CONTEXT DEPENDENCE OF ACTION SELECTION
IN THE BASAL GANGLIA AND MIDBRAIN
BY
MARIO JAMES LINTZ
B.S., Metropolitan State University, 2007
A dissertation submitted to the
Faculty of the Graduate School of the
University of Colorado in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Neuroscience Program
2016
This dissertation for the Doctor of Philosophy degree by
Mario James Lintz
has been approved for the
Neuroscience Program
by
Angeles Ribera, Chair
Gidon Felsen, Advisor
Tim Lei
Abigail Person
Diego Restrepo
Date: August 19, 2016
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Lintz, Mario James
Context Dependence of Action Selection in the Basal Ganglia and Midbrain
Thesis directed by Assistant Professor Gidon S. Felsen
ABSTRACT
Decision making is mediated by balancing external influences and internal motivations in the
selection of one among multiple alternatives. Decisions about movements in response to external
stimuli are well-studied, however, where and how internal motivations influence movement selection
is less well understood. In this dissertation, I implement a novel behavior paradigm designed to
examine the differences between stimulus-guided and internally-specified decisions.
In the first study, I examined how movement direction and decision context modulate neural
activity in the principal output nucleus of the basal ganglia, the substantia nigra pars reticulata (SNr).
During the decision making phase of the task, SNr neurons exhibit a significant direction preference
with nearly equal numbers preferring ipsiversive and contraversive movements. I show that the SNr
promotes internally-specified over stimulus-guided movements and convey that trial-type differences
of neural activity cannot be explained by task-related variables such as trial difficulty, reaction time
and previous trial history. Decision context influences activity in a regular manner dependent on
whether the trial is in the neuron’s preferred or antipreferred direction.
In the second study I applied the same techniques in the superior colliculus (SC), a structure
integral to driving orienting movements. Like the SNr, which directly inhibits SC activity, the SC
shows significant direction dependent activity during decision making, though with a strong bias for
contraversive movements. Neural activity during this period was modulated by decision context,
though in a manner unlike that observed in the SNr. Unilateral inhibition of the SC revealed that
internally-specified decisions are more impervious to modulation. This supports my conclusion that
internally specified motivations drive movement selection via the SC in a unique manner that could
not be appreciated in previous studies examining the role of stimulus-guided movements alone.
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I conclude that both the SNr and the SC play a role in balancing stimulus-guided and
internally-specified motivations to facilitate movement selection. This context dependent modulation
of neural activity enhances our understanding of the roles the SNr and SC play in guiding decisionmaking and indicate more cognitive roles in this process overall.
The form and content of this abstract are approved. I recommend its publication.
Approved: Gidon S. Felsen
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DEDICATION
Dedicated to my wonderful wife, Monica and my three amazing kids: Delilah, Oliver and Josephine
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ACKNOWLEDGEMENTS
For the people in my life who believed in me, stood by my side, and helped shape me into the person
I am today. I’d like to acknowledge the following people:
My wonderful wife, Monica, for joining me in this journey of life, and all the happy
memories and support. For the encouragement and patience when things get tough, and for always
loving me with all of her heart.
My kiddos, Delilah, Oliver and Josephine, for always putting a smile on my face, teaching me
new levels of love and understanding and reminding me of all the beauty of life.
My parents, James and Michelyn, for always believing in me and teaching me that I could do
anything I put my mind to and for all of their support, guidance, love, and of course meals and
babysitting.
My sisters, Elishia and Lashell who know me as well as anyone could, for keeping me
accountable, putting up with my brother antics, and for all of your love and support.
My Oma for the spiritual guidance and all the wonderful conversations about life and the
mysteries and beauties of the brain.
My aunts, uncles, and cousins for the love and support.
My in-laws, Greg, Teresa, Val, Vangie, Evita, Tony and Anna for welcoming me to the
family, showing interest in what I do and encouraging me along the way.
My brother-in-law, Anthony for the guidance getting to and staying on this path and for all
the support.
My advisor, Gidon, for the training, teaching, understanding, open ear, advice and support
throughout the years of my graduate training.
Everyone in the Felsen Lab during my time here: Jamie, Beth, John, Quang, Jackie, Andrew,
and Sammie.
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My thesis committee, Drs. Ribera, Person, Lei, Restrepo, Felsen for pushing me to think
critically about the interpretation of my results and their implications.
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TABLE OF CONTENTS
CHAPTER
I. INTRODUCTION ............................................................................................................... 1
Motivation ............................................................................................................................. 1
Overview of dissertation ....................................................................................................... 1
Decision making .................................................................................................................... 2
The role of reward in decision making ........................................................................... 2
The role of internally specified information in decision making .................................... 3
Modern methods to study decision making .................................................................... 3
Behavioral assays used to assess decision making in animal models ............................. 4
Basal ganglia ......................................................................................................................... 6
Basal ganglia anatomy .................................................................................................... 6
Substantia nigra pars reticulata ....................................................................................... 8
Superior colliculus................................................................................................................. 10
Superior colliculus anatomy ........................................................................................... 11
GABAergic input to the superior colliculus from the substantia nigra pars reticulata .......... 12
II. BASAL GANGLIA OUTPUT REFLECTS INTERNALLY-SPECIFIED
MOVEMENTS .................................................................................................................... 16
Introduction ........................................................................................................................... 16
Materials and methods........................................................................................................... 17
Results ................................................................................................................................... 24
Behavioral assay dissociates stimulus-guided and internally-specified
movements ..................................................................................................................... 24
SNr activity differs for stimulus-guided and internally-specified
movements ...................................................................................................................... 26
Modulation of SNr activity by task-relevant variables ................................................... 31
Discussion ............................................................................................................................. 35
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III. ACTION SELECTION MODE SWITCHING IN THE SC DEPENDS ON DECISION
CONTEXT ........................................................................................................................... 39
Introduction ........................................................................................................................... 39
Materials and methods........................................................................................................... 41
Results ................................................................................................................................... 46
Behavioral assay dissociates stimulus-guided and internally-specified movements ...... 46
Neurons in the mouse SC display three commonly described firing
profiles: burst, buildup and fixation ................................................................................ 49
SC buildup neurons are engaged differently between internally-specified and
stimulus-guided trials ...................................................................................................... 51
Unilateral inhibition in the SC biases movement differently depending on the
epoch of stimulation........................................................................................................ 55
Discussion ............................................................................................................................. 57
IV. CONCLUSION .................................................................................................................... 61
Summary of novel results ...................................................................................................... 61
Summary of the nigrotectal pathway..................................................................................... 63
Study strengths ...................................................................................................................... 65
Limitations of approach......................................................................................................... 66
Implications of results ........................................................................................................... 67
REFERENCES
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LIST OF TABLES
TABLE
2.1 Direction preference and activity change during the delay epoch ................................................ 28
x
LIST OF FIGURES
FIGURE
1.1 Summary of the disinhibition model of basal ganglia control...................................................... 9
2.1 Behavioral task and performance ................................................................................................. 25
2.2 Confirmation of recording sites and spike clustering ................................................................... 27
2.3 SNr activity during the delay epoch depends on movement direction and trial type ................... 29
2.4 Activity depends on the extent to which movements are internally specified ............................. 30
2.5 Dependence of firing rate on trial type cannot be explained by discrimination difficulty or an
associated variable .............................................................................................................................. 32
2.6 SNr activity is influenced by several task-relevant factors throughout the trial ........................... 34
2.7 Model proposing how the observed activity of ipsiversive preferring SNr neurons could facilitate
internally-specified movements relative to stimulus-guided movements. ......................................... 37
3.1 Behavioral task and performance ................................................................................................. 48
3.2 Confirmation of SC recording sites .............................................................................................. 50
3.3 SC activity during the prestimulus and delay epochs depend on movement direction ................ 51
3.4 The SC is engaged differently between SG and IS trials.............................................................. 52
3.5 Unilateral inhibition in the SC shows time dependent differences in behavior modulation ........ 56
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CHAPTER I
INTRODUCTION
Motivation
Decision making is a fundamental aspect of daily living and survival. Ultimately, we strive to
make decisions that will result in what we believe to be the best possible outcome, using all of the
information available at the time to decide what’s best in the current situation. Traditionally this
process has been considered a higher order brain function, though recent research has implicated
subcortical regions to play an important role in the decision-making process as well. Due to its vast
implications, understanding the neural substrates of decision making is a fundamental goal of systems
neurobiology.
I investigated different modalities of decision-making as they are processed in the subcortical
nigrotectal pathway, extending from the substantia nigra pars reticulata (SNr) to the superior
colliculus (SC). The SNr is the principle output structure of the basal ganglia whose GABAergic
projections modulate motor activity via inhibitory control of the thalamus and brainstem (Deniau et
al., 2007). The SC is a midbrain structure modulated by the SNr involved in orienting movements to
points in egocentric space via excitatory projections to premotor nuclei in the brainstem and spinal
cord (Wolf et al., 2015). With knowledge that both the SNr and SC modulate, to some degree,
movement based decision making, I aimed to determine how these two regions drive decisions
differently in the context of two classes of movement that I describe below: stimulus-guided versus
internally-specified movements.
Overview of dissertation
This dissertation will cover the unique modulation of neural activity that occurs in the SNr
and SC during the decision-making phases of a novel behavior task. The present chapter begins with a
brief overview of decision making and the factors that influence this process. Next it covers methods
of studying decision making and provides pertinent background about our current understanding of
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the role of the SNr and SC in this process. Chapters two and three cover the novel data contributed by
this dissertation. Chapter two presents the results of the context-dependent modulation of neural
activity in the SNr of mice performing full-body orienting movements in response to environmental
cues. In chapter three, the same task is used to understand how a target of the SNr, the SC is
modulated under the same behavioral contexts. Chapter four summarizes the novel results and creates
a framework of how the brain regions studied here work in concert during decision making, why this
is important and how it builds upon our current knowledge of this process.
Decision making
Decision making is the cognitive process resulting in the selection of one action among
several alternatives. The values, preferences and knowledge of the decision maker determine how
alternatives are identified and chosen. At the core of this process is the analysis and ranking of a finite
set of alternatives where a decision maker must select the most attractive alternative when all criteria
for decision-making are considered simultaneously. This process frequently involves the combination
of explicit (concrete and transferrable) knowledge with tacit (perceptual and skill based) knowledge
to select the best course of action. The ability to combine and consider the relevant factors when
making decisions is important for survival. Throughout this dissertation, decision making will refer to
the process of selecting a target direction for movement (action selection).
The role of reward in decision making. In the selection of one among many alternatives,
the expected outcomes of future events substantially impact the decisions we make. The appetitive vs
aversive results of our actions is an area of intense study in decision making, as the prediction of
reward or punishment play a major role in driving learning (Schultz et al., 1997). Evolutionarily,
prediction of reward and punishment have large implications in survival. For an organism to adapt
and thrive in its environment, it must be able to predict, based on certain cues, the presence of food,
mates, and potential dangers. The perceived value of reward associated with a stimulus varies as a
function of an animal’s previous experiences with that stimulus and as a function of the animal’s
2
internal state at the time. The function of a reward to drive reinforcement and learning varies
according to behaviors elicited (Schultz et al., 1997).
The role of internally specified information in decision making. Rational decision making
requires the thoughtful consideration of all factors relevant to the current situation. These factors
include a combination of environmental (external) cues and their evaluation alongside internallyspecified variables encompassed by explicit and tacit knowledge. I operationally define internallyspecified variables as: internal factors and knowledge that accompany external cues and guide the
decision-making process. The previously discussed expectation of reward is an example of an
internally-specified variable. Prior experience and situation context are two additional internallyspecified variables relevant to the studies presented here. These variables are closely related as the
consideration of context requires knowledge of previous experiences for comparison. The influence
of previous experiences to inform decision making is imperative for survival, allowing us to apply
previously learned principles to present situations to guide decisions with the best outcomes.
Modern methods to study decision making. Decades of behaviorally based decisionmaking studies in humans have provided the basis for understanding the factors that drive our
decisions (Santos and Rosati, 2015). Human studies are frequently coupled with noninvasive
neuromonitoring procedures, primarily functional magnetic resonance imaging (fMRI), to identify
which brain areas are involved in perceptual decision making. While convenient, these noninvasive
neural recording techniques are hampered by their course spatiotemporal resolution (Thakor, 2012).
Such techniques are useful for accessing broad scale information of the neural activity underlying
behavioral processes. However, obtaining focal data at the spatiotemporal resolution necessary to
identify the neural substrates of behavioral processes requires – for now – more invasive procedures
not suitable for human subjects. Use of model organisms coupled with sophisticated neural recording
techniques provides the resolution necessary to investigate and understand the neural processes
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underlying decision making and other behaviors. Rodents and primates are frequently used in such
studies where the subject’s motor response is used to report their decision.
Nonhuman primates have been the standard and preferred organism for studying the
neurophysiology of cognitive functions such as decision making. Primates were believed to be the
only organisms evolved enough for use in studies aimed to better understand the higher order
cognitive functions of humans. While primate studies paved the way for studying the
neurophysiology of cognition, in this century rodents have emerged as a viable and preferred model
for many cognitive studies including the investigation of decision making.
While primates are better suited for performing highly complex tasks, the use of rodents
offers several advantages. They exhibit functional homology with both the human and primate
nervous systems, are quick to learn decision-making tasks and are suitable for chronic
electrophysiology. Additionally, rodents are relatively inexpensive, allowing for the use of an animal
for only a single experiment, opposed to primates which are frequently used for multiple studies.
Their relatively low cost permits replicative experiments, useful for assessing the reproducibility and
significance of results. Finally, the availability of advanced molecular tools and genetically diverse
strains creates the ability to record from individual, cell-type specific neurons in both normal and
disease models.
Behavioral assays used to assess decision making in animal models. In the use of animal
models for decision-making studies, behavioral tasks are designed to exploit an animal’s natural
characteristics and strengths. Accordingly, due to the well-developed visual system of primates, these
studies typically utilize visual cues to guide behavior. Multiple variations of visually guided saccade
tasks are frequently used, though the core tenets of the paradigm remain similar, i.e. trials begin with
visual fixation at a set point followed by presentation of a visual target to indicate the movement
direction required to obtain a reward. The specifics of the task can be modified as needed to addresses
different questions underlying the decision-making process. As an example, comparison of SNr
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activity between memory-guided and visually-guided saccade tasks revealed higher firing rates
(Hikosaka and Wurtz, 1983a) and greater susceptibility to neuromodulation during the memoryguided variant (Basso and Liu, 2007). In another version, the use of multiple potential targets
demonstrated the SNr’s role in target selection (Basso and Wurtz, 2002).
Behavioral paradigms originally developed for primate studies have provided excellent
templates for adaptation of similar studies in rodents. Because of their rudimentary visual systems
rodents primarily rely on their hearing and smell to guide behavior (Abbott, 2010). Thus, rodent
based studies traditionally utilize either olfactory or auditory stimuli to drive behavior. Furthermore,
due to their smaller size, the use of rodent models permits the study of unrestricted full-body orienting
movements to report decisions. These paradigms frequently require animals to move between a series
of ports to sample stimuli or receive a reward during the course of the task.
A commonly used approach for studying neural decision making in animal models is the twoalternative forced choice task (2AFC), where a subject has to choose between two alternatives within
a certain amount of time. The basic components of a two alternative forced choice task are 1) twoalternative choices are presented simultaneously, 2) a delay interval to select a response, 3) a response
indicating choice of one stimuli over the alternative. Here mice were trained on a 2AFC odor
discrimination task where a binary odorant mixture represents the two-alternative stimulus. Mice are
required to decipher the dominant component of the mixture to determine the movement direction
required for reward (Uchida and Mainen, 2003). I’ve adapted this task to include two unique trial
types to differentiate between stimulus-guided and internally-specified modes of decision making.
The details of this novel paradigm will be described in depth in the material and methods section of
the next chapter. The combination of this novel behavioral approach with in vivo recording techniques
allows me to investigate the role of two regions critically important in movement initiation, the basal
ganglia and superior colliculus, in the decision-making process.
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Basal ganglia
The basal ganglia (BG) are well known to be involved in motor control (Hikosaka and Wurtz,
1989; Mink, 1996), where they function in the classical sense to modulate movement via inhibition of
competing downstream motor structures (Basso and Wurtz, 2002; Di Chiara et al., 1979; Hikosaka
and Wurtz, 1985). Executing movements requires a shift in the dynamic balance between postural and
active states. The transition between the two encompasses two complementary processes to
accomplish a single goal, i.e. turning off postural control while subsequently or simultaneously
activating motor pattern generators (MPGs) to drive movement (Mink, 1996). The BG functions as a
gate in this process by selectively inhibiting competing motor mechanisms to prevent them from
interfering with voluntary movements generated by other structures in the central nervous system
(Mink, 1996). This inhibitory control is critical for overriding stimulus-driven behavioral responses
that would produce interfering movements.
Disease in the basal ganglia highlights its role in the normal control of movement and
simultaneously illustrates its involvement in other processes as well. Damage to this region generally
presents as a disturbance of normal movement, in either hypokinetic/akinetic (marked decrease in
movement with rigidity) disorders such as Parkinson’s disease or hyperkinetic (excess/abnormal
involuntary movements) disorders including Huntington’s disease (Marsden, 1982). In addition to
disorders of movement, disease in the BG is also associated with non-motor disorders such as
Tourette’s syndrome and obsessive compulsive disorder (Deniau et al., 2007). The diverse array of
symptoms associated with disease in the basal ganglia suggests its role in processing neural
information from multiple brain areas.
Basal ganglia anatomy. The basal ganglia was suggested to play a role in movement control
in the early 1900’s when S. A. Kinnier Wilson first described Wilson’s disease (Wilson, S. A. K.,
1912), proposed to cause damage to an extrapyramidal system (basal ganglia) responsible for
involuntary movements (Wilson, 1928). Subsequent studies revealed projections to motor cortical
6
areas via the thalamus (Nauta and Mehler, 1966), suggesting the BG was prepyramidal rather than
extrapyramidal and leading to the idea that the BG initiate movement through outputs ultimately
projecting to the motor cortex (Mink, 1996). Additional anatomical studies demonstrated most of the
output from the BG goes through both the thalamus and brainstem (Parent and De Bellefeuille, 1982),
suggesting the BG to be both pre- and extra- pyramidal. Finally, direct projections to areas of the
frontal lobe involved in cognitive function (Alexander et al., 1986; Middleton and Strick, 1994),
suggest involvement in cognitive and emotional-motivational processes as well (Deniau et al., 2007).
The basal ganglia are composed of six primary structures; two input structures, two intrinsic
structures, and two output structures. The two inputs of the basal ganglia are the striatum (caudate and
putamen) and the subthalamic nucleus (STN). The striatum receives excitatory input from nearly all
areas of the cerebral cortex while the STN receives excitatory input from the motor areas of the
frontal lobe. The intrinsic nuclei of the basal ganglia are the globus pallidus pars externa (GPe) and
the substantia nigra pars compacta (SNc). The GPe receives excitatory input from the STN and
inhibitory input from the striatum while the SNc is primarily composed of dopaminergic neurons and
it both sends and receives most of its information to and from the striatum. The outputs of the basal
ganglia are the globus pallidus pars interna (GPi) and the substantia nigra pars reticulata (SNr). Each
structure receives excitatory input from the STN and inhibitory input from the striatum. Both outputs
send inhibitory GABAergic projections to motor areas in the thalamus and brainstem, however, there
are no direct outputs to spinal or brainstem motor neurons (Mink, 1996).
Cortical input to the striatum is both convergent and divergent (Flaherty and Graybiel, 1991;
Selemon and Goldman-Rakic, 1985). The principal cell in the striatum is the GABAergic (Ribak et
al., 1979) medium spiny neuron (MSP) (Gerfen, 1988; Kemp and Powell, 1971), with radial
dendritic trees that span up to 500 um (Wilson and Groves, 1980), allowing a single MSP to receive
input from several cortical areas. The compartmental organization of the striatum, cortical inputs and
patterns of output provide an anatomical basis for focusing information in the striatum, suggesting a
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means to output a signal that is both spatially and temporally focused and can be modified by
behavioral contexts (Mink, 1996).
The substantia nigra pars compacta consists of mostly dopaminergic neurons and is integral
in mechanisms of reward and reinforcement (Wickens and Kötter, 1995). Dopamine released in the
nigrostriatal pathway can stimulate activity in the direct pathway via D1 receptors or inhibit activity
via the indirect pathway via D2 receptors. There are five different dopamine G protein coupled
receptors grouped into two families based on their response to agonist where D1 receptors stimulate,
and D2 receptors inhibit -adenyl cyclase activity, (Sibley and Monsma, 1992). While the cortical
inputs to the striatum terminate on the heads of MSPs, the dopaminergic projections from the SNc
terminate on the shafts of these same neurons, providing a potential means to modulate the
corticostriatal influence (Bouyer et al., 1984). Coincident firing from both cortex and SNc would
increase the synaptic strength of cortical inputs, thus strengthening certain input combinations while
weakening others. This has implications in shaping the pattern of MSP activity causing it to fire more
in some situations and less in others.
Compared to the striatum, input to the STN is more simplified. It receives excitatory,
glutamatergic input from motor areas of the ipsilateral cerebral cortex. Excitatory projections from
the STN stimulate the GPi, GPe and SNr. The net result of the arrangement in the basal ganglia is that
motor commands from the cortex result in broad excitation of GPi and SNr and focused inhibition of
specific GPi or SNr neurons in a behaviorally specific manner (Mink, 1996).
Understanding this anatomy provides insight into the complex and diverse array of
information that is processed in the basal ganglia. Information processed here is ultimately collected
in the SNr for final processing prior to transmission to downstream motor targets in the brainstem and
thalamus.
Substantia nigra pars reticulata. The substantia nigra pars reticulata is a sparsely cellular,
primarily GABAergic structure (Carpenter, 2011). The SNr is the principle output nucleus of the
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basal ganglia, and is frequently viewed as a final processing center for information transmitted
through this region. Information from nearly the entire cortex is ultimately transmitted through the
SNr via the striatonigral pathway (Deniau et al., 1996; McGeorge and Faull, 1989). The SNr exerts its
inhibitory modulatory effects on downstream nuclei in the thalamus and brainstem. Evidence suggest
that the spatial arrangement of SNr neurons provides a channeling mechanism largely preserving the
corticostriatal inputs through the SNr (Deniau and Chevalier, 1992; Deniau and Thierry, 1997;
Deniau et al., 1996; Mailly et al., 2001, 2003) cats (Tokuno et al., 1990) primates (Alexander et al.,
1986; Kitano et al., 1998; Tokuno et al., 1993).
Early interpretation of the role of the SNr in movement can be summarized by the
“disinhibition model,” which indicates that tonic firing of the SNr inhibits downstream motor regions
from engaging in unwanted movements. A decrease in SNr neural activity around the time of
movement initiation effectively disinhibits downstream structures, allowing movements to occur
[(Hikosaka et al., 2006), Figure 1.1]. However, as described in the final section of this chapter, the
disinhibition model fails to account for several observations. Here I
Figure 1.1 Summary of the
disinhibition model of basal
ganglia control.
(A) Simplified view of the
saccade control system.
Cortex sends excitatory
projections to the SC and BG
resulting in excitation of the
SC through both pathways.
(B) Saccade related
mechanism of the BG. Tonic
inhibition from the SNr
prevents unwanted
movements via inhibition of
the SC. Disruption of this
tonic activity mediated by the
caudate nucleus (CD)
disinhibits the SC, permitting
movement initiation.
aim to expand upon the basic understanding of the SNr in its role in
movement initiation and action selection to determine how the SNr functions more diversely and
what role it plays in modulating unique contexts of decision making. Specifically, I test the
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hypothesis that preparation for internally-specified movements would be promoted relative to
otherwise-identical stimulus-guided movements, by first determining whether SNr activity preceding
movement predicts the direction of upcoming movement and second by testing whether SNr activity
preceding movements depends on whether the movement direction is internally-specified or stimulusguided. I focus on these results with the consideration of how activity in the SNr influences activity in
the SC to modulate action selection and movement initiation.
Superior colliculus
The superior colliculus (SC) serves as a major hub in translating sensory stimulation into
motor commands producing egocentric orienting movements towards or away from a stimulus.
Information from external sensory stimuli is coupled with cognitive factors to inform decisions
(Gandhi and Katnani, 2011) and guide behavior.
Several studies have suggested that SC activity - directly modulated by specific sensory
modalities - reflects decision making variables (Horwitz et al., 2004; Kim and Basso, 2008; Lee and
Keller, 2006). Recently the SC has been implicated in playing a more general role in decision making,
by displaying decision related activity in response to sensory cues not directly processed by the SC
(Felsen and Mainen, 2012). I believe this makes the SC an especially interesting target for the
investigation of decision modulation.
Behavioral responses of neurons in the SC have been divided into three categories based on
their activity profiles during visual saccade tasks (Munoz and Guitton, 1991; Munoz and Wurtz,
1993, 1995). The classes include burst, buildup and fixation neurons. Burst neurons emit high
frequencies of discharge prior to saccade initiation. Buildup neurons display low level discharge that
builds gradually during sensorimotor integration frequently transitioning into a high frequency burst
during saccade initiation. Finally, fixation neurons discharge at a tonic rate and pause only during
some saccades. The designation of these classes of neurons has helped shape the view of how the SC
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modulates the decisions and movements required of visually stimulated saccade tasks. However, this
limited view of the SC does not account for responses to stimuli not processed in the SC.
The functional role of the SC has been continuously refined, from implications in spatial
attention, to directing motor output, to involvement in both sensory dependent and sensory agnostic
target selection. Here I set out to determine how the SC, as a target of the SNr, functions in decision
making under unique contexts of stimulus presentation, aiming to test the hypothesis that target
selection in the SC depends on the context in which those targets are selected (internally-generated vs
stimulus-guided) by first examining whether SC activity preceding movement depends on whether
the movement direction is internally-specified or stimulus-guided and next by testing whether
unilateral inhibition of the SC differentially affects the selection of internally specified vs stimulus
guided actions.
Superior colliculus anatomy. The SC receives input from a numerous array of brain regions.
To cover them all would be beyond the scope of this research, so instead I’ll focus on those most
relevant to the work presented here. The SC receives glutamatergic, excitatory input from several
regions of the cortex, and GABAergic inhibitory signals from the SNr/BG. These projections
converge in discrete zones of the deep subdivision of the SC (Illing and Graybiel, 1985) resulting in
compartmentalized organization throughout the region.
The SC is organized into superficial, intermediate and deep functional subdivisions. The
superficial layer receives primarily visual input retinotopically organized from the retina and the
cortex. The intermediate and deep layers - collectively referred to as the deep layers - receive visual,
somatosensory, and auditory sensory input (May, 2006). The motor map of the intermediate layer
corresponds to the retinotopic visual sensory map lying above it (Mays and Sparks, 1980), with
somatosensory and auditory maps also present in the SC; mouse (Drager and Hubel, 1975), monkey
(Groh and Sparks, 1996; Jay and Sparks, 1987), rat (McHaffie et al., 1989). Multisensory
convergence from both cortical and noncortical influences enhance the SC’s role in converting
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sensory information into motor action. These polysensory neurons are common in some regions of
SC; cat (Meredith et al., 1992), monkey (Jay and Sparks, 1987; Wallace et al., 1996), and include
over half the tectoreticulospinal neurons in cats (Meredith et al., 1992). These neurons produce
enhanced responses when stimuli of different modalities have the same location. Most sensory inputs
end in discrete patches within the SC, however extensive dendritic fields of predorsal bundles allow
these cells to gather information from broad areas of the SC (May, 2006).
GABAergic input to the superior colliculus from the substantia nigra pars reticulata
In general, the basal ganglia are thought to control movement initiation via phasic release
from tonic inhibition of downstream motor structures (Gulley et al., 2002; Mink, 1996; Mink and
Thach, 1993); previously referred to as the “disinhibition model.” The GABAergic projection from
the SNr to the SC mediates basal ganglia control of orienting movements to spatial targets (Hikosaka
et al., 2006; Utter and Basso, 2008). Accordingly, some of the first studies of SNr activity, SC
activity, and orienting movements were consistent with the disinhibition model: in tonically-active
nigrotectal neurons in primates and cats, a decrease in activity corresponded to a burst of activity in
the ipsilateral SC and the initiation of a contraversive orienting movement (Hikosaka and Wurtz,
1983b; Joseph and Boussaoud, 1985). A similar relationship between the activity of SNr and
tectospinal SC motor output neurons was observed in rats (Chevalier et al., 1985). Projections of the
nigrotectal pathway are segregated in a manner similar to the organization of the output channels
from the striatum to the SNr and from the SC to the brainstem (Deniau and Chevalier, 1992;
Redgrave et al., 1992; Williams and Faull, 1985). These studies suggest a simple serial process by
which specific striatonigral neurons inhibit specific nigrotectal neurons, which control the initiation of
orienting movements to spatial targets (Hikosaka et al., 2000). However, several pieces of evidence
complicate this view, and suggest that SNr input to the SC may play a broader role in motor control,
perhaps including the selection of targets for orienting movements (Basso and Sommer, 2011; Deniau
et al., 2007; Shires et al., 2010).
12
One set of studies focused on the relationship between SNr activity and orienting movements.
These studies showed that the activity of many SNr neurons is modulated well before movement
initiation (Handel and Glimcher, 1999), depends on the magnitude of reward associated with the
movement (Bryden et al., 2011; Handel and Glimcher, 2000; Sato and Hikosaka, 2002), and depends
on the number of potential targets for movements (Basso and Wurtz, 2002). Notably, SNr activity
was often found to increase preceding (and even during) contralateral movement initiation (Gulley et
al., 2002; Handel and Glimcher, 1999; Sato and Hikosaka, 2002). In addition, microstimulating SNr
neurons had a greater effect on saccades to remembered targets than on visually-guided saccades
(Basso and Liu, 2007), particularly when the stimulation occurred during the delay period preceding
saccade initiation (Mahamed et al., 2014). Thus, the relationship between SNr activity and
contraversive orienting movements is more complicated than the disinhibition model would predict.
A second set of studies suggesting a broader role for SNr input to the SC examined the
anatomy and physiology of this input. First, while the disinhibition model is based on ispilaterallyprojecting (i.e., “uncrossed”) nigrotectal neurons, some (“crossed”) nigrotectal neurons project
contralaterally (Beckstead et al., 1981; Chevalier et al., 1981; Deniau et al., 1977; Gerfen et al., 1982;
Jayaraman et al., 1977; Liu and Basso, 2008), and a small number project to both SCs (Cebrián et al.,
2005). Given that crossed nigrotectal projections are also inhibitory, they would appear to promote
movements ipsiversive to the phasically-inactivated SNr, inconsistent with the disinhibition model.
Second, in addition to the influence of SNr activity on bursting SC neurons that initiate movements
(Hikosaka and Wurtz, 1983b), SNr input modulates the activity of buildup SC neurons (Liu and
Basso, 2008) thought to subserve the selection of targets for movement. Finally, uncrossed nigrotectal
neurons project to GABAergic SC neurons – which may mediate the inhibition between SC circuits
representing competing movements – as often as they project to glutamatergic SC neurons (Kaneda et
al., 2008), further suggesting a broader role for the SNr than simple disinhibition.
13
Given these findings, how might input from the SNr modulate SC activity underlying the
selection of orienting movements? Can these findings be reconciled with the idea that the SNr
controls motor output via phasic release from tonic inhibition? One possibility is that the diversity of
SNr activity preceding movement can be explained by the diversity of downstream targets of SNr
neurons. SNr neurons project to several motor-related regions besides the SC, including the thalamus
and the pedunculopontine tegmental nucleus (Saitoh et al., 2003; Utter and Basso, 2008), as well as
non-motor regions such as the substantia nigra pars compacta (Pan et al., 2013; Saitoh et al., 2004;
Tepper et al., 1995). The range of activity profiles observed in SNr may reflect the distinct roles of
these downstream pathways. Even within the nigrotectal projection, the targeted SC (ipsilateral or
contralateral to the source of the projection) may explain why the activity of some SNr neurons
increase, while others decrease, preceding identical movements. Specifically, a crossed nigrotectal
neuron exhibiting an increase in activity, and an uncrossed nigrotectal neuron exhibiting a decrease in
activity, would promote the common goal of contraversive movement (relative to the side of the SNr).
This idea is supported by a study in cat that found that the activity of crossed nigrotectal neurons
increases, while the activity of uncrossed neurons decreases, in response to visual stimuli (Jiang et al.,
2003). Similarly, it is possible that the activity profile relates to the type of neurons (e.g.,
glutamatergic or GABAergic) to which the SNr neuron projects. Given that some neurons have
complex activity profiles that change during the course of the trial (Handel and Glimcher, 1999), it
may be the case that individual neurons contribute to multiple aspects of motor control, including
selecting the target, planning the movement, and initiating the movement (Basso and Sommer, 2011).
Future experiments can begin to test these ideas by targeting recordings to specific types of SNr
neurons – for example, based on whether they project to the ipsilateral or contralateral SC – using
techniques like antidromic stimulation (Hikosaka and Wurtz, 1983b) or optogenetics (Gradinaru et
al., 2010; Lima et al., 2009).
14
Although many details remain to be determined, it is clear that the SNr plays a broader role in
modulating SC activity than via simple disinhibition. As discussed in the next chapter, one idea is that
the SNr provides value-related information to the target selection process by biasing activity in SC
circuits such that spatial targets associated with the highest value are most likely to be selected
(Hikosaka et al., 2006). In Chapter 2, I examine this prospect and build upon it to suggest that perhaps
more generally, SNr activity is modulated by internally-specified variables rather than reward value
alone.
15
CHAPTER II
BASAL GANGLIA OUTPUT REFLECTS INTERNALLY-SPECIFIED MOVEMENTS
Introduction
As we interact with the world, our movements are selected based on external sensory stimuli
and internal variables representing action value, learned stimulus-response contingencies, and prior
experiences (Gold and Shadlen, 2007). Selecting the movement associated with the most desirable
outcome requires appropriately weighting each of these factors. While the neural substrates for
movements based on external sensory stimuli have been the focus of much research (Hall and
Moschovakis, 2003), where, how, and when internal goals influence movement selection is less well
understood. The basal ganglia (BG) are known to be involved in motor control (Hikosaka and Wurtz,
1989; Mink, 1996), contributing to movement selection by modulating inhibition on competing
downstream motor structures (Basso and Wurtz, 2002; Di Chiara et al., 1979; Hikosaka and Wurtz,
1985). In particular, the BG have been thought to bias the selection of movements towards those
associated with the highest value (Hikosaka et al., 2006). This “value-biasing” hypothesis is supported
by much evidence showing that activity in several BG nuclei is modulated, prior to stimulus
presentation, by reward expectation (Bryden et al., 2011; Handel and Glimcher, 2000; Hikosaka et al.,
2006; Kawagoe et al., 1998; Sato and Hikosaka, 2002) such that movements toward high-value targets
are disinhibited relative to movements toward low-value targets (Hikosaka et al., 2006; Lauwereyns et
al., 2002). Anatomical evidence is consistent with a primary role for the BG in mediating the integration
of value-based information into motor plans (Bolam et al., 2000; Gerfen and Surmeier, 2011). However,
movement selection may also be guided by other internal representations, such as recent movements
and their outcomes (Corrado et al., 2005; Fecteau and Munoz, 2003; Lau and Glimcher, 2005). I
therefore asked whether BG activity mediates the influence of internal goals, in addition to value, on
movement selection.
16
I reasoned that, if this were the case, BG output would differ when selecting equally valuable
stimulus-guided and internally-specified movements. Specifically, I would expect that internallyspecified movements would be promoted relative to otherwise-identical stimulus-guided movements,
just as more valuable movements have been shown to be promoted relative to otherwise-identical less
valuable movements (Hikosaka et al., 2006; Sato and Hikosaka, 2002). Notably, it has been proposed
that Parkinsonian patients exhibit more pronounced bradykinesia when initiating internally-specified
than stimulus-guided movements because the latter engage pathways outside of the BG (Glickstein and
Stein, 1991). However, whether the BG themselves are differentially engaged by these two types of
movements has not been tested.
I distinguished between these two possibilities by recording from neurons in the substantia
nigra pars reticulata (SNr), an output nucleus of the BG critical for orienting movements (Basso and
Sommer, 2011; Handel and Glimcher, 1999; Hikosaka and Wurtz, 1983a), in mice performing a
behavioral task in which a sensory stimulus either was or was not informative of the rewarded direction
of an orienting movement. Using a design akin to that of other recent studies (Ito and Doya, 2015;
Pastor-Bernier and Cisek, 2011; Seo et al., 2012), in alternating blocks of trials, the rewarded direction
was either determined by a sensory cue or by internal representations informed by recent trial history.
Critically, I designed the task such that correct movements were equally valuable in both conditions. I
found that SNr activity predictably differed between these two conditions, supporting the idea that the
BG mediate the influence on movement selection of internal goals. I interpret these results, in the
context of a simple model of BG output (Hikosaka et al., 2006), as suggesting that internally-specified
movements may be promoted over stimulus-guided movements by BG activity.
Materials and methods
Animal subjects. All experiments were performed according to protocols approved by the
University of Colorado School of Medicine Institutional Animal Care and Use Committee. I used male
adult C57BL/6J mice (n = 4, determined by estimating the number of neurons required for the analyses
17
and by the number of neurons recorded per mouse in initial experiments; aged 7 - 14 months at the start
of experiments; Jackson Labs) housed in a vivarium with a 12-hour light/dark cycle with lights on at
5:00 am. Food (Teklad Global Rodent Diet No. 2918; Harlan) was available ad libitum. Access to water
was restricted to the behavioral session to motivate performance; however, if mice did not obtain ~1
ml of water during the behavioral session, additional water was provided for ~2 - 5 minutes following
the behavioral session (Smear et al., 2011; Thompson and Felsen, 2013). All mice were weighed daily
and received sufficient water during behavioral sessions to maintain >85% of pre-water restriction
weight.
Behavioral task. In general, mice were first trained to perform an odor-guided spatial choice
task – which was comprised of “stimulus-guided” (SG) trials – as described in Stubblefield et al. (2013),
and were then trained to perform “internally-specified” (IS) trials. Briefly, each mouse was waterrestricted and trained to interact with three ports (center: odor port; sides: reward ports) along one wall
of a behavioral chamber (Island Motion). In each trial, the mouse entered the odor port, triggering the
delivery of an odor; waited 488 ± 104 ms (mean ± SD) for a go signal (auditory tone); exited the odor
port; and entered one of the reward ports (Figure 2.1A). Premature exit from the odor port resulted in
the unavailability of reward on that trial. Odors were comprised of binary mixtures of (+)-carvone and
(-)-carvone, commonly perceived as caraway and spearmint, respectively; an enantiomeric odor pair
was selected to control for differences in molecular structure of odorant stimuli. In each SG trial, one
of seven odor mixtures was presented via an olfactometer (Island Motion): volume (+)-carvone/(-)carvone = 95/5, 80/20, 60/40, 50/50, 40/60, 20/80, or 5/95. Mixtures in which (+)-carvone > (-)-carvone
indicated reward availability only at the right port and mixtures in which (-)-carvone > (+)-carvone
indicated reward availability only at the left port [we therefore refer to (-)-carvone as the “left odor”
(e.g., Figure 2.1D) for simplicity]. In trials in which (+)-carvone = (-)-carvone, the probability of reward
at the left and right ports, independently, was 0.5. Reward, consisting of 4 μl of water, was delivered
by transiently opening a calibrated water valve 10 - 100 ms after reward port entry. Odor and water
18
delivery were controlled, and port entries and exits were recorded, using custom software (available at
https://github.com/felsenlab; adapted from C. D. Brody) written in MATLAB (MathWorks).
Mice learned to perform SG trials within ~48 sessions (1 session/day); detailed training stages
are described in Stubblefield et al. (2013). Mice required an additional ~5 sessions to learn to perform
interleaved blocks of SG and IS trials. In every IS trial the 50/50 mixture was presented, and reward
was available only at one side throughout the block. Mice were first introduced to interleaved blocks,
each of which required 25 correct trials to advance to the next block. Once they performed ~70% of
trials in the session correctly, the number of correct trials required per block was increased to 50. Mice
performed 5 blocks (SG, IS, SG, IS, SG) per session (Figure 2.1B); the side associated with reward
switched between each IS block. Upon completing training, mice were implanted with microdrives for
neural recording (see below). During each of the 54 recording sessions, mice performed 321.81 ± 89.49
(mean ± SD) trials.
Surgery. Details of the surgical procedure are provided in Thompson and Felsen (2013).
Briefly, once the mouse was fully trained on the task, it was anesthetized with isoflurane and secured
in a stereotaxic device, the scalp was incised and retracted, 2 small screws were attached to the skull,
and a craniotomy targeting the left SNr was performed, centered at 3.27 mm posterior from bregma and
1.4 mm lateral from the midline (Paxinos and Watson, 2004). A VersaDrive 4 microdrive (Neuralynx),
containing 4 independently adjustable tetrodes, was affixed to the skull via the screws, luting (3M), and
dental acrylic (A-M Systems). A second small craniotomy was performed in order to place the ground
wire in direct contact with the brain. After the acrylic hardened, a topical triple antibiotic ointment
(Major) mixed with 2% lidocaine hydrochloride jelly (Akorn) was applied to the scalp, the mouse was
removed from the stereotaxic device, the isoflurane was turned off, and oxygen alone was delivered to
the animal to gradually alleviate anesthetic state. Mice were administered sterile isotonic saline (0.9%)
for rehydration and an analgesic (Ketofen; 5 mg/kg) for pain management. Analgesic and topical
19
antibiotic administration was repeated daily for up to 5 days, and animals were closely monitored for
any signs of distress.
Electrophysiology. Neural recordings were collected using four tetrodes, wherein each tetrode
consisted of four polyimide-coated nichrome wires (Sandvik; single-wire diameter 12.5 μm) gold
plated to 0.2-0.4 MΩ impedance. Electrical signals were amplified and recorded using the Digital Lynx
S multichannel acquisition system (Neuralynx) in conjunction with Cheetah data acquisition software
(Neuralynx).
Tetrode depths were adjusted approximately 23 hours before each recording session in order
to sample an independent population of neurons across sessions. To estimate tetrode depths during each
session I calculated distance traveled with respect to rotation fraction of the screw that was affixed to
the shuttle holding the tetrode. One full rotation moved the tetrode ~250 μm and tetrodes were moved
~62.5 μm between sessions. The final tetrode location was confirmed through histological assessment
using electrolytic lesions and tetrode tracks (see below).
Offline spike sorting and cluster quality analysis was performed using MClust software
(MClust-3.5, A.D. Redish, et al.) in MATLAB. Briefly, for each tetrode, single units were isolated by
manual cluster identification based on spike features derived from sampled waveforms (Figure 2.2B).
Identification of single units through examination of spikes in high-dimensional feature space allowed
us to refine the delimitation of identified clusters by examining all possible two-dimensional
combinations of selected spike features. I used standard spike features for single unit extraction: peak
amplitude, energy (square root of the sum of squares of each point in the waveform, divided by the
number of samples in the waveform), and the first principal component normalized by energy. Spike
features were derived separately for individual leads. To assess the quality of identified clusters I
calculated two standard quantitative metrics: L-ratio and isolation distance (Schmitzer-Torbert et al.,
2005). Clusters with an L-ratio of less than 0.70 and isolation distance greater than 6.5 were deemed
single units, which resulted in the exclusion of 12% of the identified clusters. Although units were
20
clustered without knowledge of interspike interval, only clusters with few interspike intervals less than
1 ms were considered for further examination. Furthermore, I excluded the possibility of including data
from the same neuron twice by ensuring that both the waveforms and response properties sufficiently
changed across sessions. If they did not, I conservatively assumed that I was recording from the same
neuron, and only included data from one session.
Lesioning and histology. To verify final tetrode location, I performed electrolytic lesions (100
μA, ~1.5 min per lead) after the last recording session. One day following lesion, mice were overdosed
with an intraperitoneal injection of sodium pentobarbital (100 mg/kg) and transcardially perfused with
saline followed by ice-cold 4% paraformaldehyde (PFA) in 0.1 M phosphate buffer (PB). After
perfusion, brains were submerged in 4% PFA in 0.1 M PB for 24 hours for post-fixation and then
cryoprotected for 24 hours by immersion in 30% sucrose in 0.1 M PB. The brain was encased in the
same sucrose solution, and frozen rapidly on dry ice. Serial coronal sections (60 μm) were cut on a
sliding microtome for reconstruction of the lesion site and tetrode tracks. Fluorescent Nissl
(NeuroTrace, Invitrogen) was used to identify cytoarchitectural features of the SNr and verify tetrode
tracks and lesion damage within or below the SNr. Images of SNr (see Figure 2.2A) were captured with
a 10x objective lens, using an LSM 5 Pascal series Axioskop 2 FS MOT confocal microscope (Zeiss).
Analyses and Statistics. All analyses were performed in MATLAB.
Direction preference. To quantify the selectivity of single neurons for movement direction, I
used an ROC-based analysis (Green and Swets, 1966). This analysis calculates the ability of an ideal
observer to classify whether a given spike rate was recorded in one of two conditions (here, preceding
leftward or rightward movement). I defined “preference” as 2(ROCarea – 0.5), a measure ranging from
-1 to 1, where -1 signifies the strongest possible preference for left, 1 signifies the strongest possible
preference for right, and 0 signifies no preference (Feierstein et al., 2006). Statistical significance was
determined with a permutation test: I recalculated the preference after randomly reassigning all firing
rates to either of the two groups arbitrarily, repeating this procedure 500 times to obtain a distribution
21
of values, and calculated the fraction of random values exceeding the actual value. I tested for
significance at α = 0.05. Trials in which the movement time (between odor port exit and reward port
entry) was > 1.5 s were excluded from all analyses. Neurons with fewer than 100 trials of each type
(SG and IS) or with a firing rate below 2.5 spikes/s for either trial type or across the entire session (Fc,
described below), were excluded from all analyses.
Sign of activity change during delay epoch. I calculated the normalized response (NR) for
each neuron as NR = Ft/Fc where Ft is the mean firing rate in the “test” window (delay epoch) and Fc
is the mean firing rate in the “control” window (Sato and Hikosaka, 2002) across all trials in the
preferred direction of the neuron (or, for neurons with no direction preference, across all trials). Since
the structure of my task does not include a natural “control” epoch – i.e., in which the animal is in a
motionless state unaffected by task demands – my control window was defined as the time of odor port
entry to reward port exit (i.e., the duration of the trial). Neurons with NR < 1 were defined as decreasing
and neurons with NR > 1 were defined as increasing (Table 1). Note that, by convention, a decreasing
neuron that decreases more for contraversive than ipsiversive movement would be considered to have
an ipsiversive direction preference (as calculated above), because firing rate is higher for ipsiversive
movement (cf. Sato and Hikosaka, 2002).
Reinforcement learning model. In order to estimate the value associated with each direction
of movement, I iteratively updated the value of each direction in each trial as �
(
� , −
−�
� , −
), where
� , −
� ,
=�
� , −
+
is the reward for the given direction in the previous trial in which
that direction was chosen [0 for unrewarded (which includes trials in which the correct choice was
made but the odor port was exited before the go signal) and 1 for rewarded] and
(we set
is the learning rate
= . ; values of 0.03 and 0.3 did not affect the results). The value of each direction was
updated independently. This estimate is based on the Q-learning algorithm (Sutton and Barto, 1998;
note that I excluded a term for maximum future value because this was independent of the choice on
the current trial). �
� ,
therefore ranged from 0 to 1. Since I calculated �
22
� ,
in each trial of the session
(including SG and IS trials), it tended to start near 0.5 (but was not exactly 0.5) at the beginning of each
IS block. In well-behaved IS blocks, �
� ,
consistently returned to the rewarded port. �
tended to asymptotically approach 1 as the mouse
� ,
was calculated separately for the ipsiversive and
contraversive directions and was only updated in trials in which that direction was selected.
Regression model. To assess the influence of several factors on SNr activity (Figure 2.6), I fit
=
the electrophysiological data with a multi-variable linear regression model of the form
�
�
ℎ �
� � �
�
�
ℎ �
�
�
ℎ �
� ��
�
� � �
�
�
�
� � �
�
ℎ �
+
, where
�
ℎ �
�
ℎ �
+
� ��
�
� ��
is the firing rate during the delay epoch,
− �or an ipsiv�rsiv� choic�
={
in the previous trial,
�or no choic�
�or a contrav�rsiv� choic�
={
−
={
− �or an ipsiv�rsiv� choic�
in the current trial,
�or no choic�
�or a contrav�rsiv� choic�
�
= �
�
,
�
�
,
represents the mean firing rate across trials during the delay epoch, and
�
ℎ �
+
�
+
,
� ��
�
, and
� � �
�
�
�
ℎ �
,
represent the influence on firing rate of the previous
choice, the current choice, the trial type, and the reaction time corresponding to that trial. Positive values
for
�
�
ℎ �
and
�
ℎ �
indicate that firing rate is increased by contraversive choices
and negative values indicate that firing rate is increased by ipsiversive choices. Positive values for
� ��
�
indicate that firing rate is increased by stimulus-guided trials and negative values indicate
that firing rate is increased by internally-specified trials. The sign of
� � �
�
indicates the sign
of the correlation between reaction time and firing rate. I used the MATLAB function fitlm to estimate
the βs and calculate their significance and confidence intervals. I also performed this same regression
analysis with two additional terms, for value [estimated in IS trials with the reinforcement learning
model (see above) and in SG trials as the average performance by mixture ratio within the block (Figure
23
2.2D)], and the interaction between value and trial type. I found that, while the firing rate of some
neurons were influenced by these additional factors, as expected given the value-biasing view of BG
function (Hikosaka et al., 2006), including them – or including the log of the value – did not affect the
overall results shown in Figure 2.6A. To examine how firing rate throughout the trial depended on these
factors (Figure 2.6B, C), I repeated the above analysis with respect to firing rate in overlapping 100 ms
bins, shifted by 10 ms, aligned to three behavioral events: odor valve open, odor port exit and reward
port entry.
Results
Behavioral assay dissociates stimulus-guided and internally-specified movements. I
trained mice on a delayed-response spatial choice task comprised of interleaved blocks of “stimulusguided” (SG) trials, in which the direction of movement is selected based on a sensory stimulus (Uchida
and Mainen, 2003), and “internally-specified” (IS) trials, in which the direction of an otherwiseidentical movement is selected based on internal representations informed by recent trial history (see
Materials and Methods; Figure 2.1A, B). In each trial of the task, the mouse is presented with a binary
odor mixture at a central port, waits for an auditory go cue, and moves to the left or right reward port
for a water reward. In SG trials, the dominant component of the odor mixture – which varies trial by
trial – determines the side at which reward will be delivered, while in IS trials, a balanced mixture of
the two odors is always presented but reward is delivered at only one side throughout the block (see
Materials and Methods; Figure 2.1B). Thus, while both trial types require the mouse to sample the
stimulus, in SG trials the stimulus indicates that the rewarded side is determined by the odor mixture
and in IS trials the stimulus indicates that the rewarded side is determined by the recent history of
choices and outcomes. I found that mice were able to infer (unsignaled) transitions between the SG and
IS blocks and switch their response mode accordingly: during SG blocks, mice were equally likely to
choose the left and right port (Figure 2.1C, gray boxes) reflecting a dependence on the odor mixture
(Figure 2.1D), while during IS blocks, mice reliably returned to the same (rewarded) port in each trial
24
Figure 2.1. Behavioral task and performance.
(A) Timing of events in each trial. The mouse enters the odor port, receives an odor mixture, waits for the
go signal, exits the odor port, moves to one of the reward ports, and receives a water reward for a correct
choice. Gray box, delay epoch. (B) Organization of SG (gray) and IS (white) blocks within a session. All
sessions start with an SG block and alternate between SG and IS blocks. In SG blocks, reward side
corresponds to the dominant odor in the mixture [(-)-carvone, left; (+)-carvone, right]; when the odors are
balanced ([(-)-carvone] = [(+)-carvone]), the probability of reward at both reward ports is 0.5. In IS blocks,
odors are balanced in every trial and reward is available at the same side in each trial. Thickness of
horizontal lines corresponds to probability of reward. SG, stimulus guided; IS, internally specified; L, left;
R, right. (C) Fraction of left choices across block types throughout the session. Dashed line shows an
example session (boxcar smoothed over 7 trials), solid black line shows mean over all sessions (54, from 4
mice), horizontal black lines show block means, horizontal gray lines show ideal block means (if all choices
were correct). To account for different numbers of trials per block across sessions, trials that occur in < 60%
of sessions are excluded. In SG blocks only difficult trials [(+)-carvone/(-)-carvone = 60/40, 50/50, or
40/60] are shown. (D) Mean performance in SG blocks over all sessions, separated by rewarded side of first
IS block in the session. Lines show best fit to p=1/(1+e^(-a-bx)), where x is the proportion of the left odor
[(-)-carvone)] in the mixture, p is the fraction of right choices, and a and b are free parameters. While
choices were slightly biased by the rewarded direction in the first IS block (center panels), they were much
more strongly influenced by the stimulus. (E) Performance in IS blocks. Histograms of percent correct
choices (top) and number of error events (run of consecutive incorrect choices, bottom) across blocks over
all sessions. (F) Mean reaction time in easy SG trials plotted against mean reaction time in IS trials in the
corresponding session, separately for each direction of movement.
25
(Figure 2.1C, white boxes). I quantified performance in IS blocks by calculating, for each block, the
percentage of correct trials and the number of error events, defined as a run of consecutive incorrect
choices (Figure 2.1E). Finally, I reasoned that if a mouse were to recognize that a given trial belonged
to an IS block, it could prepare its movement in advance and would therefore be able to reach the reward
port faster (Niemi and Näätänen, 1981; Poulton, 1950; Seo et al., 2012). Indeed, across the population
of sessions, I found that reaction time – defined as the time from the go cue to reward port entry – was
shorter in IS trials than in the “easy” SG trials in the same session [Figure 2.1F; I used only easy SG
trials (mixture ratios of 95/5, 80/20, 20/80, and 5/95) to control for a potential dependence of reaction
time on difficulty; population of sessions: p = 1.7 x 10-11, paired t-test; individual sessions: IS shorter
than SG in 46/108, SG shorter than IS in 3/108, p < 0.05, Wilcoxon rank-sum test; ipsiversive and
contraversive trials compared separately]. Together, these data suggest that, as intended, the direction
of movement in SG blocks is selected based on the stimulus while the direction of movements in IS
blocks is selected based on recent trial history. I therefore utilized this behavioral assay to compare how
stimulus-guided and internally-specified movements are mediated by the BG, as described below.
SNr activity differs for stimulus-guided and internally-specified movements. If the BG
integrates not only value but also other internal representations, then stimulus-guided and internallyspecified movements may differentially engage the BG despite being equally valuable. In this case, I
would predict that BG output would depend on whether the movement was internally specified or
stimulus guided, and specifically, in my task, on the degree to which recent trial history is informative
of rewarded direction. To test this prediction I examined activity in the SNr, a BG output known to be
involved in orienting movements (Basso and Sommer, 2011; Handel and Glimcher, 1999, 2000,
Hikosaka and Wurtz, 1983a, 1983b). I recorded from 296 well-isolated left SNr neurons (see Materials
and Methods; Figure 2.2) in four mice performing the task. Data from one example neuron are shown
in Figure 2.3A, B, segregated by reward port selected (ipsilateral vs. contralateral to the recording side)
and by trial type (SG vs. IS). The activity of this neuron clearly depends on both movement direction
26
Figure 2.2.
Confirmation of
recording sites and
spike clustering. (A)
Schematic (left) shows
targeted recording extent
(bar) within SNr;
coronal section (right,
3.3 mm caudal from
bregma) shows
representative tetrode
track (arrow) in SNr. (B)
Left, peaks of
waveforms from lead 1
plotted against peaks of
waveforms from lead 3
of one tetrode for a
representative recording
session. Note that
clustering was
performed using
additional features to
those shown here. Red
and green points show
waveform peaks
recorded from neurons
considered to be distinct.
Right, waveforms (mean
± SD) corresponding to
red and green points.
and trial type. To examine these dependencies across the population of neurons I first examined firing
rate during the delay epoch, defined as the time from odor valve open to the time of odor port exit
(Figure 2.1A, gray box), which most directly captures, across trial types, activity underlying selection
of the direction of movement (but since activity in IS trials may, by design, reflect direction selection
even before stimulus delivery, I subsequently examine activity in other epochs). Based on the firing
rate during this epoch in SG and IS trials, I then calculated direction preference (see Materials and
Methods). This value ranges from -1 (strongly “prefers” ipsiversive) to 1 (strongly prefers
contraversive), where 0 represents no preference. I found that 216/296 neurons displayed a significant
direction preference (p < 0.05) during the delay epoch, with about as many preferring ipsiversive
(94/216) as contraversive (122/216) movements (Figure 2.3C). Since SNr activity has been shown to
27
exhibit both movement-related increases and decreases (Bryden et al., 2011; Gulley et al., 1999, 2002;
Handel and Glimcher, 1999; Sato and Hikosaka, 2002), I next asked whether a relationship existed
between direction preference and the sign of activity change during the delay epoch, relative to baseline
(see Materials and Methods). I found that 188/296 neurons exhibited an increase in activity during this
epoch, 91/296 exhibited a decrease, and 17/296 exhibited no change (Table 1), consistent with previous
studies (Bryden et al., 2011; Gulley et al., 1999, 2002, Handel and Glimcher, 1999, 2000; Sato and
Hikosaka, 2002). Within these groups, neurons exhibited a preference for ipsiversive, contraversive, or
neither direction in roughly equal numbers (Table 1).
Preference
Contraversive
Ipsiversive
Nonselective
Total
Increase
88
52
48
188
32%
19%
17%
67%
27
38
26
91
Decrease
10%
14%
9%
33%
Total
115
90
74
279
41%
32%
27%
100%
Table 2.1. Direction preference and activity change during delay epoch. Neurons are grouped by direction
preference and whether activity in the preferred direction increased or decreased relative to baseline (see
Materials and Methods), during the delay epoch. Numbers and percentages of grand total (279) are shown;
note that 17 neurons exhibited no change in activity and are not included here.
I next examined whether activity during the delay epoch of direction-selective neurons (Figure
2.3C, black bars) differed between SG and IS trials, in two complementary ways. First, I examined
whether the difference in activity between ipsiversive and contraversive trials depended on whether the
movement was stimulus guided or internally specified. Across the population, neurons tended to show
a larger difference in firing rate preceding ipsiversive and contraversive movements in IS than in SG
trials [Figure 2.3D; ipsiversive-preferring neurons: p = 2.2 x 10-10, paired t-test; contraversivepreferring neurons: 4.0 x 10-4, paired t-test]. Second, I determined whether neurons were trial-typedependent by comparing firing rates between SG and IS trials in which the selected movement was
correct and in the preferred direction of the neuron; I then repeated this comparison for the antipreferred
direction. For trials in the preferred direction, I found that the activity of approximately half of the
direction-selective neurons was modulated by trial type (101/216; p < 0.05, unpaired t-test), with more
neurons exhibiting higher activity in IS trials than SG trials (84/101 vs. 17/101; p = 2.6 x 10-11, Χ2 test).
28
Conversely, for trials in the antipreferred direction, I again found that the activity of approximately half
of the direction-selective neurons was modulated by trial type (109/216; p < 0.05, unpaired t-test;
158/216 direction-selective neurons were modulated by trial type in at least one direction), but that
more neurons exhibited higher activity in SG trials than IS trials (76/109 vs. 33/109; p = 3.8 x 10-5, Χ2
test). Therefore, while I found that neurons were about equally likely to prefer upcoming ipsiversive
and contraversive movements (Figure 2.3C), their activity depended, in a predictable manner, on trial
type (Figure 2.3D).
Figure 2.3. SNr activity during the delay epoch depends on movement direction and trial type. (A)
Rasters for an example neuron grouped by movement direction (rows) and trial type (columns). For each
raster, each row shows spikes (black ticks) in one trial, aligned to time of odor valve open (red line) and sorted
by duration of delay epoch. Green ticks, times of go signal; blue ticks, times of odor port exit. Fifty pseudorandomly selected trials are shown per group. (B) Peri-event histograms showing average activity, separately
by direction, in stimulus-guided (left) and internally-specified (right) trials. Shading, ± SEM. Histograms are
smoothed with a Gaussian filter (σ = 15 ms). Ipsi., ipsiversive; Contra., contraversive. (C) Histogram of
direction preferences during delay epoch across population of neurons. Arrowhead corresponds to example
neuron in A. (D) Difference in delay-epoch firing rate between ipsiversive and contraversive trials in SG vs.
IS trials in the same session, separately for ipsiversive-preferring neurons (left subpanel, corresponding to
left black bars in C) and contraversive-preferring neurons (right subpanel, corresponding to right black bars
in C). Only correct trials are included; all choices on 50/50 SG trials were considered correct regardless of
whether they were rewarded. Dashed lines show x = 0, y = 0, and x = y. Red marker corresponds to example
neuron from A and B. FR, firing rate.
29
While these findings suggest that the BG differentially mediate internally-specified and stimulusguided movements, as I had predicted, a few differences between SG and IS trials may have contributed
to this observation. I therefore sought to identify these differences and determine their influence, in
several ways. First, I reasoned that, if neural activity indeed reflects trial type, firing rate would
systematically change during the IS block as the mouse increasingly based its movement choice on
internal representations instead of the stimulus (recall that the transitions from SG to IS blocks were
unsignaled). To test this idea, I calculated the correlation between the trial-by-trial firing rate during
the delay epoch and the extent to which the mouse had learned that its movement choice should be
internally specified, estimated with a reinforcement learning algorithm (see Materials and Methods). I
performed this analysis on the 158 neurons with firing rates that depended on both direction and trial
type, separately for choices in the preferred and antipreferred direction. Figure 2.4A shows data from
an example neuron displaying a significant correlation for trials in the preferred direction of the neuron
(r = 0.65, p = 7.0 x 10-9), and no correlation for trials in the antipreferred direction (r = 0.066, p = 0.60).
Figure 2.4. Activity depends on the extent to which movements are internally specified. (A) Firing rate
during delay epoch plotted as a function of the value of the rewarded side, estimated via reinforcement
learning (� � , ), for both IS blocks in a session, for one example neuron. Each circle corresponds to one trial.
(B) Correlations (as in panel A) for ipsiversive movement plotted against contraversive movement, for the
population of ipsiversive-preferring neurons (left black bars in Figure 3C) with activity that depended on trial
type (SG vs. IS). Each circle corresponds to one neuron. (C) Same as B, for contraversive-preferring neurons
(right black bars in Figure 3C). Black box corresponds to example neuron from A.
30
Overall, 77/158 neurons exhibited a significant correlation (p < 0.05) between firing rate and the
number of consecutive correct trials for either direction [Figure 2.4B, C; 35/77 for trials in the preferred
direction (red circles), 29/77 for trials in the antipreferred direction (blue circles), and 13/77 for trials
in both directions (purple circles)], with more positive correlations for trials in the preferred direction
(p = 2.4 x 10-6, Χ2 test) and negative correlations for trials in the antipreferred direction (p = 5.9 x 10-6,
Χ2 test), as I would expect given the pattern of results shown in Figure 2.3D. These results support the
idea that SNr activity reflects the degree to which movements are selected based on internal
representations.
Modulation of SNr activity by task-relevant variables. I next examined the potential
influence of other factors on the observed difference in neural activity during SG and IS trials. One
difference between these trial types, by design, is that in IS trials the decision (to move left or right) is
relatively easy, while in some SG trials this decision is more difficult (Figure 2.1D). The difficulty of
this decision – or an associated variable, such as uncertainty, or the estimated value of each movement
direction – could, in principle, affect SNr activity. Were this the case, I would expect to observe a
difference in activity between those SG trials requiring an easy discrimination (mixture ratios of 95/5,
80/20, 20/80, and 5/95) and those SG trials requiring a “difficult” discrimination (mixture ratios of
60/40, 50/50, and 40/60), since easy trials resulted in a larger fraction of correct choices (p = 3.4 x 10 27
, paired t-test; see Figure 2.1D), corresponding to a higher likelihood of reward. I therefore compared
firing rate during the delay epoch between easy and difficult SG trials, separately for trials in the
ipsiversive (Figure 2.5A) and contraversive (Figure 2.5B) direction, for the 216 direction-selective
neurons (Figure 2.3C, black bars). I found that the activity of some individual neurons depended on
difficulty (or an associated variable) (ipsiversive direction: 39/216 neurons; contraversive direction:
31/216 neurons, p < 0.05, 1-way ANOVA across mixture ratios, Figure 2.5A, B), as would be predicted
by the value-biasing view of BG function. However, there was little overlap (purple circles) between
this small population of difficulty-dependent neurons (blue circles) and those neurons that I classified
31
as trial-type-dependent [ipsiversive direction: 21/109 trial-type-dependent, and 18/107 non-trial-typedependent, neurons exhibited difficulty dependence (these ratios did not differ: p = 0.32, Χ 2 test);
contraversive direction: 16/101 trial-type-dependent, and 15/115 non-trial-type-dependent, neurons
exhibited difficulty dependence (these ratios did not differ: p = 0.56, Χ2 test); p < 0.05, 1-way ANOVA
across mixture ratios]. These results suggest that differences in decision difficulty, uncertainty, and the
value associated with the direction of movement do not account for trial-type dependence or the
differences in activity between SG and IS trials shown in Figure 2.3D.
Figure 2.5. Dependence of firing rate on trial type cannot be explained by discrimination difficulty or
an associated variable. (A) Mean normalized change from baseline (Fc, see Materials and Methods) during
delay epoch of easy vs. difficult ipsiversive SG trials of direction-selective neurons (black bars in Figure 3C).
Each circle corresponds to one neuron. Red circles indicate that activity differs between SG and IS trials, and
does not depends on mixture ratio (or an associated variable such as discrimination difficulty). (B) Same as
A, for contraversive trials.
I then examined whether reaction time (which differs between trial types; Figure 2.1F) and the
choice on the previous trial (which, by design, is more likely to correlate with the current choice in IS
blocks than SG blocks; Figure 2.1C) could explain the difference in activity between SG and IS trials.
Preliminary analyses of each of these factors in isolation indicated that, as opposed to discrimination
difficulty or an associated variable such as value (Figure 2.5), they often correlated with firing rate
during the delay epoch. In order to determine the relative influence of these factors, as well as other
32
factors that correlate with firing rate – current choice (Figure 2.3C) and trial type (Figure 2.3D) – on
neural activity during the delay epoch, I performed a linear regression analysis with previous choice,
current choice, trial type and reaction time as predictor variables (see Materials and Methods). By
considering all of these factors simultaneously, this analysis provides an unbiased method for
determining their influence on neural activity.
Across the population of neurons, the vast majority were influenced by at least one of these
factors (281/296, p < 0.05), and I found neurons with firing rates influenced by all possible
combinations of factors (Figure 2.6A). Consistent with the results shown in Figures 2.3C and 2.3D,
respectively, this analysis confirms that, as the mouse is selecting its direction of movement, the activity
of many SNr neurons was modulated by current choice (167/296) and trial type (142/296). I also found
that the activity of many neurons depended on reaction time (111/296). Surprisingly, the largest fraction
of neurons exhibited activity modulated by previous choice (188/296). This is particularly interesting
because this variable is critical for determining, in an IS block, which direction is associated with
reward.
Given that movements can initially be selected earlier in IS than SG trials (Figure 2.3A, B), I
wondered how firing rate at other times during the trial depended on previous choice, current choice,
trial type, and reaction time. I therefore extended my regression analysis to examine how firing rate is
modulated by these factors during overlapping 100 ms windows throughout the trial (see Materials and
Methods). In the example shown in Figure 2.6B, the activity of the neuron is modulated by previous
choice (cyan line) – i.e., the confidence interval (shading) for this coefficient does not include 0 – even
before the odor is delivered (odor valve open), and this influence persists until the movement is initiated
(odor port exit). The current choice (black line) does not influence neural activity until ramping up just
prior to movement initiation, but then continues to exert an influence for the remainder of the trial. The
trial type (magenta line), meanwhile, exerts a moderate influence on the firing rate – specifically,
activity is higher for IS trials – until just before movement initiation, after which this influence is
33
diminished. Reaction time was a relatively poor predictor of firing rate (not shown, for clarity). To
examine the dynamics of the weights of these factors across the population of neurons, I calculated the
fraction of neurons with significant weights in each time window (Figure 2.6C). The pattern of results
Figure 2.6. SNr activity is influenced by several task-relevant factors throughout the trial. (A) Venn
diagram showing the number of neurons whose firing rate during the delay epoch was significantly influenced
(p < 0.05) by previous choice, current choice, trial type, reaction time, and all combinations of these factors,
or by no factor. (B) β coefficients estimated based on firing rate in 100 ms bins aligned to three different trial
events for one example neuron (reaction time coefficient not shown, for clarity). Shading, ± 95% confidence
interval. (C) Fraction of neurons with a significant β coefficient corresponding to each predictor variable in
each 100 ms bin, aligned as in panel B. All 296 neurons were included in this analysis.
was similar to that shown in the example neuron (Figure 2.6B). Before the odor is delivered, the firing
rates of about half of the neurons are influenced by the previous choice. However, as the odor is
sampled, the influence of the previous choice decreases and the influence of the current choice
increases, with about two thirds of neurons exhibiting a significant weight for the current choice by the
time the movement is initiated. Interestingly, trial type modulates the activity of about one third of
neurons throughout the trial. The influence of reaction time is strongest during movement but has
relatively little influence on the population (not shown). These results indicate that SNr activity
dynamically reflects trial type and other task-relevant variables throughout the trial, as would be
34
expected if the BG are differentially involved in mediating stimulus-guided and internally-specified
movements.
Discussion
I have shown that SNr activity preceding orienting movements depends on whether the
direction of movement was indicated by a stimulus or was specified by internal variables (Figures 2.3,
4). While I designed the task such that correct movements were equally valuable across these conditions
(Figure 2.1), given imperfect (and stochastic) choice behavior, the experienced value was not
necessarily identical. However, the dependence on trial type could not be accounted for by differences
in the estimated value of each movement direction – or an associated variable, such as difficulty or
uncertainty in selecting the movement – between the trial types (Figure 2.5). In some neurons this
dependence could be explained, in part, by the choice on the previous trial (Figure 2.6A), which is
informative of the rewarded direction in IS blocks. Over the course of the trial, while the influence on
SNr activity of the previous choice decreased and that of the current choice increased, as might be
expected given the demands of the task, the influence of trial type remained relatively constant (Figure
2.6C). These results suggest that the SNr is differentially engaged by stimulus-guided and internallyspecified movements.
Previous studies in primates have shown that movement-related SNr activity was higher for
memory-guided than visually-guided saccades (Hikosaka and Wurtz, 1983a), and that SNr stimulation
had a larger effect on memory- than visually-guided saccades (Basso and Liu, 2007). Movements
selected based on a remembered stimulus can be thought of as internally specified, and in this sense my
results (Figure 2.3D) are consistent with these findings and demonstrate that they generalize across
species and movement types (saccades and full-body orienting). However, the rewarded direction in
both memory- and visually-guided trials was indicated by the stimulus, which was not the case in the
IS trials, in which I sometimes observed that direction preference emerged even before stimulus
delivery (see example in Figure 2.3A, B). Further, the difference between direction preference in IS
35
and SG trials during the delay epoch was correlated with the difference between preference in IS and
SG trials during the epoch from odor port entry to odor valve open (r = 0.47, p = 4.8 x 10 -18). These
results demonstrate that, in IS trials, the direction of movement was initially selected independent of
the stimulus, which contributes to the difference in activity during the delay epoch that I observe
between SG and IS trials. In addition, while Hikosaka and Wurtz (1983a) examined only neurons that
exhibited a decrease in activity around the time of contraversive saccades, I examined increasing and
decreasing neurons that prefer both ipsiversive and contraversive movement (Gulley et al., 1999, 2002;
Handel and Glimcher, 2000; Sato and Hikosaka, 2002) and found that all of these groups exhibited a
difference in activity between stimulus-guided and internally-specified movements. Therefore, the
differences I observed between internally-specified and stimulus-guided movements extend our
understanding of SNr function.
Interestingly, patients with Parkinson’s disease and other BG pathologies have been reported
to exhibit greater deficits in the initiation of internally-specified than visually-guided movements
(Azulay et al., 1999; Forssberg et al., 1984; Laplane et al., 1984). While the neural basis for this
phenomenon is not well understood and remains an active area of study (Distler et al., 2016), it has
been suggested that visual cues engage (intact) sensorimotor pathways outside of the BG, such as the
cerebellum (Glickstein and Stein, 1991). My results suggest that differential processing of internallyspecified and visually-guided movements within the BG themselves may also contribute to this clinical
observation.
As noted above, other studies have found that movement-related SNr activity is modulated by
the relative value associated with a movement (Bryden et al., 2011; Sato and Hikosaka, 2002), including
whether the movement will be rewarded at all (Handel and Glimcher, 2000). This value dependence
likely arises from dopaminergic input to the BG that is thought to convey reward-related information
(Schultz et al., 1997), and has been accounted for by a model in which, prior to stimulus presentation,
reward expectation modulates striatal inputs to the SNr in order to bias downstream superior colliculus
36
(SC) activity such that the most valuable movement is facilitated (Hikosaka et al., 2006; Wolf et al.,
2015). I propose that a similar model can also explain how internally-specified movements, more
generally, are facilitated (Figure 2.7).
To illustrate this idea, consider how, given the data
presented here, the relative activity between ipsiversivepreferring left and right SNr neurons would relate to an
upcoming rightward movement (I consider relative, rather
Figure 2.7. Model proposing how the
observed activity of ipsiversivepreferring SNr neurons could
facilitate
internally-specified
movements relative to stimulusguided movements. (A) Line
thickness corresponds to level of
activity. Activity preceding stimulusguided rightward movement. A left
SNr neuron is moderately weakly
active, providing moderately weak
inhibition to the left SC (superior
colliculus). A right SNr neuron is
moderately strongly active, providing
moderately strong inhibition to the
right SC. This pattern of activity in the
SC moderately promotes rightward
movement. (B) Activity preceding
internally-specified
rightward
movement. Compared to A, a left SNr
neuron is very weakly active,
providing very weak inhibition to the
left SC; and a right SNr neuron is very
strongly active, providing very strong
inhibition to the right SC. This pattern
of activity in the SC strongly promotes
rightward movement.
than absolute, activity since this is most directly relevant to the decision – move left vs. move right –
required by my task). Left and right SNr neurons would exhibit a larger difference in activity in IS trials
than in SG trials (Figure 2.3D, left). If ipsiversive-preferring SNr neurons primarily project to the
ipsilateral SC (Hikosaka and Wurtz, 1983b), then a downstream left SC neuron, the activity of which
promotes rightward movement (Felsen and Mainen, 2012; Horwitz and Newsome, 2001; Stubblefield
et al., 2013) will receive less inhibition from the left SNr when the movement is internally specified,
thereby facilitating rightward movements that are internally specified (Figure 2.7). Preceding the same
movement, contraversive-preferring left and right SNr neurons would also exhibit a larger difference
in activity in IS trials than in SG trials (Figure 2.3D, right). If contraversive-preferring SNr neurons
37
comprise the “crossed” projection to the contralateral SC (Jiang et al., 2003), then a downstream right
SC neuron would receive more inhibition from the left SNr when the movement is internally specified,
again facilitating the rightward movement. However, I observed that slightly more SNr neurons prefer
contraversive than ipsiversive movement (Figure 2.3C) but many fewer SNr neurons project to the
contralateral than ipsilateral SC, particularly in rodents (Beckstead et al., 1981; Deniau et al., 1977;
Gerfen et al., 1982; Jayaraman et al., 1977), and contraversive-preferring SNr neurons may
preferentially project to non-tectal targets. Thus, SNr activity may be consistent with the facilitation of
internally-specified contraversive movements. These results therefore extend the model underlying the
value-biasing view of BG function (Hikosaka et al., 2006) by suggesting that the influence of the SNr
on downstream motor regions is modulated by internal representations in addition to value.
In summary, I have shown that SNr activity depends on whether otherwise-identical
movements are specified by internal representations of task variables or guided by an external stimulus.
I suggest that this dependence may reflect a facilitation for internally-specified movements, consistent
with the view that, although movements are often made in response to sensory stimuli, internal
representations of priors play a critical role in guiding motor output (Wolpert and Landy, 2012). My
results are sufficiently consistent with results in primate SNr (Handel and Glimcher, 1999, 2000;
Hikosaka and Wurtz, 1983a; Liu and Basso, 2008; Sato and Hikosaka, 2002) that they can inform the
interpretation of previous studies (e.g., my proposed extensions of the model explaining the valuebiasing role of the BG described above), while also offering novel insight into BG function. Future
studies can utilize the task established here, in the experimentally-advantageous awake-behaving
mouse model (Carandini and Churchland, 2013), to examine whether the difference in SNr activity
preceding internally-specified and stimulus-guided movements is established by local processing or via
striatal inputs (Hikosaka et al., 2006; Lauwereyns et al., 2002) and to further elucidate how the BG
control goal-directed movements.
38
CHAPTER III
ACTION SELECTION MODE SWITCHING IN THE SC DEPENDS ON DECISION
CONTEXT
Introduction
Our interactions with our environment and the decisions we make as a result of these
interactions frequently rely on our senses to quickly and accurately extract information from our
surroundings. This perceptual decision making deals primarily with the evaluation of external sensory
stimuli. In the case of action selection, movements are selected based on external sensory stimuli and
internally-specified variables such as learned response-stimulus contingencies and prior experiences
(Gold and Shadlen, 2007). Ultimately, we weigh the factors relevant to a current situation in order to
make a decision resulting in the most desirable outcome, by balancing the evaluation of sensory input
with the consideration of internally specified-variables. Understanding the neural substrates
underlying decisions based on external sensory stimuli has been an area of focus in recent years (Hall
and Moschovakis, 2003). However, how this sensory information is integrated with internal variables
remains an important question in the study of decision making. While cognition is traditionally
considered a higher order brain function (Keuken et al., 2014), recent studies indicate subcortical
regions play significant roles in decision making as well (Felsen and Mainen, 2012).
The superior colliculus is a midbrain structure involved in directing orienting movements especially those cued by external sensory stimuli- to specific points in egocentric space (Krauzlis et
al., 2004; Wolf et al., 2015). While the role of the SC in movement initiation is well established,
several studies have implicated the SC to play a role in both the selection of a target for movement
and initiation of that same movement (Krauzlis, 2004; Mays and Sparks, 1980) Along these lines,
neurophysiological data from cortical studies suggest that selecting and generating actions may be
complementary components of a single process (Cisek, 2007; Cisek and Kalaska, 2010; Gold and
Shadlen, 2000).
39
The direct visual input to the SC from the visual cortex and retina make the SC an ideal
candidate for studying oculomotor output in response to visual stimuli. Studies of action selection in
the SC frequently utilize head-fixed primates performing visually guided saccade tasks see (Wurtz
and Albano, 1980) for review. Recent studies in rodents provide a renewed interpretation of the SC’s
role in action selection. While the SC receives direct sensory input from visual, auditory, and
somatosensory regions of the brain, there is no known direct input providing sensory information
from the olfactory system (Wolf et al., 2015). SC studies in which rodents made full-body orienting
movements in response to olfactory stimuli indicate a more general role in sensorimotor decision
making and demonstrate that previous findings in primates generalize across species and movement
types (Felsen and Mainen, 2012).
Buildup or prelude neurons are of particular interest in the study of decision making in the
SC. These neurons are defined by their activity during sensorimotor integration, suggesting a
potential role in the decision making process. This process in the SC is thought to be mediated
through an inter-SC competition between circuits that correspond to distinct targets in space. While
the exact nature of this competition is not known, results of several studies support this mechanism
(Lee et al., 1988a; Lo and Wang, 2006). I view my results through this framework suggesting interSC competition is maximized during difficult trials while minimized during easy trials (Felsen and
Mainen, 2012). I reason that internally specified decisions, which are formed independently of an
external sensory stimulus, would be similar to easy trials in this respect and thus SC competition
would be at a minimal state.
Here I recorded from the left SC of mice performing a behavioral task in which a sensory
stimulus either was or was not informative of the rewarded direction of an orienting movement. In
alternating blocks of trials, the rewarded direction was either determined by a sensory cue, or by
internal representations informed by recent trial history. To understand how choice context modulates
decision making in the SC, I focus on the activity of buildup neurons during two critical decision
40
making phases of the task. My results demonstrate that the SC engages in two unique modes of action
selection between these two contexts. Unilateral inhibition in the SC corroborates this finding and
sheds light on a more diverse role of the SC in the decision making process.
Materials and methods
Animal subjects. All experiments were performed according to protocols approved by the
University of Colorado School of Medicine Institutional Animal Care and Use Committee. I used
male adult C57BL/6J mice (electrophysiology: n = 3 in determined by estimating the number of
neurons required for my analyses and by the number of neurons recorded per mouse in initial
experiments; Optogenetics: n = 7; aged 7 - 14 months at the start of experiments; Jackson Labs)
housed in a vivarium with a 12-hour light/dark cycle with lights on at 5:00 am. Food (Teklad Global
Rodent Diet No. 2918; Harlan) was available ad libitum. Access to water was restricted to the
behavioral session to motivate performance; however, if mice did not obtain ~1 ml of water during
the behavioral session, additional water was provided for ~2 - 5 minutes following the behavioral
session (Smear et al., 2011; Thompson and Felsen, 2013). All mice were weighed daily and received
sufficient water during behavioral sessions to maintain >85% of pre-water restriction weight.
Behavioral task. In general, mice were first trained to perform an odor-guided spatial choice
task – which was comprised of “stimulus-guided” (SG) trials – as described in Stubblefield et al.
(2013), and were then trained to perform “internally-specified” (IS) trials. Briefly, each mouse was
water-restricted and trained to interact with three ports (center: odor port; sides: reward ports) along
one wall of a behavioral chamber (Island Motion). In each trial, the mouse entered the odor port,
triggering the delivery of an odor; waited 483.8 ± 68.3 ms (mean ± SD; neural recording sessions)
and 355.0 ± 70.2 ms (mean ± SD; optogenetic stimulation sessions) for a go signal (auditory tone);
exited the odor port; and entered one of the reward ports (Figure 3.1A). Premature exit from the odor
port resulted in the unavailability of reward on that trial. Odors were comprised of binary mixtures of
(+)-carvone and (-)-carvone, commonly perceived as caraway and spearmint, respectively; an
41
enantiomeric odor pair was selected to control for differences in molecular structure of odorant
stimuli. In each SG trial, one of seven odor mixtures was presented via an olfactometer (Island
Motion): volume (+)-carvone/(-)-carvone = 95/5, 80/20, 60/40, 50/50, 40/60, 20/80, or 5/95. Mixtures
in which (+)-carvone > (-)-carvone indicated reward availability only at the right port and mixtures in
which (-)-carvone > (+)-carvone indicated reward availability only at the left port [we therefore refer
to (-)-carvone as the “left odor” (e.g., Figure 3.1D) for simplicity]. On trials in which (+)-carvone = ()-carvone, the probability of reward at the left and right ports, independently, was 0.5. Reward,
consisting of 3 μl of water, was delivered by transiently opening a calibrated water valve 10 - 100 ms
after reward port entry. Odor and water delivery were controlled, and port entries and exits were
recorded, using custom software (adapted from C. D. Brody) written in MATLAB (MathWorks).
Mice learned to perform SG trials within ~39 sessions (1 session/day); detailed training
stages are described in Stubblefield et al. (2013). Mice required an additional ~5 sessions to learn to
perform interleaved blocks of SG and IS trials. In every IS trial the 50/50 mixture was presented, and
reward was available only at one side throughout the block. Mice were first introduced to interleaved
blocks, each of which required 25 correct trials to advance to the next block. Once they performed
~70% of trials in the session correctly, the number of correct trials required per block was increased
to 50. Mice performed 5 blocks (SG, IS, SG, IS, SG) per session (Figure 3.1B); the side associated
with reward switched between each IS block. Upon completing training, mice were implanted with
microdrives for neural recording (see below). For three mice, during each of the 67 recording
sessions, mice performed 302.76 ± 65.46 (mean ± SD) trials. For seven mice, during each of the 179
optogenetic stimulation sessions, mice performed 305.13 ± 54.51 (mean ± SD) trials.
Surgery. Details of the surgical procedure are provided in Thompson and Felsen (2013).
Briefly, once the mouse was fully trained on the task, it was anesthetized with isoflurane and secured
in a stereotaxic device, the scalp was incised and retracted, 2 small screws were attached to the skull,
and a craniotomy targeting the left SC was performed, centered at 3.88 mm posterior from bregma
42
and 1.0 mm lateral from the midline (Paxinos and Watson, 2004). A VersaDrive 4 microdrive
(Neuralynx), containing 4 independently adjustable tetrodes, was affixed to the skull via the screws,
luting (3M), and dental acrylic (A-M Systems). A second small craniotomy was performed in order to
place the ground wire in direct contact with the brain. After the acrylic hardened, a topical triple
antibiotic ointment (Major) mixed with 2% lidocaine hydrochloride jelly (Akorn) was applied to the
scalp, the mouse was removed from the stereotaxic device, the isoflurane was turned off, and oxygen
alone was delivered to the animal to gradually alleviate anesthetic state. Mice were administered
sterile isotonic saline (0.9%) for rehydration and an analgesic (Ketofen; 5 mg/kg) for pain
management. Analgesic and topical antibiotic administration was repeated daily for up to 5 days, and
animals were closely monitored for any signs of distress.
For surgeries involving implantation of optogenetic instruments and injection of viral
constructs, the aforementioned procedure was modified in the following ways. A single a craniotomy
targeting the left SC was performed, centered at 3.88 mm posterior from bregma and 1.0 mm lateral
from the midline (Paxinos and Watson, 2004). To achieve expression of eArch 3.0 in the SC, each
adult mouse was injected with 600 nl of pAAV-CaMKIIa-eArch 3.0-EYFP (all viruses were obtained
from the University of North Carolina Vector Core with permission from Dr. Karl Deisseroth
(Stanford University)). An optic fiber was permanently implanted as part of a moveable drive housing
(Anikeeva et al., 2012) in order to deliver light to opsin-expressing neurons located ventral to the
fiber tip.
Electrophysiology. Neural recordings were collected using four tetrodes, wherein each
tetrode consisted of four polyimide-coated nichrome wires (Sandvik; single-wire diameter 12.5 μm)
gold plated to 0.2-0.4 MΩ impedance. Electrical signals were amplified and recorded using the
Digital Lynx S multichannel acquisition system (Neuralynx) in conjunction with Cheetah data
acquisition software (Neuralynx).
43
Tetrode depths were adjusted approximately 23 hours before each recording session in order
to sample an independent population of neurons across sessions. To estimate tetrode depths during
each session I calculated distance traveled with respect to rotation fraction of the screw that was
affixed to the shuttle holding the tetrode. One full rotation moved the tetrode ~250 μm and tetrodes
were moved ~62.5 μm between sessions. The final tetrode location was confirmed through
histological assessment using electrolytic lesions and tetrode tracks (see below).
Offline spike sorting and cluster quality analysis was performed using MClust software
(MClust-3.5, A.D. Redish, et al.) in MATLAB. Briefly, for each tetrode, single units were isolated by
manual cluster identification based on spike features derived from sampled waveforms. Identification
of single units through examination of spikes in high-dimensional feature space allowed us to refine
the delimitation of identified clusters by examining all possible two-dimensional combinations of
selected spike features. I used standard spike features for single unit extraction: peak amplitude,
energy (square root of the sum of squares of each point in the waveform, divided by the number of
samples in the waveform), and the first principal component normalized by energy. Spike features
were derived separately for individual leads. To assess the quality of identified clusters I calculated
two standard quantitative metrics: L-ratio and isolation distance (Schmitzer-Torbert et al., 2005).
Clusters with an L-ratio of less than 0.82 and isolation distance greater than 3 were deemed single
units. Although units were clustered without knowledge of interspike interval, only clusters with few
interspike intervals less than 1 ms were considered for further examination. Furthermore, I excluded
the possibility of including data from the same neuron twice by ensuring that both the waveforms and
response properties sufficiently changed across sessions. If they did not, I conservatively assumed
that I was recording from the same neuron, and only included data from one session.
Optogenetic stimulation. Light was delivered via diode-pumped, solid-state lasers (532 nm;
Shanghai Laser & Optics Century) coupled to the optic fiber. Lasers were calibrated daily with an
optic power meter (Melles Griot). The power range was measured to be 25–30 mW for 532 nm light,
44
a range previously demonstrated to hyperpolarize Arch3.0-expressing neurons. Power output for the
532 nm laser was 140–160 mW/mm2, which is near a previously demonstrated range to hyperpolarize
NpHR-expressing neurons (Gradinaru et al., 2008; Tye et al., 2011). At these ranges of power output,
I estimate that effective light stimulation was restricted to the intermediate and deep layers of the left
superior colliculus (Figure 3.5B), given precise histological confirmation of the ventral tip of optic
fiber (cannula) location. Yellow-green light was delivered continuously for 300 ms to activate Arch.
Lesioning and histology. To verify final tetrode location, I performed electrolytic lesions
(100 μA, ~1.5 min per lead) after the last recording session. One day following lesion, mice were
overdosed with an intraperitoneal injection of sodium pentobarbital (100 mg/kg) and transcardially
perfused with saline followed by ice-cold 4% paraformaldehyde (PFA) in 0.1 M phosphate buffer
(PB). After perfusion, brains were submerged in 4% PFA in 0.1 M PB for 24 hours for post-fixation
and then cryoprotected for 24 hours by immersion in 30% sucrose in 0.1 M PB. The brain was
encased in the same sucrose solution, and frozen rapidly on dry ice. Serial coronal sections (60 μm)
were cut on a sliding microtome for reconstruction of the lesion site and tetrode tracks. Fluorescent
Nissl (NeuroTrace, Invitrogen) was used to identify cytoarchitectural features of the SC and verify
tetrode tracks and lesion damage within or below the SC. Images of the SC (see Figure 3.2) were
captured with a 10x objective lens, using a 3I Marianis inverted spinning disc confocal microscope
(Zeiss). Verification of Arch expression and fiber depth followed the same procedures with the
exception of electrolytic lesioning.
Analyses and statistics. All analyses were performed in MATLAB.
Direction preference. To quantify the selectivity of single neurons for movement direction, I
used an ROC-based analysis (Green and Swets, 1966). This analysis calculates the ability of an ideal
observer to classify whether a given spike rate was recorded in one of two conditions (here, preceding
leftward or rightward movement). I defined “preference” as 2(ROCarea – 0.5), a measure ranging from
-1 to 1, where -1 signifies the strongest possible preference for left, 1 signifies the strongest possible
45
preference for right, and 0 signifies no preference (Feierstein et al., 2006). Statistical significance was
determined with a permutation test: I recalculated the preference after randomly reassigning all firing
rates to either of the two groups arbitrarily, repeating this procedure 500 times to obtain a distribution
of values, and calculated the fraction of random values exceeding the actual value. I tested for
significance at α = 0.05. Trials in which the movement time (between odor port exit and reward port
entry) was > 1.5 s were excluded from all analyses. Neurons with fewer than 100 trials of each type
(SG and IS) or with a firing rate below 2.5 spikes/s for either trial type or across the entire session
(Fc, described below), were excluded from all analyses.
Percent and direction of shift. To quantify the direction and magnitude of the behavior shift
during light stimulation trials, I compared the fraction of choices in both ipsiversive and contraversive
trials, separately for the light on and light off conditions using the following equations:
Left trials:
�
�
ℎ�
=
� ℎ �
−
Right trials:
�
ℎ�
� ℎ �
� ℎ
� ℎ
+
ℎ �
� ℎ �
=
−
ℎ �
ℎ �
� ℎ �
� ℎ
+
� ℎ
� ℎ
ℎ �
ℎ �
� ℎ
� ℎ
+
ℎ �
� ℎ
ℎ �
+
� ℎ
ℎ �
� ℎ
−
� ℎ
� ℎ
Left trial shift is multiplied by -1 so that positive values correspond to a rightward shift during light
on trials and negative values correspond to a leftward shift during light on trials.
Results
Behavioral assay dissociates stimulus-guided and internally-specified movements. Mice
were trained on a delayed-response spatial choice task comprised of interleaved blocks of “stimulusguided” (SG) trials, in which the direction of movement is selected based on a sensory stimulus
46
(Uchida and Mainen, 2003), and “internally-specified” (IS) trials, in which the direction of an
otherwise-identical movement is selected based on internal representations informed by recent trial
history (see Materials and Methods; Figure 3.1A, B). In each trial of the task, the mouse is presented
with a binary odor mixture at a central port, waits for an auditory go cue, and moves to the left or
right reward port for a water reward. In SG trials, the dominant component of the odor mixture –
which varies trial by trial – determines the side at which reward will be delivered, while in IS trials, a
balanced mixture of the two odors is always presented but reward is delivered at only one side
throughout the block (see Materials and Methods; Figure 3.1B). Thus, while both trial types require
the mouse to sample the stimulus, in SG trials the stimulus indicates that the rewarded side is
determined by the odor mixture and in IS trials the stimulus indicates that the rewarded side is
determined by the recent history of choices and outcomes. I found that mice were able to infer
(unsignaled) transitions between the SG and IS blocks and switch their response mode accordingly:
during SG blocks, mice were equally likely to choose the left and right port (Figure 3.1C, gray boxes)
reflecting a dependence on the odor mixture (Figure 3.1D), while during IS blocks, mice reliably
returned to the same (rewarded) port in each trial (Figure 3.1C, white boxes). These data suggest that,
as intended, the direction of movement in SG blocks is selected based on the stimulus while the
direction of movements in IS blocks is selected based on recent trial history. Therefore, I utilized this
behavioral assay to compare how stimulus-guided and internally-specified movements are mediated
by the SC, as described below.
I examined neural activity between SG and IS trials during two periods relevant to the
decision making process. First, the prestimulus epoch is defined as the time from odor port entry to
100 ms after odor valve opening (Figure 3.1A, dark gray box). The 100 ms designation is used to
account for the delay, empirically measured from odor valve opening to odor detectability in the port
(Thompson and Felsen, 2013). The delay epoch is defined as the period starting 100 ms after odor
47
Figure 3.1. Behavioral task and performance. (A) Timing of events in each trial. The mouse enters the
odor port, receives an odor mixture, waits for the go signal, exits the odor port, moves to one of the reward
ports, and receives a water reward for a correct choice. Dark gray box, prestimulus epoch. Light gray box,
delay epoch. (B) Organization of SG (gray) and IG (white) blocks within a session. Gray, SG blocks; white,
IG blocks. All sessions start with an SG block and alternate between SG and IG blocks. In SG blocks,
reward side corresponds to the dominant odor in the mixture [(-)-carvone, left; (+)-carvone, right]; when the
odors are balanced ([(-)-carvone] = [(+)-carvone]), the probability of reward at both reward ports is 0.5. In
IG blocks, odors are balanced in every trial and reward is available at the same side in each trial. Thickness
of horizontal lines corresponds to probability of reward. SG, stimulus guided; IG, internally generated; L,
left; R, right. (C) Fraction of left choices across block types throughout the session. Dashed line shows an
example session (boxcar smoothed over 7 trials), solid black line shows mean over all sessions (67, from 3
mice), horizontal black lines show block means, horizontal gray lines show ideal block means (if all choices
were correct). To account for different numbers of trials per block across sessions, trials that occur in < 60%
of sessions are excluded. In SG blocks only difficult trials [(+)-carvone/(-)-carvone = 60/40, 50/50, or
40/60] are shown. (D) Mean performance in SG blocks over all sessions, separated by rewarded side of first
�
IG block in the session. Lines show best fit to � =
, where x is the proportion of the left odor [(-)�+�− − �
carvone)] in the mixture, p is the fraction of right choices, and a and b are free parameters.
valve opening to the time of the auditory go signal (Figure 3.1A, light gray box). The prestimulus
epoch captures activity underlying action selection in IS trials compared to the relatively neutral
activity during SG trials. Meanwhile, the delay period most directly captures activity underlying
selection of movement direction in both trial types.
48
Neurons in the mouse SC display three commonly described firing profiles: burst,
buildup and fixation. Saccadic studies of the SC in primates have traditionally separated neurons
into three different classes based on their firing profiles relative to saccadic events (Basso and Wurtz,
1997; Glimcher and Sparks, 1992; Kustov and Lee Robinson, 1996; Munoz and Wurtz, 1993, 1995).
These include buildup neurons which discharge at a low frequency, build gradually during
sensorimotor integration and frequently transition into a high frequency burst during saccade
initiation (Figure 3.3A-C); burst neurons which discharge at a high frequency prior to saccade
initiation but are otherwise relatively quiescent (Figure 3.3D-F); and fixation neurons which display
tonic activity over the course of the behavior, only pausing during the initiation of some, but not all
saccades (Figure 3.3G-I). While these classes were defined in primates responding to visual stimuli, I
identified the same firing patterns in mice making full-body orienting movements in response to
olfactory stimuli. These findings suggest homology between the rodent and primate SC, and support
evidence for a more general role of the SC in decision-making, independent of the stimulus modality
used.
We recorded from 172 well-isolated left SC neurons (see Materials and Methods, Figure 3.2)
in three mice performing the behavioral task. Recording sites were verified histologically and are
shown for all three animals (Figure 3.2). Data from three example neurons, one representing each of
the three classes is shown in Figure 3.3. Firing rates are separated by the reward port selected
(ipsilateral vs. contralateral to the recording side). For each neuron, firing rate is shown from odor
port entry (yellow tick marks) to odor port exit (cyan tick marks) and aligned to the time of odor
valve opening, capturing both the prestimulus and delay epochs. I calculated direction preference
during both epochs using all trials per session for each cell type (see Materials and Methods). The
resulting preference value ranges from -1 (strongly “prefers” ipsiversive) to 1 (strongly prefers
contraversive), where 0 represents no preference.
49
Figure 3.2. Confirmation of SC
recording sites. Coronal section (left,
3.88 mm caudal from bregma) shows
representative tetrode tracks (arrows) in
SC; schematic (right) shows targeted
recording extent (colored bars) within
SC for each animal in the study.
We found that 116 of 172 neurons fit the definition of one of the three classically described
categories (Munoz and Wurtz, 1995), resulting with 49 burst neurons, 50 buildup neurons and 17
fixation neurons. I classified cells into one of the three categories based on characteristics of
movement related discharge during the behavioral task. Cells were qualitatively processed by visual
inspection of neural activity as shown in raster plots (see Figure 3.3A, D, G). I categorized a cell as a
buildup cell if it showed a change in discharge between the delivery of the stimulus to the onset of
movement (Figure 3.3A, B). Conversely, burst and fixation cells were classified first by a lack of
changing activity during this same period, and second by an increase (burst; Figure 3.3D, E) or
decrease (fixation; Figure 3.3G, H) in activity immediately preceding and/or during movement
initiation. The low frequency, gradually increasing response of buildup neurons has been potentially
attributed to motor preparation, target selection, attention and working memory (Gandhi and Katnani,
2011).
We found that 31/50 buildup neurons displayed a significant direction preference (p <0.01)
during the delay epoch, with more preferring contraversive (21/31) than ipsiversive (9/31) movements
(Figure 3.3C, right). Among burst and fixation neurons, 10/49 (Figure 3.3F, right) and 6/17 (Figure
3.3I, right) respectively displayed a significant direction preference (p < 0.01), with equal numbers
preferring contraversive (5/10 and 3/6) and ipsiversive (5/10 and 3/6) movements. During the
prestimulus epoch 26/50 buildup cells showed a significant direction preference (14/26 contra, 12/26
50
ipsi) but with no apparent overall direction selectivity (Figure 3.3C, left). Meanwhile, 13/49 burst
cells showed a significant preference with slightly more directional bias [9/13 ipsi, 4/13 contra;
(Figure 3.3F, left)], similar to the 9/17 fixation cells that showed significant preference [2 ipsi, 7
contra; (Figure 3.3I, left)]. Burst and fixation cells are mentioned here for completeness, however,
because buildup cells are active during the decision making process (Kim and Basso, 2008), I focus
on this population for the remainder of the study.
Figure 3.3. SC activity during the delay and prestimulus epoch depends on movement direction. (A)
Rasters for an example buildup neuron grouped by movement direction. Each row shows spikes (black
ticks) in one trial, aligned to time of odor valve open (red line) and sorted by duration of delay epoch.
Yellow ticks, time of odor port entry; green ticks, times of go signal; blue ticks, times of odor port exit.
Fifty pseudo-randomly selected trials are shown per group. (B) Peri-event histograms showing average
activity within groups of trials shown in A. Shading, ± SEM. Histograms are smoothed with a Gaussian
filter (σ = 15 ms). Ipsi., ipsiversive; contra., contraversive. (C) Histogram of direction preferences of
buildup neurons during the prestimulus epoch (left) and delay epoch (right). D-F, same as A-D for burst
neurons. G-I, same as A-D for fixation neurons.
SC buildup neurons are engaged differently between IS and SG trials. I expanded my
analysis of direction preference to include trial-type to determine if decision context affects cell
preference. Reexamination of the buildup neuron shown in Figure 3.3 revealed trial-type dependent
51
modulation of neural activity during the prestimulus and delay epochs, for both the ipsiversive
(Figure 3.4A, top panel) and contraversive direction (Figure 3.4A, bottom panel). Next I analyzed
direction preference independently for IS and SG trials to determine how each account for the
preference observed in Figure 3.3.
Figure 3.4. The SC is engaged differently between SG and IS trials. (A) Peri-event histograms shown in
3B for trials grouped by movement direction (top: ipsiversive; bottom: contraversive) and trial type.
Shading, ± SEM. Histograms are smoothed with a Gaussian filter (σ = 15 ms). Ipsi., ipsiversive; contra.,
contraversive. (B) Comparison of direction preferences between SG and IS cells for buildup neurons during
the prestimulus epoch. Each circle corresponds to one neuron. Black box corresponds to example neuron
from A. (C) Same as B during the delay epoch. Black box corresponds to example neuron from A. (D)
Mean normalized change from baseline firing rate of buildup neurons in both prestimulus and delay epoch
for stimulus-guided trials vs. internally-generated trials in the corresponding session, separated by trial
direction. Only correct trials are included; all choices on 50/50 SG trials were considered correct regardless
of whether they were rewarded. Vertical black bars represent standard error of the mean.
52
During the prestimulus epoch I found that 26/50 buildup neurons displayed a significant
direction preference during IS trials (Figure 3.4B, red circles), no cells showed a significant
preference during SG trials alone, and 5/50 neurons display significant direction preference in both IS
and SG trials (Figure 3.4B, magenta circles). As a population, there is no directional bias during IS
trials with 13/26 preferring ipsiversive and 13/26 preferring contraversive movements. Likewise, for
cells showing a direction preference during both trial types, findings are similar with 3/5 preferring
contraversive and 2/5 neurons preferring ipsiversive movements (Figure 3.4B, colored points above
vs below the horizontal line).
Due to the repetitive nature of movements during IS trials and the dependence on previous
trial history to inform the current decision, I predict that animals can formulate decisions earlier
during these trials. Conversely, during SG trials animals rely on the olfactory stimulus to inform
current decisions (Figure 3.1D). The results shown in Figure 3.4B largely support my prediction that
direction selection is represented earlier during IS trials, with the exception of the five neurons that
also display a prestimulus preference during SG trials. While this prestimulus preference is somewhat
surprising, these results align with previous studies where both direction selective and non-direction
selective SC neurons exhibited significant direction preference prior to stimulus presentation
(Horwitz and Newsome, 2001).
In the delay epoch, I found 17/50 buildup cells showed a direction preference in IS trials only
(Figure 3.4C, red circles), 9/50 showed a significant direction preference during SG trials only
(Figure 3.4C, blue circles) and 13/50 showed a significant direction preference during both IS and SG
trials. Similar to the prestimulus epoch, I found that during IS trials the population is not biased
towards movements in either direction with 6/17 preferring contraversive movements and 11/17
preferring ipsiversive movements (p = 0.225, chi-squared test). Among neurons displaying a
significant preference during SG trials, either solely or coupled with a preference during IS trials, I
found that of 21/22 neurons prefer contraversive versus 1/22 preferring ipsiversive movements (p =
53
2.0 x 10-5, chi-squared test). Interestingly, among the neurons displaying a significant preference
during both trial types, a small number of neuron’s direction preference reversed depending on trial
type, with 3/13 showing a contraversive preference during SG trials but an ipsiversive preference
during IS trials (Figure 3.4C, magenta circles, quadrant IV).
Because SC activity is traditionally correlated with contralateral movements, I found it
surprising that during both the prestimulus and the delay epochs of IS trials, there was no overall
directional preference among the population. This finding suggests that while the SC is engaged to
drive contralateral movements during SG trials, across the population, activity during IS trials does
not guide movements in the same general way.
I wanted to determine whether the shift towards contralateral preference during the delay
epoch in SG trials, as compared to IS trials was due to a change in the overall firing rate of neurons
across the population. I calculated the mean firing rate separately for ipsilateral and contralateral trials
as well as for IS and SG trials during both the prestimulus and delay epoch. I found that during the
prestimulus epoch, while there was a general tendency for firing rate to increase, the relative increase
between the four trial types was nearly identical (Figure 3.4D, left panel). During the delay epoch I
found that changes in firing rate depend on trial type (Figure 3.4D, right panel). The greatest increase
during this period was for contralateral SG trials, which combined with the corresponding decrease in
firing rate observed in ipsilateral SG trials correlates with the overall contralateral preference during
SG trials. The relatively similar firing rates between ipsilateral and contralateral IS trials is similar to
what I observed in the prestimulus epoch and suggest that the lack of relative fluctuation of firing
rates during IS trials may correspond to the roughly equal direction preference across the population
during these trials in both the prestimulus and delay epoch.
The fact that most neurons display a direction preference during IS trials in the prestimulus
epoch while no neurons show a direction preference exclusive to SG trials during the same period,
suggests that activity in the SC guides action selection differently between the two trial types. The
54
unbiased direction preference of the population largely persists as the trial enters the delay epoch
during IS trials. Stimulus delivery during SG trials appears to cue the SC to engage in action selection
in the traditional contralateral preferring fashion. However, delivery of the directionally ambiguous
stimulus during IS trials does not appear to substantially impact the overall direction preference
during IS trials.
Unilateral inhibition in the SC biases movement differently depending on the epoch of
stimulation. I unilaterally inhibited neural activity in the left SC to determine if this modulation
would differently affect movements during IS versus SG trials (Materials and methods, Figure 3.5). I
injected a viral construct (pAAV-CaMKIIa-eArch 3.0-EYFP) to express the light activated protonpump, eArch 3.0 under the control of the glutamatergic cell type specific promoter, CamKIIa. I
confirmed expression and optic fiber placement using histologic techniques (Materials and methods,
Figure 3.5B). During behavioral experiments, I stimulated via an optical fiber connected to a
computer-controlled 532 nm laser.
I stimulated the SC for 300 ms during either the prestimulus epoch or the delay epoch,
depending on the session. In the prestimulus epoch I stimulated for the 300 ms from the time of odor
port entry to the time of odor valve opening (Figure 3.5A, left green box). In the delay epoch, I
stimulated for the 300 ms prior to odor port exit up until odor port exit (Figure 3.5A, right green box).
Similar to recent studies (Kopec et al., 2015), unilateral inhibition during the delay epoch produced a
small yet significant ipsilateral shift in choice selection as represented by the example session (Figure
3.5C, right). However, unilateral SC inhibition during the prestimulus epoch produced a significant
shift in the contralateral direction, represented by the example session (Figure 3.5C, left). I analyzed
both shift direction and magnitude across the population using a generalized linear model (MATLAB,
glmfit) to measure the influence on choice direction by odorant and optic stimulation. I averaged the
mean coefficient estimates of all sessions and plotted the results for both the prestimulus and delay
55
epoch (Figure 3.5D, horizontal black lines). I observed a significant contralateral shift during the
prestimulus epoch compared to a smaller magnitude, yet still significant
Figure 3.5. Unilateral inhibition in the SC shows time dependent differences in behavior modulation.
(A) Timing of events in each trial as shown in Fig. 1A, here highlighting the periods of optogenetic
inhibition. Left green box, prestimulus epoch. Right green box, delay epoch. (B) Coronal section (3.88 mm
caudal from bregma) shows expression of Arch 3.0-EYFP in the left SC (green), with Nissl shown in blue.
(C) Performance in SG blocks over a single session, comparing light on (solid black line) and light off
�
(dashed gray line) trials. Lines show best fit to � =
, where x is the proportion of the left odor [(-)�+�− − �
carvone)] in the mixture, p is the fraction of right choices, and a and b are free parameters. IG trials shown
for comparison (black and gray squares). Example shown for each epoch; left – prestimulus, right – delay.
(D) Diagram showing the magnitude and direction of shift during light on trials for the prestimulus and
delay epochs. Ordinate: β coefficients estimated based fit of generalized linear model. Horizontal lines
indicate measured shift, their width indicates period of stimulation during experimental sessions (black
solid) and control sessions (gray dashed). Vertical lines, ± standard error. Asterisk indicates significance (p
< 0.05). (E) Percent and direction of shift (Materials and methods) by trial type during the prestimulus
epoch, separated by session type: experimental – left, control – right. Black vertical lines, ± standard error.
(F) Same as E for the delay epoch.
56
ipsilateral shift when stimulating during the delay epoch. The contralateral shift during the
prestimulus epoch is unexpected and may be explained by a potential rebound effect of optogenetic
inhibition during the prestimulus epoch. Due to the shift in the intended direction during the delay
epoch, I speculate that the rebound in activity is at least partially dependent on the timing of the
stimulus. Control sessions in both epochs did not produce a significant shift in either direction (Figure
3.5D, horizontal dashed gray lines).
We expected that modulation of SC activity would differently affect SG and IS trials. To
determine how the effect of unilateral inhibition varied by trial type and difficulty, I calculated the
fractional difference of left and right choices between light on and light off trials (Materials and
methods, Figure 3.5E, F). Where positive values indicate a contralateral shift and negative values
indicate an ipsilateral shift, I found that during the prestimulus epoch, difficult trials were most
susceptible to modulation and IS trials were the least susceptible. The shift during difficult trials was
significantly greater than during IS trials (p < 0.05, paired t-test; Figure 3.5E, left panel) for trials in
both the ipsilateral and contralateral direction. Stimulation during the delay period did not produce
significant shifts among any single trial type despite the global ipsilateral shift in behavior (Figure
3.5F, left panel). Control data from both sessions show a substantial amount of variability (Figure
3.5E, F, left panels)
Discussion
Here I have shown that buildup neurons in the SC exhibit trial-type dependent modulation of
neural activity and direction preference. Interestingly, the modulation of individual neurons does not
translate to a population effort to drive movement in one direction over the other. The results of the
optogenetic studies support the idea that the SC engages differently between trial types as unilateral
inhibition of the SC has the least influence during IS trials. This result suggests that movements are
less likely to be influenced for decisions that have already been committed. This is the case during IS
blocks where mice could potentially begin the trial with a preformed decision based on recent trial
57
history. While I recognize that odor sampling is necessary to confirm block type, this does not
preclude the animal from forming a contingency based decision during the earliest phases of the trial
(Figure 3.4A, B).
While early studies correlated activity in the deep layers of the SC with orientating saccadic
eye movements to specific visual targets in contralateral space (Goldberg and Wurtz, 1972a, 1972b;
Lee et al., 1988b; Wurtz and Goldberg, 1972), more recent studies indicate a correlation with
orienting movements more generally (Felsen and Mainen, 2012). Although classically described for
its role in modulating oculomotor responses to visual cues, activity in the SC has recently been shown
to be representative of orienting movements of the head and body as well (Corneil et al., 2002; Felsen
and Mainen, 2008; Freedman et al., 1996; Gandhi and Katnani, 2011; Guillaume and Pélisson, 2001;
Walton et al., 2007).
Initial indication that buildup neurons play a role beyond simply movement initiation came
from observations that activity during the delay period reflected the probability of selecting a visual
target in the response field of a specific neuron (Basso and Wurtz, 1997; Dorris and Munoz, 1998;
Krauzlis, 2004; McPeek and Keller, 2002). Further suggesting a more complex role for the SC,
modulation of prelude activity by task difficulty revealed relatively high firing rates when presented
with difficult stimuli for movements away from preferred targets (Felsen and Mainen, 2012; Horwitz
and Newsome, 2001; Ratcliff et al., 2007; Thevarajah et al., 2009), suggesting that prelude activity
correlates with movement targets that are considered but not ultimately selected. Current theories
suggest that action selection in the SC is mediated through an inter-SC competition where situations
with greater uncertainty result in closer competition, ultimately increasing the likelihood of selecting
an incorrect target (Wolf et al., 2015).
Building on the role of the SC in action selection, these results offer new considerations of
the role of buildup neurons during decision making by dissociating stimulus-guided from internallyspecified decisions. By focusing on the period of the task prior to and following odorant delivery,
58
we’re able to decipher the unique role these neurons play in guiding decisions. We’ve shown that
during the prestimulus epoch of IS trials, activity in individual neurons reflect a preference for the
direction of the upcoming movement, though an overall directional bias is absent across the
population (Figure 3.4B). During IS trials of the delay epoch I observed a similar trend. While the SC
typically exhibits preference for targets selected in contralateral space, this conventional preference
becomes less apparent in the presence of ambiguous stimuli with no inherent directional association.
This indicates that the preference of the SC for contralateral targets, primarily applies when external
sensory stimuli directly indicate target selection in contralateral space. This conclusion would suggest
that during IS trials, direction of movement was initially selected independent of the stimulus. This
idea is supported by the fact that the same population of cells, in the presence of a direction encoding
stimulus, displays the contralateral preference characteristic of SC neurons (Figure 3.4C, blue
circles).
In support of this idea, the results of unilateral optogenetic inhibition revealed that action
selection during IS trials was less susceptible to modulation than during SG trials. During the
prestimulus epoch of IS trials, some SC buildup neurons are engaged in the action selection of the
upcoming movement. As a result, inhibition during this period has a minimal effect on trial outcome.
Conversely, previous trial history during SG blocks is less reliable for predicting upcoming
movements. Thus, during the prestimulus epoch of SG trials, SC neurons await external cues to
engage in action selection. SC neurons in this anticipatory state are more susceptible to modulation.
In consideration of inter-SC competition, I would expect that difficult trials be more susceptible to
modulation than easy trials. Because the balance and competition between the two SC will be greatest
during difficult trials, moderate manipulation of neural activity is more likely to tip the scale in a way
that interferes with normal action selection. Conversely, during easy trials SC competition is
presumed to be more one-sided, leaving little room for choice modulation via short durations of
inhibition.
59
In summary, I have shown that the activity of SC buildup neurons, widely implicated in the
process of action selection, is modulated in a context-dependent manner depending on whether
otherwise-identical movements are selected by internal representations of task variables or guided by
an external stimulus. This context-dependent processing of decisions enhances our overall
understanding of the role of the SC in decision making. While the SC is well-established in its role of
driving movements in response to external sensory cues, internal representations of priors play a
critical role in guiding motor output as well (Wolpert and Landy, 2012); here we’ve shown how the
superior colliculus is involved in this critical aspect of decision making.
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CHAPTER IV
CONCLUSION
Summary of novel results
Here I tested the hypothesis that activity in the nigrotectal pathway is modulated by the
context in which decisions are made. I proposed that substantia nigra pars reticulata activity uniquely
mediates the preparation of internally-specified vs otherwise-identical, stimulus-guided orienting
movements by modulating superior colliculus activity in order to facilitate those movements. The
novel results presented in this dissertation focus on the results of these questions. I built on previous
studies investigating the role of the SNr and SC in decision making by applying a novel task designed
to dissociate two modes of decision making based on two unique contexts of stimulus and trial
presentation. I found that animals were able to learn, understand and reliably perform this task. I
believe these studies are important for the following reasons: 1) understanding of subcortical regions
such as the SNr and the SC continues to expand with increasing complexity in the roles they play in
driving regular behavior. 2) Decision-making is a multifaceted process, an important aspect of our
lives and an area of intensely studied research. While a number of studies have aimed to characterize
the role of the SNr and the SC, either independently or in tandem, in the decision-making process,
there is a void in the consideration of how internal motivations affect this process. Here we’ve
examined how these internal motivations modulate activity in both regions of interest, implicating
their role in consideration of choice context during the decision-making process.
In the first study I focused on the SNr’s role in movement preparation where I investigated
the hypothesis that: preparation for internally-specified movements is promoted relative to otherwiseidentical stimulus-guided movements. This hypothesis is split into two separate questions where I
asked first: does SNr activity preceding movement predict the direction of upcoming movement, and
second: does SNr activity preceding movements depend on whether the direction of movement is
internally generated or stimulus guided? I recorded neural activity from the left SNr of mice during a
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behavior task comprised of IS and SG trials, and focused my analyses on the decision-making phase
of the task i.e. the delay epoch. I focus first on this epoch because this period most directly captures
differences between the two separate modes of decision making. In this study I focused on the entire
population of SNr cells in my dataset. I found that during this period, the majority of SNr neurons
display a significant direction preference with nearly equal numbers preferring the ipsilateral vs.
contralateral direction. Across the population I found that during internally-specified trials, SNr
neurons showed a consistent increase in activity in the neurons preferred direction and consistent
decrease in the neuron’s antipreferred direction. I concluded that the SNr promotes internallygenerated over stimulus-guided movements. Building upon an established model of the SNr’s role in
reward guided decision making, I introduced a model to consider how the observed contextdependent modulation would drive downstream activity in the superior colliculus.
In the second study I focused on a major target of the SNr, the superior colliculus, where I
tested the hypothesis that: target selection in the SC depends on the context in which those targets are
selected (internally-generated vs stimulus-guided). I first asked: does SC activity preceding
movements depend on whether the movement direction is internally-specified or stimulus-guided, and
second: does unilateral inhibition of the SC differentially affect the selection of internally-specified vs
stimulus-guided movements? I recorded extracellular signals from the SC using the same task
described in the first study. Here I shifted my focus to include two distinct but connected epochs: the
prestimulus epoch and the delay epoch. My interest in the delay epoch derives from the same reason
for focusing on this period in the first study. However, I additionally focus on the prestimulus epoch
because during this period there is a clear distinction between SG and IS trials. Specifically, during IS
trials, it is likely that a decision is already being assembled and reflected in the neural activity, while
in SG trials I conclude that because animal’s decisions are driven by odorant mixtures (Figure 3.1),
during this period they do not yet have enough information to form a decision. For this reason, I
deemed this epoch an ideal time period for unilateral inhibition in the SC to determine how IS and SG
62
trials are differentially affected, and found a stronger effect during SG trials. In my analyses of neural
recording data, I focus on the buildup/prelude cells of the SC as they are most frequently implicated
in the decision-making process. I found that among this population, there was a strong contralateral
preference during the delay epoch with very little direction bias during the prestimulus epoch. Here I
combine the results of the two studies to infer conclusions about the nigrotectal pathway based on
these independent analyses.
Summary of the nigrotectal pathway
Here we’ll revisit the model proposed in chapter 2 (Figure 2.7) and build from it based on the
observed results of my SC studies. To review, I considered how the activity of ipsiversive- and
contraversive-preferring SNr neurons would relate to an upcoming rightward movement. A left,
ipsilateral preferring SNr neuron would exhibit a lower firing rate for an internally-specified than a
stimulus-guided movement. If ipsiversive-preferring SNr neurons primarily project to the ipsilateral
SC (Hikosaka and Wurtz, 1983b), then a downstream left SC neuron, the activity of which promotes
rightward movement (Felsen and Mainen, 2012; Horwitz and Newsome, 2001; Stubblefield et al.,
2013) will receive less inhibition from the left SNr when the movement is internally generated,
thereby facilitating rightward movements that are internally generated (Figure 2.7A, B). Preceding the
same movement, a left SNr neuron with a rightward direction preference would exhibit a higher firing
rate for an internally-specified than a stimulus-guided movement (Figure 2.3D, red circles). If
contraversive-preferring SNr neurons comprise the “crossed” projection to the contralateral SC, then
a downstream right SC neuron would receive more inhibition from the left SNr when the movement
is internally generated, again facilitating the rightward movement (Figure 2.7C, D).
To examine how this model reflects my SC results, I review the predicted SC activity based
on the proposed model. I predicted that during a rightward movement of either an IS or SG trial, I
would see an increase in the activity of a left SC neuron. Considering the competitive nature of interSC interactions, I expect to see a larger difference in firing rate (less competition) during IS than SG
63
trials. I presume that the increase in SC activity would simultaneously result in greater excitation of
downstream nuclei and greater inhibition of the competing SC.
Our results partially agree with these predictions. While I observed higher firing rates during
contraversive- vs. ipsiversive- movements, my results indicate that SC discharge is greatest during
contralateral SG trials and least during ipsilateral SG trials (Figure 3.4D). During IS trials I observed
slightly elevated discharge during contralateral vs. ipsilateral trials, corresponding to a subtle
contralateral preference. These results therefore suggest that inter-SC competition during IS trials
may lie somewhere between the highly competitive nature of difficult trials and the low competition
state of easy trials.
Application of these conclusions to the optogenetic study suggest that the observation of IS
trials as the least prone to modulation, may not be attributed directly to the level of inter-SC
competition during these trials but instead due to the timing of inhibition. I have proposed that during
IS trials, animals can formulate decisions earlier than during SG trials which is supported by the
results shown in Figure 3.4B.
Consider that during the prestimulus epoch of SG trials, unilateral inhibition biases the
system prior to presentation of the stimulus. This inhibitory modulation may effectively function to
replace the role of the odor stimulus during affected trials. This is consistent with the observation that
unilateral inhibition has a stronger effect during SG vs IS trials. During IS trials, due to the reliance
on the previous trial, the system is directionally biased prior to the onset of the inhibitory stimulation.
This bias functionally supersedes the inhibitory stimulus in IS trials in the same manner the inhibitory
stimulus supersedes the effect of the odor stimulus during SG trials. The finding that unilateral
inhibition has a greater impact during difficult vs easy SG trials suggest that the odor stimulus can
override inhibitory modulation when there is greater certainty about the identification of the odorant.
The ability of the odor stimulus to override inhibitory modulation is evidenced by the observation that
unilateral inhibition modulates trial outcome in the minority of trials.
64
Unilateral inhibition during the delay epoch produced a muted affect across trial types. This
result supports the interpretation of inhibition during the prestimulus epoch as it suggests that during
SG trials once the odorant is sampled, decisions are less susceptible to modulation, equivalent to the
prestimulus epoch of IS trials. In regard to the susceptibility to behavior modulation, SG trials during
the delay epoch are akin to IS trials during the prestimulus epoch. Furthermore, SG trials during late
phases of the delay epoch are similar to IS trials during early phases of the delay epoch in a relative
comparison of how long the movement decision has persisted by these points in the respective trials.
This interpretation can also explain the larger effect on SG vs IS trials during the delay epoch as the
decision in IS trials is more robust as it was formulated earlier relative to the onset of unilateral
inhibition.
If these interpretations are accurate, I would expect that during unstimulated sessions, mice
would perform poorly on the first SG trial following an IS block when that first trial is in the opposite
direction of the rewarded side of the preceding IS block. We’d expect that the sensory stimulus of the
first SG trial would minimally affect action selection in the same way light inhibition has a minimal
effect during IS trials i.e. the committed decision is less likely to affected by a subsequent stimulus.
The directionally indicative odorant signals the end of the IS block so that by the second trial in the
SG block, the animal has switched its decision mode to rely on the odorant instead of the previous
trial. When the first trial SG trial following an IS block, is in the same direction, mice get the trial
correct 97.2% of the time, compared to 37.4% of the time when the first trial is in the opposite
direction. Ultimately this suggests that the longer a decision to move has existed, the less likely that
decision is to be modulated by additional factors and stimuli.
Study strengths
The use of mice in these experiments allows us to pursue these results in future studies that
can take advantage of advanced recording, stimulation and imaging techniques most readily available
in rodent models. The use of a freely-moving behavioral task allowed us to capture neural activity
65
while animals perform natural orienting movements, reducing the potential to obfuscate results by
restraining animals or mandating unnatural movements. The neural recording techniques let us
analyze and interpret activity at the level of individual neurons, providing fine spatiotemporal
resolution for interpretation of my results. Histological verification of recording sites from all
experiments confirmed that I recorded from and modulated activity in the intended regions of study.
The novel behavioral task introduced with these studies is one that mice can perform with ease and is
well suited to abstract independent and unique modes of decision making. Use of a stimulus for IS
blocks that is also used during SG blocks (50/50 odorant) allows us to conclude that differences in
neural activity between trial types is not attributable to a novel cue. The organization of trials into
large blocks provides animals with the repetition necessary to ensure understanding of the task
structure so they may perform well on a trial to trial basis.
Limitations of my approach
Here I independently studied two interconnected brain regions, producing novel results based
on sound experimentation. As I view the two regions in tandem to draw conclusions about the
nigrotectal pathway from these results, I am faced with some obstacles that cloud my interpretation.
Most importantly, while the SC is modulated by the SNr, both regions make numerous connections
with other structures in the brain. The SNr additionally sends projections to the thalamus and other
regions of the brainstem, while the SC receives input from many areas including the cortex, retina,
and pedunculopontine tegmental nucleus (PPN). Thus, while I speculate about the nigrotectal
pathway in light of these results, I cannot draw any conclusions with certainty about these regions
when considered together. My conclusions about the SNr and the SC when considered independently,
enhance our understanding of how these structures are modulated and potentially drive movements
differently between stimulus-guided and internally-specified movements. However, determining the
direct effect on the SC from the SNr under these unique contexts will require techniques that provide
the ability to identify and focally record from individual neurons in the nigrotectal pathway.
66
Implications of results
Overall these studies indicate that activity in both the SNr and the SC are modulated by the
context in which decisions are made. It is well established in the field of decision making that
decisions necessarily rely upon a combination of external cues and internal knowledge. The finding
that both the SNr and SC are modulated in a manner that considers these elements, suggests an
enhanced cognitive component of these regions that has been previously unappreciated. The fact that
activity in the SC is not a direct reflection of activity in the SNr, suggest that the context-dependent
modulation of the SNr is likely communicated to the thalamus as well as other brainstem structures in
addition to the SC. Likewise, the context-dependent modulation of activity in the SC that is not
reflective of activity in the SNr suggests that decision context is likely reflected in structures upstream
of the SC in addition to the SNr.
To address these potential questions, one could feasibly investigate activity in individual
circuits along this pathway. However, perhaps a more interesting endeavor for now would be to
continue collecting neural data in a broad structural approach, applying it to regions upstream of both
the SC and the SNr, such as the striatum, PPN, or regions of the cortex, to determine their roles in the
consideration of decision context. Ultimately, a comprehensive understanding of the action-based
decision-making process will likely require efforts along both trajectories. As we continue to probe
the decision-making process with new techniques and behavioral paradigms that push the envelope of
how much information we can extract during this process, we will eventually disentangle the neural
substrates underlying decision making.
67
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