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Optically Imaging Forgetting in the Hippocampus
by
Adam I. Ramsaran
A thesis submitted in conformity with the requirements for the degree of Master of Arts
Department of Psychology University of Toronto
© Copyright by Adam I. Ramsaran (2016)
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Optically Imaging Forgetting in the Hippocampus
Adam I. Ramsaran
Master of Arts
Department of Psychology
University of Toronto
2016
Abstract
Ongoing neurogenesis in the hippocampus has been demonstrated to have a causal role in
forgetting. It remains unclear how this forgetting is represented in the brain at the level of
neuronal ensembles. This thesis attempts to answer this question by utilizing in vivo calcium
imaging to record activity from hundreds of neurons in subfield CA1 while mice form and
retrieve a contextual fear memory. We trained mice in a contextual fear conditioning task and
tested memory expression before and after enhancing neurogenesis with voluntary exercise, or
control conditions. Our preliminary findings indicate that memory formation is associated with a
reduction in activity within CA1 and increased correlated activity among CA1 neurons.
Enhancing neurogenesis after learning disrupted correlated activity during memory retrieval.
Thus, decreased correlated activity in CA1 is concomitant with behavioral forgetting, suggesting
that perturbed correlated activity resulting from circuit reorganization by hippocampal
neurogenesis may underlie forgetting.
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Acknowledgments
The thesis would not have been possible without the mentorship and support from numerous
individuals over the past year.
First, I would like to thank Paul Frankland for giving me the opportunity to conduct research that
is truly exciting within a lab environment that manages to be both intellectually rigorous and
overwhelmingly positive. These qualities along with Paul’s ongoing support are a motivating
force, and I look forward to seeing what the next few years in the lab has in store.
Second, I am immensely grateful for the mentorship I received from my collaborators on this
project, Mazen Kheirbek and Jessica Jiménez. Mazen and Jessica were kind enough to take on
this research collaboration within my first months of joining the Frankland lab (i.e., when I had
no idea what I was doing) and walked me through every stage of performing calcium imaging
experiments. Both Mazen and Jessica continue to be invaluable resources. The data presented in
this thesis is the result of our first calcium imaging experiment.
I would also like to thank the remaining members of my thesis committee, Sheena Josselyn and
Iva Zovkic, for their encouragement and constructive critique of this work; the members of Team
Chendoscope, Chen Yan, Alex Jacob, and Valentina Mercaldo, for always being available to talk
about mini-microscopes and data analysis methods; Moriam Ahmed, for her assistance with
immunohistochemistry for this study; and finally, the entire Josselyn/Frankland lab for the many
enjoyable scientific and non-scientific discussions over the past year.
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Table of Contents
Acknowledgments........................................................................................................................... ii
Table of Contents ........................................................................................................................... iv
List of Tables ................................................................................................................................ vii
List of Figures .............................................................................................................................. viii
Chapter 1 General Introduction .......................................................................................................1
Chapter 2 Literature Review ............................................................................................................3
2.1 Memory and the hippocampus .............................................................................................3
2.1.1 Multiple memory systems ........................................................................................3
2.1.2 Functional anatomy of the hippocampus .................................................................5
2.2 Adult hippocampal neurogenesis .......................................................................................11
2.2.1 Proliferation and integration of adult-born neurons ...............................................11
2.2.2 Enhanced excitability and neuronal competition ...................................................12
2.2.3 Adult-born neurons in memory processing............................................................13
2.3 Forgetting: Psychological and neurobiological perspectives .............................................16
2.3.1 Theories of forgetting ............................................................................................16
2.3.2 Infantile amnesia ....................................................................................................17
2.3.3 Neurobiological basis of forgetting .......................................................................18
2.4 In vivo calcium imaging .....................................................................................................20
2.4.1 Calcium indicators and imaging preparations ........................................................20
2.4.2 Current implementations in memory research .......................................................22
Chapter 3 Objectives and Hypotheses ...........................................................................................24
3.1 Objectives ..........................................................................................................................24
3.2 Hypotheses .........................................................................................................................24
3.2.1 Behavior .................................................................................................................24
3.2.2 CA1 calcium activity .............................................................................................24
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Chapter 4 Methods .........................................................................................................................26
4.1 Mice and stereotaxic surgery .............................................................................................26
4.1.1 Mice .......................................................................................................................26
4.1.2 Virus injections and GRIN lens implantation ........................................................26
4.1.3 Baseplate attachment .............................................................................................27
4.2 Behavioral testing ..............................................................................................................28
4.2.1 Apparatus ...............................................................................................................28
4.2.2 Contextual fear conditioning..................................................................................28
4.3 Neurogenesis manipulation ................................................................................................29
4.4 Histology ............................................................................................................................29
4.4.1 Tissue preparation ..................................................................................................29
4.4.2 Immunohistochemistry and quantification ............................................................29
4.5 In vivo calcium imaging ....................................................................................................30
4.5.1 Hardware, software, and data acquisition ..............................................................30
4.5.2 Pre-processing ........................................................................................................30
4.5.3 Post-processing ......................................................................................................31
4.6 Statistical analysis ..............................................................................................................34
Chapter 5 Results ...........................................................................................................................35
5.1 Post-encoding neurogenesis promotes forgetting of contextual fear memory...................35
5.2 Post-encoding neurogenesis does not alter general activity in CA1 measured using
calcium events ....................................................................................................................36
5.3 Post-encoding neurogenesis disrupts correlated activity within CA1 neuronal
populations .........................................................................................................................38
Chapter 6 Discussion and Conclusions ..........................................................................................44
6.1 Results summary ................................................................................................................44
6.2 Functional calcium activity patterns during memory formation and expression ...............45
6.2.1 Neural correlates of memory in the hippocampus .................................................45
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6.2.2 Limitations of current calcium imaging approach .................................................49
6.3 Future directions ................................................................................................................49
6.3.1 Further defining circuit mechanisms of forgetting with cell-type specific
imaging ..................................................................................................................49
6.3.2 Restoring forgotten memories and memory-related neuronal activity ..................50
6.4 Conclusions ........................................................................................................................50
References ......................................................................................................................................52
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List of Tables
Table 1. Design of different contexts used for contextual fear conditioning. ...............................26
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List of Figures
Figure 1. Multiple memory systems. ..............................................................................................4
Figure 2. Hippocampal system and trisynaptic circuit. ...................................................................6
Figure 3. Post-encoding neurogenesis promotes forgetting. .........................................................16
Figure 4. Miniature microscope for in vivo calcium imaging in freely behaving mice. ...............23
Figure 5. Neuronal populations and calcium traces extracted from calcium imaging videos. ......32
Figure 6. Increased hippocampal neurogenesis in mice given running wheel access for 28 days.
........................................................................................................................................................35
Figure 7. Increased hippocampal neurogenesis causes forgetting of contextual fear memory. .....37
Figure 8. Increased hippocampal neurogenesis does not alter the rate of calcium events or
amount of neurons activated in CA1. ...........................................................................................39
Figure 9. Increased hippocampal neurogenesis disrupts correlated activity in CA1 neuronal
populations. ...................................................................................................................................41
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Chapter 1 General Introduction
Attempt to recall in detail today’s events beginning from the moment when you woke up this
morning. Remembering what time you woke up, what you ate for breakfast, who you spoke to,
etc. is probably an easy task considering these events occurred just hours ago. Now try to recall
similar events from a week, month, or year ago. This task is probably much more difficult, if not
impossible for the longest time delay, and even if you can remember events from months or
years ago those memories are unlikely to be vivid. Our most recent, commonplace memories are
retrieved easily and in great detail, however this becomes less likely for memories as time
progresses.
It is no surprise then that most memories acquired throughout the lifespan are forgotten. This
makes sense considering the constant deluge of episodic information that could hypothetically be
encoded and stored to memory, most of which (like what you ate for breakfast specifically on
this day) has little to no utility. Although it is unknown how much information (e.g., in bytes) is
associated with a particular memory or what the human brain’s true memory capacity is (Marr
once estimated the hippocampus’ daily capacity to be 105 events, approximately equal to the
number of seconds in a day (Willshaw, Dayan, & Morris, 2015)), the number of neurons or
synapses that potentially can serve as physical substrates of memories (Josselyn, Kohler, &
Frankland, 2015), while incredibly large, are not infinite. Therefore, forgetting as a cognitive
process and neurobiological mechanism likely exists to prevent “overcrowding” of mental and
physical (neural) space.
Why and how forgetting occurs remain fundamental questions in psychology, neuroscience, and
intersecting fields (Wixted, 2004). While the former question has more predominately been
addressed by human neuropsychology research (Wimber, Alink, Charest, Kriegeskorte, &
Anderson, 2015), the more recent application of genetic and cellular techniques in behavioral
neuroscience has renewed interest in the latter and led to predictions on how forgetting could
occur at the circuit and cellular level (Frankland, Kohler, & Josselyn, 2013; Josselyn &
Frankland, 2012). Notably, our lab recently identified hippocampal neurogenesis, the
proliferation and integration of new dentate gyrus granule cells into hippocampal circuitry, as a
mechanism that regulates forgetting throughout the lifespan (Akers et al., 2014). Using a
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combination of environmental, pharmacological, and genetic approaches, this study
demonstrated a causal link between post-learning neurogenesis and forgetting in various
mammalian species. While these results provide compelling support for the neurogenic
hypothesis of forgetting (Frankland et al., 2013), it remains unclear how neurogenesis promotes
forgetting in the brain at the level of neuronal ensembles in structures like the hippocampus.
The current thesis begins to address this issue by employing optical imaging technology to
record large-scale neuronal activity in vivo in behaving mice in an attempt to understand how
dynamic neuronal activity in the hippocampus relates to memory persistence versus forgetting.
The thesis is organized primarily in two sections. First, a review of literature relevant to the
current thesis is given in Chapter 2. This includes literature related to the neurobiology of
memory (with an emphasis on the mnemonic functions of the hippocampus), forgetting, adult
hippocampal neurogenesis, and calcium imaging techniques. Following the literature review, the
thesis research is covered in Chapters 3, 4, and 5 (objectives and hypotheses, methods, and
results, respectively). Finally, in Chapter 6, the implications of this ongoing research are
discussed in the context of the neurogenic hypothesis of forgetting and the broader biological
basis of memory and memory persistence. Future directions including further studies and
ongoing technique advancements are briefly discussed.
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Chapter 2 Literature Review
2.1 Memory and the hippocampus
2.1.1 Multiple memory systems
Decades of neuropsychological research has upheld that memory is not a singular function of the
mind (Squire, 2004). Rather, learning can give rise to classes of memory that are mediated by
different memory systems rooted within the biology of the central nervous system (Fig. 1). The
earliest evidence supporting the notion of multiple memory systems came from Brenda Milner’s
pioneering studies of Henry Molaison (H.M.), a patient that underwent bilateral resection of his
medial temporal lobe (MTL) system—including the anterior two-thirds of his hippocampi—in
order to cure his intractable epilepsy. Removal of H.M.’s MTL produced profound memory
impairments including temporally-graded retrograde amnesia and complete anterograde amnesia.
However, despite his inability to remember the episodic details of individual testing sessions,
H.M.’s capacity for short-term memory, motor-skill learning (famously tested by the mirror-
drawing task), and perceptual memory remained intact (Milner, 1962; Scoville & Milner, 1957).
Most importantly, Milner’s studies demonstrated that cognitive and procedural memory are
separate phenomena in the mind and brain, and suggested that the neural substrate of the former
resided in the MTL.
Since Milner’s seminal work with H.M., countless studies have supported the role of the MTL
and particularly the hippocampus in mediating declarative memory. Research on animal learning
has strongly implicated the hippocampus in cognitive functions including contextual learning
and discrimination (Frankland, Cestari, Filipkowski, McDonald, & Silva, 1998; J. J. Kim &
Fanselow, 1992; Rudy, 2009; S. H. Wang, Teixeira, Wheeler, & Frankland, 2009; but see
Wiltgen, Sanders, Anagnostaras, Sage, & Fanselow, 2006), spatial memory and navigation (E. I.
Moser, Kropff, & Moser, 2008; M. B. Moser, Rowland, & Moser, 2015; O'Keefe & Dostrovsky,
1971), recognition memory (Cohen et al., 2013; Martinez, Villar, Ballarini, & Viola, 2014),
temporal order memory (Hoge & Kesner, 2007; Kesner, Gilbert, & Barua, 2002), memory for
configural associations (e.g., negative patterning tasks) (McKenzie et al., 2014; McKenzie et al.,
2015; Rudy, 2009; Rudy & Sutherland, 1989, 1995), and others. The role of the hippocampal
subregions in select memory tasks will be discussed in detail in section 2.1.2. Importantly,
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behavioral paradigms testing these abilities in animals are considered “episodic-like” because
they model components of human episodic memory (“what-where-when” memory) that require
hippocampal function. Thus, contextual fear conditioning, spatial navigation in the water maze,
and other behavioral paradigms commonly used to assess hippocampus-dependent memory in
rodents and other species can provide insight into how the human hippocampus functions to
support declarative memory.
While the multiple memory systems model has provided insight to the relative contributions of
brain structures to different forms of learning and memory, it should be noted that work over the
past decade has challenged the classical notion that particular brain regions are necessary
components of memory systems. That is to say that plasticity within brain memory systems
allows alternate circuits to compensate to support memory when there is loss of function within
the primary circuit. Evidence for this stems from studies of conditioned fear in rodents, for which
the neural circuitry responsible for memory and behavior generation are well-known (Fanselow
& Poulos, 2005). For example, although populations of neurons in lateral nucleus of the
amygdala (LA) are normally necessary for encoding and expressing conditioned fear (Berndt et
al., 2016; Han et al., 2009; J. Kim, Kwon, Kim, Josselyn, & Han, 2014; Rashid et al., 2016; Yiu
et al., 2014), in the absence of LA function conditioned fear can be acquired via an alternate
circuit in which the central nucleus of the amygdala or bed nucleus of the stria terminalis is the
critical substrate (Ponnusamy, Poulos, & Fanselow, 2007; Poulos, Ponnusamy, Dong, &
Fanselow, 2010; Zimmerman & Maren, 2011; Zimmerman, Rabinak, McLachlan, & Maren,
2007). A similar phenomenon is observed in contextual fear conditioning where prefrontal
Figure 1. Multiple memory systems. Long-term memory systems and associated
mammalian brain structures thought to be important for each form of learning. Adapted from
Squire, 2004.
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(prelimbic and infralimbic) cortices compensate for loss of hippocampal function prior to
learning (Wiltgen et al., 2006; Zelikowsky et al., 2013).
Notably, learning by these alternate circuits is less efficient (e.g., prolonged training is required
for successful encoding and memories decay quickly) indicating that the nature of memories
within alternate circuits may be different. However, these results highlight the need for a
reappraisal of the multiple memory systems model in which alternate memory circuits are
considered within the context of the damaged or diseased brain. Nonetheless, in the intact brain,
the hippocampus’ role in mediating declarative memory has stood the test of time, and more
complex analytical techniques now allow researchers to understand the nuances of how the
hippocampus and related brain structures encode and represent information across time
(McKenzie et al., 2014; McKenzie et al., 2015).
2.1.2 Functional anatomy of the hippocampus
2.1.2.1 Overview of the hippocampal system
This section will examine the anatomy of the hippocampus within the larger hippocampal system
and briefly mention how information processing is thought to occur within this network of
structures. The following sections will focus on the three of the major subdivisions of the
hippocampus—the dentate gyrus (DG), CA3, and CA1—and discuss how these structures
contribute to encoding, storage, and retrieval of spatial and contextual memories.
The anatomy and physiology of the hippocampal system are critical to its function in memory.
The hippocampus is the central structure in the so-called hippocampal system—a grouping of
neocortical and limbic structures that are largely responsible for processing episodic and
episodic-like memories (Fig. 2A). The hippocampal formation, which can further be divided into
hippocampus proper (subfields CA1-3), DG, and subiculum, is highly interconnected with
structures in the parahippocampal region, especially the lateral and medial entorhinal cortices
(van Strien, Cappaert, & Witter, 2009). Axons of entorhinal neurons project to all divisions of
hippocampus proper, but densely innervate the DG via the perforant path. The perforant path
together with two other important projections between hippocampal subfields—the mossy fiber
pathway (DG-CA3) and Schaffer collateral pathway (CA3-CA1)—form the trisynaptic circuit
(Fig. 2B), which has long been considered a hub for Hebbian plasticity and learning (Bliss &
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Lomo, 1973; Stepan, Dine, & Eder, 2015). Information flow through this circuit is mostly
unidirectional, with CA1 projecting back to the parahippocampal region directly or via the
subiculum.
a
b
Figure 2. Hippocampal system and trisynaptic circuit. (A) The hippocampus (blue) and
associated parahippocampal structures (green) form the hippocampal system. Information
from cortical regions is received by the hippocampus via entorhinal cortex inputs, and is sent
out of the hippocampus through projections to the entorhinal cortex. (B) Most information
flow in the within the hippocampus follows the trisynaptic circuit. Perforant path (EC-DG),
mossy fibers (DG-CA3), and Schaffer collaterals (CA3-CA1) are the three critical pathways
within the circuit. CA2 is omitted for simplicity. (DG) Dentate gyrus; (LEC) Lateral
entorhinal cortex; (MEC) Medial entorhinal cortex; (MF) Mossy fibers; (PER) Perirhinal
cortex; (POR) Postrhinal cortex; (PP) Perforant path; (PRS) Presubiculum; (SC) Shaffer
collaterals; (SUB) Subiculum.
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While this description of hippocampal connectivity may seem simple, a high degree of
heterogeneity exists within the hippocampal network. For example, the trisynaptic circuit can be
segregated along the proximodistal axis into two parallel circuits that functionally differ with
respect to their roles in supporting spatial memory (Nakazawa, Pevzner, Tanaka, & Wiltgen,
2016). Even greater heterogeneity exists along the dorsoventral axis of the hippocampus,
including differences in dorsal and ventral hippocampus’ ability to control learned- and anxiety-
related behavior (Kheirbek et al., 2013; Kheirbek, Klemenhagen, Sahay, & Hen, 2012).
Importantly, there is a stark contrast between inputs and outputs of the dorsal and ventral
hippocampus, and these differences in connectivity likely underlie their different roles in
modulating behavior (Tannenholz, Jimenez, & Kheirbek, 2014). The remainder of this thesis will
focus on mnemonic processes mediated by the dorsal hippocampus, which is more
conventionally studied in learning and memory fields.
2.1.2.2 Dentate gyrus
As previously noted, the DG received input from the entorhinal cortex in the form of perforant
path fibers. These axons synapse on granule cells, the round, glutamatergic principal cells in the
DG. Accordingly, granule cells are found within the principal, or granule cell layer, with granule
cell dendrites occupy the molecular layer of the DG (Amaral, Scharfman, & Lavenex, 2007).
Other cell types exist in the DG, including mossy cells in the polymorphic layer, or hilus, and
basket cells (Amaral et al., 2007). Immature (adult-born) granule cells in the inner granule cell
layer can also be considered a unique cell type of the DG, however these will be discussed at
length in section 2.2.2.
A key feature of the DG is the sheer number of granule cells within the structure. The rat dentate
gyrus contains approximately 1.2 million granule cells (Amaral et al., 2007); about four times
more cells than its output, CA3 (Treves & Rolls, 1994). This ratio is consistent across species,
and has clear implications for information processing by the DG.
Functionally, the DG is thought to perform a computation known as pattern separation. Pattern
separation is the process by which similar overlapping input representations are transformed into
orthogonal output representations (Treves & Rolls, 1994; Willshaw et al., 2015). In the context
of the trisynaptic circuit, incoming activation from the entorhinal cortex activates sparse
populations of DG granule cells which effectively separates similarly patterned signals. In turn,
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representations in CA3 exhibit less overlap because incoming information has been “sparsified”
within the DG. Presumably, this computation allows for efficient coding of similar memories in
CA3 so that potentially ambiguous retrieval cues do not elicit inappropriate recall (i.e., recall of
the wrong memory or multiple memories). Convincing evidence for pattern separation in the DG
has only emerged within the past decade, with the strongest evidence coming from
electrophysiological recordings demonstrating drastically dissimilar DG representations for
similar stimuli (Leutgeb, Leutgeb, Moser, & Moser, 2007; Neunuebel & Knierim, 2014).
Moreover, the behavioral analogue of pattern separation, pattern or context discrimination,
requires the NMDA receptor-mediated plasticity in DG granule cells (Kheirbek, Tannenholz, &
Hen, 2012; McHugh et al., 2007), implicating the DG in not only biological, but also behavioral
correlates of pattern separation. Lastly, adult-born granule cells in the DG heavily contribute to
pattern separation and discrimination (Kheirbek, Klemenhagen, et al., 2012; Nakashiba et al.,
2012; Niibori et al., 2012; Sahay et al., 2011)—these studies will be discussed in greater detail in
section 2.2.3.
In addition to its role in pattern separation, the DG has been demonstrated to be an important hub
for mediating encoding and retrieval of hippocampal memories. Contemporary studies
examining the nature of memory storage in the brain have converged on the notion that engrams
(enduring physical substrates of memory) are distributed throughout the brain (Cowansage et al.,
2014; Josselyn et al., 2015; Wheeler et al., 2013). However, within these distributed networks,
certain regions, like the DG, disproportionately contribute to memory functions. With respect to
encoding, granule cells in the DG are allocated to memory traces based on their relative intrinsic
excitability, which is influenced both by cell age (Kee, Teixeira, Wang, & Frankland, 2007) and
expression of the transcription factor CREB (cyclic adenosine monophosphate response element-
binding protein) (Park et al., 2016). Furthermore, this neuronal allocation is constrained by
somatostatin+ interneurons in the DG (Stefanelli, Bertollini, Luscher, Muller, & Mendez, 2016).
Recent studies employing optogenetics and chemogenetics to manipulate DG engrams have
shown that ensembles of granule cells active during encoding are necessary and sufficient for
contextual memory retrieval (Denny et al., 2014; Liu et al., 2012; Ramirez et al., 2013; Roy et
al., 2016; Ryan, Roy, Pignatelli, Arons, & Tonegawa, 2015; Stefanelli et al., 2016). Collectively,
these data indicate that the DG exhibits powerful control over hippocampal memories and
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suggests a larger role for the DG in mobilizing the larger, distributed engram during memory
retrieval.
2.1.2.3 CA3
Subfield CA3 is the second subregion along the trisynaptic circuit. Unlike the DG, principal cells
in CA3 are glutamatergic pyramidal neurons, of which there are nearly 300,000 in the rat (Treves
& Rolls, 1994). CA3 pyramidal neurons receive input from mossy fibers originating in the DG.
Each mossy fiber forms approximately 15 synapses on dendrites of CA3 pyramidal neurons with
each CA3 neuron receiving up to 50 synapses from upstream granule cells (Amaral et al., 2007;
Treves & Rolls, 1994). The most interesting feature of CA3, though, is its network of recurrent
collateral connections. Each CA3 pyramidal neuron forms approximately 12,000 synapses with
incoming collateral axons originating from the same region (Treves & Rolls, 1994). This
architectural property of CA3 is closely linked to its purported role in memory, which is
discussed below.
Anatomical organization of the CA3, chiefly its recurrent collateral network, has implicated it in
pattern completion computations. Pattern completion, the converse of pattern separation, is a
process by which degraded or incomplete input representations can elicit complete activation of
stored output representations. CA3 as an autoassociative network is an ideal substrate for this
process. Pattern completion and therefore memory retrieval by CA3 and downstream structures
is hypothesized to occur by rapid modification of synaptic weights over time within the recurrent
network so that the final activity state represents the encoded memory (Neunuebel & Knierim,
2014; Treves & Rolls, 1994). Remarkably, this occurs in spite of degraded input from the DG
(Neunuebel & Knierim, 2014), suggesting that CA3 likely is the site of encoding and retrieval in
the hippocampus. It is important to not understate the importance of pattern separation in the
DG, though; these processes are critically intertwined such that a disruption in pattern separation,
for example by genetic perturbation of neurogenesis in the DG, impairs pattern completion and
memory reactivation in CA3 (Niibori et al., 2012). These findings and others support a critical
role for CA3 in encoding and retrieval of hippocampal memories (Denny et al., 2014).
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2.1.2.4 CA1
CA1, the third segment of the trisynaptic circuit, shares similar characteristics with CA3. The
principal cells in this region are glutamatergic pyramidal neurons. CA1 receives few inputs from
other regions; the two primary CA1 afferents are Schaffer collateral fibers from CA3 and direct
projections from entorhinal cortex layer 3 (Amaral et al., 2007; van Strien et al., 2009). The
primary outputs of CA1 are the subiculum and entorhinal cortex layers 5 and 6, which then
project to other brain regions. In addition, a novel monosynaptic projection between CA1 (and
CA3) and the medial prefrontal cortex in the mouse was recently identified (Rajasethupathy et
al., 2015).
Probably the most recognized physiological property of CA1 neurons is their preferential firing
within precise locations within an environment (i.e., place fields). For their spatially-tuned firing
patterns, these neurons are known as place cells (O'Keefe & Dostrovsky, 1971). Because place
cell receptive fields are spatial locations, they are thought to be critical to spatial navigation and
spatial memory (Colgin, 2016; Langston et al., 2010; O'Keefe & Dostrovsky, 1971). However,
only recently has a causal role for place cell activity and spatial memory been demonstrated (de
Lavilleon, Lacroix, Rondi-Reig, & Benchenane, 2015). In this study, de Lavilleon and colleagues
identified place cells within an open field and then cleverly paired medial forebrain bundle
(MFB) stimulation with replayed place cell firing during sleep, which allowed a dissociation
between place cell firing and the current location of the animal. When replaced into the open
field, mice preferred to spend more time in the place field paired with MFB stimulation (de
Lavilleon et al., 2015). Memory for contexts requires CA1 neurons (presumably, place cells), as
optogenetic activation or silencing of CA1 neuronal ensembles facilitates or prevents memory
expression, respectively (Nakazawa et al., 2016; Ryan et al., 2015; Tanaka et al., 2014).
While the current thesis focuses on contextual memory, it should be noted that information
coding in CA1 (and other regions of the hippocampus) is not explicitly spatial. Recent work has
shown that the hippocampus is responsive to many different features of stimuli, including item
identity, context, location, valence, etc., and usually, CA1 cell firing exhibits high dimensionality
with many cells responding to conjunctions of these dimensions (McKenzie et al., 2014).
Interestingly, hierarchical organization of memory task dimensions shows that context and
location are represented more prominently than dimensions such as valence, supporting the
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notion that the hippocampus is essential in encoding spatial information (McKenzie et al., 2014;
McKenzie et al., 2015).
2.2 Adult hippocampal neurogenesis
2.2.1 Proliferation and integration of adult-born neurons
Neurogenesis, the birth and integration of neurons into functional circuits, is common throughout
early life brain development. This is no surprise since neural circuits and systems are still being
established during this period. Interestingly, neurogenesis does indeed continue in select regions
of the mammalian brain during adulthood. This discovery, first reported by Joseph Altman in
1963 (Altman, 1963), was initially met with skepticism because it challenged the dogma of the
era that the production of new neurons ceased prior to adulthood (Altman, 2011). Decades later,
however, neurogenesis in the adult brain is recognized as a unique form of plasticity in the adult
brain and widely studied in neuroscience fields.
Neurogenesis in the adult mammalian brain occurs in two canonical sites: the subventricular
zone (SVZ) of the lateral ventricles and the subgranular zone (SGZ) of the hippocampus. New
neurons originating from neural progenitor cells (NPCs) in the SVZ migrate along the rostral
migratory stream and incorporate into the olfactory bulb as interneurons, whereas cells
originating in the SGZ travel a shorter distance to the inner granule cell layer of the DG to
integrate into hippocampal circuits (Christian, Song, & Ming, 2014; Zhao, Deng, & Gage, 2008).
The majority of adult-born cells in the DG differentiate into principal granule cells, and reach
maturity approximately 6-8 weeks post-mitosis in the rodent brain (Christian et al., 2014; Zhao,
Teng, Summers, Ming, & Gage, 2006). Despite this, adult-born granule cells become functional
within hippocampal circuits before reaching full maturity. The developmental trajectory of adult-
born granule cells is described below.
Morphological development of adult-born granule cells precedes their functional integration into
hippocampal circuits. Axons from immature granule cells extend toward CA3 and enter the
subfield approximately 1.5 weeks after birth (Zhao et al., 2006), and around the same dendrites
reach the molecular layer of the DG (Christian et al., 2014; Zhao et al., 2006). Dendritic size and
complexity increases over the next few days with spinogenesis also beginning around 2.5 weeks
of age (Zhao et al., 2006). A few days later, afferents from local mature granule cells (which
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provide transient input to immature granule cells during their earliest stages of development)
diminish and are replaced by inputs from perforant path fibers (Vivar et al., 2012), and mossy
fibers contact CA3 neuron dendrites and hilar interneurons (Restivo, Niibori, Mercaldo,
Josselyn, & Frankland, 2015). After establishing these input and output connections, immature
granule cells are functionally integrated into hippocampal circuits at 3-4 weeks of age (van Praag
et al., 2002). Interestingly, the developmental trajectory of adult-born granule cells is similar to
that of postnatally-born granule cells (Restivo et al., 2015; Zhao et al., 2006), which suggests a
degree of functional homogeneity between the two neuronal populations (Stone et al., 2011).
2.2.2 Enhanced excitability and neuronal competition
The important changes in adult-born neuron connectivity described above make 4-week-old
granule cells more excitable than their mature counterparts (Christian et al., 2014). Specifically,
these younger cells have a lower activation threshold than their older counterparts, biasing them
toward low input specificity and a highly-responsive state (Marin-Burgin, Mongiat, Pardi, &
Schinder, 2012). The increased excitability of adult-born neurons and constant addition of these
cells to the DG produces neuronal competition between new and existing granule cells for
synaptic inputs and outputs. As immature granule cell dendrites and axons form synapses
respectively in the entorhinal cortex and CA3, it is likely that they will share or replace synaptic
connections with mature granule cells, rather than be “out-competed” by these older neurons.
Previous work has shown that in the preweanling rodent brain, axons from immature granule
cells inactivate adjacent mature granule cell axons on the postsynaptic cell, causing the older
axons to retract (Yasuda et al., 2011). In contrast, in the adult brain, although silencing of mature
granule cells after “losing” their synapses to immature granule cells is observed, there is less
evidence for elimination of the mature axons (Lopez et al., 2012). With the addition of 9,000
new neurons each day to the rat DG (i.e., >1% of the total granule cell population added each
week) (Cameron & McKay, 2001), there is constantly competition between newborn neurons
and their neighboring cells for survival and therefore a high rate of synaptic turnover in the
hippocampus. Clearly, adult neurogenesis continuously remodels existing hippocampal circuits
throughout the lifetime. What requires further clarification is what the functional consequences
of this circuit reorganization are, especially with regard to behavior and cognition.
13
2.2.3 Adult-born neurons in memory processing
2.2.3.1 Role in encoding and retrieval
As mentioned above, adult-born neurons undergo a transient stage of increased excitability
around 4 weeks of age (Christian et al., 2014; Marin-Burgin et al., 2012). Accordingly, this
period is also when immature granule cells make significant contributions to encoding and
retrieval of hippocampal memories. Four-week-old granule cells are preferentially recruited to
hippocampal memory traces relative to their older counterparts (Kee et al., 2007). Modulating
the excitability of random subsets of neurons in the DG (and other brain regions) by CREB-
overexpression also biases memory allocation (Han et al., 2009; J. Kim et al., 2014; Park et al.,
2016; Rashid et al., 2016; Yiu et al., 2014), suggesting that increased excitability of immature
granule cells is responsible for their preferred recruitment into memory traces. Indeed, in vivo
imaging of adult-born versus mature granule cells has revealed that younger neurons are more
active overall and respond with less specificity to changing contextual stimuli than mature
granule cells (Danielson et al., 2016). Like for memory encoding, the role of adult-generated
neurons in memory retrieval depends on their maturational stage. Genetic ablation of immature
neurons shortly after learning produces a deficit in memory retrieval (Arruda-Carvalho,
Sakaguchi, Akers, Josselyn, & Frankland, 2011), presumably because a significant subset of the
DG engram has been killed. Optogenetic inactivation of 4-week-old, but not 6- or 8-week-old,
granule cells also impairs memory retrieval (Danielson et al., 2016; Gu et al., 2012), which
further suggests that excitability of immature cells in the DG and their preferential recruitment to
memory wanes after 4 weeks. Taken together, these studies indicate that adult-born neurons, if
allocated to a memory trace during their period of heightened excitability, exhibit powerful
control over hippocampal memories.
2.2.3.2 Anterograde manipulation
Due to adult-born neurons’ notable role in encoding and retrieving memories, much research has
focused on determining how memories are affected by different rates of adult hippocampal
neurogenesis. Historically, studies of this type have used anterograde manipulations. In other
words, these studies employ manipulations that increase or decrease adult hippocampal
neurogenesis prior to learning, and later observe the effect of these manipulations on memory
retrieval. Many methods are commonly used to manipulate neurogenesis. Notably, in the SGZ,
14
cell proliferation and survival are influenced by factors including voluntary exercise (Akers et
al., 2014; van Praag et al., 2002), antidepressants (Akers et al., 2014), stress (Denny et al., 2014),
x-ray irradiation (Denny et al., 2014), environmental enrichment (Bergami et al., 2015), etc.
Additionally, transgenic mouse lines have been developed in which the rate of neurogenesis is
constitutively or conditionally regulated (Arruda-Carvalho et al., 2014; Burghardt, Park, Hen, &
Fenton, 2012; Imayoshi et al., 2008; Sahay et al., 2011).
The importance of ongoing adult neurogenesis in learning and memory is demonstrated by
numerous studies. Genetic ablation or x-ray irradiation of new neurons in the DG causes deficits
in contextual fear conditioning, spatial discrimination, trace conditioning, and other
hippocampus-dependent memory tasks (Arruda-Carvalho et al., 2014; Burghardt et al., 2012;
Denny et al., 2014; Niibori et al., 2012; Shors et al., 2001). Reduction in adult hippocampal
neurogenesis also likely underlies memory deficits resulting from chronic stress (Denny et al.,
2014). It should be noted that these deficits are not universal since stronger training can
overcome some of these memory impairments (Denny et al., 2014). Therefore, a reduction in
hippocampal neurogenesis impairs some, but not all hippocampal learning. Likewise, studies
have reported mixed results on whether increasing neurogenesis prior to learning facilitates
memory (Akers et al., 2014; Epp, Silva Mera, Kohler, Josselyn, & Frankland, 2016; Frankland et
al., 2013). More research is needed to determine what variables (e.g., behavioral task,
maturational stage of adult-born neurons, etc.) contribute to the facilitation of memory by
neurogenesis, and what memory processes (e.g., encoding, consolidation, retrieval) are improved
by adult-born neurons.
One process that neurogenesis has been shown to directly modulate is DG pattern separation. As
discussed previously, the DG is thought to perform pattern separation computations to support
efficient encoding of memories to non-overlapping neuronal ensembles (Deng, Aimone, & Gage,
2010; Kheirbek, Klemenhagen, et al., 2012). Increasing hippocampal neurogenesis is sufficient
for improving context discrimination (Sahay et al., 2011), a behavioral task that requires DG
pattern separation. Consistent with this finding, other studies have found that genetic reduction
of adult-born granule cells, but not mature granule cells, impairs context discrimination and
memory separation in CA3 (Nakashiba et al., 2012; Niibori et al., 2012).
15
Immature neurons have a critical role in orchestrating pattern separation for a few reasons. First,
newly generated granule cells undergo a transient period of heightened excitability which allows
them to be preferentially recruited to memory traces (Kee et al., 2007). Different cohorts of
adult-born neurons are constantly passing in and out of this maturational stage, meaning that two
learning experiences separated by a sufficient amount of time likely recruit different subsets of
immature neurons (Aimone, Wiles, & Gage, 2009; Deng et al., 2010). How then can pattern
separation be performed when the two experiences occur closely in time? Adult-born granule
cells also contribute to sparse coding by controlling excitation/inhibition balance within the DG.
Immature granule cells indirectly modulate excitability of mature granule cells via intermediary
hilar interneurons and mossy cells (Drew et al., 2016; Restivo et al., 2015). Increasing the rate of
adult hippocampal neurogenesis consequently leads to more inhibitory tone in the DG (Ikrar et
al., 2013), which represents at the population level more sparse activation of mature granule cells
during an experience (Drew et al., 2016). Thus, DG coding is made sparser by increasing
hippocampal neurogenesis, and this allows multiple experiences—even those occurring closely
in time—to be encoded by nonoverlapping neuronal ensembles.
2.2.3.3 Retrograde manipulation
Studies examining the effects of increased or decreased neurogenesis on memory almost
exclusively fall into the anterograde category. Until recently, it was unknown how the integration
of adult-born neurons into hippocampal circuits affected previously encoded memories. Our lab
has identified a causal role for hippocampal neurogenesis in regulating forgetting across
mammalian species (Akers et al., 2014). This study demonstrated for the first time that
enhancing neurogenesis after encoding impairs subsequent retrieval of hippocampal, but not
hippocampus-independent, memories (Fig. 3). Conversely, suppressing neurogenesis had the
opposite effect; memory retention in these cases was prolonged. These effects were observed
using a variety of behavioral tasks and in three different rodent species, suggesting that
neurogenesis’ role in regulating forgetting is conserved across mammalian species (but see
Kodali et al., 2016). In addition, in infant mice that exhibit rapid forgetting, suppressing
neurogenesis was able to mitigate infantile amnesia, thus providing a mechanism for this
phenomenon (Josselyn & Frankland, 2012). Notably, it was necessary to promote neurogenesis
for a period of 4 weeks to cause forgetting, which is consistent with the maturational stage of
newborn neurons integrating into the DG. This will be discussed further in section 2.3.3.2, in
16
which research examining the neurobiological basis of forgetting is reviewed. More recent
research from our lab has shown that neurogenesis-mediated forgetting is adaptive in that is
facilitates acquisition of new, conflicting information by reducing proactive interference from
previously encoded memories (Epp et al., 2016). An important question that remains is how the
integration of new neurons into hippocampal circuits produces forgetting at the level of cellular
memory traces. This question is key to this thesis, and will therefore be revisited in the following
sections on forgetting and in vivo imaging.
2.3 Forgetting: Psychological and neurobiological perspectives
2.3.1 Theories of forgetting
Plainly stated, forgetting is the inability to remember information that could be recalled at an
earlier time (Tulving, 1974). Although profound forgetting is associated with many disorders and
brain injury, this discussion will mostly be limited to natural forgetting. There is over a century’s
worth of psychological research examining why humans and non-human animals forget. Two
dominant accounts of forgetting emerged from this work; forgetting due to memory decay or
interference (Wixted, 2004). Decay versus interference studies differ experimentally in whether
extraneous information is presented to subjects—in experiments examining memory decay, time
Figure 3. Post-encoding neurogenesis promotes forgetting. (A) Retrograde (top) and
anterograde (bottom) manipulations of neurogenesis within a contextual fear conditioning
task. (B) Hypothetical data demonstrating that enhancing neurogenesis after encoding leads to
forgetting (top), while increasing neurogenesis prior to encoding has no discernable effect on
memory retrieval (bottom).
17
(i.e., retention interval) is the only manipulated variable, whereas in interference experiments
additional learning is given before (for proactive interference) or after (for retroactive
interference) the to-be-remembered information (Hardt, Nader, & Nadel, 2013; Wixted, 2004).
Decay and interference both contribute to forgetting in a time-dependent manner such that decay
increases with time and interference is effective within a short temporal window around the time
of learning the target information (Hardt et al., 2013; Wixted, 2004).
Another enduring question regarding forgetting is whether the inability to remember represents a
failure of memory storage or retrieval. In other words, does forgetting represent the inability to
properly maintain a memory within the brain, or are forgotten memories successfully stored but
just inaccessible? As alluded to, the question of storage or retrieval failure deals more with
neural processes, and therefore it will be revisited in section 2.3.3. It should be mentioned
however that this question has been of interest long before the advent of modern neuroscience
techniques that allow probing memory content within neural circuits. Findings from early
behavioral studies support the view that forgetting represents a failure in memory retrieval
(Smith & Spear, 1984), and now contemporary behavioral neuroscience are providing
complementary biological evidence that memory traces are not lost even when behavioral
forgetting is observed.
2.3.2 Infantile amnesia
One special case of forgetting, infantile forgetting (infantile amnesia) has garnered special
attention within the field of forgetting research (Josselyn & Frankland, 2012; Madsen & Kim,
2016). This is because infant forgetting is robust across altricial species and likely shares
common mechanisms with adult forgetting (Akers et al., 2014; Josselyn & Frankland, 2012;
Madsen & Kim, 2016). Thus, infant animals are appropriate model for studying forgetting, and
in some cases more advantageous since infant forgetting is rapid when compared to adult
forgetting (Akers et al., 2014; Robinson-Drummer & Stanton, 2015). In line with this logic, a
better understanding of the neurobiology of infantile amnesia can provide insight to how
forgetting occurs throughout the lifespan (Madsen & Kim, 2016).
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2.3.3 Neurobiological basis of forgetting
2.3.3.1 Breaking connections
If memory formation involves making connections within the brain (i.e., plasticity mechanisms),
it follows that forgetting should involve breaking connections. This idea has dominated
contemporary forgetting research. This section will primarily focus on the neurobiology of
forgetting in the hippocampus but also provide a few key insights from studies examining
forgetting of non-hippocampal memories. First, a superficial overview of plasticity underlying
memory formation is given, followed by current insights into how reversal of memory-related
synaptic modifications might underlie forgetting.
Formation of enduring memories in the hippocampus has been linked to long-term potentiation
(LTP), a physiological phenomenon triggered by learning that initiates a cascade of molecular
events thought to stabilize memories within hippocampal circuits (Bliss & Lomo, 1973; Kandel,
Dudai, & Mayford, 2014). Accordingly, successful memory formation is associated with
alterations at synapses that undergo LTP, such as increased spine density and growth, efficacy of
synaptic transmission, and AMPA receptor trafficking at synapses (Kandel et al., 2014; Nabavi
et al., 2014; Roy et al., 2016; Ryan et al., 2015). These changes within memory circuits are
thought to facilitate reactivation of memory traces during successful retrieval (Denny et al.,
2014; Josselyn et al., 2015; Kandel et al., 2014; Nakazawa et al., 2016; Ryan et al., 2015; Tayler,
Tanaka, Reijmers, & Wiltgen, 2013). In short, learning-induced plasticity effectively builds
connections within the hippocampus (and other brain regions) that can maintain memories over
time.
If memory formation involves making connections, then logically forgetting should involve
breaking them. Indeed, memory-associated dendritic spines in the hippocampus and motor cortex
have been demonstrated to be critical for memory retention (Hayashi-Takagi et al., 2015; Roy et
al., 2016). Specifically, optical shrinkage (Hayashi-Takagi et al., 2015) or pathological
degeneration (Roy et al., 2016) of learning-induced spines impairs subsequent memory retrieval.
Furthermore, just as LTP within neuronal ensembles is sufficient for memory formation, LTD in
the same cells abolishes memory (Nabavi et al., 2014), presumably by reversing morphological
and molecular synaptic plasticity. Are memories lost forever when the connections supporting
them are broken? Consensus is growing that the answer to this question is no. Numerous studies
19
have demonstrated recovery of memory by optogenetic and pharmacological manipulations in
instances of low or absent learning-induced synaptic plasticity and infantile amnesia (J. H. Kim,
McNally, & Richardson, 2006; Madsen & Kim, 2016; Roy et al., 2016; Ryan et al., 2015; Tang,
McNally, & Richardson, 2007). It is largely unknown how memories are retrieved in the absence
of synaptic modifications thought to be necessary for memory stability, but one suggestion is that
inherent cellular connectivity within memory circuits stores memories while plasticity
mechanisms occur to facilitate memory retrieval by natural retrieval cues (Ryan et al., 2015).
Nonetheless, forgotten memories, while unable to be recalled, likely remain successfully stored
in the brain.
2.3.3.2 Catastrophic interference by neurogenesis
An important question stemming from our neurogenesis research is how the integration of new
neurons into the hippocampus “breaks” connections to disrupt memory retrieval. As mentioned
earlier, our lab’s studies on neurogenesis and forgetting have shown that approximately 4 weeks
of enhanced neurogenesis is required to disrupt memory retrieval (Akers et al., 2014; Epp et al.,
2016). Importantly, this amount of time is congruent with the functional development of adult-
born neurons (Christian et al., 2014; Restivo et al., 2015; van Praag et al., 2002). That is to say
that adult-born granule cells do not impede memory until they have integrated into hippocampal
circuitry. Our current model of neurogenesis-induced forgetting is based around this idea; that
integration of new neurons into the DG after learning impairs memory by remodeling
hippocampal circuits, thereby disrupting later memory retrieval. This phenomenon, known as
catastrophic interference (Hardt et al., 2013), is a property of hippocampal neurogenesis
predicted by computational models. Two different models support the notion of adult-born
neurons disrupting previously encoded memories by showing that the probability of forgetting
learned patterns increases over time in the presence of ongoing neurogenesis (Meltzer, Yabaluri,
& Deisseroth, 2005; Weisz & Argibay, 2012). This may occur in vivo through immature granule
cells contributing interference or noise by nonspecific activation during memory retrieval
(Danielson et al., 2016), or by highly excitable immature granule cells silencing mature synapses
after they “win” the competition for synaptic connections (Lopez et al., 2012; Yasuda et al.,
2011). To determine how new neurons contribute to forgetting, methods to probe memory
content should be applied to our model of neurogenesis-induced forgetting. Ideally, real-time
monitoring of hippocampal circuitry during memory retrieval will allow comparison of
20
functional activity underlying successful and failed memory retrieval. The following section will
overview methods to accomplish this, specifically in vivo calcium imaging methods that are used
in this thesis.
2.4 In vivo calcium imaging
2.4.1 Calcium indicators and imaging preparations
Optical imaging techniques like calcium imaging are widely being adopted by researchers
interested in the relationship between brain and behavior, and for good reason. Calcium imaging
provides the unprecedented ability to monitor large populations of neurons over extended periods
of time (Hamel, Grewe, Parker, & Schnitzer, 2015). This is typically achieved by injection of a
calcium sensitive dye or virus containing the sequence for a genetically encoded calcium
indicator (GECI) into the brain region of interest. GECIs have gained popularity in studying the
mammalian brain because unlike dyes, expression of the indicator protein can be targeted to
specific cell types using viral-mediated expression (Chen, Kim, Peters, & Komiyama, 2015;
Danielson et al., 2016; Lovett-Barron et al., 2014). Calcium indicator molecules, like those in the
popular GCaMP6 class, dynamically change their fluorescence in response to calcium flux
across the cell membrane, which can be used as a proxy for neural activity (Hamel et al., 2015).
However, it is difficult to deconvolve recorded calcium transients to their underlying action
potentials due to the poor cooperativity (linearity of fluorescence in response to increasing action
potentials) of current GECIs (Badura, Sun, Giovannucci, Lynch, & Wang, 2014). Yet, calcium
imaging still offers a reliable means for monitoring neuronal activity, and despite the reduced
temporal precision optical imaging versus electrophysiological recordings.
The benefits of in vivo calcium imaging are clear when compared to other methods for imaging
activity in the brain, mainly quantification of IEG or transgene expression within brain circuits
and electrophysiological recordings. The comparisons here will be made within the context of
memory research, however these differences are not limited to one field. IEG and transgenic
approaches (usually exploiting IEG promoters, e.g., TetTag mouse (Reijmers, Perkins, Matsuo,
& Mayford, 2007)), allow for the identification of neurons active during an experience
(Guzowski, McNaughton, Barnes, & Worley, 1999; Tanaka et al., 2014; Tayler et al., 2013). To
visualize activity in the brain using these methods, animals are killed after memory encoding or
retrieval and their brain tissue is processed using immunohistochemistry to identify IEG-
21
expressing neurons or fluorophores expressed in response to IEG activation. While these
methods offer the ability to identify activated neurons within whole brain structures, it provides
no temporal resolution because animals must be killed during the peak of IEG expression.
Conversely, using electrophysiological recordings, researchers can obtain high-precision
temporal firing information from small groups of neurons in the intact brain (Leutgeb et al.,
2007; McKenzie et al., 2014; McKenzie et al., 2015; Neunuebel & Knierim, 2014), but it is not
usually feasible to identify spatially where these neurons are or track the same neurons over long
periods of time.
Calcium imaging offers a viable solution for large-scale in vivo imaging by combining aspects of
genetic labelling and electrophysiology methods. As mentioned previously, in vivo calcium
imaging allows recording of neuronal populations during awake behavior. While temporal
precision is low compared to electrophysiological recordings (Badura et al., 2014), the number of
neurons that can be recorded from the same animal is 2-3 orders of magnitude greater than
possible with multi-unit electrodes (Hamel et al., 2015). Therefore, neuronal dynamics within
microcircuit can easily be observed (Rajasethupathy et al., 2015) and even localized to distinct
classes of neurons (Chen et al., 2015; Danielson et al., 2016; Lovett-Barron et al., 2014).
Moreover, using the stability of virally-expressed GECIs permit experimenters to repeatedly
monitor the same population of neurons for months (Ziv et al., 2013), which provides the unique
opportunity to discover how activity within neural circuits transforms over time, for example
over the course of systems consolidation.
Two different calcium imaging preparations are compatible with awake behavior in the intact
rodent (in vivo calcium imaging is also used in non-human primate models, but will not be
considered here). First, calcium dynamics can be recorded during behavior using 2-photon
microscopy in head-fixed mice. Because mice must be fixed under the microscope objective
during imaging, the behavioral repertoire amenable with these experiments is somewhat limited,
and at times criticized for being unnatural and not ecologically relevant (Minderer, Harvey,
Donato, & Moser, 2016). Even so, virtual versions of common behavioral tasks such as
environment exploration (Danielson et al., 2016; Dombeck, Khabbaz, Collman, Adelman, &
Tank, 2007), fear (Lovett-Barron et al., 2014; Rajasethupathy et al., 2015) and eyeblink (Modi,
Dhawale, & Bhalla, 2014) conditioning, and motor learning (Chen et al., 2015; Peters, Chen, &
Komiyama, 2014) have been paired with 2-photon calcium imaging in cortex and hippocampus.
22
These studies benefit from the high spatial resolution afforded by 2-photon microscopes, and
enable visualization of calcium dynamics from neuronal processes (Chen et al., 2015; Lovett-
Barron et al., 2014).
Calcium imaging can also be performed in the freely-moving mouse using miniature
microscopes. These microscopes are light enough (approximately 2 g) to be mounted on the head
of a mouse and allow imaging of calcium dynamics from neuronal populations in superficial or
deep brain regions through implantable gradient refractive index (GRIN) lenses (K. K. Ghosh et
al., 2011; Hamel et al., 2015; Resendez et al., 2016) (Fig. 4). Because of size limitations, these
microscopes have low spatial resolution and therefore only allow imaging cell bodies (Resendez
et al., 2016). However, the main benefit of 1-photon epifluorescence imaging is its compatibility
with most behaviors routinely studied in laboratory settings including spatial exploration and
spatial discrimination (Cai et al., 2016; Kitamura et al., 2015; Rubin, Geva, Sheintuch, & Ziv,
2015; Ziv et al., 2013), consummatory behaviors (Jennings et al., 2015), and many others.
Miniature microscopes for in vivo calcium imaging are still a relatively new tool, and therefore
have yet to be implemented to study circuit activity involved in learned behaviors.
2.4.2 Current implementations in memory research
Few studies have recorded calcium dynamics associated with learned behavior, and even fewer
that have focused these efforts on the hippocampus. With respect to the use of miniature
microscopes (the method employed in this thesis), coding of different environments by
ensembles of CA1 neurons has been examined. Notably, one study has reported that place cell
activity can be monitored in dorsal CA1 for up to 35 days (and probably longer) (Ziv et al.,
2013), suggesting that calcium dynamics carry similar information compared to action potentials
in the hippocampus. One other main finding has emerged from similar hippocampal and
entorhinal imaging studies; that different environments are differentially coded by hippocampal
neurons (Cai et al., 2016; Kitamura et al., 2015) and generalize over time (Rubin et al., 2015),
but these findings have been shown previously using other methods (Denny et al., 2014; Tayler
et al., 2013). What is more interesting is how population activity is modified by learned
associations and behaviors, which is one aim in the current thesis. This question has been
addressed in two studies imaging principle hippocampal neurons during context fear or trace
eyeblink conditioning tasks (Modi et al., 2014; Rajasethupathy et al., 2015). Both studies
23
converge on the notion that learning is associated with an emergence of high correlated activity
among within a subset of neurons in CA1 (Modi et al., 2014) and CA3 (Rajasethupathy et al.,
2015) cell populations. This functional activity likely results from learning-induced plasticity,
and is congruent with the emergence of spatiotemporal activity patterns in motor cortex during
acquisition of a learned motor behavior (Chen et al., 2015; Peters et al., 2014). In the context of
the current thesis, it is unknown how these emergent activity patterns would be affected by
forgetting. This question is the primary motivation for this work.
GRIN lens
LED
CMOS
sensor
baseplate
microscope
a b
c
d
Figure 4. Miniature microscope for in vivo calcium imaging in freely behaving mice. (A)
Miniature microscopes (microscope used in current study shown) are compact and weight
approximately 2 g. (B) The microscope can be secured to a baseplate (outlined in white)
attached above the implanted GRIN lens on the mouse’s head. (C) Imaging of cell
populations is permitted through the GRIN lens implanted above the brain region of interest.
This allows simultaneous recording of calcium dynamics from large populations of cells in
the region of interest. (D) Video recordings (max intensity projection shown here) can be
processed to extract cells and corresponding calcium traces.
24
Chapter 3 Objectives and Hypotheses
3.1 Objectives
The current thesis is an extension of our previous research on neurogenesis-induced forgetting
(Akers et al., 2014; Epp et al., 2016). Here, we aimed to determine how alterations in calcium
activity recorded from hippocampal subfield CA1 relates to forgetting, based on the idea that
memory formation is associated with plasticity-driven alterations in hippocampal cell population
dynamics (Rajasethupathy et al., 2015) and therefore forgetting may represent a perturbation of
this activity (Frankland et al., 2013). To this end, we utilized in vivo calcium imaging with a
miniature microscope in combination with a mouse model of rapid forgetting mediated by
enhanced neurogenesis (Akers et al., 2014). This allowed monitoring of neuronal populations in
CA1 during memory formation, and successful or unsuccessful memory retrieval in a contextual
fear conditioning task. Ultimately, the goal of this ongoing research is to determine relationships
between neurogenesis, memory retention, and cellular activity patterns in the hippocampus.
Defining these relationships will provide insight into how memories in the brain are maintained
or forgotten over time, and how ongoing plasticity in the form of hippocampal neurogenesis
modulates this process.
3.2 Hypotheses
3.2.1 Behavior
Consistent with our model of forgetting regulated by hippocampal neurogenesis (Frankland et
al., 2013), we predicted that enhanced neurogenesis (quantified here by doublecortin+ immature
granule cells in the DG) after encoding a context fear memory would disrupt subsequent memory
retrieval. We have demonstrated this finding numerous times, across different hippocampal
memory tasks and rodent species (Akers et al., 2014; Epp et al., 2016).
3.2.2 CA1 calcium activity
We did not form explicit hypotheses regarding the calcium imaging data at the outset of the
experiment, primarily because there are no standard analyses for this type of data. Furthermore,
there are no published data describing functional calcium activity during unrestrained fear
25
conditioning, so definite hypotheses that indicated how CA1 activity would be affected by
forgetting could not be formulated.
With this in mind, we set criteria to define memory-related activity that would not be limited by
any particular analysis. Specifically, for any measure of calcium activity to be unambiguously
associated with memory formation and subsequent forgetting, the following criteria needed to be
met: 1) there should be an increase or decrease in the activity measure from context fear training
to testing, which would represent successful memory formation; 2) the activity measure should
be near baseline (training levels) during testing in a novel context where no fear memory was
formed; and most importantly, 3) the increase or decrease in the activity measure observed
during successful memory retrieval should persist in mice that have normal levels of
neurogenesis and memory retention (a dampening of the effect should be permitted over the long
time period of the experiment) but severely attenuated (toward baseline) in mice in which
neurogenesis is promoted and forgetting is observed. We adopted this approach for the additional
reason that it allowed us to determine which analyses were most appropriate for our data set. For
example, if an activity measure did not change between training and testing (as does freezing
behavior), it likely did not reflect memory encoding and therefore would neither reflect
forgetting. Finally, a relationship between the observed calcium activity patterns and behavioral
memory persistence would be strongly supported by a correlation between the activity measure
and performance in the behavioral task. The data in this these represent our efforts in defining
memory-related activity in the hippocampus to this point, and work with this data set is still
ongoing.
26
Chapter 4 Methods
4.1 Mice and stereotaxic surgery
4.1.1 Mice
Adult (at least 8 weeks of age) male C57Bl/6J mice obtained from Jackson Laboratories and
housed at the New York State Psychiatric Institute are were used in this study. Mice were
maintained on a 12 h light-dark cycle (lights on at 0600 h) and group-housed (3-5 mice per cage)
prior to surgical procedures. Following surgery, mice were individually housed with enrichment
domes unless otherwise specified. All mice had ad libitum access to food and water throughout
the duration of the study. Mice were assigned to one of two groups, Sedentary or Running, which
differed in housing conditions during the 28-day retention interval (see section 4.3). The
experiment was conducted in accordance with guidelines set by the US National Institutes of
Health and with the approval of the Institutional Animal Care and Use Committees at Columbia
University and New York State Psychiatric Institute. Surgical procedures and behavioral imaging
sessions (described below) were performed at the New York State Psychiatric Institute (New
York, NY, USA), and histology was performed at the Hospital for Sick Children (Toronto, ON,
Canada).
4.1.2 Virus injections and GRIN lens implantation
Mice received virus injections and microendosope implants at approximately 8-10 weeks of age.
For surgical procedures, mice were anesthetized with isofluorane (induced at 3.0%, maintained
with 1.0-1.5%), placed into a stereotaxic frame, and pre-treated with carpofen (5 mg/kg). The
skull was exposed and 4% lidocaine was applied directly on the incision site. Using a 2.3 mm
trephine, a craniotomy was performed above right dorsal CA1 (craniotomy centered at AP -2.15
mm, ML +1.30 mm from bregma), and the skull fragment and underlying dura were carefully
removed with surgical forceps. The edges of the craniotomy were aspirated using a 26-gauge
needle attached to a vacuum pump while the area was continuously irrigated with sterile saline.
A small amount of cortical tissue was removed during this process. Following the aspiration, a
glass micropipette containing AAV-DJ-CaMKIIα-GCaMP6f (Stanford University, CA) was
lowered into the hippocampus (AP -2.15 mm, ML +1.85 mm from bregma, DV -1.45, -1.65 from
skull at injection site), and the virus was injected into dorsal CA1 in 32 nl increments to a total of
27
approximately 250 nl per DV location (500 nl total per mouse). The micropipette tip was left in
the tissue for an additional 10 min after the last injection for adequate virus diffusion.
For lens implantation, the arm of the stereotaxic instrument was replaced with a clamp holding
the GRIN lens microendoscope (1.0-mm diameter, 4.0-mm length), which was positioned
perpendicularly to the surface of the skull. The GRIN lens was slowly lowered through the
cortical tissue and underlying fibers of the corpus callosum to its position above dorsal CA1 (AP
-2.15 mm, ML +1.30 mm from bregma, DV -1.30 mm from skull at site). To avoid widespread
tissue damage around the lens, the microendoscope was retracted +0.10 mm for every -0.20 mm
increment, which allowed the underlying tissue to properly settle around the lens as it was
lowered. Three anchor screws and dental cement were used to secure the microendoscope in its
location. Additional dental cement was used to build a protective bowl-like headcap around the
lens, which was then covered with a fast-drying silicone elastomer (Smooth-On, Inc., Macungie,
PA). The incision was closed by affixing loose skin to the cement heacap using Vetbond tissue
adhesive. Post-surgery, mice were treated with lidocaine on the incision site and placed in
individual cages with dome enrichment. In most cases, mice were active within 5 min of surgery.
Post-operative monitoring continued for three days.
4.1.3 Baseplate attachment
Mice were allowed to recover from surgery for at least 4 weeks before attaching the baseplate,
which interfaces with and supports the miniature microscope during behavioral imaging sessions
(Fig. 4). For this procedure, mice were anesthetized with isoflurane, placed into a stereotaxic
frame, and the silicone covering the GRIN lens was removed with forceps. The miniature
microscope (Inscopix, Palo Alto, CA) with the baseplate attached was held in an adjustable
gripper (Inscopix, Palo Alto, CA) and positioned above the implanted lens such that the objective
lens in the microscope and the implant were parallel and separated by 1-2 mm. The microscope’s
blue LED (475-nm wavelength) was powered using the nVista HD imaging software (Inscopix,
Palo Alto, CA) which allowed visualization of the tissue through the microendoscope. With the
LED powered, the microscope was lowered toward the top of the implanted lens by a motorized
micromanipulator (Scientifica, Uckfield, East Sussex) until a field of view containing cells
exhibiting dynamic cell fluorescence indicative of spontaneous neuronal activity appeared in
focus. If no such field of view of observed, for example, if no cell fluorescence or static cell
28
fluorescence was visualized, the microscope was removed, the GRIN lens was re-covered with
fast-drying silicone, and the baseplating procedure was attempted again 1-2 weeks later. Once a
desired field of view was identified, the baseplate was fixed to the existing headcap using dental
cement mixed with black carbon powder. The microscope was detached from the baseplate and a
baseplate cover (Inscopix, Palo Alto, CA) was screwed into place to protect the lens between
imaging sessions.
4.2 Behavioral testing
4.2.1 Apparatus
Contextual fear conditioning occurred in a chamber (20 cm × 20 cm) with two plexiglass walls
(north and south walls), two aluminum walls (east and west), and a shock-grid floor. The
chamber was placed inside a Med Associated box and arranged in two configurations to create
two distinct contexts, referred to as Context A and Context B, and described in Table 1.
4.2.2 Contextual fear conditioning
For contextual fear conditioning training, mice were placed into the chamber for 2 min, after
which three foot shocks (0.5 mA, 2 s duration, separated by 1 min) were delivered. Mice
remained in the chamber for an additional 1 min following the third shock. Mice were tested for
conditioned fear 1 and 29 days after training in Context A, and 30 days after training in Context
B. A subset of mice were not tested in Context B because this testing session was added after the
start of the study. Calcium activity was recorded from CA1 neurons during all contextual fear
sessions—methods for in vivo calcium imaging are described in a following section. Freezing
Table 1. Design of different contexts used for contextual fear conditioning.
29
behavior was recorded during all sessions using a webcam mounted above the chamber and
Ethovision XT software (Noldus). Freezing behavior (absence of all movement except
movement involved in respiration) was scored offline by an observer blind to the experimental
condition of each mouse.
4.3 Neurogenesis manipulation
We used running wheels (i.e., voluntary exercise) to increase neurogenesis and promote
forgetting selectively in the Running mice. Following the first testing session (1 day after
training), mice in the Running group were allowed access to a running wheel for 28 days.
Running wheels were removed prior to the second testing session (29 days after training) and
mice returned to standard housing conditions for the remainder of the experiment. Sedentary
mice remained in standard housing conditions for the duration of the experiment.
4.4 Histology
4.4.1 Tissue preparation
Mice were transcardially perfused with 0.1 M phosphate-buffered saline (PBS) followed by 4%
paraformaldehyde (PFA) solution. Brains were post-fixed in PFA within the skull for 48 h to
allow tissue fixation around the GRIN lens. Brains were then removed and transferred to 30%
sucrose and stored at 4C until sectioning. Sections (50 µm) were taken with a cryostat along the
entire anterior-posterior axis of the dentate gyrus using a 1/4 sampling fraction to create four sets
of sections at 200 µm intervals. Sections were kept free-floating in a cryoprotectant solution
(20% glycerol, 30% ethylene glycol in PBS) at -20C until further processing.
4.4.2 Immunohistochemistry and quantification
For doublecortin (DCX) labeling, sections were incubated for 48 h in goat anti-doublecortin
primary antibody (Santa Cruz) diluted 1:600 in 4% normal donkey serum and 0.5% Triton-X.
Sections were then incubated in Alexa488 donkey anti-goat secondary antibody diluted 1:300 in
PBS for 2 hr. Sections were counterstained with DAPI (1:10000) and coverslipped with
PermaFluor mounting medium (Thermo Scientific).
DCX+ cells were counted using an epifluorescent microscope under a 40× objective. Cells were
counted along the entire anterior-posterior axis of the dentate gyrus sample, and cell counts were
30
multiplied by 4 to approximate the total number of immature cells in the full dentate gyrus
volume.
4.5 In vivo calcium imaging
4.5.1 Hardware, software, and data acquisition
We used an integrated miniature fluorescence microscope (Inscopix, Palo Alto, CA) to record
GCaMP6f signals from CA1 neurons during contextual fear conditions sessions. The miniature
microscope has been described previously (K. K. Ghosh et al., 2011). In brief, the light-weight
microscope (approximately 1.9 g) can be mounted on the baseplate attached to the mouse’s head
to allow for cellular-level imaging through the implanted microendoscope. A blue LED emits
475-nm wavelength light that is directed by a dichroic mirror down into the tissue sample to
excite the GCaMP6f-expressing neurons. Fluorescence from the sample returns through the
GRIN lens, passes through an emission filter, and is focused on a CMOS camera that records the
fluorescence signal. To record the imaging data, the microscope was wired to a data acquisition
(DAQ) box which interfaced with a computer running the nVista HD software. Microscope
settings such as LED power, gain, etc. could be adjusted using the nVista HD software.
In the current study, mice were briefly anesthetized with isofluorane (less than 2 min) during
microscope attachment and allowed 30 min in the home cage to acclimate to the weight of the
microscope and ensure clearance of isofluorane from the brain. Fear conditioning training or
testing sessions were conducted after this period. All recordings were acquired at 15 fps with an
LED power and image sensor gain set between 20-80% and 1.0-4.0, respectively. Additionally,
behavioral and imaging recordings were synchronized using a Noldus I/O box, which allowed
imaging recordings by nVista HD to be triggered on at the onset of behavioral recordings by
Ethovision XT.
4.5.2 Pre-processing
Pre-processing (referring to steps involved in transforming raw imaging videos to data sets
composed of calcium signals emitted from individual neurons) was performed using Mosaic
software (Inscopix, Palo Alto, CA). All videos per mouse (typically 4 videos) were spatially
downsized using a spatial binning factor of 4 to reduce processing time. Spatial resolution is
reduced during this step; however, biological structures (blood vessels, neurons, etc.) are still
31
clearly visible (Resendez et al., 2016). Videos were then motion-corrected to reduce the
likelihood of motion artifacts during calcium transient extraction and then subsequently cropped
to maintain an identical field of view for all recordings per mouse. We normalized videos to
represent change in fluorescence over baseline (DF/F) using a minimum z-projection image for
the entire video (computed for each pixel across time) as baseline fluorescence. These DF/F
videos were concatenated and temporally binned to obtain a single video per mouse including all
sessions at 5 fps (200 ms bins).
An automated cell-sorting algorithm that employs principal component analysis followed by
independent component analysis (Mukamel, Nimmerjahn, & Schnitzer, 2009) was applied to the
concatenated DF/F videos to isolate calcium signals from putative neurons within the field of
view (Fig. 5B). An observer visually inspected the extracted signals to determine whether the
calcium activity traces and spatial units accurately represented known dynamics of GCaMP6f
calcium transients from pyramidal cell bodies (Badura et al., 2014). Atypical independent
components (spatial units and their paired activity traces) were discarded. The remaining spatial
units were further processed by applying an adaptive threshold which removed background
noise. Additionally, activity traces were processed to isolate individual calcium events. This
involved performing a non-negative deconvolution step on each trace to identify calcium
transients with amplitudes exceeding 6 median absolute deviations. Calcium transients exceeding
this threshold were considered calcium events. Calcium events were placed at the peak time for
the corresponding calcium transient, and all other time bins were zeroed (Fig. 5C). Calcium
event data were used in all analyses presented in this thesis.
4.5.3 Post-processing
4.5.3.1 General activity measures
CA1 calcium activity was quantified by session for each mouse with two different measures.
First, we calculated the rate of calcium events (in seconds) per cell for each 300 s session
(typically 4 sessions) and averaged the calcium event rates of all cells for each mouse.
32
Second, we calculated the proportion of neurons in the imaged cell population that was “active”
during each session for each mouse. A neuron was considered to be active if it displayed 10 or
more calcium events during a session. The number of neurons considered as active during the
session was then divided by the size of the imaged cell population for the corresponding mouse
to obtain a percentage. The same results were obtained when varying the minimum activity
threshold between 1-20 calcium events (data not shown).
a b
c
Figure 5. Neuronal populations and calcium traces extracted from calcium imaging
videos. (A) Representative brain section showing GCaMP6f expression in CA1 and GRIN
lens tract above the same region. (B) Examples of spatial units (randomly colored) extracted
from calcium imaging videos representing putative neurons expressing GCaMP6f. In total we
imaged calcium activity from 1866 neurons in 13 mice. (C) All calcium transient traces (left)
and corresponding deconvolved calcium event traces (right) for one mouse during two
different fear conditioning sessions. Traces are colored to corresponding to their associated
spatial unit shown in panel B.
33
4.5.3.2 Correlated activity
Correlated activity within the CA1 cell populations was analyzed using custom-written
MATLAB scripts and based on an analysis performed in a recent 2-photon imaging study
(Rajasethupathy et al., 2015). First, event amplitude was removed from calcium event traces so
that all data sets were binary (0 = no calcium event, 1 = calcium event). Cells that did not exhibit
at least 1 calcium event in each session were removed so the same cell pairs were represented in
the analysis for each session. Then, all traces were binned into 1 s epochs by summing the
number of calcium events across adjacent sets of 5 bins. The resultant ntime × pcell matrices (one
per session per mouse) contained the number of calcium events per 1 s bin for each cell within
the imaged population. We computed an autocorrelation for each matrix which gave the
Pearson’s correlation coefficient for each pair of cells’ calcium event traces within the imaged
population for each session (see Fig. 9A).
Two measures were obtained from these data. The mean correlation was obtained by averaging
the Pearson’s correlation coefficients for all unique cell pairs for each session. This measure
represented the overall linear dependence of all neurons within the population at different time
points in the experiment (i.e., different sessions). A similar measure referred to as “correlated
pairs” was adapted from a recent study (Rajasethupathy et al., 2015). This was done by finding
the number of cell pairs during each session with a Pearson’s correlation exceeding 0.3 (referred
to as a correlated pair) and then determining the average number of correlated pairs per cell
during each session. Correlated pairs have a high connection probability in vivo (Ko et al., 2011;
Rajasethupathy et al., 2015), therefore change in this measure over time represents functional
connectivity within the cell population. Because the number of correlated pairs is linearly related
to the size of the imaged cell population (data not shown), we normalized all mean correlation
and correlated pairs values using the values obtained on Day 1 as baseline (i.e., normalized
values were percent change from baseline). Mice with 0 correlated pairs on Day 1 were excluded
from the analysis (Sedentary n = 1, Running n = 1). We also analyzed the proportion of
correlated pairs that were “reactivated” between test sessions. To do this, we calculated the
proportion of cell pairs that were considered correlated pairs during test sessions on Day 2 and
Day 29, and multiplied these values to obtain the proportion of correlated pairs that would persist
from Day 2 to Day 29 by chance. We then found the actual proportion of correlated pairs that
34
persisted from Day 2 to Day 29 and compared these values to chance to determine whether
correlated pairs persisted over time.
4.6 Statistical analysis
Behavioral freezing and doublecortin+ cell count data were analyzed using paired and
independent-samples t-tests based on our a priori hypotheses. We analyzed imaging data using
nonparametric Wilcoxon matched pairs tests (for within-group comparisons) and Mann-Whitney
U tests (for between-group comparisons). To reduce the number of comparisons performed and
because there were unequal sample sizes between days, we did only the tests that were consistent
with the criteria for imaging data described in 3.2.2. These included between-group comparisons
on each day to observe differences between treatment groups (Sedentary versus Running)
(criteria 3), within-group comparisons (by treatment) of measures on Day 1 and subsequent
testing days to determine how the measures changed across each session (criteria 1), and within-
group comparisons (by treatment) between measures on Day 30 and Day 31 to observe the
context-specificity of the activity measures (criteria 2).
35
Chapter 5 Results
5.1 Post-encoding neurogenesis promotes forgetting of contextual fear memory
Mice in both treatment groups were trained in contextual fear conditioning (Day 1) and tested for
conditioned freezing behavior on three subsequent occasions (Days 2, 30, and 31) (Fig. 7A).
Importantly, running wheel access was provided to mice in the Running group between the first
and second testing session on Days 2 and 30. Voluntary exercise promoted the rate of
neurogenesis in Running mice such that they had more doublecortin+ immature neurons across
the total volume of the dentate gyrus when compared to Sedentary mice (t10 = 2.86, P < 0.05)
(Fig. 6).
Freezing behavior was assessed during contextual fear conditioning (5-min training protocol)
and retrieval of contextual fear memory during three 5-min test sessions (Fig. 7A). The amount
of freezing behavior did not differ between Sedentary and Running groups during training in
context A on Day 1 (t11 = 0.11, P > 0.10) (Fig. 7B) or during the first testing session in context A
on Day 2 (t11 = 0.95, P > 0.10) (Fig. 7C). Thus, memory retention did not differ between
treatment groups prior to the neurogenesis manipulation. Mice were tested twice more for
memory retention, 28-days later (during which running wheel access was given to Running
Figure 6. Increased hippocampal neurogenesis in mice given running wheel access for 28
days. (A) Representative images of the dorsal DG showing doublecortin+ immature neurons.
(B) Mice with running wheel access for 28 days showed more doublecortin+ neurons in the
DG than mice that remained sedentary during the same period (Sedentary n = 5, Running n =
7; t-test). *P < 0.05. Data represent mean + SEM.
36
mice) on Day 30 in context A and on Day 31 in context B. On Day 30 in context A, mice in the
Running group exhibited lower conditioned freezing compared to mice in the Sedentary group
(t11 = -2.20, P < 0.05), and spent less time freezing relative to Day 2 (t6 = 2.45, P < 0.05) (Fig.
7C). In contrast, freezing observed in Sedentary mice in context A did not differ across days (t5 =
-1.20, P > 0.10). We calculated the change in freezing across context A testing sessions as the
difference between freezing levels on Day 2 and Day 30 and found that this measure was
reduced (i.e., less freezing on Day 30 compared to Day 2) in Running mice compared to
Sedentary mice (t11 = -2.28, P < 0.05) (Fig. 7D). When tested in novel context B, freezing
behavior did not differ between Sedentary and Running groups (t6 = 0.14, P > 0.10) (Fig. 7C).
As expected, promoting neurogenesis after encoding the contextual fear memory led to
forgetting. This was further supported by a strong negative linear relationship between the
number of doublecortin+ cells counted per mouse and the time spend freezing on Day 30 (R210 =
0.39, P < 0.05) and the change in freezing between Days 2 and 30 (R210 = 0.36, P < 0.05), but not
freezing on Day 31 in context B (R25 = 0.05, P > 0.10) (Fig. 7E-G).
5.2 Post-encoding neurogenesis does not alter general activity in CA1 measured using calcium events
During each session of the contextual fear conditioning paradigm, we recorded calcium activity
from a population of CA1 neurons expressing GCaMP6f in each mouse. We processed these
video recordings to obtain calcium event data (described in detail in section 4.5.2), which were
further analyzed for two measures of general activity to determine whether CA1 neuronal
responses to the conditioning context would be altered by learning in a similar manner to lateral
amygdala neurons in response to tone fear conditioning (S. Ghosh & Chattarji, 2015; Quirk,
Repa, & LeDoux, 1995).
We first calculated the average rate of calcium events recorded from the cell populations during
each session (Fig. 8A). We found that the frequency of calcium events decreased between Day 1
and Day 2 in Sedentary and Running mice (Sedentary: Z = 2.20, P < 0.05; Running: Z = 1.85, P
< 0.07; Wilcoxon matched pairs test), indicating a learning-induced reduction in cellular activity.
The calcium event rate on Day 1 was not different to the rate on Days 30 and 31 (all Ps > 0.10).
The rate of calcium events increased between Day 30 (testing in context A) and Day 31 (testing
in context B) in both groups of mice (Sedentary: Z = 1.83, P < 0.07; Running: Z = 1.83, P <
37
0.07), suggesting that previously observed reduction in calcium activity was context-specific.
Importantly, we did not observe rate differences between groups on any day (all Ps > 0.10;
Figure 7. Increased hippocampal neurogenesis causes forgetting of contextual fear
memory. (A) Mice were trained in context A on Day 1 and tested for fear memory on Days 2
and 30 in context A and Day 31 in context B. Running mice had access to running wheels
between Days 2 and 30. (B) Freezing behavior did not differ between groups during training
(Sedentary n = 6, Running n = 7; t-test). (C) Freezing was decreased on Day 30 in mice that
ran for 28 days compared to mice that remained sedentary (Sedentary n = 6, Running n = 7; t-
test) and the same mice on prior to running (Sedentary n = 6, Running n = 7; paired t-test).
Freezing did not differ between groups when tested in context B (Sedentary n = 4, Running n
= 4; t-test). (D) Change in freezing over time was reduced in mice that ran (Sedentary n = 6,
Running n = 7; t-test). (E-G) The number of immature neurons in the DG was linearly
correlated with freezing behavior on Day 30 (E), change in freezing behavior between Days 2
and 30 (F), but not freezing behavior on Day 31 (G). For all figures, black bars represent
between-group comparisons and gray bars represent within-group comparisons, *P < 0.05.
38
Mann-Whitney U test), meaning that behavioral forgetting was not reflected in the calcium event
rate.
We further examined the percentage of active neurons within the cell populations during each
session using a minimum number of calcium events per session as an activity threshold (Fig.
8B). For the data presented here, the threshold was set at 10 calcium events (i.e, a neuron was
considered “active” during a session if it exhibited 10 or more calcium events during the 300 s
session) to avoid ceiling and floor effects. It should be noted that similar data were obtained
when setting the threshold between a minimum 5 and 20 calcium events (data not shown).
Similar to the rate of calcium events, the percentage of neurons that were considered active
decreased between Day 1 and Day 2 for Sedentary and Running groups mice (Sedentary: Z =
2.20, P < 0.05; Running: Z = 1.69, P < 0.10; Wilcoxon matched pairs test), but did not differ
between Day 1 and other days (all Ps > 0.10). Again, we found that this reduction in activity was
context specific such that the percentage of neurons displaying 10 or more calcium events
increased between testing in context A on Day 30 and testing in context B on Day 31 (Sedentary:
Z = 1.83, P < 0.07; Running: Z = 1.83, P < 0.07). There were however no differences between
Sedentary and Running mice on any day (all Ps > 0.10; Mann-Whitney U test). Thus, the size of
the active neuronal population (based on number of evoked calcium events) did not reflect the
behavioral forgetting observed in the same mice.
5.3 Post-encoding neurogenesis disrupts correlated activity within CA1 neuronal populations
Memory formation in a head-fixed contextual fear conditioning preparation was recently shown
to enhance correlated activity within hippocampal CA3 cell populations (Rajasethupathy et al.,
2015). Here we asked whether this was true for neuronal populations in CA1, and additionally
examined whether neurogenesis would perturb this correlated activity. For this analysis, we
binned calcium event data into 1 s epochs and computed Pearson’s correlation coefficients for
each unique pair of cells by correlating the cells’ calcium event traces. This was repeated for all
sessions and mice (Fig. 9A). By averaging the correlation coefficients for each session, we
obtained the mean correlation which represented the overall level of correlated activity within
the cell population. We then normalized these values using each mouse’s Day 1 mean
correlation. We found that the normalized mean correlation increased between Day 1 and Day 2
39
in Sedentary mice (Z = 1.99, P < 0.05; Wilcoxon matched pairs test), but not Running mice (Z =
1.01, P > 0.10), and the normalized mean correlation on subsequent days did not differ from
baseline levels (all Ps > 0.10) (Fig. 9B). There was also no difference between the normalized
mean correlation on Day 30 and Day 31 when mice were tested in different contexts (all Ps >
0.10). However, on Day 30 where we observed forgetting in Running mice relative to Sedentary
mice, we also saw decreased correlated activity (Z = -2.07, P < 0.05; Mann-Whitney U Test).
The mean correlation metric is somewhat problematic because activity of most cell pairs within
the imaged CA1 populations are weakly or not correlated (Fig. 9A). Therefore, most of the
Figure 8. Increased hippocampal neurogenesis does not alter the rate of calcium events
or amount of neurons activated in CA1. (A) The rate of calcium events observed in CA1
neuronal populations decreased in both groups of mice after contextual fear training
(Sedentary n = 6, Running n = 7; Wilcoxon matched pairs test) and increased when tested in
novel context B (Sedentary n = 4, Running n = 4; Wilcoxon matched pairs test). Rate of
events did not differ between groups on any day. (B) The amount of neuron that were
activated during context exposures decreased after contextual fear training (Sedentary n = 6,
Running n = 7; Wilcoxon matched pairs test) and increased when mice were exposed to novel
context B (Sedentary n = 4, Running n = 4; Wilcoxon matched pairs test). The amount of
neurons active during each session did not differ between Sedentary and Running groups.
Gray bars represent within-group comparisons, *P < 0.05, ^P < 0.10. Data represent median
and interquartile ranges.
40
correlation coefficient values contributing to the mean correlation metric are noise and these low
correlations are unlikely to be functional with regard to memory. To somewhat address this
issue, we used the correlated pairs metric, which represents the average number of significantly
correlated partner cells per neuron based on a Pearson’s correlation coefficient greater than 0.3
(Rajasethupathy et al., 2015). This correlation coefficient threshold is associated with high
connection probability between the cell pair in vivo (Ko et al., 2011), and thus, correlated pairs
can be used as an measure of functional connectivity within microcircuits. We calculated the
number of correlated pairs per cell for each session and again normalized the values by the
baseline (Day 1) number of correlated pairs per mouse. Again, we found that the normalized
correlated pairs increased between Day 1 and Day 2 in the Sedentary (Z = 2.20, P < 0.05;
Wilcoxon matched pairs test), but not Running mice (Z = 0.10, P > 0.10) (Fig. 9C). Unlike we
the mean correlation however, the proportion of correlated pairs was reduced on Day 31
compared to Day 30 (Z = 1.60, P < 0.10), suggesting that this metric is more consistent with the
context-dependent nature of fear memory. In addition, the normalized correlated pairs in the
Running group was decreased compared to the Sedentary group on Day 30 (Z = -2.28, P < 0.05;
Mann-Whitney U test), suggesting again that neurogenesis disrupted correlated activity within
the CA1 cell populations.
With one exception, the correlated pairs metric met the criteria set out in the objectives of this
thesis for defining memory-related activity. We observed an increase in the number of correlated
pairs between contextual fear training and the first testing session (although, only in the
Sedentary group), the number of correlated pairs was context-specific, and enhancing
neurogenesis reduced the number of correlated pairs in the Running group. Thus, correlated
activity in CA1 neuronal populations in the form of significantly correlated cell pairs may
contribute to memory maintenance over time. To further test this idea, we examined the extent to
which correlated pairs were “reactivated” between testing sessions, as is commonly done using
overlap of reporter proteins and IEG expression (Denny et al., 2014; Han et al., 2009; Liu et al.,
2012; Nakazawa et al., 2016; Rashid et al., 2016; Tanaka et al., 2014; Tayler et al., 2013; Yiu et
al., 2014). To do this, we calculated the proportion of cell pairs that were considered correlated
pairs (number correlated pairs/total number of unique cell pairs) on Day 2 and Day 30, and
multiplied these values to obtain the percentage of correlated pairs from Day 2 that would persist
until Day 30 by chance. We then found the actual percentage of correlated pairs on Day 2 that
41
Figure 9. Increased hippocampal neurogenesis disrupts correlated activity in CA1
neuronal populations.
-
Correlation
+
42
were also correlated pairs on Day 30, and compared this value to chance (Fig. 9D). There was
significant “reactivation” of correlated pairs in Sedentary mice, but not Running mice, compared
to chance levels (Sedentary: Z = 2.20, P < 0.05; Running: Z = 0.36, P > 0.10; Wilcoxon matched
pairs test), and a greater percentage of “reactivated” correlated pairs in Sedentary mice compared
to Running mice (Z = -2.07, P < 0.05; Mann-Whitney U test). This further suggests that
correlated pairs are functional units associated with memory maintenance over time. Finally, we
asked whether normalized correlated pairs per mouse was correlated with doublecortin+ cells or
freezing behavior, which would further support the relationship between neurogenesis, CA1
population activity, and memory expression. The amount of correlated pairs, however, did not
covary with the number of doublecortin+ neurons (R28 = 0.04, P > 0.10) or freezing behavior on
Day 30 (R29 = 0.20, P > 0.10) (Fig. 9E-F). These results show that the amount of correlated pairs
Figure 9. Increased hippocampal neurogenesis disrupts correlated activity in CA1
neuronal populations. (A) Pairwise correlations (left) and correlated pairs (Pearson’s
correlations > 0.3) (right) for a subset of mice on each day. (B) Contextual fear training
increased the normalized mean correlation of CA1 neuronal population in Sedentary but not
Running mice (Sedentary n = 6, Running n = 7; Wilcoxon matched pairs test). Correlated
activity was decreased in mice with increased neurogenesis on Day 30 (Sedentary n = 6,
Running n = 7; Mann-Whitney U test). (C) Normalized correlated pairs increased in
Sedentary mice after contextual fear training in Sedentary but not Running mice (Sedentary n
= 5, Running n = 6; Wilcoxon matched pairs test). Correlated pairs were decreased in mice
with increased neurogenesis on Day 30 (Sedentary n = 5, Running n = 6; Mann-Whitney U
test), and decreased in novel context B (Sedentary n = 3; Wilcoxon matches pairs test). (D) A
higher percentage of correlated pairs persisted from Day 2 to Day 30 in Sedentary mice
(Sedentary n = 6, Running n = 7; Mann-Whitney U test) and was above chance in mice with
normal levels of neurogenesis (Sedentary n = 6, Running n = 7; Wilcoxon matched pairs test).
(E-F) Despite difference in normalized correlated pairs on Day 30, the amount of correlated
pairs was not strongly correlated with the number of doublecortin+ neurons (E) or freezing
behavior (F). For all figures, black bars represent between-group comparisons and gray bars
represent within-group comparisons, *P < 0.05, ^P < 0.10.
43
in CA1 is not linearly related to memory expression or impacted by ongoing hippocampal
neurogenesis.
44
Chapter 6 Discussion and Conclusions
6.1 Results summary
The current work represents a first attempt at elucidating a neural circuit mechanism underlying
neurogenesis-mediated forgetting. To do this, we employed in vivo calcium imaging in freely
behaving mice to record neuronal activity from hundreds of hippocampal neurons while mice
formed and later successfully or unsuccessfully retrieved a contextual fear memory. While we
have yet to uncover a measure of neural activity in hippocampal subfield CA1 that
unambiguously represents memory formation and subsequent forgetting, the data presented in
this thesis nonetheless provide key insights into how forgetting might occur at the level of
neuronal ensembles.
Like in our previous reports (Akers et al., 2014; Epp et al., 2016), we found that enhancing
neurogenesis (Fig. 6) after memory formation in a contextual fear conditioning task led to
weakened memory expression during a subsequent retrieval test, or forgetting of the contextual
fear memory (Fig. 7). Increasing neurogenesis however did not alter freezing to a novel context,
which is a new insight provided by this work. Forgetting of stimulus attributes is noted to be an
aspect of forgetting that leads to generalization of memories (Jasnow, Cullen, & Riccio, 2012;
Wiltgen & Silva, 2007). We did not observe increased fear generalization in mice with enhanced
neurogenesis in the current experiment, suggesting that neurogenesis-induced forgetting is not
merely reduced generalization decrement. Alternatively, in our paradigm, the memory retrieval
test on Day 30 may serve as a reminder that makes the remote context memory more specific
(Wiltgen & Silva, 2007), and thereby reducing generalization when mice are tested in the novel
context on Day 31. Nonetheless, intact context discrimination in mice with basal and enhanced
levels of neurogenesis allowed us to demonstrate that neuronal activity recorded from CA1
during memory retrieval was context-specific (see below).
Calcium activity recorded from CA1 neuronal populations during contextual fear memory
encoding and retrieval was analyzed in a few different ways in this thesis. At the outset of this
work, we did not specify a particular analysis that would be used to examine CA1 neuronal
activity, but instead outlined general criteria that would be met if an activity metric appropriately
reflected memory. These included: 1) that there should be change in the activity metric after
45
contextual fear conditioning, 2) the metric should be near baseline (training) levels when mice
are tested in a novel context not paired with shocks (i.e., change in the metric due to learning
would be specific), and 3) in animals that exhibit neurogenesis-induced forgetting, the metric
should again be closer to baseline than in mice that display successful memory retrieval. All of
these criteria were met in our analyses of the calcium imaging data, albeit not all with the same
metric.
Memory formation was associated with a decrease in the rate of calcium events recorded from
CA1 neurons, as well as the amount of neurons active per session (Fig. 8). These measures were
also memory-specific, because the learning-induced decrease in activity were reversed when
mice were tested in a novel context. However, forgetting was not reflected in the calcium event
rate or percentage of active neurons because these measures did not differ between groups of
mice with enhanced or basal levels of neurogenesis. This key difference was instead detected in
measures of correlated activity (Fig. 9), which described the extent to which activity from
individual neurons within the imaged CA1 cell populations covaried with one another during
fear conditioning sessions. The correlated pairs metric also showed memory-specificity, although
a robust increase in correlated pairs was not observed after training in both groups of mice (this
may have been due to low sample sizes in this analysis). Furthermore, similar to reactivation of
memory trace neurons measured by IEG activity (Denny et al., 2014; Tanaka et al., 2014; Tayler
et al., 2013), correlated pairs were reactivated across testing sessions in mice showing intact
memory, but not mice that forgot. Thus, correlated activity in the form of emergent correlated
pairs within the CA1 network may be necessary for memory retention.
6.2 Functional calcium activity patterns during memory formation and expression
6.2.1 Neural correlates of memory in the hippocampus
Memories at the level of cellular ensembles are thought to be represented by precise patterns of
spatiotemporal activity (Josselyn et al., 2015). Contemporary studies have linked spatial and
temporal patterning of neuronal activity in the hippocampus to memory formation and
expression, albeit separately using different methodologies. Sparse populations of neurons in the
DG, CA3, and CA1 express activity-dependent IEGs in response to memory encoding
(Guzowski et al., 1999; Tayler et al., 2013; Wheeler et al., 2013), and activation of these same
46
neurons are necessary and sufficient for memory retrieval at later time points (Denny et al.,
2014; Liu et al., 2012; Park et al., 2016; Ramirez et al., 2013; Roy et al., 2016; Ryan et al., 2015;
Tanaka et al., 2014). While studies of this kind have shown a definite role for spatial patterns of
neuronal activation in mediating memory encoding and retrieval, they provide no insight into
how temporal activity within and among engram neurons contributes to mnemonic processing.
On the other hand, electrophysiological recordings have demonstrated a role for temporal firing
in the hippocampus as memory representations. Notably, activity of place cells in the
hippocampus are spatially tuned to locations within an environment such that they fire when an
animal is within in the associated place field or when an animal is remembering that place field
(i.e., memory replay) (de Lavilleon et al., 2015; Langston et al., 2010; O'Keefe & Dostrovsky,
1971). Place cell firing is a neural substrate of spatial memory because spatial memories can be
modified by pairing stimuli with offline (replayed) temporal activation of place cells (de
Lavilleon et al., 2015). Electrical oscillations in the hippocampus, like theta rhythm and sharp
wave ripples, are also important for memory (Colgin, 2016), but are beyond the scope of this
work since we are interested in cellular-resolution brain activity. Electrophysiological recordings
from behaving rodents have been critical for defining precise firing properties of neurons within
the hippocampus, but have limitations including the lack of spatial resolution and time
constraints for recording. These are not issues for the in vivo calcium imaging method that were
used in the current study.
As mentioned before, the benefit of the in vivo calcium imaging approach used in this thesis is
that it allows recording of integrated spatiotemporal activation of neurons during behavior. Thus,
we were able to track activity of individual neurons over time to examine both spatial activation
of neurons (e.g., percentage of neurons activated) and temporal activity of neurons (e.g., rate of
calcium events). Consistent with reports of altered neuronal activity in the LA after fear
conditioning, we observed an overall reduction in activity (rate of calcium events and percentage
of activated neurons) in CA1 after mice were trained in contextual fear conditioning that was
specific to the training context. These measures were not affected by neurogenesis-induced
forgetting, which will be speculated on in the next section. We could not however examine
evoked calcium events in relation to a discrete stimulus, like a tone, which has been done with
neuronal responses recorded during tone fear conditioning (S. Ghosh & Chattarji, 2015; Quirk et
al., 1995). Instead, a more logical future approach to analyzing this data is to examine how
47
activity of place cells in CA1 are affected by memory formation and forgetting. Other groups
performing in vivo calcium imaging with miniature microscopes have found that place cells can
be identified using evoked calcium events instead of neuronal firing (Cai et al., 2016; Rubin et
al., 2015; Ziv et al., 2013), but this has only been done while mice explore open fields or traverse
linear tracks. Hippocampal place cells undergo remapping after contextual fear conditioning, and
these emergent place representations are stable for at least a few days (M. E. Wang et al., 2012).
Using calcium imaging in freely behaving mice, it may be possible to detect this property in CA1
place cells over much longer periods of time and observe whether the spatial representations in
the hippocampus that emerge after conditioning are degraded by neurogenesis-induced
forgetting. This analysis and others will be conducted in the future to further define how ongoing
neurogenesis impairs memory-related hippocampal activity and consequently memory retrieval.
A potential substrate of memory examined in this study was correlated activity among CA1
neurons in the form of correlated cell pairs (pairs with correlation coefficient exceeding 0.3). A
study by Ko and colleagues elegantly demonstrated that cortical neurons with similar functional
activity in vivo (i.e., neurons that responded more similarly to visual stimuli) have a high
connection probability, and this connection probability was especially high when the correlation
coefficient between calcium signals from imaged neurons exceeded 0.3 (Ko et al., 2011). We
leveraged this finding to determine whether functional connectivity within the CA1 neuronal
population would be increased after learning (as demonstrated by a recent study examining
memory-related activity in CA3 (Rajasethupathy et al., 2015)), and additionally to examine
whether this functional connectivity would be lost when mice forgot. We found that the amount
of correlated pairs in CA1 did increase following conditioning (although, not in one group of
mice) in a context-dependent manner. These findings were congruent with an increase in
correlated pairs in CA3 after conditioning (Rajasethupathy et al., 2015), suggesting memory
formation increases correlated activity throughout the hippocampus, and within and between
other brain structures (Wheeler et al., 2013). It further suggests that these neurons might be
responding more similarly to aspects of the context during memory retrieval. Further analysis of
place cell activity may provide insight to whether this is true for spatial information.
The critical question in this thesis was whether memory-related activity patterns would be
perturbed by hippocampal neurogenesis similarly to memory expression. This was the case with
respect to correlated activity among the CA1 neuronal populations, which was decreased during
48
memory retrieval in mice that displayed increased neurogenesis and forgetting. It should be
noted that correlated activity in CA1 was not linearly related to levels of neurogenesis or
memory expression (although a strong relationship was seen between neurogenesis and memory
expression). Correlated pairs still may have a privileged role in maintaining memories, as seen
by a higher percentage of persisting correlated pairs over time in mice with basal neurogenesis
compared to mice that ran. This parallels numerous studies demonstrating reactivation of
encoding neurons at memory retrieval (Denny et al., 2014; Ryan et al., 2015; Tanaka et al., 2014;
Tayler et al., 2013), and suggests that memory trace neurons may share functional connectivity,
although more research is needed to test this. Likewise, further studies are needed to determine
whether neurons belonging to correlated pairs fit the criteria of engrams (persistence, ecphory,
content, dormancy) (Josselyn et al., 2015). Further characterization of correlated pairs within the
CA1 cell population will lead to a better understanding of how activity within functionally
connected groups of neurons maintains or loses memories over time.
A question emerging from this work is how ongoing neurogenesis disrupts functional activity
within the hippocampus, including subfield CA1. Our current model of neurogenesis-induced
forgetting predicts that forgetting is a result of a failure to reinstate patterns of neural activity
present at encoding (Frankland et al., 2013). This was partially supported by our data showing
that correlated activity during retrieval was disrupted by enhances neurogenesis. This hypothesis
should be amended to reflect our findings that patterns of activity during encoding and memory
retrieval are markedly different, at least with respect to the rate of evoked events, percentage of
active cells, and correlated activity among cell populations. Thus, forgetting is more likely a
failure in reinstating activity patterns that emerge as a result of encoding, such as correlated
activity demonstrated here. This might occur in a few different ways. It is possible that the
addition of new neurons into hippocampal circuitry, by way of their increased excitability (Kee
et al., 2007; Marin-Burgin et al., 2012) and somewhat nonspecific activation (Danielson et al.,
2016), contribute noise that muddies memory-related activity patterns that should be reinvoked
during memory retrieval. In addition or alternatively, the silencing of mature, memory-related
synapses (Lopez et al., 2012; Yasuda et al., 2011) by new neurons would prevent reactivation of
memory-related activity patterns. Future studies with more sophisticated imaging techniques will
be able to answer this question (see section 6.3.1).
49
6.2.2 Limitations of current calcium imaging approach
Despite the many benefits of the calcium imaging approach used in this study, there are still
limitations related to its use in studying memory-related activity. First, as mentioned previously,
memories in the hippocampus (and other brain regions) are sparsely encoded within populations
of neurons such that only a small percentage of cells become necessary for maintaining the
memory trace (Josselyn et al., 2015; Rashid et al., 2016; Stefanelli et al., 2016; Tayler et al.,
2013). Our expression of GCaMP6f within subfield CA1 was not restricted to neurons activated
during learning, making it difficult, but not impossible, to define memory trace neurons.
Therefore, it is possible that imaged cells that are not part of the memory trace are contributing
noise to our analyses. Future technology developments such as miniature microscopes capable of
dual-channel imaging will solve this issue by allowing simultaneous imaging of memory trace
and non-memory trace cells within the freely behaving mouse. By genetically labelling memory
trace cells with a red fluorophore (e.g., tdTomato) and expressing GCaMP in the entire neuronal
population, we will be able to identify and characterize activity from memory trace neurons (red
signal+) which will allow us to later “decode” activity in whole populations of neurons to predict
which cells are likely to be critically involved in the memory trace.
An additional, general limitation to calcium imaging is the poor temporal resolution of current
GECIs. The slower kinetics (relative to electrical activity) of popular indicators such as GCaMP6
is further compounded by poor linearity (Badura et al., 2014). These properties make it near
impossible to use recorded calcium transients to predict underlying electrical activity, which is a
more direct measure of neuronal activity than calcium flux, which is buffered by GECIs and
other molecules, released from internal stores, etc. (Hamel et al., 2015). The development and
wider availability of fast, genetically encoded voltage indicators will alleviate this issue and
allow imaging of millisecond-scale electrical activity from large-scale cell populations.
6.3 Future directions
6.3.1 Further defining circuit mechanisms of forgetting with cell-type specific imaging
The ability monitor neuronal activity over long periods of time in genetically-defined cell
populations has opened up many possibilities with regard to studying memory persistence and
forgetting. Of these, of primary interest is determining how the activity of new neurons causes
50
forgetting in real-time. Activity of immature neurons in the DG during context exploration was
imaged in real time for the first time recently, and it was found that these neurons are more
active and less spatially tuned than their mature counterparts (Danielson et al., 2016). What
remains unknown is how these neurons act during retrieval of an associative memory, especially
after promoting neurogenesis, and how their activity during memory retrieval affects the activity
of surrounding mature neurons. Again, the future development of dual-channel miniature
microscopes will allow us to address this question by labelling immature neurons in the DG with
a red fluorophore which would allow us to separate signals from new and old neurons in the DG.
As alluded to here and above, the advent dual-channel miniature microscopes will open up
incredible possibilities for understanding how different subpopulations of neurons contribute to
the overall function of neural circuits.
6.3.2 Restoring forgotten memories and memory-related neuronal activity
Another question of interest is how reminders operate to reinstate forgotten memories.
Reminders treatments such as brief exposure to memory-related stimuli or pharmacological
manipulations are effective at making remote memories more specific (Wiltgen & Silva, 2007)
and restoring forgotten memories (J. H. Kim et al., 2006; Madsen & Kim, 2016; Tang et al.,
2007). How would this happen? If forgetting is associated with perturbed activity patterns in the
hippocampus, it should follow that reversal of forgetting is accompanied by reinstatement of
these patterns. This can be tested using the same methods described in this thesis with the
addition of a reminder treatment that is effective at restoring forgotten contextual fear memories.
This future experiment will provide causal support for the link between complex neural activity
and memory retention.
6.4 Conclusions
The data presented in this thesis provide initial insight into how the ongoing additional of adult-
generated neurons into existing hippocampal circuitry disrupts functional activity to cause
forgetting. We found that contextual memory formation may lead to an increase in correlated
activity among neurons in hippocampal subfield CA1, and enhancing neurogenesis after
encoding impairs both correlated activity in CA1 and memory expression. These results suggest
that correlated activity within CA1 may be a substrate that maintains memories over time, and
perturbation of functional connectivity between neurons underlies forgetting. The disruption of
51
correlated activity in CA1 is likely caused by circuit reorganization by neurogenesis, preventing
the proper, memory-associated patterns of activity from occurring during memory retrieval.
Thus, we have begun to elucidate a neural circuit mechanism for neurogenesis-mediated
forgetting. Further work in this area will determine how the activity of immature neurons directly
contributes to forgetting. In the future, this information can have important implications for
memory disorders, as disruption of activity within hippocampal circuits likely underlies memory
failure associated with various neuropathologies.
52
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