Browsing by Subject "Neurosciences"

Severe constipation can be life-threatening and disproportionately affects patients who may not benefit from conventional treatments. Sacral nerve stimulation (SNS) is an alternative to laxatives and pharmaceuticals, and it modulates propulsive action in the colon. Conventional SNS failed to treat slow-transit constipation. I hypothesized bursts of nerve stimulation interleaved by quiescent periods increase colonic transit more effectively than continuous nerve stimulation. I electrically stimulated the colon directly in computational models and the isolated mouse colon to characterize properties of the colonic motor complex (CMC), and I used optical and fluorescent imaging, electromyography, and manometry to compare the effect of pelvic and sacral nerve stimulation on colonic motility. I developed a computational model of colonic motility and compared the effects of burst and conventional nerve stimulation on pellet velocity and colonic emptying under normal and slow transit conditions. Burst nerve stimulation evoked more frequent calcium and pressure waves, and increased fecal pellet output than continuous nerve stimulation in the isolated mouse colon, anesthetized rat, and computational model, respectively. Burst nerve stimulation with optimized burst frequency, duration, and interval more effectively produced prokinetic motility than continuous nerve stimulation, suggesting that burst SNS may be a viable clinical treatment for severe and slow transit constipation.

Changing environmental conditions may present substantial challenges to organisms experiencing them. In animals, the fastest way to respond to these changes is often by altering behavior. This ability, called behavioral flexibility, varies among species and can be studied on several levels. First, the extent of behavioral flexibility exhibited by a species can be determined by observation of that species' behavior, either in nature or in experimental settings. Second, because the central nervous system is the substrate determining behavior, neuroanatomy can be studied as the proximate cause of behavioral flexibility. Finally, the ultimate causation can be examined by studying ecological factors that favor the evolution of behavioral flexibility. In this dissertation, I investigate behavioral flexibility across all three levels by examining the relationship between habitat structure, the size of different structures within the brain and total brain size, and behavioral flexibility in six closely-related species of Puerto Rican Anolis lizards. Anolis lizards provide an excellent taxon for this study as certain species, including those used here, are classified as belonging to different ecomorphs and are morphologically and behaviorally specialized to distinct structural habitat types.

In order to determine the presence of behavioral flexibility in Anolis, I first presented Anolis evermanni with a series of tasks requiring motor learning and a single instance of reversal learning. Anolis evermanni demonstrated high levels of behavioral flexibility in both tasks.

To address the pattern of brain evolution in the Anolis brain, I used a histological approach to measure the volume of the whole brain, telencephalon, dorsal cortex, dorsomedial cortex, medial cortex, dorsal ventricular ridge, cerebellum, and medulla in six closely-related species of Puerto Rican Anolis lizards belonging to three ecomorphs. These data were analyzed to determine the relative contribution of concerted and mosaic brain evolution to Anolis brain evolution. The cerebellum showed a trend toward mosaic evolution while the remaining brain structures matched the predictions of concerted brain evolution.

I then examined the relationship between the complexity of structural habitat occupied by each species and brain size in order to determine if complex habitats are associated with relatively large brains. I measured brain volume using histological methods and directly measured habitat complexity in all six species. Using Principal Component Analysis, I condensed the measures of habitat structure to a single variable and corrected it for the scale of each lizard species' movement, calling the resulting measurement relevant habitat complexity. I tested the relationship between relative volume of the telencephalon, dorsal cortex, dorsomedial cortex, and whole brain against both relative habitat complexity and ecomorph classification. There was no relationship between the relative volume of any brain structure examined and either relevant habitat complexity or ecomorph. However, relevant habitat complexities for each species did not completely match their ecomorph classifications.

Finally, I tested the levels of behavioral flexibility of three species of Anolis, A. evermanni, A. pulchellus, and A. cristatellus, belonging to three distinct ecomorphs, by presenting them with tasks requiring motor and reversal learning. Anolis evermanni performed well in both tasks, while A. pulchellus required more trials to learn the motor task. Only a single Anolis cristatellus was able to perform either task. Anolis evermanni displayed lower levels of neophobia than the other species, which may be related to its superior performance.

In combination, this research suggests that Anolis of different ecomorphs display different levels of behavioral flexibility. At the proximate level, this difference in behavioral flexibility cannot be explained by changes in the relative size of the total brain or brain structures associated with cognitive abilities in other taxa. At the ultimate level, the size of the brain and several constituent structures cannot be predicted by habitat complexity. However, behavioral flexibility in certain tasks may be favored by utilization of complex habitats. Flexibility in different tasks is not correlated, rendering broad comparisons to a habitat complexity problematic.

Vocal learning is the ability modify vocal output based on auditory input and is the basis of human speech acquisition. It is shared by few distantly related bird and mammal orders, and is thus very likely to be an example of convergent evolution, having evolved independently in multiple lineages. This complex behavior is presumed to require networks of regulated genes to develop the necessary neural circuits for learning and maintaining vocalizations. Deciphering these networks has been limited by the lack of high throughput genomic tools in vocal learning avian species and the lack of a solid computational framework to understand the relationship between gene expression and behavior. This dissertation provides new insights into the evolution and mechanisms of vocal learning by taking a top-down, systems biology approach to understanding gene expression regulation across avian and mammalian species. First, I worked with colleagues to develop a zebra finch Agilent oligonucleotide microarray, including developing programs for more accurate annotation of oligonucleotides and genes. I then used these arrays and tools in multiple collaborative, but related projects, to measure transcriptome expression data in vocal learning and non-learning avian species, under a number of behavioral paradigms, with a focus on song production. To make sense of the avian microarray data, I compiled microarray data from other sources, including expression analyses across over 900 human brain regions generated by Allen Brain Institute. To compare these data sets, I developed and performed a variety of computational analyses including clustering, linear models, gene set enrichment analysis, motif discovery, and phylogenetic inference, providing a novel framework to study the gene regulatory networks associated with a complex behavior. Using the developed framework, we are able to better understand vocal learning at different levels: how the brain regions for vocal learning evolved and how those brain regions function during the production of learned vocalizations. At the evolutionary level, we identified genes with unique expression patterns in the brains of vocal learning birds and humans. Interesting candidates include genes related to formation of neural connections, in particular the SLIT/ROBO axon guidance pathway. This algorithm also allowed us to identify the analogous regions that are a part of vocal learning circuit across species, providing the first quantitative evidence relating the human vocal learning circuit to the avian vocal learning circuit. With the avian song system verified as a model for human speech at the molecular level, we conducted an experiment to better understand what is happening in those brain regions during singing by profiling gene expression in a time course as birds are producing song. Surprisingly, an overwhelming majority of the gene expression identified was strongly enriched in a particular region. We also found a tight coupling between the behavioral function of a particular region and the gene expression pattern. To gain insight into the mechanisms of this gene regulation, we conducted a genomic scan of transcription factor binding sites in zebra finch. Many transcription factor binding sites were enriched in the promoters of genes with a particular temporal patterns, several of which had already been hypothesized to play a role in the neural system. Using this data set of gene expression profiles and transcription factor binding sites along with separate experiments conducted in mouse, we were able uncover evidence that the transcription factor CARF plays a role in neuron homeostasis. These results have broadened our understanding of the molecular basis of vocal learning at multiple levels. Overall, this dissertation outlines a novel way of approaching the study of the relationship between genes and behavior.

Anxiety is a behavioral state induced by low-threat, uncertain situations in which perceived danger is diffuse. The anxiety state is then accompanied by increased vigilance and risk assessment to one’s surroundings. Recent studies have shown that the brain regions responsible for encoding anxiety are widely located in the frontal cortex and extended limbic system; however, the network architecture responsible for hypervigilance has yet to be elucidated. Here, I propose to employ a data-driven method of using in vivo recordings of electrical activity across multiple brain regions concurrently as mice freely explore classic ethological anxiety-related behavioral assays and are administered pharmacological agents that modulate the anxiety state. Using novel machine-learning techniques, I have generated neural models that reflect the network-level activity engaged during the performance of these tasks. I have then validated the structure of this anxiety network in its ability to generalize to other anxiety-related tasks and models of disease. I anticipate that this strategy will discover an independent network that is correlated with anxiety-related behaviors. Thus, successful completion of the proposed work will lead to a network-level understanding of anxiety. Furthermore, the framework discovered through this study has the potential to facilitate the development of new revolutionary approaches for anxiety disorders.

The development and function of the nervous system relies on complex regulation of gene expression programs. MicroRNAs (miRNAs) are small RNAs that have diverse functions in mammalian development and disease. In concert with the RNA-induced silencing complex, miRNAs repress translation by binding to target mRNAs. The nervous system contains the largest proportion of miRNAs, yet few have been functionally characterized in vivo.

miR-133b is a highly conserved miRNA embedded in the sequence of 7H4, a noncoding RNA that is enriched at the neuromuscular junction (NMJ), a large synapse that is essential for eliciting muscle contraction and movement. I have found that, like 7H4, miR-133b expression is enriched at the NMJ and upregulated postnatally, coinciding with important events in synaptic maturation, including synaptic growth and elimination. Knockdown of miR-133b in postnatal muscle by electroporation of modified antisense oligonucleotides gave rise to abnormally large synapses, indicating a role for miR-133b in synaptic maturation. To specifically remove miR-133b in vivo, I generated a mouse containing a targeted deletion of the miR-133b stemloop. NMJ maturation and synapse elimination proceeded normally in miR-133b knockout mice, suggesting that miR-133b may have other functions at the synapse. The expression of 7H4 and miR-133b is upregulated following nerve transection, consistent with a role in synaptic regeneration. Indeed, NMJ reinnervation is delayed in miR-133b KO mice following nerve crush, but not nerve cut. These data suggest that miR-133b may have a specific protective function at the synapse that could be relevant to disease states, including amyotrophic lateral sclerosis (ALS), where NMJ denervation occurs following motor neuron cell death. However, loss of miR-133b did not affect survival or disease progression in the SOD1(G93A) mouse model, differentiating its role from that of miR-206, another miRNA found in 7H4.

miR-133b has recently been proposed to regulate the development and maintenance of midbrain dopaminergic (mDA) neurons. mDA neurons have critical functions in the control of movement and emotion, and their degeneration leads to motor and cognitive defects in Parkinson's disease. miR-133b is enriched in the midbrain and regulates mDA neuron differentiation in vitro by targeting Pitx3, a transcription factor required for appropriate development of substantia nigra DA neurons. However, the function of miR-133b in the intact midbrain has not been determined. miR-133b KO mice have normal numbers of midbrain dopaminergic neurons during development and aging. Moreover, dopamine neurotransmitter levels are unchanged in the striatum and other brain regions, while expression of dopaminergic genes including Pitx3 is also unaffected. Finally, miR-133b null mice display normal motor coordination and activity, suggesting that miR-133b does not play a significant role in the development or maintenance of the mDA neuron population.

Motivated behaviors are essential for the survival and maintenance of life. The ventral tegmental area (VTA) is a midbrain region that has been implicated in motivational processes, such as seeking reward and avoiding harm. It contains dopamine (DA) neurons that project to limbic brain areas and give rise to the prominent mesolimbic DA pathway. In addition, it contains gamma-aminobutyric acid (GABA) neurons that also project to the limbic system as well as other major brain regions, such as the hindbrain and prefrontal cortex. Despite decades of research, the functions of VTA neurons remain mysterious and controversial. According to an influential hypothesis, VTA DA neurons encode a reward prediction error (RPE), a teaching signal that updates the value of learned associations (Schultz, Dayan, & Montague, 1997). It has also been proposed that VTA GABA neurons represent reward expectancy and provide the subtraction needed (actual reward minus expected reward), to compute a RPE (Eshel et al., 2015). According to another prominent hypothesis, however, VTA neurons encode the amount of effort, vigor, or ‘incentive’ we attribute to motivationally relevant stimuli (Salamone & Correa, 2012; Berridge & Robinson 1998). In most studies that attempt to relate VTA neural activity with reinforcement learning (RL) algorithms, the animals are often head-fixed and behavioral measures are usually limited to limb movements or licking. However, restraining the animal does not mean that they do not attempt to move their head and body. This creates a significant confound in all past research on DA neurons encoding RPE. I will argue that the conflict between the two prominent hypotheses of VTA function arises from both conceptual and empirical limitations, including the lack of precise and continuous behavioral measurements. To address these concerns, I first developed a novel head-fixation device that measures the forces exerted by the head in three orthogonal directions (up/down, left/right, forward/backward), as well as the forces exerted by the body (Chapter 2). The device contains load cells that convert analog voltage signals into continuous measures of force while the mice engage in traditional head-fixed tasks. By recording VTA neurons using in vivo electrophysiology and optogenetics while simultaneously measuring the continuous forces exerted by the mice, I found that VTA DA neurons encode the impulse vector (the magnitude and direction of force exerted over time) rather than RPE (Chapter 3). Moreover, according to the impulse-momentum theorem, I show how dynamic vector representations from head-fixed experiments can be translated into kinematic vector representations during freely moving behavior. According to the impulse-momentum theorem, impulse is equal to a change in momentum. In other words, a change in force is equivalent to a change in velocity assuming a constant mass, linking both dynamic and kinematic vector quantities. Then, by using the same continuous force measurements and manipulating the spatial location of reward during a traditional Pavlovian conditioning task, I falsified several key predictions from the RPE hypothesis (Chapter 4). By delivering the same reward in different locations (e.g., keeping the value and prediction constant), I was able to disambiguate an RPE signal from force exertion. I found that VTA DA neurons more precisely represented the impulse vector and not an RPE. Moreover, using a leaky integrator model, single unit activity of DA neurons could be used to predict the forces exerted across time regardless of reward predictability, as well as across multiple timescales. Then, I demonstrated that optogenetic manipulation of phasic DA activity has no impact on learning but directly modulates performance. At the same time, by using the same manipulations, I falsified the expectancy hypothesis of VTA GABA neurons and demonstrated they also represent vector quantities of force rather than expectancy. I found that VTA GABA neurons show opponent activity (increases or decreases of their firing rate) based on the direction of movement, despite the same level of expectancy and value (Chapter 4). Moreover, I utilized the leaky-integrator model to show that VTA GABA neurons represent the integral of DA activity. Finally, using in vivo electrophysiology, optogenetics, in vivo calcium imaging, and 3D motion capture during freely moving behavior in a novel reward tracking task, I found that a subset of VTA GABA neurons precisely represent three-dimensional rotational kinematics (Chapter 5). Taken together, these results demonstrate that the VTA controls the kinematics and dynamics necessary to control all motivated behaviors such as orientation, approach, and avoidance; whether to seek reward or avoid harm. These data unite the directional and activational components of motivation and provide precise physical quantities to influential concepts such as effort and vigor. Furthermore, I show the computational interaction between VTA DA and GABA neurons and demonstrate how they both participate in controlling the force vectors. Consequently, I made significant steps towards understanding how the VTA controls motivated behaviors and also falsified several key predictions of the RPE hypothesis, as well as improved the effort-related hypotheses. Thus, I have developed a new and comprehensive framework of VTA functioning.

The goal of this dissertation is to characterize goal directed proactive behavioral responses to threat as well as reactive responses to threat exposure, and to identify the neural and personality correlates of individual differences in these responses. Three specific studies are reported wherein participants completed a novel shock avoidance paradigm while concurrent measures of behavioral, muscular, and sympathetic autonomic activity were collected; self-report was used to measure mood and trait personality; and blood oxygen-level dependent functional magnetic resonance imaging (BOLD fMRI) was used to measure individual differences in threat-related amygdala reactivity and intrinsic connectivity within the corticolimbic circuit.

Results from Study 1 demonstrate that during threat exposure, participants exhibit increased avoidance behavior, faster reaction times, and increased muscular and sympathetic activity. Moreover, results demonstrate that two broad patterns characterize individual differences in how participants respond during avoidance: 1) a generalized tendency to exhibit magnified threat responses across domains; and 2) a tendency to respond either with proactive behavioral responses or reactive autonomic responses. Heightened state anxiety during the shock avoidance paradigm, and increased trait anxiety were both associated with the generalized tendency to exhibit magnified threat responses. However, gender moderated the relationship between trait anxiety and generalized increases in threat responses during avoidance, such that only male participants exhibited a positive relationship between these two factors. Study 2 demonstrates that intrinsic connectivity between the dorsomedial prefrontal cortex and centromedial region of the amygdala prospectively predicts whether participants will respond proactively or reactively during active avoidance. Finally, Study 3 provides evidence that responses to threat-related facial expressions within the centromedial region of the amygdala are associated with more reactive and less proactive responses during avoidance.

These results demonstrate that patterns observed in animal models of avoidance, specifically the competition between proactive and reactive responses to threat cues, extend to human participants. Moreover, our results suggest that while anxious mood during performance and heightened trait anxiety are associated with a generalized facilitation of threat responses across domains, measures of neural circuit function within the corticolimbic system predict whether individuals will exhibit increased proactive or reactive responses during active avoidance. In addition to facilitating the search for the neural processes underlying how the brain responds dynamically to threat, these results have the potential to aide researchers in characterizing the symptoms and neural processes underlying anxiety disorders.

The formation and retention of distinct membrane domains in the fluidic membrane bilayer is the key process in establishing spatial organization for mediating physiological functions in metazoans. The spectrin-ankyrin network organizes diverse membrane domains including T-tubule and intercalated disc of cardiomyocytes, basolateral membrane of epithelial cells, costameres of striatal muscle, and axon initial segments and nodes of Ranvier in nervous system. This thesis identifies a novel function of 480 kDa ankyrin-G, an alternatively spliced isoform of the ankyrin family, in promoting somatodendritic GABAA receptor synaptogenesis both in vitro and in vivo. In the nervous system, an insertion of a neuronal specific exon (exon 37) occurs in ankyrin-G polypeptide which results in a 480 kDa isoform. 480 kDa ankyrin-G (giant ankyrin-G) has been shown to coordinate formation and maintenance of the axon initial segment (AIS) and nodes of Ranvier. This thesis research began with the discovery that giant ankyrin-G, previously thought to be confined to the axon initial segment, forms developmentally-regulated and cell-type specific somatodendritic "outposts" on the plasma membrane of pyramidal neurons. This somatodendritic 480 kDa ankyrin-G outpost forms micron-scale membrane domains where it associates with canonical AIS binding partners including voltage-gated sodium channel and neurofascin. This thesis further discovered that the giant insert of 480 kDa ankyrin-G interacts with GABARAP, a GABAA receptor-associated protein. Both the interaction with GABARAP and the membrane association through palmitoylation of giant ankyrin-G are required for the formation of somatodendritic GABAergic synapses. This work further found that ankyrin-G associates with extrasynaptic GABAA receptors and stabilizes receptors on the extrasynaptic membrane through opposing endocytosis. This story demonstrates for the first time the existence of giant ankyrin-G somatodendritic outpost as well as its function in directing the formation of GABAergic synapses that provides a rationale for studies linking ankyrin-G genetic variation with psychiatric disease and neurodevelopmental disorders.

Additional work presented in the Appendix characterized novel ankyrin-G full length transcripts in the heart and kidney with unique domain compositions though alternative splicing. The preliminary work further identified biochemical properties and potential role of an insert C in the C-terminus of ankyrin-G in mediating cytokinesis and cellular migration in mouse fibroblasts. Together, this thesis work expands the knowledge of giant ankyrin-G functions in the nervous system and offers insights into the diversified roles of distinct ankyrin-G peptides acquired from alternative splicing in organizing specific membrane domains and interacting with defined intracellular pathways in different tissues.

Many of the skills we value most as humans, such as speech and learning to play musical instruments, are learned in the absence of external reinforcement. However, the model systems most commonly used to study motor learning employ learning paradigms in which animals perform behaviors in response to external rewards or punishments. Here I use the zebra finch, an Australian songbird that can learn its song as a juvenile in the absence of external reinforcement as well as modify its song in response to external cues as an adult, to study the circuit mechanisms underlying both internally and externally reinforced forms of learning. Using a combination of intersectional genetic and microdialysis techniques, I show that a striatonigral pathway and its downstream effectors, namely D1-type dopamine receptors, are necessary for both internally reinforced juvenile learning and externally reinforced adult learning, as wells as for song modification in response to social cues or to deafening. In addition, I employ optogenetic stimulation during singing to demonstrate that this striatonigral projection is sufficient to drive learning. Interestingly, I find that neither the striatonigral pathway nor D1-type dopamine receptors are necessary for recovery of pitch after externally driven pitch learning. In all, I establish that a common mechanism underlies both internally and externally reinforced vocal learning.

Ever since the initial discovery of general anesthetics almost 170 years ago, how general anesthesia (GA) induces loss of consciousness remains a century-long mystery. In addition, whether diverse anesthetic drugs and sleep share a common neural pathway is hotly debated and largely unknown. Previous studies have established that many GA drugs inhibit neural activity through targeting GABA receptors. Here, by using Fos staining, ex vivo brain slice recording, and eventually in vivo multichannel extracellular electrophysiology, we discovered a core ensemble of hypothalamic neurons in and near the supraoptic nucleus, consisting primarily of peptidergic neuroendocrine cells, which are surprisingly and persistently activated by multiple classes of GA drugs. Strikingly, chemogenetic or optogenetic stimulation of these anesthesia-activated neurons (AANs) strongly potentiated slow-wave sleep and prolonged GA, whereas conditional ablation through diphtheria toxin receptor strategy or inhibition of AANs with optogenetics led to reduced slow-wave oscillation in the brain, significant loss of slow-wave and rapid-eye movement sleep, and shortened durations under GA. Together, these findings identify a previously unknown common neural substrate underlying diverse GA drugs and natural sleep, and further illustrate a crucial role of the neuroendocrine system in regulating global brain states.

It has long been appreciated that the process of learning might invoke a physical change in the brain, establishing a lasting trace of experience. Recent evidence has revealed that this change manifests, at least in part, by the formation of new connections between neurons, as well as the modification of preexisting ones. This so-called structural plasticity of neural circuits – their ability to physically change in response to experience – has remained fixed as a primary point of focus in the field of neuroscience.

A large portion of this effort has been directed towards the study of dendritic spines, small protrusions emanating from neuronal dendrites that constitute the majority of recipient sites of excitatory neuronal connections. The unique, mushroom-like morphology of these tiny structures has earned them considerable attention, with even the earliest observers suggesting that their unique shape affords important functional advantages that would not be possible if synapses were to directly contact dendrites. Importantly, dendritic spines can be formed, eliminated, or structurally modified in response to both neural activity as well as learning, suggesting that their organization reflects the experience of the neural network. As such, elucidating how these structures undergo such rearrangements is of critical importance to understanding both learning and memory.

As dendritic spines are principally composed of the cytoskeletal protein actin, their formation, elimination, and modification requires biochemical signaling networks that can remodel the actin cytoskeleton. As a result, significant effort has been placed into identifying and characterizing such signaling networks and how they are controlled during synaptic activity and learning. Such efforts have highlighted Rho family GTPases – binary signaling proteins central in controlling the dynamics of the actin cytoskeleton – as attractive targets for understanding how the structural modification of spines might be controlled by synaptic activity. While much has been revealed regarding the importance of the Rho GTPases for these processes, the specific spatial and temporal features of their signals that impart such structural changes remains unclear.

The central hypotheses of the following research dissertation are as follows: first, that synaptic activity rapidly initiates Rho GTPase signaling within single dendritic spines, serving as the core mechanism of dendritic spine structural plasticity. Next, that each of the Rho GTPases subsequently expresses a spatially distinct pattern of activation, with some signals remaining highly localized, and some becoming diffuse across a region of the nearby dendrite. The diffusive signals modify the plasticity induction threshold of nearby dendritic spines, and the spatially restricted signals serve to keep the expression of plasticity specific to those spines that receive synaptic input. This combination of differentially spatially regulated signals thus equips the neuronal dendrite with the ability to perform local biochemical computations, potentially establishing an organizational preference for the arrangement of dendritic spines along a dendrite. Finally, the consequences of the differential signal patterns also help to explain several seemingly disparate properties of one of the primary upstream activators of these proteins: brain-derived neurotrophic factor (BDNF).

The first section of this dissertation describes the characterization of the activity patterns of one of the Rho family GTPases, Rac1. Using a novel Förster Resonance Energy Transfer (FRET)- based biosensor in combination with two-photon fluorescence lifetime imaging (2pFLIM) and single-spine stimulation by two-photon glutamate uncaging, the activation profile and kinetics of Rac1 during synaptic stimulation were characterized. These experiments revealed that Rac1 conveys signals to both activated spines as well as nearby, unstimulated spines that are in close proximity to the target spine. Despite the diffusion of this structural signal, however, the structural modification associated with synaptic stimulation remained restricted to the stimulated spine. Thus, Rac1 activation is not sufficient to enlarge spines, but nonetheless likely confers some heretofore-unknown function to nearby synapses.

The next set of experiments set out to detail the upstream molecular mechanisms controlling Rac1 activation. First, it was found that Rac1 activation during sLTP depends on calcium through NMDA receptors and subsequent activation of CaMKII, suggesting that Rac1 activation in this context agrees with substantial evidence linking NMDAR-CaMKII signaling to LTP in the hippocampus. Next, in light of recent evidence linking structural plasticity to another potential upstream signaling complex, BDNF-TrkB, we explored the possibility that BDNF-TrkB signaling functioned in structural plasticity via Rac1 activation. To this end, we first explored the release kinetics of BDNF and the activation kinetics of TrkB using novel biosensors in conjunction with 2p glutamate uncaging. It was found that release of BDNF from single dendritic spines during sLTP induction activates TrkB on that same spine in an autocrine manner, and that this autocrine system was necessary for both sLTP and Rac1 activation. It was also found that BDNF-TrkB signaling controls the activity of another Rho GTPase, Cdc42, suggesting that this autocrine loop conveys both synapse-specific signals (through Cdc42) and heterosynaptic signals (through Rac1).

The next set of experiments detail one the potential consequences of heterosynaptic Rac1 signaling. The spread of Rac1 activity out of the stimulated spine was found to be necessary for lowering the plasticity threshold at nearby spines, a process known as synaptic crosstalk. This was also true for the Rho family GTPase, RhoA, which shows a similar diffusive activity pattern. Conversely, the activity of Cdc42, a Rho GTPase protein whose activity is highly restricted to stimulated spines, was required only for input-specific plasticity induction. Thus, the spreading of a subset of Rho GTPase signaling into nearby spines modifies the plasticity induction threshold of these spines, increasing the likelihood that synaptic activity at these sites will induce structural plasticity. Importantly, these data suggest that the autocrine BDNF-TrkB loop described above simultaneously exerts control over both homo- and heterosynaptic structural plasticity.

The final set of experiments reveals that the spreading of GTPase activity from stimulated spines helps to overcome the high activation thresholds of these proteins to facilitate nearby plasticity. Both Rac1 and RhoA, the activity of which spread into nearby spines, showed high activation thresholds, making weak stimuli incapable of activating them. Thus, signal spreading from a strongly stimulated spine can lower the plasticity threshold at nearby spines in part by supplementing the activation of high-threshold Rho GTPases at these sites. In contrast, the highly compartmentalized Rho GTPase Cdc42 showed a very low activation threshold, and thus did not require signal spreading to achieve high levels of activity to even a weak stimulus. As a result, synaptic crosstalk elicits cooperativity of nearby synaptic events by first priming a local region of the dendrite with several (but not all) of the factors required for structural plasticity, which then allows even weak inputs to achieve plasticity by means of localized Cdc42 activation.

Taken together, these data reveal a molecular pattern whereby BDNF-dependent structural plasticity can simultaneously maintain input-specificity while also relaying heterosynaptic signals along a local stretch of dendrite via coordination of differential spatial signaling profiles of the Rho GTPase proteins. The combination of this division of spatial signaling patterns and different activation thresholds reveals a unique heterosynaptic coincidence detection mechanism that allows for cooperative expression of structural plasticity when spines are close together, which in turn provides a putative mechanism for how neurons arrange structural modifications during learning.

Astrocytes, the perisynaptic glial cells of the brain, display a complex morphology that is strongly linked to their functions at the synapse. Primary processes radiating from the astrocyte cell soma branch out to secondary and tertiary processes, which further ramify into tiny perisynaptic astrocyte processes, giving a mature astrocyte its characteristic arborized structure. Astrocyte processes dynamically ensheath the pre- and post-synapse to provide instructive cues for synapse formation, maturation, and function. Perturbations in astrocyte-synapse interactions result in synaptic deficits, leading to excitation/inhibition imbalance and aberrant neural circuitry. However, the mechanisms linking astrocyte morphology and function to neuronal contact and synaptic adhesion are poorly understood. In a candidate-based reverse genetic screen utilizing rodent cortical neurons and astrocytes, I identified two genes, HepaCAM and CTNND2, as regulators of astrocyte morphogenesis in response to neuronal adhesion.HepaCAM is an astrocyte-enriched cell adhesion molecule that participates in cell-cell and cell-ECM interactions to regulate cell migration and proliferation. shRNA-mediated silencing of hepaCAM expression in astrocytes resulted in decreased astrocyte complexity in vitro and in vivo. HepaCAM stabilizes the gap junction protein connexin 43 (Cx43) at cell-cell junctions. We used stimulated emission depletion (STED) microscopy to show that hepaCAM and Cx43 colocalize at astrocyte processes in the mouse cortex and performed native affinity purifications followed by liquid chromatography-coupled high-resolution mass spectrometry (AP-MS) to demonstrate that Cx43 binds to hepaCAM. Finally, utilizing the same shRNA silencing approach, we found that hepaCAM and Cx43 were epistatic to each other in the regulation of astrocyte morphogenesis. Through mosaic analysis with double markers (MADM), we found that hepaCAM knockout astrocytes lost their ability to tile and had mislocalized Cx43. Consequently, gap junction coupling is impaired in astrocytes without hepaCAM. Additionally, we found decreased colocalization of hepaCAM puncta with synapses, a marked decrease in inhibitory synapses density, and a significant decrease in amplitude of miniature inhibitory postsynaptic currents, suggesting that loss of astrocytc hepaCAM disrupts the balance between synaptic excitation and inhibition. During development, astrocytes need to form non-overlapping territories within which they dynamically ensheathe synapses within discrete regions of neuropil. Taken together, our findings suggest that hepaCAM and Cx43 are critical proteins at the intersection of these two processes to ensure the proper molecular regulation of astrocyte self-organization and territory formation for normal circuit formation and function. Next, we identified Ctnnd2 (protein: δ-catenin) as another key regulator of astrocyte morphological complexity. δ-catenin was previously thought to be a neuron-specific protein that regulates dendrite morphology. Utilizing RNA fluorescence in situ hybridization (RNA-FISH) and immunohistochemistry, we found Ctnnd2 mRNA and δ-catenin is also highly expressed by astrocytes during the critical period of astrocyte morphological maturation and synapse formation during cortical development. shRNA-mediated silencing of Ctnnd2 expression in astrocytes resulted in decreased astrocyte complexity in vitro and in vivo. δ-catenin is hypothesized to mediate transcellular interactions through the cadherin family of cell adhesion proteins. We used structural modeling and surface biotinylation assays in both HEK293T and purified astrocyte cultures to reveal that δ-catenin interacts with N-cadherin juxtamembrane domain to promote N-cadherin surface expression. An autism-linked δ-catenin point mutation impaired N-cadherin cell surface expression and reduced astrocyte complexity. In the developing mouse cortex, only lower-layer cortical neurons express N-cadherin. Remarkably, when we silenced astrocytic N-cadherin throughout the cortex, only lower-layer astrocyte morphology was disrupted. These findings show that δ-catenin controls astrocyte-neuron cadherin interactions that regulate layer-specific astrocyte morphogenesis.

Emotions are pervasive in daily life, and a rich literature has documented how emotional stimuli and events disrupt ongoing processing and place heightened demands on control. Yet the executive control mechanisms that subsequently resolve that interference have not been well characterized. Although many failures of executive control have emotion at their core, numerous questions remain in the field regarding interactions between emotion and executive control. How do executive processes act on affective representations? Are emotional representations less amenable to control? Do distinct processes or neural networks govern their control? The present dissertation addresses these questions by investigating the neural systems and cognitive processes that support executive control in the face of interference from affective sources. Whereas previous research has emphasized the bottom-up impact of emotion on cognition, this dissertation will investigate how top-down executive control signals modulate affect's influence on cognition. Combining behavioral techniques with neuroimaging methodologies, this dissertation characterizes the interactive relationship between affective processes and top-down executive control and the ramifications of that interaction for promoting adaptive behavior.

Cognitive and behavioral phenomena related to affective interference resolution are explored in a series of research projects spanning attention and memory. Task-irrelevant affective representations may disrupt performance, but this interference appears to be dependent on top-down factors and can be resolved by executive mechanisms. Interference resolution mechanisms act on representations both of stimuli in the environment and information stored in memory. The findings reported here support emotion's capacity to disrupt executive processing but also highlight the role executive control plays in overcoming that interference in order to promote adaptive behavior.

Preliminary evidence demonstrates that age-related differences in episodic memory performance become greater in tasks that have greater perceptual demands (e.g., task stimuli are visually degraded), but are attenuated in tasks that have greater semantic demands (e.g., task requires utilizing previous knowledge). This work suggests that age-related differences in how perceptual and semantic information are represented in the brain have an impact on episodic memory. Broadly, the goal of this thesis was to investigate this idea. To investigate this goal, while undergoing functional magnetic resonance imaging scanning, samples of younger and older adults studied and later retrieved their memories of pictures of either scenes (Study 1 and 2) or objects (Study 3). The first two studies found that, compared to younger adults, in older adults, (1) in occipitotemporal cortex, the quality of perceptual-related representations was attenuated, but, intriguingly, (2) in anterior temporal lobes and prefrontal cortex, the quality of semantic-related representations was similar and even enhanced; these effects were found to be related to episodic memory. Study 1 demonstrated this pattern in individual brain regions and Study 2 demonstrated that this pattern was also present in how information was distributed across the whole-brain network. In Study 3 it was found that these age-related differences in functional neural representations are the result of age-related visual signal loss and compensatory semantic-enhancing mechanisms. Taken together, the three studies highlight that age-related differences in neural representations have an impact on cognition and especially episodic memory.

Advanced age is associated with substantial changes in the brain. These changes can be attributed to many difference sources, such as detrimental effects of aging, brain’s compensatory responses to such negative effects, and cognitive or neural resources acquired over lifespan. As a result, under the same cognitive task, healthy older adults (OAs) often show recruitment of brain regions that are different from healthy young adults (YAs). These observations have been drawn from functional magnetic resonance imaging (fMRI) studies on aging and cognition, which have been largely based on univariate analysis that relates experimental conditions to activity level in individual brain region. While univariate analysis reveals the age differences in the recruitment of brain regions, much remains unknown regarding how these regions are playing their roles. Meanwhile, recent methodological advances in cognitive neuroscience have provided the opportunities to examine 1) functional communications across brain regions, and 2) information stored in the distributed neural representation in a region. In this dissertation, I described age-related differences in these two novel perspectives in a series of fMRI studies on episodic memory, a domain of cognition that is particularly affected by aging. In these studies, healthy YAs and OAs encoded and later retrieved images of scenes or objects inside the scanner. Analyses on functional brain network and neural representations were conducted on the neuroimaging data. These analyses revealed three main findings. First, neural representation and functional connectivity revealed reduced involvements of the core task regions in OAs. During encoding, early visual cortex (EVC) in OAs exhibited reduced representation of visual information. During retrieval, medial temporal lobe (MTL) in OAs exhibited reduced reconfiguration of functional connectivity associated with successful remembering. Second, enhanced recruitments of additional neural resources in OAs were also observed. During encoding, anterior temporal lobe (ATL) in OAs exhibited enhanced semantic representation. During retrieval, prefrontal cortex (PFC) in OAs showed enhanced functional connectivity and stronger reconfiguration of connectivity associated with successful remembering. Finally, we found that schematic knowledge affected functional communication in PFC and semantic representation in ATL differently in the two age groups, suggesting that schema-related strategies may be preferentially selected by OAs. Taken together, these studies depicted the detrimental effect of aging and brain’s adaptive changes in two novel perspectives: functional communication and information processing, which may contribute to a more comprehensive understanding of episodic memory function in aging populations.

Hypoxia is a profound stressor of the central nervous system implicated in numerous neurodegenerative diseases. While it is increasingly evident that the early effects of hypoxia cause impairment at the level of the axon, the precise mechanisms through which hypoxia compromises axonal structure and function remain unclear. However, links between hypoxia-induced axonopathic disease and the amyloid cascade, as well as the upregulation of amyloid precursor protein (APP) and amyloid beta (Aβ) by hypoxic stress, give rise to the hypothesis that proteolytic cleavage of APP into Aβ may be specifically responsible for axonopathy under conditions of hypoxia.

The goal of this dissertation was thus to understand dependence of hypoxia-induced axonal morphological and functional impairment on APP cleavage and the production of Aβ. I have developed a model of hypoxia-induced axonopathy in retinal explants. Using this model, I have experimentally addressed the core hypothesis that APP cleavage, and in particular the formation of Aβ, is necessary and sufficient to mediate morphological and functional axonopathy caused by hypoxia. I have found that there is a dissociation between the mechanisms responsible for hypoxia-induced morphological and functional impairment of the axon in the explanted retina, with the former being dependent on APP-to-Aβ processing and the latter likely being dependent on cleavage of a non-APP substrate by the enzyme BACE1. These findings shed light on mechanisms of hypoxia-induced axonopathy.

The ability to learn and to modify complex vocal sequences requires extensive practice coupled with performance evaluation through auditory feedback. An efficient solution to the challenge of vocal learning, stemming from reinforcement learning theory, proposes that an “actor” learns correct vocal behavior through the instructive guidance of an auditory “critic.” However, the neural circuit mechanisms supporting performance evaluation and even how “actor” and “critic” circuits are instantiated in biological brains are fundamental mysteries. Here, I use a songbird model to dissociate “actor” and “critic” circuits and uncover biological mechanisms for vocal learning.

First, I employ closed-loop optogenetic methods in singing birds to identify two inputs to midbrain dopamine neurons that operate in an opponent fashion to guide vocal learning. Next, I employ electrophysiological methods to establish a microcircuit architecture underlying this opponent mechanism. Notably, I show that disrupting activity in these midbrain dopamine inputs precisely when auditory feedback is processed impairs learning, showing that they function as “critics.” Conversely, I show that disrupting activity in a downstream premotor region prior to vocal production prevents learning, consistent with an “actor” role. Taken together, these experiments dissociate discrete “actor” and “critic” circuits in the songbird’s brain and elucidate neural circuit and microcircuit mechanisms by which “actors” and “critics” working cooperatively enable vocal learning.

Memory is a fundamentally important process that guides our future behavior based on past experience. Its importance is underscored by the fact that a major feature of many neurodegenerative disorders is memory loss, which is disabling to an increasing portion of the aging population. However, the underlying electrophysiological processes underlying memory formation and retrieval in humans remains very poorly understood, and in turn, limits our abilities to provide effective therapy for patients suffering from these disorders. Here, we endeavored to investigate the underpinnings of human memory through intracranial recordings in human epilepsy patients undergoing routine monitoring for potential resective surgery. This unprecedented access to the human brain during awake behavior allowed us to make several inroads into understanding human memory. First, we investigated fast frequency oscillations in the brain, termed ripples, and their relevance during human episodic memory. We found that during a paired associates verbal memory task, ripples coupled between the medial temporal lobe (MTL) memory system and the temporal association cortex, and this coupling preceded the reinstatement of memory representations from the memory encoding period. Next, we measured single unit spiking activity from anterior temporal lobe in order to examine if temporal patterns of activity may serve as a general neural code that is replayed during memory retrieval. We found that verbal memories corresponded to item-specific sequences of cortical spiking activity, these sequences replayed during memory retrieval, and replay was preceded by ripples in the MTL. Finally, to develop a more mechanistic understanding of our findings, we used a randomly connected recurrent leaky integrate and fire neural network model to investigate the characteristics needed for significant spike sequence generation. We found that randomly connected networks can generate sequences under many parameter regimes with just white noise inputs, the specific output sequence was inherently related to the connectivity of the network, and these models could make quantitative predictions about dynamic excitatory and inhibitory balance during spiking sequences in the human data. Taken together, our results demonstrate a flexible mode of communication between the MTL and cortex in the service of episodic memory, and we provide a theoretical framework for understanding the generation of these neural patterns in the human cortex.