2 Brain Slice Preparation

Neurons are very sensitive to the chemical composition of the fluid in which they

reside. During the slicing procedure two distinct artificial cerebral spinal fluid (ACSF)

recipes (‘cutting-ACSF’ and ‘recovery-ACSF’) were used. Our cutting recipe was based on

the recommendations of Hájos and Mody (2009). Our recovery recipe, modified from

Peça, et al. (2011), step reduced excitotoxicity in the minutes immediately after slicing

by substituting N-methyl-d-glucamine (NMDG) for sodium chloride, thereby preventing

sodium-based action potentials; increasing magnesium, which reduces excitability by

blocking NMDA receptors; and reducing calcium, which is a primary mediator of

excitotoxicity. A third recipe (‘recording-ACSF’) was used during the recording period.

We lowered the concentration of magnesium and calcium in our recording solution to 1

mM (compared to 2 mM in cutting ACSF), as it has been reported that recurrent

29

network activity is more prevalent in acute slices under this condition (Sanchez-Vives

and McCormick, 2000; Fanselow and Connors, 2010). All ACSFs were made with

deionized water. The specific chemical compositions of all ACSFs are summarized in

Table 2. All ACSFs were continuously bubbled with ‘carbogen’, a gaseous mixture of

oxygen (95%) and CO2 (5%), except during the slicing phase of the preparation.

Species Cutting Recovery Recording

NaCl 125 0 125 NMDG 0 92 0

HCl 0 92 0 KCl 2.7 2.5 2.7

NaHCO3 25 30 25 NaH2PO4 1.22 1.2 1.22 Dextrose 10 25 10

CaCl2 2 0.5 1 MgSO4 2 10 1

Ascorbic Acid 1 1 1 Thiourea 2 2 2

Na-Pyruvate 3 3 3 Taurine 2 2 0

Table 2. Concentrations (in mM) of the chemical components of the ACSF recipes used for slicing, recovery and recording.

Slices were prepared from mice in accordance with guidelines put forth by

Brown University’s Institutional Animal Care and Use Committee. For each experiment,

the mouse was deeply anesthetized by intraperitoneal injection of a high dose of

ketamine and xylazine dissolved in sterile saline. Upon induction of anesthesis, as

determined by non-response to a pinch of a hind paw, the mouse was decapitated and

the head was immediately submerged in ice-cold cutting ACSF. The brain was quickly

extracted from the skull and moved to a second dish of cold ACSF, where the most

caudal aspect of the brain was cut off with a razor blade to create a flat surface. This

30

surface was then glued to a platform, which was inserted into the sample chamber of a

vibratome, also filled with near-freezing ACSF. At this point, the left hemisphere of the

brain was removed. The vibratome was then used to cut 300 μm thin coronal sections

of tissue from the brain. We chose 300 μm following the conventions of mouse slice

electrophysiology, as this thickness is generally considered to balance the desire to cut

thick slices that preserve as much of the microcircuitry within the slice as possible with

the need to cut thin slices to ensure the tissue sections receive sufficient oxygenation to

remain viable throughout the length of an experiment (~5-8 hours).

As each slice detached from the brain, it was gently sucked into a transfer

pipette and placed into a bath of warm (32 °C) ACSF for recovery. Most slices underwent

a two-chamber recovery process in which they were first placed in warm recovery-ACSF

for approximately 5 minutes before being moved into a chamber of cutting-ACSF for

storage. In all cases, the recovery chambers were kept at 32 °C for about 30 minutes

following the slicing, and the holding chamber was subsequently removed from

temperature control and allowed to reach room temperature. Both chambers were

gently and continuously bubbled with carbogen. All slices were allowed to recover for at

least one hour after slicing before conducting any experiments.

2.1.3 Selection of Neuronal Populations

For data collection, individual slices were transferred into a slice chamber

underneath an upright microscope (Nikon Eclipse E600FN). Each slice was placed onto a

glass slide coated in poly-L-lysine, which served to prevent the slice from drifting

31

substantially throughout the duration of the recording. Carbogenated ACSF flowed

continuously through the slice chamber at a rate of 2-3 mL/min. Each slice was

inspected at low magnification (4X) in both differential interference contrast (DIC) and

epifluorescence modes, and the medial prefrontal cortex was identified based on

criteria of anatomy, visible fluorescence, slice health and experimental goals. If a good

area was found, the microscope objective was switched to high magnification (40X) and

the region was inspected in detail. By adjusting the x-y position of the objective, as well

as the focal plane, many candidate neighborhoods of neurons within the desired region

were considered. Data was only collected from regions which contained many healthy-

looking cells based on cell morphology, size and calcium fluorescence. Due to the

limitations of single-photon epifluorescence microscopy, all imaged populations were

located at depths within ~50 μm of the slice surface.

2.1.4 Patch-Clamp Recording

Pipettes for patch-clamp recordings were made in-house, using a Sutter P-97

Flaming/Brown Micropipette Puller. We used pipettes with a series resistance of 1-5

MΩ, and tip diameter of 1-1.5 μm. Just prior to each experiment, a pipette was back-

filled with internal solution composed of (in mM): 130 K-gluconate, 4 KCl, 2 NaCl, 10

HEPES, 0.2 EGTA, 4 ATP-Mg, 0.3 GTP-Tris, 14 phosphocreatine-Tris.

Electrophysiological recordings were obtained using an Axoclamp 2B amplifier in

current-clamp mode, and the HS-2A model headstage. Signals were sampled at 50 kHZ

using a Digidata 1440 digitizer and recorded with the pClamp software package. Further

32

post-processing (filtering, spike detection) of these electrophysiological signals was

conducted offline in MATLAB using custom-written functions.

Within a selected population of neurons, a single neuron was selected for patch-

clamp recording. This selection was, again, based on the criteria of cell health (as

ascertained by morphology and size) and visible calcium fluorescence. Neurons within

the periphery of the field of view were preferred, so as to minimize the image distortion

caused by the patch pipette.

To obtain the loose-patch configuration, the following general procedure was

followed. Under the microscope, the pipette was first moved to just above the surface

of the selected neuronal population. The amplifier’s voltmeter was zeroed at this point.

A small amount of positive pressure was applied to keep the tip clean. The pipette’s

resistance was measured by recording the voltage change during injections of a series of

step pulses of current. After these measurements, a smaller periodic test pulse train (0.5

nA) was used to monitor the resistance at the tip in real time during the patching

process. The pipette tip was then slowly moved through the slice to the target cell until

it was close enough that the pipette pressure caused a slight dimpling of the cell

membrane. Pressure was then immediately released, sucking a patch of the cell

membrane onto the pipette tip. If needed, further suction was gently applied until a

stable seal was formed.

Upon forming a stable patch, the health of the cell was reevaluated by several

criteria. First, the spontaneous electrophysiological behavior of the cell was observed.

33

Any spiking during this period was taken as an indication that the cell was alive. Second,

the cell’s response to injection of various currents was observed. Cells that showed no

response beyond the passive Ohmic response were not used. Third, each cell’s calcium

fluorescence before and after patching was considered. Cell death is associated with a

massive uptick in intracellular calcium. Under calcium imaging, this influx could be

observed as an unambiguous change in the fluorescence of the cell. Neurons that

showed substantial brightening of fluorescence upon patching were rejected.

2.1.5 Calcium Imaging

Neuronal populations expressing GCaMP were imaged under a Nikon Eclipse

E600FN upright microscope in epifluorescence mode, using a Nikon 40X water-

immersion objective (NA = 0.8). A Polychrome V light source provided 490 nm excitation

illumination, at 1 mW average power. A band-pass (505-540 nm) filter in front of the

camera separated the blue excitation light from the green light emitted by the

fluorophore. Images were acquired with an Andor iXon 887 EMCCD camera (gain = 1)

cooled to -60 °C, using MetaFluor software, with 10-30 ms exposure time per frame

(depending on what was needed to achieve sufficient signal-to-noise ratio), 256 x 256

pixel resolution, and 16 bit bit-depth. The final movies ranged from 20 – 60 Hz frame

rate, with ~30 Hz being the most common.

Brain tissue is highly scattering in the visible wavelengths, so we restricted

imaging to the first 50 μm depth from the slice surface, where individual neurons could

be clearly resolved by eye. For each neuronal population, 10-30 movies (typically around

34

3 minutes per movie) were recorded across various experimental conditions. When

necessary, small adjustments of focus or x-y position were made between movies to

ensure that the same focal plane and cell population were maintained throughout the

experiment. In all experiments, neuronal populations were imaged under the baseline

condition for at least 10 minutes before bath application of any drugs.

2.1.6 Bath Application of Drugs

Recording ACSF was divided into control and drug bottles. For each experiment,

the drug under study (NMDA or picrotoxin) was dissolved in deionized water to make a

stock solution, vortexed for 1 hour, and then diluted into the drug ACSF bottle to

achieve the desired concentration. Drugs were applied to the slices by switching a valve

controlling which bottle fed the slice chamber. In this way, the drug-laden ACSF

gradually washed into the bath on a time scale of 2-5 minutes.

2.2 Analysis

2.2.1 First-Pass Image Segmentation

Each movie was carefully inspected visually. Neuronal somata were identified by-

eye on the basis of morphology and the time course of fluorescence. For each identified

neuron, we drew an elliptical or rectangular region of interest (ROI) around the soma

using MetaFluor software. These ROIs were deliberately drawn somewhat larger than

the perceived sizes of the somata, to allow for a slight (< 5 pixel) translational drift

within or between movies. A background ROI was also defined by all pixels not included

35

in any neuronal first-pass ROI. After this initial segmentation, all further analysis was

conducted in MATLAB.

2.2.2 Signal Processing

Time-series representing the average fluorescence of the ith neuron, Fi(t), were

constructed as the arithmetic mean of the values of all pixels within the ith ROI for each

frame. A background trace, Fback(t), was similarly constructed for each movie as the

arithmetic mean value over all background pixels for each frame. Background

subtraction consisted simply of subtracting Fback(t) from each Fi(t) for each movie. To

suppress shot noise, the background-subtracted traces were then digitally filtered,

forwards and backwards, with a third-order low-pass Bessel filter with a cut-off

frequency of 10 Hz.

Statistical noise for a given data set, σ, was estimated using a two-step process.

First, each filtered, background-subtracted fluorescence trace, Fi,k (ith neuron, kth movie),

was standard deviation-filtered, using a 20 s sliding window. In the second step, the

minimum of each S.D.-filtered trace was then taken as an estimate of the statistical

noise. All such minima were then pooled, and the final, overall noise estimate was

obtained by taking the median value of this pool.

2.2.3 Slice Motion Estimation

Despite efforts to minimize slice motion, we observed slight translation in the xy-

plane in some data sets. We quantified this motion using image registration functions

36

from the MATLAB Image Processing toolbox. For each movie, we generated a

normalized mean projection image. By registering each projection image to that of the

previous movie, we estimated the trajectory of the slice throughout the data set. Data

sets in which the slice moved more than 5 pixels (~4 μm) from its starting position were

not used for further analysis.

2.2.4 First-pass Event Detection and Subevents

Detection of fluorescent events, corresponding to brightened GCaMP

fluorescence due to increased intracellular calcium concentration, was achieved through

an iterative algorithm optimized specifically for the characteristics of GCaMP, as

determined in simultaneous electrophysiology/imaging experiments. For each

fluorescence trace Fi(t), the algorithm first identified all sequences of frames in which

fluorescence increased consecutively, dubbed ‘rises’. For each rise, a local baseline

value, F0, was defined as the value of Fi(t) during the first frame of the rise. Only rises

whose height, ΔF = Fpeak – F0, exceeded a threshold value of 2.5σ, were retained. Each

surviving rise was then matched with a falling phase, extending from the peak of the rise

to the next local minimum below the threshold value. The series of frames from the

beginning of each rise until the end of the falling phase was defined as an event.

To approximate the timing of the underlying spiking within a given event, each

event was further divided into subevents, defined as all rising phases (selected by the

same criteria as events) contained within the event. The relative magnitude of each

subevent was defined as ΔF/F0. The duration of each subevent was defined as the time

37

interval from the first frame of the subevent to the rise peak. The concepts of events

and subevents, and their relationship with neuronal spiking are exemplified in Figure

2.1.

Figure 2.1. A representative example of the correspondence between neuronal firing (bottom panel) and GCaMP6f fluorescence response (top). Black bars signify the durations of two events. Blue bars represent durations of subevents detected within these events. Black dashes indicate timing of action potential peaks.

2.2.4 Second-Pass Image Segmentation

The ROIs drawn in the first-pass segmentation stage consisted of ellipses and

rectangles drawn by eye around each neuron. Since neurons are not ellipsoids or

rectangular prisms, these shapes are not ideal for calcium imaging. We used a second-

stage segmentation step to semi-automatically draw ROIs tailored to each neuron’s

morphology. Our segmentation method exploited the fact that many neurons that were

38

dim under baseline conditions brightened substantially during calcium events, briefly

revealing their morphology in greater detail.

Having discovered the brightest events from each neuron during first-pass event

detection, we generated sets of summary images for each event by taking the mean and

standard deviation, over the event’s frames, for each pixel within a rectangular

neighborhood defined by the length and width of the first-pass ROIs. We also generated

mean and standard deviation summary images of each neuron using all frames from

each movie. All summary images were then median filtered (2x2 pixel filter kernel) to

reduce noise, and thresholded at the 90th percentile, generating sets of binary images

that approximated the putative cellular morphology. These binary images were then

presented one-by-one to the user, who visually compared the binary images with the

corresponding summary image and identified examples where the binary image

captured the morphology of the neuron. If no good binary images were identified for a

neuron, that neuron was excluded from any further analysis. In practice, we obtained

satisfactory segmentation on 93 ± 1% of identified neurons. Neurons that failed the

second-pass segmentation were typically dim under baseline conditions and exhibited

little or no activity. In rare cases, the presence of a bright, highly active neighboring

neuron or dendrite interfered with second-pass segmentation.

The selected binary images were then padded to account for slice motion, and

then merged to make one final composite binary image for each neuron. Merging was

accomplished by setting each pixel that was set to 1 in any selected image to 1 in the

39

composite image. This composite image was smoothed by taking the convex hull around

all pixels set to 1. Finally, any pixel that was shared by two or more composite images

was assigned to whichever image it was closest to the centroid of. Thus, we obtained

final ROIs that captured the morphology of each neuron with minimal background pixels

and allowed for minor (< 5 pixels) slice motion. An example of the final product of this

algorithm is shown in Figure 2.2.

Figure 2.2. Representative example of the final product of image segmentation. Each colored region represents a ROI. Numbered areas without such regions indicate ROIs identified in the first pass that did not survive the second-pass segmentation.

40

2.2.5 Subevent Validation

To ensure that each detected subevent could be attributed to the activity of the

underlying neuron, and not to neuropil contamination or fluorescence from neighboring

neurons leaking into the ROI, a validation step was implemented. Validation was

premised on the assumption that, during spiking, [Ca2+] should rise throughout the cell

body, not just in one compartment. Therefore, we expected that during each real

subevent, there should be substantial correlation between the time series of individual

pixels in the ROI and the mean fluorescence trace, Fi(t), from which the subevent was

derived.

For each ROI, a local neighborhood was defined including the ROI itself and all

pixels within 20 μm of the ROI. For each subevent, a local correlation map, ρ(x,y), was

formed by calculating the Pearson correlation coefficient between Fi(t), the mean

fluorescence time-series for the ith ROI, and Gi(x,y,t), the fluorescence time series of

pixel (x,y) within the neighborhood of the ith ROI over the frames of the subevent.

Thresholding ρ above the 90th percentile and retaining only sets of 10 or more

contiguous pixels (‘blobs’) yielded an estimate of the spatial distribution of the source(s)

of each subevent. Blobs whose centroid and maximum fell outside the ROI were

discarded. A subevent was rejected if less than 75 blob pixels overlapped with the ROI,

or if less than 40% of the blob pixels overlapped with the ROI. These parameters were

determined by examination of the fluorescence response to spiking in patched neurons.

The process of subevent validation is demonstrated in Figure 2.3.

41

Figure 2.3. Representative examples of the subevent validation procedure applied to a true subevent (top row) and a spurious subevent (bottom row) detected from two neighboring ROIs (dashed blue curves). Increased fluorescence from one neuron is indicated by the standard deviation projections over the subevent frames (left column). The correlation maps (middle column) show the Pearson correlation between the mean fluorescence trace from each ROI and the intensity trajectories of each pixel in a neighborhood around them, during the subevent in question. Thresholding the correlation maps (right column) and comparing the source pixels with the known locations of each neuron leads to acceptance of the top subevent and rejection of the bottom subevent, which is better explained by leakage of fluorescence into a neighboring ROI than neuronal activation.

2.2.6 Binarization

The final representation of neural activity was generated by converting each

fluorescence time series, Fi(t) to a binary vector Bi(t): Bi(t) was set to 1 if the tth frame

was included in a subevent from the ith neuron, and set to 0 otherwise (Figure 2.4).

Although this process reduced the total information contained in each time series by

42

ignoring the relative magnitudes of subevents, it was necessary for two reasons. First, as

we showed in the event validation process, the raw fluorescence signal, Fi(t), was often

corrupted by activity from neighboring neurons or neuropil, making it an unreliable

record of neural activity. Second, while the relative magnitude provides a qualitative

sense of the underlying activity, we found that the correspondence between spiking and

fluorescence varied considerably from neuron to neuron (see section 3.1), making the

interpretation of relative magnitude difficult. Binarization avoids these issues by only

considering the timing of validated activity.

Overall population-level activity, A(t), was quantified as the sum of the activity

over all neurons at each frame. The activity rate was calculated by binning A(t) into 10

second bins, summing A(t) over the duration of each bin, and dividing the result by the

bin width multiplied by the number of neurons imaged.

2.2.7 Measures of Synchrony

Correlation between pairs of binary vectors, Bi and Bj, was calculated using the

Jaccard index (Jaccard, 1901). For pairs of binary vectors, the Jaccard similarity index is

given by

J = M11/(M10 + M01 + M11),

where Mxy represents the number of frames where Bi = x and Bj = y. Since our analysis

was relatively insensitive to low-frequency spiking (see Section 3.1), it cannot be

43

assumed that failure to detect activity from calcium imaging implies no activity.

Therefore, the Jaccard index is more appropriate for measuring pairwise correlation

than the standard Pearson correlation coefficient.

The Jaccard index is suitable for measuring pairwise correlation between

neurons, but is not easily generalized to higher-order correlations. To look for synchrony

between larger numbers of neurons, we used a complementary measure: synchronous

events (not to be confused with the fluorescence events described in Section 2.2.4). This

measure is based on the notion that network activity will induce multiple neurons to fire

synchronously, resulting in strongly time-locked subevents from multiple neurons. To

detect these times, we first read off the frames corresponding to the peaks of the time

derivatives (dF/dt) of each subevent. We then constructed a vector wherein each

element contained the number of peaks, across all neurons, that occurred within ± 50

ms of each frame. Finally, we found all local maxima of this vector greater than 1, and

read off the identities of the neurons involved in the corresponding synchronous event,

along with various other properties such as the distances between the participant

neurons, and the relative magnitudes and durations of the constituent subevents. This

process is demonstrated in Figure 2.4.

44

Figure 2.4. Reconstruction of neuronal activity and synchrony from fluorescence data. Top panel: Processed fluorescence traces from all regions of interest within a movie. Black segments show the detected subevents. A major bout of synchrony is evident around 737 s. Middle panel: Binarized representation of the activity shown in the top panel. Dots within each dash show the timing of the derivative peak. Bottom panel: Total number of synchronous (within ±50 ms) peaks at each frame. Black stars denote synchronous events.

2.2.8 Surrogate Data Sets

For many observations in our data, it is important to answer the question, “How

probable is this observation under the null hypothesis of independent activity?” This is a

difficult question to fully address, because the probability distribution under the null

hypothesis is unknown. Under these conditions, one common approach is to generate,

via Monte Carlo methods, surrogate data sets from the observed data that preserve

45

some relevant properties (such as the overall activity rate) but scramble other details

(such as the relative timing of events) (Sasaki, 2006; Lopes-dos-Santos, 2013). This

method sets up the permutation test, a model-free test of statistical significance. By

generating N surrogate data sets, one can estimate an upper limit on the p-value of a

given test statistic, S, using:

p ≤ (n + 1)/(N + 1)

where n is the number of surrogates data sets where S is more extreme than the

measured S in the actual data (Phipson and Smyth, 2010). Alternatively, one can obtain

confidence intervals on S from its distribution in the surrogate data, and then compare

the measurement with these intervals to test against the null hypothesis.

We generated 1000 surrogates for each data set. For analysis of the significance

of observed values of correlation and synchrony, we used circular shifts to generate

surrogate data. Each binary time series Bi,k(t), corresponding to the activity of the ith

neuron in the kth movie, was circularly shifted by a pseudorandom number of frames,

Δt, drawn from a uniform distribution on the interval [0, Nframe-1], where Nframe is the

number of frames within the relevant movie:

Bi,k(t) → Bi,k(t + Δt).

In this way, the surrogate data sets preserved the overall activity rate, the distribution

of activity among neurons, and the sequence of activity (up to a circular shift) but

scrambled the timing of activity relative to other neurons.

46

2.2.9 Multitaper Spectral Analysis

To estimate the Fourier power spectra of each binarized time-series, we used the

multitaper method for point-binned processes, as implemented by the Chronux Toolbox

(Bokil, et al., 2010) for MATLAB. This method is superior to direct calculation of the

discrete Fourier transform from the full trace, in that it reduces noise and bias by

averaging many distinct representations of each binarized trace. Briefly, the multi-taper

approach uses a set of orthonormal windowing functions (tapers) to calculate

independent estimates of the Fourier spectrum, S(f), of some random process, X(t). A

final estimate of the Fourier power spectrum is then obtained from the average of these

spectra (Thomson, 1982):

S(f) = sumk( |xk(f)|2 )/K

where K is number of tapers used, and xk(f) is the Fourier transform of X(t) convolved by

the kth taper. Chronux implements this procedure using the discrete prolate spheroid

sequence (Slepian and Pollak, 1960) as the tapers, providing optimal spectral

concentration (Mitra and Bokil, 2008). For each movie, K was determined by

K = 2TW - 1

where T is the total length of the movie and W is the desired spectral resolution (Mitra

and Bokil, 2008). For our analysis, W was set to 0.2 Hz.

We also used the Chronux Toolbox to test for coherence between oscillatory

neurons. After obtaining the Fourier power spectra of all traces, we checked each movie

47

for pairs of neurons whose spectra peaked at frequencies above 0.2 Hz (our spectral

resolution). For each pair of neurons that met this criterion, we calculated the spectral

coherence

Cij(f) = Dij/(DiDj)1/2

where Dij is the cross-spectral density between the ith and jth neurons, and Di is the

autospectral density of the ith neuron. We also calculated a confidence level, Cconf, on

|Cij(f)| using jackknife resampling (Mitra and Bokil, 2008), such that the probability of

observing |Cij(f)| > Cconf under the null hypothesis was 0.01. We checked |Cij(f)| for

peaks great than Cconf within a frequency range defined as [fpeak,1 – W, fpeak,2 + W], where

fpeak,1 is the lower of the two neuronal peak frequencies, and fpeak,2 is the higher

frequency. Pairs of neurons that met these criteria were considered coherent and we

extracted the magnitude, phase and frequency of peak coherence. This process is

illustrated in Figure 3.8.

2.2.10 Identification of Epochs of Rhythmic Activity

To identify rhythmic activity, we searched each trace for sequences of subevents

in which the timing and duration of subevents were predictable. For each subevent in a

trace, the timings of the nearest neighbor subevent, before and after the subevent

under consideration, were predicted by assuming that the two inter-subevent intervals

would be equal:

48

ti+1 = Ti + (Ti - Ti-1) = 2Ti – Ti-1

ti-1 = Ti - (Ti+1 - Ti) = 2Ti – Ti+1

where Ti denotes the observed timing of the peak of the ith subevent, and ti is the

predicted timing of the ith subevent. The accuracy of each prediction was evaluated by

the relative error (RE):

RE = (t – T)/T

Similarly, the duration of each nearest neighbor subevent was predicted to be equal to

the duration of the subevent under consideration, and relative error was again used to

evaluate the predictions. The first and last subevents in each trace had only one nearest

neighbor, and so only one prediction was made. Sequences of 5 or more consecutive

subevents in which all predictions had RE < 0.5 were considered as epochs of rhythmic

activity.

We characterized each epoch in terms of a number of basic properties. Duration

was defined as the difference between the timing of the end of the last constituent

subevent and the start of the first. Frequency was estimated as the arithmetic mean of

the inverse of all inter-subevent intervals within the epoch. We calculated the duty

cycle for each epoch as the ratio of the mean duration of all constituent subevents to

the mean inter-subevent interval.

49

2.2.11 Statistics

All statistical analysis was performed in MATLAB. Mean ± standard error of the

mean (SEM) is reported, except when specified otherwise. Distributions of test statistics

of interest (such as the subevent rate) often violated the assumption of normality, and

so non-parametric hypothesis tests were used. Significance bars presented in figures

follow the convention: 1 star: p < 0.05; 2 stars: p < 0.01; 3 stars: p < 0.001. In some

cases, distributions are presented as box plots. For these figures, box edges represent

25th and 75th percentiles, red lines represents median values, red plus-signs represent

outlier data points, and whiskers extend to the most extreme non-outlier data points.

Most datasets consisted of a series of movies collected in baseline ACSF,

followed by a series of movies collected in drug-laden ACSF. Since the same neurons

were recorded in both conditions for each data set, statistical analysis of drug effects

were made using hypothesis tests appropriate for dependent samplesm, usually the

Wilcoxon signed rank test. Effect sizes and confidence intervals were calculated using

the Measure of Effect Size Toolbox (Hentschke and Stüttgen, 2011), and interpretation

of effect sizes followed the guidelines of the toolbox’s documentation. For non-

parametric statistical tests, we used Cohen’s U3 as a measure of effect size (Cohen,

1988). For two samples, X and Y, with medians x and y, U3 is calculated by:

U3 = N(X < y) + 0.5 N(X = y) N(X)

where N(…) gives the number of elements that satisfy the logical criteria contained in

the argument.

50

CHAPTER 3: RESULTS

3.1 GCaMP Expression and Calibration

In total, we imaged populations from 34 slices, cut from 16 mice, for a total of

1381 neurons. A detailed summary of our data sets is provided in Table 3.

Date (m/d/y)

Age (days)

Indicator Drug Conc. (μM) Electrophys. Neurons Imaged

11/27/2012 23 GCaMP5 Picrotoxin 25 Yes 31

11/27/2012 23 GCaMP5 Picrotoxin 25 Yes 30

11/29/2012 25 GCaMP5 Picrotoxin 2.5 No 58

3/27/2013 17 GCaMP5 Picrotoxin 30 Yes 24

4/17/2013 66 GCaMP5 Picrotoxin 30 Yes 27

4/17/2013 66 GCaMP5 Picrotoxin 5 Yes 49

5/1/2013 21 GCaMP5 Picrotoxin 30 Yes 7

5/1/2013 21 GCaMP5 Picrotoxin 30 Yes 21

9/10/2013 16 GCaMP6f Picrotoxin 5 Yes 41

9/10/2013 16 GCaMP6f Picrotoxin 5 Yes 32

9/11/2013 17 GCaMP6f Picrotoxin 5 Yes 46

9/11/2013 17 GCaMP6f Picrotoxin 5 Yes 50

9/12/2013 18 GCaMP6f Picrotoxin 1 Yes 47

9/12/2013 18 GCaMP6f Picrotoxin 1 Yes 42

9/12/2013 18 GCaMP6f Picrotoxin 1 No 60

½/2014 18 GCaMP6f n/a No 39

½/2014 18 GCaMP6f n/a No 48

1/6/2014 22 GCaMP6f n/a No 49

1/6/2014 22 GCaMP6f n/a No 67

1/6/2014 22 GCaMP6f n/a No 58

1/15/2014 17 GCaMP6f NMDA 8 No 39

1/15/2014 17 GCaMP6f NMDA 8 No 40

1/15/2014 17 GCaMP6f NMDA 8 No 44

1/16/2014 18 GCaMP6f NMDA 8 No 33

1/16/2014 18 GCaMP6f NMDA 8 No 39

1/16/2014 18 GCaMP6f NMDA 8 No 42

2/27/2014 18 GCaMP6f Control No 36

2/28/2014 19 GCaMP6f NMDA 16 No 37

2/28/2014 19 GCaMP6f NMDA 12 Yes 40

7/23/2014 17 GCaMP6f NMDA 3 Yes 43

7/23/2014 17 GCaMP6f NMDA 3 Yes 46

7/23/2014 17 GCaMP6f NMDA 3 Yes 41

7/28/2014 22 GCaMP6f NMDA 3 Yes 33

7/28/2014 22 GCaMP6f NMDA 3 No 42

51

Table 3. Basic details of every data set included in this thesis. Dates containing multiple data sets indicate multiple slices from a single animal.

3.1.1 GCaMP Expression

Stereotaxic Injection of AAV-based viral vectors consistently produced

widespread expression of GCaMP throughout the Mpfc (Figure 3.1). At the level of cell

populations within layers 5 and 6 selected for imaging at high magnification (see

Methods), 44 ± 8 (mean ± SD, 25 slices from 11 animals) somata were identified by eye

for analysis, and acceptable segmentation was attained for 93 ± 1% of these neurons. A

large majority (90 ± 3%) of these cells emitted at least one subevent, suggesting that

most of the imaged neurons were alive. Neuronal resting fluorescence intensity was

estimated as the second percentile of Fi(t), excluding event-associated frames.

Dispersion of resting fluorescence between neurons was low, with coefficients of

variation ranging from 6 – 16 %.

52

Figure 3.1. A coronal slice containing mPFC, as imaged with differential interference contrast (left panel) and epifluorescence (right panel) microscopy at low magnification. Fluorescence throughout the right hemisphere confirms that viral injection produced widespread GCaMP expression throughout the ipsilateral mPFC and beyond. Scale bar = 0.5 mm.

The synapsin promoter is reported to drive highly neuron-specific expression

(Kügler, Kilic and Bähr, 2003), so we assumed that all fluorescent cells were neurons.

Neocortex is populated by many subtypes of neurons, including several classes of

interneurons. The synapsin promoter drives expression in both pyramidal cells and

interneurons. Pyramidal cells are the predominant class of neurons in neocortex, so

numerically they constitute the bulk of the neurons that we imaged. Interneurons are

known to have relatively weaker calcium fluorescence signals (Kerlin, et al., 2010), and

in pilot experiments we confirmed that they were nearly silent in baseline conditions,

and emitted detectable fluorescence responses only to very strong stimulation. These

facts suggest that the vast majority of the activity we report was from pyramidal cells,

but it is likely that some interneurons are also included in our analysis. This thesis makes

no attempt to distinguish between these classes.

3.1.2 Relating GCaMP6f Fluorescence Response to Spiking

GCaMP6f has been functionally characterized in pyramidal cells of layer 2/3

visual cortex, and is reported to be sensitive to individual action potentials (Chen, et al.

2013). However, to our knowledge, there are no published accounts of the relationship

between spiking and fluorescence response of GCaMP6f-expressing pyramidal neurons

53

in deep-layer prefrontal cortex. We therefore sought to determine whether individual

APs were detectable in our preparation, and more generally to characterize the

relationship between spiking and fluorescence response. To examine this issue, we

conducted simultaneous imaging and electrophysiological recordings. Individual APs

were easily detected in whole-cell or cell-attached recordings, and therefore served as a

“gold-standard” for relating the activity of an individual neuron to the less readily

interpretable fluorescence signal. We matched each isolated subevent (an event with

only one subevent) from the patched neuron to all spikes within a time window defined

from 200 ms prior to the onset of the subevent to 100 ms after its peak.

Of nine GCaMP6f-expressing neurons patched, seven had at least one isolated

subevent matched to a single AP. We focused on 4 neurons that had 10 or more such

subevents. We examined fluorescence response to individual spikes in terms of three

parameters: durations of the rising phase (trise) and falling phase (tfall) and relative

magnitude (ΔF/Fo). These properties are summarized, for each neuron, in Figure 3.2.

Single action potentials, when detected, evoked fluorescence responses that lasted 0.54

± 0.33 s (mean ± SD, N = 2583 subevents), and had a relative magnitude of 2.03 ± 1.67%.

However, the ranges of these parameters varied considerably from neuron to neuron

(Figure 3D), making it difficult to infer whether an observed subevent could be

attributed to a single AP without the benefit of an electrophysiological ground-truth.

54

Figure 3.2. (A) Example of the detection of individual action potentials (bottom) as fluorescent subevents (top, blue bars). (B) Overlay of all isolated subevents attributed to single action potentials, aligned to timing of the spikes. All traces are from neuron 3. (C-E) Breakdown, by neuron, of the distributions of subevent properties from single-spike subevents.

Although GCaMP6f was capable of reporting individual spikes in some neurons,

the time scale of this signal was on the order of hundreds of milliseconds. Sequences of

action potentials (spike trains) that fired with interspike-intervals (ISI) less than this time

scale would therefore be expected to appear as single subevents (see Figure 2.1), with

55

greater relative magnitude reflecting increased calcium entry. To study this case, we

searched our data for subevents that matched with multiple spikes. We quantified these

spike trains in terms of three parameters: number of spikes during the subevent time

window (Nspike), mean firing rate (Rspike), and mean firing rate weighted by the number of

spikes in the train (Nspike x Rspike). Figure 3.3 displays the relative magnitude of all isolated

subevents that matched with multiple spikes in terms of these parameters. 8 of 9

neurons showed positive correlations between all three spike train parameters and

relative magnitude of the fluorescence response. The highest correlation was obtained

for the weighted spike rate (ρ = 0.66 ± 0.15, N = 9 slices). As with the single-AP case, we

observed that individual differences from neuron to neuron complicate the

interpretation of fluorescence signals (Figure 3.3).

Figure 3.3. Calibration of relative magnitude of fluorescence response to action potentials as a function of (A) number of spikes (B) mean firing rate and (C) spike-weighted firing rate. Each marker’s color/shape combination identifies a distinct neuron.

56

3.1.3 Error Rates

Error rates were estimated by examining the correspondence between neuronal

spiking and fluorescence response. False positive errors were defined as subevents that

were not matched to underlying spiking, and the false positive error rates was

calculated as the ratio of the number of erroneous subevents to the total number of

subevents considered. False positives were relatively rare (<5%) for all neurons

analyzed. The overall false positive error rate was 4% for subevents with relative

magnitude ≤ 1%, and fell further to < 2% for all brighter subevents (Figure 3.4A).

False negative errors were defined as spike trains not detected by calcium

imaging. We estimated false negative rates by identifying isolated spikes and spike

trains, and checking for corresponding subevents. False negatives were much more

common than false positives, particularly for spike trains with less than 5 spikes and

firing rates less than 10 Hz, but fell dramatically for spike trains of higher spike number

and frequency (Figure 3.4B,C).

57

Figure 3.4. (A) False positive error rates as a function of subevent relative magnitude. (B) False negative error rate as a function of number of spikes for unmatched spike trains. (C) False negative error rate as a function of number firing rate.

3.1.4 Interpretation of Calcium Imaging Data

The results of our analysis of the relationship between spiking and GCaMP

fluorescence generally confirmed the validity of our experimental and analytical

approach for detection of neuronal activity, but also revealed some limitations. First, the

low rate of false positives assured us that nearly all subevents represent calcium entry

due to spiking, as opposed to other biological processes involving calcium (see Figure

1.5). On the other hand, we observed relatively high false negative rates for spike trains

of low spike number or firing rate, implying that there were many such spike trains (and

isolated spikes) that were not picked up by our analysis. In this sense, our reconstruction

of the activity of neural populations based on subevents is a conservative

representation of the underlying activity.

We found that we could detect single action potentials, extending the result of

Chen, et al. to layer 5 Mpfc. However, the neuron-to-neuron variability of fluorescence

response calibration curves (Figure 3.4) precluded the possibility of inferring the exact

details of the underlying spike trains from the imaging data alone. In light of these

findings we adopted a parsimonious interpretation, treating each subevent simply as an

indication that a particular neuron was active during the duration of the subevent but

58

making no assumptions about the firing rate or the precise timing of the underlying

spikes.

3.2 Spontaneous Activity

3.2.1 Spontaneous Activity – Basic Properties

We imaged 25 slices from 11 mice expressing GCaMP6f in our baseline ACSF, for

a total of 1092 neurons. We observed spontaneous activity in all slices. At a gross level,

we observed subevent rates of 4.57 ± 1.02 subevents/min/neuron (N = 25 slices).

Neuronal activity ratios (total subevent duration/total recording length) were typically

low (< 10 %), with no neuron reaching 50 % (Figure 3.5B). At the level of individual data

sets, the distribution of subevent rates among neurons was always positively skewed

(skewness = 3.58 ± 0.33, N = 25), implying that the spontaneous activity within each slice

was dominated by a few highly active neurons, while the majority of neurons emitted

few or no subevents (Figure 3.5C). We quantified the inequality of these distributions

using the Gini coefficient. Commonly used in economics, the Gini coefficient measures

the distance of a distribution from perfect equality as the area between the observed

Lorenz curve and the hypothetical Lorenz curve for a perfectly uniform distribution

(Figure 3.5D). Pooling all neurons, the Gini coefficient was 0.89, close to the theoretical

limit of 1 for a maximally unequal distribution.

59

Figure 3.5. (A) Top panel: Example raster plot of spontaneous activity. Each row represents a neuron, and black dashes signify subevents. Bottom panel: Total (sum) activity of all neurons shown in the top panel. (B,C) Histograms of activity ratio (B) and subevent rate (C) of all analyzed neurons. (D) Lorenz curves of subevent rate across all neurons (solid black curve) and uniform distribution (dashed line). The shaded area between these curves is used to calculate the Gini coefficient.

We examined two basic subevent properties: duration and relative magnitude

(ΔF/Fo). Pooling all data sets, the distributions of both variables, were highly positively

skewed, and well-fit by lognormal distributions (Figure 3.6), and as such we characterize

their location and scale in terms of their geometric mean (equivalent to median for a

lognormal distribution) and median absolute deviation. Median subevent duration was

0.25 s, with a median absolute deviation of 0.08 s. Median relative magnitude was

60

1.83 % with a median absolute deviation of 0.96 %. These numbers are comparable with

the duration and relative magnitude of responses to individual action potentials (Figure

3.2), suggesting that baseline activity is dominated by brief (< 500 ms) packets of one or

a few action potentials at low frequency.

Figure 3.6. Estimated probability densities of subevent duration (A) and relative magnitude (B) pooled from all data sets. Maximum likelihood fits to lognormal distributions are overlaid on each histogram (black curves).

3.2.2 Spontaneous Activity – Rhythmic Firing

We noticed a subset of neurons tended to oscillate between periods of activity

and inactivity (Figure 3.7A). Fourier analysis of the binarized traces (Figure 3.7B)

revealed prominent peaks in the delta frequency band (0-4 Hz). In some cases, neurons

exhibited oscillatory epochs interspersed with silence or sporadic activity (for example,

several of the neurons shown in Figure 3.5A), making interpretation of the Fourier

spectrum difficult. To identify rhythmic firing without relying on the Fourier spectrum,

61

we searched each trace for sequences of subevents in which the timing and duration of

subevents were predictable to within 50% relative error (see Methods).

14.6 ± 2.8 % of neurons showed at least one rhythmic epoch (N = 25 slices)

under baseline conditions. The probability of observing 2 or more such epochs, given

that one had been observed, was 65.5 ± 6.2 %. Duration of epochs varied widely,

ranging from a few seconds to longer than 1 minute (Figure 3.7B). Neurons were found

to oscillate at frequencies spanning the entire delta frequency band, but showed a

preference for the 0.5 – 2 Hz range (Figure 3.7D). The duty cycle of oscillations varied

widely, but largely fell within the range of 0.2 – 0.5 (Figure 3.7E).

Rhythmic activity could potentially be explained as unhealthy neurons being

unable to maintain a hyperpolarized resting potential, so we looked for evidence of poor

cell health. Maintenance of intracellular calcium concentration is often compromised in

dying neurons (Hyrc, et al. 1997), so we compared the baseline GCaMP fluorescence

levels (see Methods) of rhythmic neurons (neurons with showing 2 or more rhythmic

epochs) to their non-rhythmic counterparts within each slice. Of the 25 slices, rhythmic

neurons were significantly brighter (p < 0.01, two-sample Kolomogorov-Smirnov test)

than low-activity neurons in only one slice. Similarly, unhealthy neurons often swell and

then shrink below their normal size. We compared the size of neurons from rhythmic

and non-rhythmic groups within each slice. Size of neurons was approximated as the

total area (in pixels) of the corresponding regions of interest. Only 1 of 25 slices, showed

a significant difference in size distribution (p < 0.01, two-sample Kolomogorov-Smirnov

62

test). Overall, we found very little evidence to support the proposition that rhythmic

neurons are unhealthy.

Figure 3.7. (A) Representative example of two neurons showing highly rhythmic activity in baseline ACSF. (B) Fourier power spectra of the binarized traces derived from the two traces in A. (C-E) Histograms of basic properties of rhythmic epochs under baseline conditions.

We investigated possible interactions between pairs of rhythmic neurons using

coherence analysis. Coherence is a complex-valued measure of interaction between two

oscillators as a function of frequency: its magnitude indicates the strength of

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interaction, and its phase gives the relative delay between oscillations (see Methods).

The sign of the phase angle depends on which oscillator is considered the reference, and

is not of interest for this analysis, so we present its absolute value instead. We searched

for evidence of coherence within the frequency band between the spectral peaks of

each pair of neurons (Figure 3.8A-D). We found 1.63 ± 0.7 % (N = 25 slices) of all pairs of

neurons within a slice showed evidence of coherence. The magnitude of significant

coherence was typically low (0.384 ± 0.005, N = 522 pairs; Figure 3.8E) relative to the

theoretical maximum of 1. The distribution of phase delay among coherent pairs was

inconsistent with a uniform angular distribution (p < 0.01, Rayleigh test) and peaked

around 0 (Figure 3.8F), indicating a preference for in-phase interaction. For each slice

we compared the distribution distances between coherent pairs with the distances

between all possible pairs using the two-sample Kolmogorov-Smirnov test. Setting a

significance level of 0.01, we found no significant differences and therefore no evidence

of any spatial clustering among coherent neurons.

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Figure 3.8. (A) Example raster plots of two oscillatory neurons. (B) Fourier power spectra of neurons 1 (blue) and 2 (black) shown in A. (C) Phase angle of coherence for example in A. Black dot indicates the frequency of maximum coherence. (D) Magnitude of coherence of example in A. The red line demarcates the frequency band in which coherence is considered, and its height shows the 99% confidence level. (E) Distribution of magnitude of coherence for all coherent pairs of neurons. (F) Histogram of absolute value of phase angles for all coherent pairs.

3.2.3 Spontaneous Activity – Pairwise Correlations and Functional Connectivity

We measured the pairwise correlation between neurons using the Jaccard

similarity index, J. Under baseline conditions, J values were almost always low, with

most pairs having no correlation at all (Figure 3.9A,C). Even if cortical neurons were

completely decoupled, given sufficient activity one would expect to see some level of

coincidental overlap that would result in some non-zero Jaccard indices. To account for

this, we estimated p-values on each Jaccard index using Monte Carlo-generated

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surrogate data sets in which any latent temporal structure between traces was

destroyed (see Methods). We focused on pairs of neurons with non-zero J and p-values

< 0.01, which we considered functionally connected (Figure 3.9B). Under these criteria,

2.41 ± 0.39 % of pairs (N = 25 slices) were identified. Even among functionally connected

pairs, Jaccard indices were mostly low in an absolute sense, although a non-negligible

fraction (~3%) reached values of 0.5 or greater. The distribution of functionally

connected J values was significantly higher (p < 0.01, two-sample Kolmogorov-Smirnov

test, U3 = 0.96) than the overall distribution (Figure 3.9C).

We looked for possible spatial organization among functionally connected pairs.

We calculated the conditional probability of functional connectivity as a function of

distance (20 μm bins), and found a strong preference for connectivity among proximal

neurons (Figure 3.9D). 50 % of functionally connected neurons fell within 60 μm of each

other, and 95% fell within 140 μm. We performed a similar analysis on the orientation

of functionally connected pairs relative to the medial-lateral axis (10° bins), and

observed a preference for alignment along this axis over the dorsal-ventral axis (Figure

3.9E).

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Figure 3.9. (A) Example Jaccard correlation matrix obtained from one data set in baseline conditions. (B) Functional connections between neurons in the same data set as A. (C) Distributions of Jaccard indices under baseline conditions, pooling all pairs (red curve) or functionally connected pairs (blue) of active neurons from all data sets. (D) Conditional probability of functional connection, as a function of neuronal separation. (E) Conditional probability of functional connection, as a function of angle between the line segment connecting the pair of neurons and the medial-lateral axis (0°).

3.2.4 Spontaneous Activity – Synchronous Events

We identified synchronous events by searching for frames in which multiple

neurons emitted subevents whose time-derivatives peaked within 50 ms (see Methods;

Figure 2.3). The majority of synchronous events consisted of only 2 participant neurons

(Figure 3.10A). The overall rate of synchronous events in baseline conditions varied over

several orders of magnitude, and was very strongly correlated with the overall subevent

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rate (R = 0.97, p < 0.01, N = 25 slices). Only 1 slice of 25 had a synchronous event rate

that exceeded the 99% confidence interval derived from Monte Carlo-generated

surrogate data sets (Figure 3.10B), seemingly suggesting that most or all of the

synchrony we observed could be explained by coincidence. However, we occasionally

observed bright synchronous events involving many neurons (for example, see Figure

3.11), and it seemed implausible that these synchronous events could be due to

coincidence. For uncorrelated activity, the probability of N neurons synchronizing should

scale as the product of the neurons’ individual activation probabilities. For correlated

activity, the activation of multiple neurons enhances the probability of recruiting even

more neurons, and thus we would expect to see an excess of high-synchrony events. To

reconcile these observations, we examined the rates of synchronous events with exactly

2, exactly 3, or 4+ participant neurons separately. For each bin, we quantified the excess

rate as the difference between the observed rate and the upper 99% confidence level

(i.e. any positive rate corresponds to a statistically significant excess). Only 1 slice

exceeded its expected 2-participant rate, 2 slices had positive excesses of 3-particpant

events, and 7 slices exceeded their 4+ expectation (Figure 3.10C). Based on this

observation, we inferred that a substantial fraction of the 4+ participant synchronous

events were due to functional connectivity among neurons and not simple chance. We

redivided the data into low-synchrony (2 or 3 participants) and high-synchrony (4+

participants) groups and compared the distributions of interesting parameters such as

the mean relative magnitude of the constituent subevents (Figure 3.10D), mean

separation between all participants (Figure 3.10E) and mean angle (Figure 3.10F)

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between participants. In each case, the distributions were significantly different (p <

0.01, two-sample Kolmogorov Smirnov test).

Figure 3.10. (A) Distribution of number of neuronal participants for all synchronous events. (B) Comparison of overall baseline synchronous event rates observed in each slice (black Xs) with the 99% confidence interval (blue bars) obtained from surrogate data sets. (C) Break down of excess synchronous event rate by number of participants. Red dashed line indicates the upper 99% confidence level. (D-F) Distributions of mean relative magnitude (D), mean separation (E) and mean angle (F) of participant subevents/neurons for low-synchrony (blue) and high-synchrony (black) synchronous events.

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Figure 3.11. (A) Example frames from just before (left), and at the peak of (right), a major synchronous event. (B) Fluorescence traces from eight neurons identified as participants in the synchronous event shown in A (top), and simultaneous loose-patch recording from a participant neuron (bottom). The black fluorescence trace in the top panel corresponds to the patched neuron.

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3.3 Effects of NMDA

3.3.1 Effects of NMDA – Basic Subevent Properties

We imaged 519 neurons across 13 slices before (92 minutes total) and after (154

minutes total) wash-in of NMDA. We used NMDA concentrations of 3 μM (5 slices), 8

μM (6 slices), 12 μM (1 slice) and 16 μM (1 slice). We observed that NMDA application

coincided with dramatic upticks in neural activity after a 2-5 minute delay (Figure

3.12A). Data from this transition period was not included in our analysis. Comparing the

mean subevent rates of slices before and after wash-in we found a significant increase

under NMDA ACSF (p < 0.01, Wilcoxon sign-rank test, N = 13 slices) with a Cohen’s U3

effect size of 1, the theoretical maximum for this metric (Figure 3.12B). Thus, we found

very strong evidence that NMDA increased overall neural activity.

NMDARs are strongly implicated in persistent firing (Wang, 1999; see

Introduction), so we examined the effect of NMDA on the durations of subevents. We

compared mean subevent durations of neurons before and after wash-in. This analysis

required a reasonable sample size of subevents in each condition for each neuron, so

we only included neurons that emitted at least 5 subevents in each condition. Given the

highly unequal distribution of activity among neurons (see Figure 3.5D), this

requirement excluded many neurons. To compensate, we pooled neurons by the NMDA

dose they received: 3 μM, 8 μM or 12+ μM. Of these three pools, we found a significant

effect for the 12 + μM group (p < 0.01, Wilcoxon sign-rank test, N = 17 neurons).

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Interestingly, this groups showed a strong decrease (U3 = 0.12) in subevent duration

(Figure 3.12C).

We performed a similar analysis on subevent relative magnitude, and found

mixed results. We found significant differences for the 3 μM and 12+ μM groups, with

strong effect sizes (U3 = 0.88 and 1.0, respectively). However, the 8 μM group showed

no significant difference, and the effect size was a modest 0.57 (Figure 3.12D). Together,

these results suggest that NMDA has a complex effect on subevent magnitude that

appears to be dose-dependent.

Figure 3.12. (A) Example of the time course of activity in response to wash-in of NMDA (3 μM in this example, green region). (B) Comparison of overall subevent rates before and after NMDA application, for all NMDA data sets. (C) Comparison of distributions of

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mean neuronal subevent duration, for each NMDA concentration group. (D) Same as C, but comparing mean subevent relative magnitude.

3.3.2 Effects of NMDA – Rhythmic Activity

Having observed consistent rhythmic activity under baseline ACSF, we wondered

whether NMDA affected this rhythmicity. We identified epochs of rhythmic activity

using the same criteria of predictability. For each slice, we compared the fraction of

recording time neurons spent oscillating before and after NMDA application, and found

significant increases (p < 0.01, Wilcoxon sign-rank test) in 11 of 13 slices, spanning all

NMDA concentrations (Figure 3.13B). Effect sizes were very strong (Cohen’s U3 = 0.93 ±

0.04) for those 11 slices. In all cases (including slices with non-significant differences) a

substantial fraction of neurons (50 ± 3 %, N = 13 slices) were found to switch from non-

rhythmic (0 epochs) to rhythmic (≥ 1 epoch) after NMDA application.

To detect any possible effect on the frequency of oscillation, we looked at

neurons that showed rhythmic activity before and during NMDA application. In some

cases, neurons exhibited multiple epochs with distinct frequency ranges within one

condition, making it difficult to assign one meaningful “summary frequency” per

neuron. Instead, we compared frequency distributions by pooling the inter-subevent

intervals from all epochs within each condition, for each neuron. We checked for

significant differences between these samples using the two-sample Kolmogorov-

Smirnov test. Effect sizes were assessed using Cohen’s U3, with U3 > 0.5 signifying an

overall increase in frequency, and U3 < 0.5 signifying a decrease. We found significant

differences (p < 0.01) in 23 of 41 neurons through this analysis, 15 of which showed

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decreases (U3 = 0.08 ± 0.02), and 8 of which showed increases (U3 = 0.97 ± 0.02). The

overall spectrum of rhythmic activity under NMDA shifted towards lower frequencies

(< 1 Hz) relative to baseline (Figure 3.13C).

Figure 3.13. (A) Raster plot showing the effect of NMDA on rhythmic activity. Roughly 3 minutes (180 s) after beginning of wash-in, activity increases, and rhythmic activity in particular increases, as evidenced by the appearance of several neurons showing repetitive activations of self-similar duration and frequency. (B) Example of a neuron showing a marked decrease in frequency of rhythmic activity after NMDA application. (C) Spectra of rhythmic activity before (red) and during (black) NMDA application.

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We looked for any effects of NMDA on coherence between neurons, using the

same analysis approach as in section 3.2.2. NMDA increased the overall number of pairs

showing significant coherence (Figure 3.14A). Mirroring the overall tendency towards

lower frequency oscillations under NMDA, coherence tended to peak at lower

frequencies (Figure 3.14B), with almost all coherent pairs peaking below 1 Hz. We also

found a moderate increases in the magnitude of coherence (Figure 3.14C). As in the

baseline condition, the phase delay of coherence was widely distributed but showed a

distinct preference for low delays (Figure 3.14D).

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Figure 3.14. (A) Comparison of the fraction of pairs of neurons showing significant coherence prior to and during application of NMDA. (B-D) Distributions of frequencies of peak coherence (B), magnitude (C) and phase angle (D) for all coherent pairs.

3.3.3 Effects of NMDA – Pairwise Correlations

We assessed the pairwise correlation between active neurons under baseline

and NMDA conditions using the Jaccard similarity index. Consistent with NMDA’s

activating effect on slices overall, we found an increase in the number of pairs showing

finite correlation (Figure 3.15A,B). However, in absolute terms, pairwise correlations

were still very low compared to the theoretical limit of 1, and typically fell below 0.1

(98% of all pairs in wash condition, 92% in NMDA).

We identified functionally connected neurons by comparison with Monte Carlo-

generated surrogate data sets (p < 0.01, permutation test). The fraction of pairs that

met this criterion increased under NMDA in 12 of 13 slices (Figure 3.15C), and the

overall effect was significant (p < 0.01, Wilcoxon sign rank test) and strong (Cohen’s U3 =

0.83). A minority (12.7 ± 3.8 %) of the pairs that were identified as significantly

correlated during the baseline period were also so identified under NMDA. We

wondered whether NMDA affected the distance between correlated neurons.

Comparison of the distributions of distances for these pairs of neurons, between

baseline and NMDA, failed to find significant differences for any slice (p < 0.01, two-

sample Kolmogorov-Smirnov test).

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Figure 3.15. (A) Jaccard correlation matrix for all pairs of neurons from one example data set under baseline conditions (left panel) and NMDA (right panel). (B) Histograms of Jaccard indices pooled from all pairs of neurons under baseline (blue) or NMDA (black) condition. (C) Fraction of pairs of neurons that were identified as significantly correlated under each condition.

3.3.4 Effects of NMDA – Synchronous Events

We compared the overall rates of synchronous activity in baseline and NMDA

conditions. NMDA significantly increased the rate of synchronous events (p < 0.01,

Wilcoxon sign rank test, N = 13 slices, Cohen’s U3 = 1; Figure 3.16A). However, the rates

of synchrony under both conditions were very strongly correlated with the

corresponding overall subevent rates (R = 0.94 in baseline, 0.90 in NMDA, p < 0.01)

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suggesting that the observed synchrony might be explained primarily, if not entirely, by

the sheer volume of ongoing activity. Indeed, comparing the confidence intervals on

overall synchrony rates (obtained from surrogate data sets) to the observed values (for

example, see Figure 3.16B), we found that 11 of 13 slices fell within the 99% confidence

range, 1 slice fell below this range, and 1 above. Under NMDA, 8 of 13 slices fell within

this range, and 5 fell below it. Thus, the overall synchrony rates were generally

consistent with the null hypothesis of independent neurons or, in several cases,

underperformed this prediction.

We looked at the rates of synchronous events as a function of the number of

participants in each synchronous event. As in our baseline analysis (section 3.2.4), we

looked at the rates of synchronous events with 2, 3 or 4+ neuronal participants. Results

for the 2 and 3-participant rates were mixed, with slices generally falling within the 99%

confidence intervals both in both the baseline and NMDA conditions. However, we

consistently found excess rates of synchronous events involving 4 or more neurons

(Figure 3.16C, for example). We quantified the excess as the difference between the

observed rate and the upper 99% confidence level, for each slice and condition, and

compared the excesses between conditions. NMDA’s effect on the rate of 4+ neuron

synchronous events was statistically significant (p < 0.01, Wilcoxon sign-rank test, N = 13

slices) and strong (U3 = 1; Figure 3.16D).

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Figure 3.16. (A) The overall rate of synchronous events increased during NMDA application. (B) Example of overall synchrony rates from one slice falling within the 99% confidence intervals (blue lines) predicted from surrogate data sets. Black Xs indicate observed rates. (C) Decomposition of synchronous event rate by number of participants for the same data set as in B. Blue lines give 99% CI in baseline condition. Red lines, slightly offset, show the same in NMDA condition. Black dots indicate observed rates. (D) Excess rate of 4+ neuron synchronous events increased under NMDA condition.

3.4 Effects of Picrotoxin

3.4.1 Effects of Picrotoxin – Basic Characterization

We imaged 523 neurons across 14 slices from 8 mice before and during

application of picrotoxin. Picrotoxin concentrations used were 1 μM (2 slices), 2.5 μM (1

slice), 5 μM (5 slices), 25 μM (2 slices) and 30 μM (4 slices). For some analyses, we

combined these experiments into 3 dosage groups: low (1-2.5 μM), medium (5 μM) and

high (25-30 μM). Whereas NMDA induced unambiguous increases in activity, the effect

of picrotoxin on the activity rate was far more subtle (for example, Figure 3.17A).

Comparison of the gross subevent rates of all slices between the baseline and wash-in

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conditions failed to find a statistically significant difference (p > 0.01, Wilcoxon sign-rank

test, N = 14 slices; Figure 3.17B). We then examined each slice individually and

compared the neurons’ subevent rates between the baseline and wash conditions. Only

3 of 14 slices (including one slice from each dose group) showed significant differences

(p < 0.01, Wilcoxon signed-rank test), and of those slices two showed decreased activity

(U3 = 0.36 and 0.11) and one showed an increase (U3 = 1.0). Based on these results, we

could not conclude that picrotoxin had any effect on the subevent rate.

As in our NMDA analysis, to investigate the potential effect of picrotoxin on

subevent duration and relative magnitude, we grouped slices by dosage and pooled all

neurons within each group that emitted at least 5 subevents in each condition. Since the

low and high picrotoxin groups combined slices expressing GCaMP5 and GCaMP6f,

which are known to have different time constants (Chen, et al., 2013), we focused on

the medium picrotoxin group, which only used GCaMP6f. We found a moderate,

statistically significant increase on mean subevent duration in this analysis (p < 0.01,

Wilcoxon signed-rank test, N = 82 neurons, U3 = 0.67; Figure 3.17C). A similar

comparison of relative magnitude failed to detect any significant difference (Figure

3.17D).

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Figure 3.17. (A) Example of the time course of neural activity prior to, and during application of 5 μM picrotoxin (green region). (B) Subevent rates of all slices from the picrotoxin data sets in baseline and drug conditions. (C) Distribution of mean subevent durations for all qualified neurons in the 5 μM Ptx dataset. (D) Distribution of mean subevent relative magnitudes for all qualified neurons in the 5 μM picrotoxin dataset.

3.4.2 Effects of Picrotoxin – Rhythmic Activity

Picrotoxin had no clear effect on the prevalence of rhythmic activity. Comparing

the fraction of time spent oscillating under baseline and picrotoxin conditions, we found

a significant difference (p < 0.01, Wilcoxon signed-rank test) in only 1 of 14 slices. To

assess whether picrotoxin affected the frequency of oscillations, we focused on neurons

that showed rhythmicity under both conditions. For each such neuron, we compared

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the distributions of frequencies of the rhythmic epochs under the baseline and

picrotoxin conditions. 25 of 33 neurons showed significant differences (p < 0.01, two-

sample Kolmogorov-Smirnov test), and 23 of these neurons exhibited pronounced

decreases in frequency (U3 = 0.10 ± 0.03, N = 23 neurons; Figure 3.18A, for example),

while two neurons showed increases (U3 = 0.78 and 0.97). Examining the overall

distributions of frequencies (pooling all rhythmic epochs by condition), we observed a

bimodal distribution under baseline conditions (peaks around 0.75 and 2 Hz) that

changed to a unimodal distribution in picrotoxin, with the higher frequency band

conspicuously absent (Figure 3.18B).

Figure 3.18. (A) Example of a neuron that showed a marked reduction in frequency of rhythmic activity under 5 μM picrotoxin. (B) Spectra of rhythmic activity under baseline and picrotoxin conditions.

We examined coherence between rhythmic neurons using the same approach as

in Section 3.3.2. Picrotoxin was associated with a modest increase in high-coherence

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pairs (Figure 3.19A). As in the baseline condition for these slices, nearly all observed

coherence was found in the low-delta (< 1 Hz) frequency range (Figure 3.19B).

Distributions of phase delays were remarkably similar between the two conditions, with

a clear preference for in-phase coherence (Figure 3.19C).

Figure 3.19. Distributions of (A) magnitude of peak coherence (B) frequency of peak coherence (C) phase angle of peak coherence.

3.4.3 Picrotoxin Results – Pairwise Correlations

Picrotoxin exhibited a complex effect on neural correlation. The number of pairs

of neurons identified as functionally connected increased significantly upon application

of picrotoxin (p < 0.01, Wilcoxon signed rank test, N = 14 slices, U3 = 0.85; Figure 3.20C).

However, these Jaccard indices were weaker than their baseline counterparts (Figure

3.20A,B). Of 11 slices with functionally connected pairs detected in both conditions, 6

showed statistically significant decreases in the distribution of Jaccard indices (p < 0.01,

two-sample Kolmogorov-Smirnov test, U3 = 0 – 0.23), and no slice showed a statistically

significant increase. Among these slices, 24.4 ± 9.2 % of functionally connected pairs in

baseline conditions were also identified under picrotoxin.

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Figure 3.20. (A) Representative example similarity matrices from one slice in baseline (left) and 5 μM picrotoxin (right). (B) Distribution of Jaccard indices for all pairs of active neurons in baseline and picrotoxin conditions. (C) Fraction of pairs of neurons identified as functionally connected in baseline and picrotoxin conditions.

3.4.4 Picrotoxin Results – Synchrony

Comparing overall rates of synchronous events between baseline and picrotoxin

conditions, we found no significant effect due to picrotoxin (Figure 3.21A), consistent

with picrotoxin not exerting an excitatory effect (see above). Under baseline conditions,

only one slice exceeded its upper 99% confidence level on the rate of synchronous

events overall, whereas under picrotoxin 10 of 14 slices exceeded it (for example, Figure

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3.21B). Dissecting synchrony by number of participants, we found a strong, statistically

significant increase in the excess rate of 3 participant synchronous events (p < 0.01,

Wilcoxon signed-rank test, N = 14 slices, U3 = 1; Figure 3.21D). We also found an

increase in 4+ participant rate, but this effect was not significant at the 0.01 alpha-level

(p = 0.015). As with previous analysis on baseline and NMDA-induced activity, 2-

participant rates always fell within or below their 99% confidence levels.

Figure 3.21. (A)Comparison of rates of synchronous activity of all slices between baseline and picrotoxin condtions. (B) Representative example of the overall rates of synchronous events in baseline and picrotoxin, compared to 99% confidence intervals derived from surrogate data. (C) Breakdown of rates of synchronous events by number of participant neurons for the same example data set as B. (D) Comparison of excess 3- participant synchronous event rates over the upper 99% confidence level (red dashed line) for baseline and picrotoxin conditions.

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CHAPTER 4: DISCUSSION

Calcium imaging has emerged as a dominant technique for studying biological

systems. The ubiquity of calcium in cell physiology and the availability of many diverse

indicators have led to a proliferation of studies that use calcium-dependent

fluorescence to measure biological processes. In the context of brain science in

particular, there is an increasing emphasis on simultaneously measuring the activity of

as many individual neurons as possible. Individual neurons have been studied in

exquisite detail in terms of their electrophysiological, morphological and

genetic/molecular properties. Likewise, techniques such as functional magnetic

resonance imaging and EEG have mapped the brain in terms of macroscopic regions.

Perhaps the biggest gap in our understanding of the brain is in how the collective

actions of huge populations of neurons (and other cells) give rise to higher level

concepts like behavior and cognition.

In this context, this thesis represents a finite step towards that greater goal in

two senses. First, we developed a suite of methods for acquiring and analyzing calcium

imaging data using epifluorescence microscopy. In particular, we described a novel

semi-automated method of image segmentation, which we then exploited to ensure

that the signals we obtained were a faithful representation of the underlying neuronal

activity. Second, we applied these methods, in combination with patch-clamp

electrophysiology, to characterize the spontaneous network dynamics of the medial

prefrontal cortex. Taking advantage of the slice preparation, we then used

neuropharmacological agents that act on two of the molecular systems supporting the

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PFC’s cognitive functions to tune the synaptic coupling between neurons, and

characterized the response in our model system.

4.1. Technical Innovations

4.1.1 Semi-Automated Image Segmentation

Image segmentation is a fundamental step in any calcium imaging experiment. A

256x256 image contains 65,536 pixels, and selecting the precise pixels that correspond

to a particular cell is a notoriously difficult problem. Furthermore, a poor segmentation

could lead to dramatically wrong results. Imagine, for example, that a single neuron was

missegmented and treated as two neurons: as the neuron flickered on and off, both

ROIs would show highly correlated activity and might lead a researcher to conclude that

she had found a pair of strongly coupled neurons. Given the importance of

segmentation for all further analysis, it is crucial to get the segmentation right.

Although several automated image segmentation algorithms have been

developed (Mukamel, 2009; Wong, et al., 2010), these methods were designed for 2-

photon or confocal microscopy, where background fluorescence is minimized. These

methods failed to consistently yield satisfactory segmentation when applied to our data.

We conducted our experiments using epifluorescence microscopy, which has the

advantages of lower cost and higher frame rate (usually), but suffers from background

fluorescence problems. In our experimental protocol, GCaMP was widely expressed

throughout the brain tissue, and light from outside the focal plane contaminated our

data. Automated methods that look for clusters of statistically correlated pixels would

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pick up the flickering of impostors such as neuropil, dendrites, and out-of-focus neurons

and treat them as valid neurons for analysis. We avoided this problem by employing a 3-

stage segmentation. In stage 1, the user initially identifies each neuron by eye and

draws simple ROIs around them. In stage 2, our algorithm extracts the fluorescence

from each ROI, searches for sequences of frames in which each neuron’s morphology

should be particularly apparent, and then generates putative ROIs based on summary

images. The user is then presented with the putative ROIs and selects the high-quality

ROIs. In stage 3, the high-quality ROIs are padded to allow for slight translational

motion, and then merged to form the final ROIs.

The algorithm we present is just one implementation of a broader class of

segmentation methods. We obtained satisfactory results using mean and standard

deviation projections to reveal the neuronal morphology, but other statistics would also

be suitable. As we showed with the event validation step, pixels that cover the same

neuron tend to be strongly correlated with each other, and it seems likely that good

segmentation could be achieved using this projection also. Similarly, we used a simple

thresholding at the 90th percentile of all pixel values, but more sophisticated

thresholding techniques, such as Otsu’s method (Otsu, 1979) could also be appropriate

depending on the experimental context. The generality of our algorithm is such that it

seems likely that many other simple modifications could expand its utility to meet

diverse experimental needs.

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The involvement of human user in image segmentation is advantageous in that

even dim neurons are readily identifiable by eye, and impostors such as dendrites can

be easily rejected. However, this requirement is also a drawback, as the multiple-stage

segmentation is time-consuming. For a typical data set with 40-50 neurons in the field of

view, segmentation by a trained user would generally take 40 to 60 minutes. Clearly,

this would limit the practicality of scaling this approach up to thousands of neurons.

However, combining this approach with machine learning techniques, or crowdsourcing,

might potentially overcome this limitation. For the purposes of this thesis our novel

image segmentation approach was highly successful in drawing regions of interest that

matched the neural somata, with minimal “deadweight” pixels, in a challenging imaging

context with many background features of comparable intensity to the neural somata.

4.1.2 Subevent Detection and Subevent Validation

Segmentation is just the first step in any calcium imaging analysis. In order to

make scientific inferences, it is necessary to detect the fluorescent events that signify

action potentials. Whereas single action potentials produce relatively stereotyped rising

and falling phases (Figure 3.2), trains of action potentials with ISIs shorter than the time

scales of these transients can yield complex signals that are difficult to interpret. Since

rising intracellular calcium is strongly associated with spiking, we interpreted all time

periods in which fluorescence increased substantially above the noise level as spike-

associated subevents. It is important to consider that spiking might also occur during the

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falling phase. For instance, if a neuron fires at high frequency (say, 60 Hz) and then

relaxes continuously to a lower frequency (20 Hz, for example) the fluorescence signal

would decrease even though firing was still occurring. However, it is difficult to reliably

distinguish this case from a temporary pause in firing, and so we took the most

conservative interpretation. Thus, the activity reported in this thesis likely represents an

underestimation of the actual neural activity.

Calcium imaging experiments always require segmentation and usually involve

some event detection method. To our knowledge, no previous work has taken the

further step of event validation. We found this to be necessary, as our initial comparison

of subevents to spiking in patch-clamped neurons revealed many false positives

(subevents with no matched spikes) that, upon inspection were attributed to

fluorescence from neighboring cells bleeding into the region of interest (as exemplified

in Figure 2.3). Having derived the approximate morphology of each neuron from the

image segmentation process, we exploited this information to distinguish true activity

from artifacts. During spiking, calcium enters the soma through ion channels and

diffuses through the cytosol on a millisecond time scale, much faster than our imaging

integration time (typically 20 ms). Therefore, true action potentials should produce

correlated rises in fluorescence distributed through many or all of the pixels associated

with the soma, not just one side or compartment of the neuron. By mapping the

correlation between each pixel and the average fluorescence signal of the subevent, and

overlapping this map with the ROI, it was relatively straightforward to distinguish true

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subevents from false positives. With this step in place, we were able to achieve low type

1 error rates for even the dimmest subevents.

One weakness of this approach, however, is that it struggles with situations in

which two or more neighboring neurons were active simultaneously. In this case, pixels

outside the region of interest under consideration may legitimately be strongly

correlated with the subevent fluorescence and fool the algorithm into treating a true

subevent as a false positive. For this reason, in addition to those previously discussed,

we consider our final subevent catalog a lower bound on the actual network activity.

This bias is particularly important for our analysis of synchrony, and implies the actual

number of neurons involved in synchronous events may be higher than what we report.

4.1.3 Analysis Philosophy

Our analysis depends on assumptions and fiducial choices of parameters and at

several different stages. Whenever possible, we based these choices on consideration of

the electrophysiological ground truth, and in every case, we preferred to err on the side

of conservativism. For example, we could have achieved a greater sensitivity to single-

AP subevents by lowering the event detection threshold, or loosening the requirements

in the validation stage, at the cost of more false positives. Our view was that false

positives are more pernicious than false negatives when considering concepts such as

synchrony or correlation, because a primary cause of false positives was the bleed-

through of fluorescence from active neurons into neighboring ROIs. If this problem was

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not adequately addressed, false positive subevents would appear to be highly correlated

and synchronous with true positives, undermining our ability to accurately measure

these statistics.

This philosophy also underlies the decision to work with the binarized traces,

rather than the direct fluorescence measurement or some reconstruction of the

neurons’ spike trains. By disregarding the magnitude of subevents, we threw away a

substantial amount of information and limited the analysis to the question of “Was this

neuron likely to have spiked at least once during this frame?” rather than “How much

spiking occurred during this frame? ” Unfortunately, we could not justify a more

elaborate analysis based on the results of our calibration experiments, and we instead

took the most parsimonious interpretation of our data. Given the conservatism of our

analysis, the results presented here should be considered as useful lower bounds on the

concepts of interest, and not as definitive measurements.

4.1.4 GCaMP and the Slice Model of mPFC

A primary goal of our work was to establish calcium imaging in mPFC slices as a

useful model for studying the physical bases of cognitive functions. We were able to

consistently obtain healthy brain slices with widespread GCaMP expression just 4-7 days

after stereotaxic viral injection. The short latency from injection to good expression is

important, as physiologists generally find that slices cut from older animals tend to be of

poorer health. Although our data was obtained mostly from mice in the age range of 16-

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23 days old, by using the NMDG-based recovery ACSF step during slicing we obtained

viable slices from mice as old as 66 days. Future studies of the PFC in older animals using

the same protocol would therefore be of great interest, as the PFC is known to develop

on a longer time scale than most other brain regions (see Introduction) and it seems

likely that development might lead to changes in the local network activity.

We also characterized the performance of GCaMP at the level of individual

neurons under a wide range of spiking conditions. GCaMP6f has been reported to have

single-spike sensitivity, and we did find we were able to detect spikes in some neurons.

However, in general, the results of our experiments highlight that caution must be taken

in interpreting the fluorescence signal in terms of action potentials. We found

considerable variability from neuron to neuron in the relative magnitude and time

course of the calcium response to spiking. This likely reflects the heterogeneity of

calcium regulation between neurons: individual differences in the expression of calcium

ion channels, and/or the intracellular machinery for calcium sequestration are prime

candidates for explaining this finding. Fast-spiking interneurons, for example, are known

to have a muted calcium influx during action potentials, compared to pyramidal cells

(Kerlin, et al., 2010). Several subtypes of pyramidal cells have been identified in rodent

mPFC, and calcium currents have been implicated in the burstiness of some subtypes

(Yang Seamans and Gorelova, 1996; see Figure 1.4), so it is unreasonable to assume that

all pyramidal cells will have identical calcium dynamics during spiking.

Additionally, experimental considerations may partly account for this variability.

The level expression of the GECI itself might have a direct effect on the response and, at

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high levels, might indirectly affect the response by disrupting the normal calcium

regulation pathways. In pilot experiments (not included as data in this thesis) we found

that neurons generally appeared healthy 4-7 days after viral injection, and GCaMP

expression was restricted to cytosol (see Figures 2.2 and 2.3, for example), whereas

slices cut long (> 10 days) after injection often contained many neurons with very bright

baseline fluorescence and GCaMP penetrating into the nuclei. Similar findings have been

noted by other researchers (unpublished communications).

Despite the difficulty of interpreting the fluorescence signal in terms of spiking,

we were able to reproduce, and expand on, neuroscientific findings from other labs with

a conservative analysis that was agnostic to spiking per se. Although detailed

information about every action potential would obviously be preferable, we found that

significant insights can still be made without it. Therefore, a principal finding of this

thesis is that in vitro calcium imaging with GCaMP is a powerful technique for studying

the network activity of mPFC in and of itself.

Many variants on the experimental protocol described here could be used to

gain further information about neural networks in the mPFC. This work focused on

layers 5 and 6, but imaging the spontaneous dynamics of the more superficial layers

would be of equal interest. Likewise, repeating these experiments in disease-model

mouse strains, or with different neuropharmacological or optogenetic perturbations

would open up countless experiments of great academic and medical relevance. The

past decade has seen a rapid proliferation of transgenic mouse lines that express Cre

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recombinase under the control of various cell-type specific promoters. Using Cre-

dependent GCaMP vectors, one could interrogate the dynamics of, and interactions

between, neurons of a specific cell type. Essentially, our work represents the starting

point for a class of potential experiments studying the population dynamics of mPFC in

vitro using GECIs.

Furthermore, to our knowledge there are no reports of calcium imaging of deep-

layer mPFC in vivo. Calcium imaging in vivo has historically been limited to surface

structures at depths less than the scattering length (< 1 mm) of the infrared light used

for 2-photon microscopy. Anatomically, rodent mPFC is outside this range (see Figure

1.3). However, the depth of in vivo imaging can be expanded greatly by combining

gradient refractive index (GRIN) lenses with 2-photon microscopy (Murayama, et al.,

2007), and it seems likely that the mPFC will soon become a major subject of

population imaging studies in awake, behaving animals. The results presented in this

thesis will serve as an interesting point of comparison for these studies. In particular,

our characterization of the relationship between action potentials and GCaMP response

will be highly relevant, as it is extremely technically challenging to perform simultaneous

patch-clamp and imaging experiments in vivo in such a remote brain region.

4.2. Spontaneous Dynamics of the Deep-Layer mPFC Slice

A living brain is never completely silent. Activity within a given region will wax

and wane depending on many internal and external factors, but even during sleep some

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level of spontaneous activity is always observed (Steriade, Timofeev and Grenier, 2001).

Spontaneous activity appears in diverse regions and models, and it is implicated as

having a functional role in development (Xu, et al. 2011; Yamamoto and Lopez-Bendito,

2012), memory consolidation (Deuker, et al., 2013) and central pattern generation (Le

Bon-Jego and Yuste, 2007).

The diversity of spontaneous activity motifs in the intact neocortex reflects the

complex interactions of many players, particularly the thalamus, neuromodulatory

centers, and the cortex itself. The results of this thesis, however, were obtained in

coronal slices, where all thalamocortical and neuromodulatory afferents were severed.

On the other hand, local intracortical connectivity within neocortex consists of axons

and axon collaterals extending directly to the dendrites of nearby neurons. The

characteristic length of these connections varies depending on cell-type: interneurons

typically synapse primarily on neurons within a 100-200 um radius, for instance,

whereas pyramidal cells form longer range connections on to the scale of 1 mm

(Mountcastle, 1997). Connection probability between pyramidal neurons declines

strongly as a function of neural separation (Morishima and Kawaguchi, 2006; Voges, et

al. 2010), implying that neurons receive the majority of their synapses from local

partners. Therefore, the 300 um thick slice preparation preserves a substantial fraction

of the local network architecture. Consequently, the spontaneous activity we observed

must be attributed primarily to the intrinsic microcircuitry of the mPFC.

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Our results show that this circuitry is capable of supporting a wide range of

activity rates, spanning several orders of magnitude (0.03–23.4 subevents/min/neuron),

without any significant extracortical input. The lowest activity rates bordered on

complete silence, and the highest baseline activity exceeded the activity seen in other

slices under NMDA stimulation. In aggregate, our results showed that, in layers 5 and 6

mPFC, slices typically maintain a finite baseline of activity. It is notable that that this

activity was not equally distributed among neurons, but rather we consistently found a

minority of neurons that maintained activity for long stretches of time, and contributed

the vast majority of the overall activity rate. The highly skewed distribution of activity in

our data (Figure 3.5) comports with findings from other systems (Mizuseki and Buzsáki,

2013), although a direct comparison to that work is not possible because we are using

distinct metrics (subevent rate vs. firing rate). It is not clear, from this data, what caused

these neurons in particular to be so active. High activity could be attributed to intrinsic

properties, a high density/potency of excitatory synapses, or some combination of these

factors. Further characterization of these neurons is potentially of great interest, as they

may represent a particularly rich target for therapeutic intervention, given their

apparent outsized role in the network.

Many neurons, especially the highly active ones, exhibited a rhythmic tendency

in baseline ACSF, as evidenced by the predictability of their subevent sequences and, in

some cases, the peaks of their Fourier power spectra. The frequencies of these

oscillations spanned the entire delta band (0-4 Hz), but were mostly contained in the

range of ~0.5 – 2 Hz. Due to the time scale of GCaMP signals (Figure 3.2), sustained

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activity at higher frequencies would mostly be detected as long, continuous subevents

(Figure 2.1). Therefore, it is possible that rhythmic activity also occurred at higher

frequencies but was essentially filtered out of the analysis. The dearth of subevents with

durations longer than 1 second (Figure 3.6) argues against this possibility. It is likely,

however, that some of the rhythmic activity we observed reflected delta-band

membrane potential oscillations crowned by bouts of higher-frequency firing. We found

this to be the case in at least one patched neuron. This phenomenon has also been

observed in layer 5 pyramidal cells in acute slices of containing somatosensory cortex

(Carracedo et al., 2013), for example.

The prominence and consistency of low-delta rhythmic activity in our data

suggests that this is a fundamental property of deep-layer mPFC microcircuitry.

Disruption of thalamocortical connectivity has been associated with enhanced cortical

delta, implying that neocortex can act as an intrinsic delta generator (Amzica and

Steriade, 1998). These oscillations could be caused by intrinsic properties of the neurons

themselves, rhythmic input from elsewhere in the slice, or some combination of the

two. If the oscillating neurons were responding to some common extrinsic rhythm, they

would be predicted to exhibit strong coherence with low phase delay, as they would all

fire most strongly during the peak of that rhythm. The relative rarity of significant

coherence between neurons, the weakness of coherence between those neurons, the

wide distribution of phase delays and frequencies, and the relatively low correlations

between neurons all argue against the common-input explanation. Furthermore, the

observation that a minority of neurons (~15%) showed any oscillations, but oscillations

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were likely (68% probability) to recur in those neurons that did show one such epoch,

supports the proposition that internal properties are involved. Based on this data, and

the known electrophysiological properties of layer 5 pyramidal neurons, it seems more

plausible that the rhythmic activity we observed primarily reflects the intrinsic

properties of the neurons. Further studies are needed to fully characterize the

physiological mechanism(s) underlying these rhythms, and to elucidate their role in the

larger network.

Slow (< 1 Hz) rhythmic transitions between up- and down-states have been

observed in slices of ferret prefrontal cortex (Sanchez-Vives and McCormick, 2000).

These transitions are mediated by recurrent, balanced excitatory and inhibitory activity

within local cortical networks. We found no evidence of these transitions in mouse PFC.

Similarly, previous studies have reported that these transitions are rare or do not occur

in murine PFC slices (Seamans, 2003; Tahvildari, et al. 2012).

There is substantial evidence that prefrontal cortex mediates cognitive functions,

such as working memory, through recurrent activity within local cortical microcircuits

(Goldman-Rakic, 1995; Wang, et al. 2007). Mapping these circuits in detail is therefore

an important step towards bridging the conceptual gap between the collective activity

of neurons and the psychological notion of working memory. Unfortunately, the precise

strength of synaptic connectivity between neurons can only be directly measured by

performing multiple simultaneous whole-cell recordings and measuring the post-

synaptic responses of each neuron to spikes from other neurons in the network. This

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method is not practical for mapping large networks, as simultaneous recording of 3 or

more neurons become technically challenging, and the combinatorial explosion of the

number of pairings, multiplied by 2 due to the fact that each pair must be tested in both

directions, implies that a very large number of experiments must be conducted. A major

advantage of calcium imaging is that the activity of large numbers of neurons can be

read off simultaneously with single-cell resolution. Although the precise synaptic details

of each connection are lost, by examining the correlation between neurons, functional

connections can be inferred statistically and act as an ersatz map of the microcircuit

(Figure 3.9B). However, it is important to keep in mind the limitations of this approach.

First, it is not clear whether correlations in activity are due to direct synaptic

connections, correlated inputs, or some combination. Second, the direction and the sign

(excitatory or inhibitory) of the connections cannot be readily determined.

Nevertheless, this approach can reveal spatial information about the network that is

otherwise inaccessible.

The distribution of Jaccard correlations was dominated by very weak values, but

did contain a long tail of much stronger connections (Figure 3.9C). This finding roughly

agrees with the results of Song, et al. (2005), who found the distribution of strengths of

synaptic connection between neurons in layer 5 visual cortex to be highly skewed, with

a mass of very low strength connections giving way to a long tail of stronger

connections. Predominantly weak correlations with long tails have also been observed

in other neural systems, including hippocampus (Buzsáki and Mizuseki, 2014) and

salamander retina (Schneidman, et al. 2006).

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Similarly, under our definition of functional connectivity (p < 0.01), relatively few

(~2-3%) of pairs could be considered more correlated than chance. Connection

probabilities among pyramidal cells in neocortex have are generally reported to be

around 0.1 within 100 μm separation (Morishima and Kawaguchi, 2006; Wang et al.

2006) and fall to around 0.01 by 200 μm, in good agreement with our results (Figure

3.9D). We looked for isotropy in the microcircuit by examining the connection

probability as a function of orientation (Figure 3.9E). We found a preference for

connectivity among neurons oriented in the medial-later axis over connections in the

dorsal-ventral axis (~0.06 vs. ~0.025). Within the geometry of mPFC slices, the medial-

lateral axis corresponds to the axis of a cortical column, suggesting that layer 5 and/or 6

neurons connect more commonly within their column than with their horizontal

neighbors.

We complemented our correlation analysis by measuring synchronous events.

Whereas correlation measures the overall connection between pairs of neurons,

synchronous events captured instantaneous co-activity between 2 or more neurons. The

finding that synchronous events fall within or below the confidence interval obtained

from surrogate data would seem to suggest that the synchrony we observed was mostly

or entirely coincidental. However, when synchronous events are dissected by the

number of participant neurons, the notion of pure coincidence starts to recede. 3-

participant events were consistently on the high-end of the 99% confidence interval,

and 4+ events met or exceeded this interval in most slices. These results suggest that

pure coincidence cannot entirely explain the data; there must be at least some

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modicum of network activity involved. With this in mind, the finding that 2-particpant

events often underperformed coincidence is explainable: our surrogate data shifts each

trace independently and breaks up high-synchrony events, sending their constituent

subevents to random times, where they are much more likely to synchronize with one

other subevent than 2 or 3+. In other words, conservation of the total number of

subevents from each neuron in our surrogate data combined with the presence of high-

synchrony events biases towards an overabundance of low-synchrony events.

Nevertheless, the surrogate data still explains most of the observed synchrony,

implying that much of the synchrony we observed may be coincidental. Since the 4+

events show the strongest evidence of neuronal coupling, we consider them as a proxy

for the network component of the observed synchrony, and the 2 and 3-participant

events as the coincidence component. This view is supported by the distribution of

mean spatial separations between participants in high-synchrony events, which falls

entirely in the range of 30-150 μm (Figure 3.10E), in excellent agreement with the

finding in this thesis and elsewhere that functional connectivity is largely contained to

this spatial scale. In contrast, low-synchrony events show a much broader distribution,

lending credence to the notion that they may be largely coincidental.

The acute, submerged brain slice preparation generally exhibits dramatically

reduced activity when compared to the corresponding tissue in the intact brain (Hájos

and Mody, 2009). This is sensible considering the traumatic nature of the slicing

procedure, the severing of excitatory input from distant sources, the loss of natural

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energy supplies from vasculature, and the removal of physiological neuromodulatory

tone. Despite these headwinds, we found slices always exhibited finite activity, which in

some slices was quite brisk. This observation alone demonstrates that the mPFC

microcircuitry has some capacity for intrinsic activity built into it. Even prior to

application of any drugs, we were able to reproduce many basic observations about

neocortex (the heterogeneity of activity and the spatial distribution of connectivity, for

example), and make original observations regarding its rhythmic tendencies and

network activity from calcium imaging alone. Therefore, a principal result of this thesis is

to recommend the acute mPFC slice, combined with expression of a GECI, as a powerful

model system for mapping prefrontal cortical microcircuitry and testing hypotheses

concerning the physical mechanisms underlying higher mental functions.

4.3 NMDA and the mPFC

Having established a methodological and analytical program for probing network

dynamics in the mPFC, we sought a means of promoting the network out of its “ground

state” into a more excited state. NMDA was an ideal candidate for several reasons. First,

NMDARs are highly expressed in mPFC and strongly implicated in cognitive function and

dysfunction (see Introduction), and so their activation is of particular relevance. Second,

NMDA has been demonstrated to promote network activity in striatal microcircuits in

slice preparations (Carrillo-Reid, et al. 2008), and so it seemed reasonable that it might

have a similar action in mPFC. Third, as a pharmacological agent, NMDA could easily be

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applied to the slice in a highly controlled manner that did not interfere with imaging or

electrophysiological recording. One factor that complicates the interpretation of these

experiments, however, is that NMDA acts very broadly on the nervous system. NMDA

receptors are expressed in all cortical neural subtypes and in at least some astrocytes

(Lalo, et al., 2005), and it is impossible to determine to what extent the activation that

we observed can be ascribed to the direct actions of the drug on a given neuron’s

receptors, as opposed to the indirect effects caused by activating many of the

neighboring cells.

The basic finding that NMDA promotes activity (Figure 3.13) is not surprising;

NMDA is well-known for its excitatory effects on . However, the finding that it

consistently promoted activity in our slices, at all concentrations tested, still serves as a

useful “sanity check” before proceeding to more interesting analysis. Given NMDARs’

putative role in persistent activity, it seemed plausible that agonization might induce

prolonged spike trains that would manifest in our analysis as long-duration subevents.

However, the data did not cleanly support this hypothesis. We saw no difference at 3

μM, a modest increase at 8 μM, and a pronounced decrease at 12+ μM concentrations.

This effect might be potentially explained as some complicated interaction between

NMDA’s effects on pyramidal cells and interneurons (i.e. high doses of NMDA might

sensitize interneurons, leading to more rapid recruitment of inhibition in response to

pyramidal cell firing) but this is completely speculative. Further research is needed to

understand this result. Similarly, we expected that NMDA would promote higher

frequency firing and therefore brighter subevents. We found this effect at low and high

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doses, but not at 8 μM. Again, this mixed result does not offer a straightforward

interpretation, and calls for further investigation.

One of the most consistent and unambiguous results of our NMDA experiments

was the promotion of rhythmic activity. Wash-in of NMDA was strongly associated with

neurons spending more time engaged in delta-band rhythmic activity (Figure 3.10).

Although NMDA exerted mixed effects on the frequency-bands of individual neurons,

the overall effect of NMDA seemed to push the slices slightly towards lower-band delta.

We also found a modest increase in the strength of coherence, particularly in the < 1 Hz

range, accompanied by a slight tendency towards shorter phase delays. This may reflect

NMDA strengthening the synaptic coupling between oscillating neurons, leading to

increased synchronization.

These results are particularly interesting in light of the report of Carracedo, et al.

(2013) of an intrinsic delta-generating system in layer 5 somatosensory cortex.

Specifically, they found that delta rhythms were generated in acute slices by recurrent

networks of intrinsic-bursting pyramidal cells interacting via NMDAR-dependent

excitation. Our data matches this result well, as we observed a putative class of layer 5

pyramidal neurons that exhibit delta-frequency rhythmic tendencies and NMDA-

sensitivity. Further work is needed to definitively make this connection, but our results

seem to extend this finding from somatosensory cortex to mPFC.

Given the prominent role of NMDARs in excitatory neurotransmission, we

expected NMDA to increase the correlation between neurons, and this is precisely what

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the data showed (Figure 3.11). NMDA increased both the magnitude of Jaccard indices,

and the number of functionally connected neurons. Interestingly, a relatively small

fraction of the functionally connected pairs (~13 %) in baseline ACSF were also identified

under NMDA. This suggests that NMDA activation is not simply reinforcing and

elaborating on the pre-existing functional topography, but may be activating

connections that were dormant under baseline conditions.

The results of our NMDA synchrony analysis strengthen the basic analysis from

the baseline condition. NMDA application resulted in a substantial increase in synchrony

that was particularly acute for high-synchrony events. The consistency and extent by

which high-synchrony events exceed their surrogate predictions constitutes strong

evidence of network influence, and conforms well to the prediction that NMDA should

strengthen network activity in mPFC.

These NMDA experiments were highly successful both technically and

scientifically. From a technical standpoint, we were able to demonstrate the power of

the experimental and analytical methods described in this thesis. Within a highly

simplified formalism, and without explicit reference to any electrophysiological analysis,

many established effects of NMDA on neural networks were recovered. Additionally, we

were able to uncover a novel facet of prefrontal cortex: a population of neurons

exhibiting spontaneous, NMDA-sensitive delta-frequency activity.

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4.4 Picrotoxin and PFC

We used picrotoxin to antagonize GABAA receptors and quantified the resulting

effects on mPFC networks. These effects were generally more difficult to interpret than

those of NMDA. Reduction or removal of inhibition might be expected to have a net

excitatory effect, but we did not observe any significant increase in subevent rates after

applying picrotoxin, suggesting that the observed baseline activity levels were not

strongly restrained by GABAA-mediated inhibition. Inhibition is critical for the

termination of neural activity, as excitatory firing recruits local GABAergic interneurons

(feedback inhibition). Therefore, we might expect to observe more sustained duration

subevents under picrotoxin. We did find a statistically significant increase in duration

under picrotoxin, but the effect size was modest (U3 = 0.67) and the median duration

was only marginally longer (350 ms, vs. 300 ms in the baseline). In general, the effects of

picrotoxin on spontaneous activity at the level of individual neurons were modest or

non-existent within the sensitivity of our analytical framework.

The prevalence of rhythmic activity was unaffected by picrotoxin, implying that

GABAA receptors were not critical to the generation of delta-frequency oscillations. We

did find a consistent reduction in frequency however, suggesting that interneurons may

be involved in tuning the frequency of oscillations. We also observed a modest increase

in coherence between neurons, particularly at near-zero phase lag, suggesting that

disinhibition functionally increased the coupling between oscillatory neurons at low

frequencies.

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The correlating or decorrelating role of inhibition in neocortex is controversial

(Sippy and Yuste, 2013). Our finding that disinhibition generally led to an increase in the

number of functionally connected pairs, but a decrease in the magnitude of correlation

could suggest that inhibition can play both roles simultaneously. However, more work is

needed to clarify this result.

4.5 Summary and Future Directions

The fundamental goal of this thesis was to help uncover the physical principles

that mediate the remarkable capabilities of the prefrontal cortex. While it has long been

appreciated that the brain is a physical system, its complexity is such that the principles

governing it have been revealed only slowly, as technology enables researchers to

explore it at different scales. In this context, the major contributions of this thesis are

twofold: first the advancement of image processing methods and an analytical

framework for measuring the activity of many neurons simultaneously through

epifluorescence calcium imaging, and second the application of these methods to the

PFC in particular, where we characterized its spontaneous dynamics and used relevant

pharmacological agents to tune the synaptic coupling between neurons.

The methodology established in this thesis is very general, straightforward, and

as we showed through its applications, powerful. Exploiting a basic property of most

calcium indicators, the tendency to brighten in response to increased calcium

concentration, we developed a semi-automated method for approximating neuronal

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morphology. This boosted the signal-to-noise ratio relative to results obtained from

manual segmentation while allowing for slight translational motion, and also set up an

image processing method for rejecting false positives. We then used these methods to

obtain a simplified readout of the population dynamics of tens of neurons with temporal

resolution on the order of hundreds of milliseconds, and micron-scale spatial resolution.

We then applied this analytical toolbox to deep-layer prefrontal cortex, where

the local interactions between neurons within recurrent networks are known to

mediate information processing (decision making) and short-term storage (working

memory). Using a slice model, we characterized basic properties of the calcium indicator

GCaMP6f. We then examined the spontaneous activity of the mPFC in terms of

fundamental concepts such as diversity, isotropy, rhythmicity, coherence, correlation

and synchrony. We used NMDA to strengthen the excitatory coupling between neurons,

or picrotoxin to weaken inhibition, and quantified how those concepts were affected by

these perturbations. Comparing our results to the extant literature on mPFC and

neocortex in general, we confirmed that our simplified representation of cortical activity

captured many known properties of our model system. Finally, we achieved a novel

neuroscientific discovery: the existence of a population of neurons in layer 5/6 mPFC

that appears to act as an intrinsic delta-generating microcircuit, with strong sensitivity

to NMDA-agonization and a possible modulatory role from GABAergic inhibition.

Given the diverse topics covered in this thesis, there are many avenues for

further research. Our analytical framework was highly successful in achieving the goals

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of this thesis, but it could likely be developed further in terms of the image

segmentation algorithm, the event validation algorithm, and especially the final neural

readout. By binarizing our data, information about the detailed neural dynamics was

destroyed. If a highly reliable method of inferring the detailed spiking from GCaMP6f

can be established, a great deal more information would be available. Alternatively, a

number of genetically-encoded voltage indicators have recently been published (Jin, et

al. 2012; Hochbaum, et al. 2013), with purported millisecond-scale time resolution. The

methods described in this thesis should apply equally to these fluorophores, and

combining these techniques could enable the unambiguous readout of every action

potential from large populations of neurons with maximized SNR, obviating the

event/subevent formalism presented here.

Our major neuroscientific claim, intrinsic delta generation in deep-layer the

mPFC, demands further research. We consistently observed delta-frequency subevents

occurring spontaneously under baseline conditions, and especially after application of

NMDA. However, we could not definitively state whether these neurons are intrinsic

bursting pyramidal cells, as would be predicted by analogy to the circuit described by

Carracedo et al. in somatosensory cortex. Furthermore, although we characterized

these cells’ local interactions in terms of coherence, correlation and synchrony, their

interactions with the larger network (other cortical layers and distant regions) are

unknown. Detailed electrophysiological and optogenetic experiments may lead to

further insights on the origins of prefrontal delta-waves and the possible mechanisms

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through which they play a role in sleep, memory and cognition. These cells may also

present a powerful target for therapeutic interventions.

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