This thesis is comprised of two parts. In the first part, we investigated a) the pharmacology of GNTI, a selective kappa opioid receptor antagonist, as a scratch-inducing compound in mice and b) possible mediators and receptors that may be involved in GNTI-induced scratching (itch). We studied if GNTI induces scratching through opioid, histamine, gastrin-releasing peptide (GRP) and/or muscarinic M1 receptors. In the second part, we established similarities and differences between pain and itch using GNTI-induced scratching and formalin-induced nociception models in mice. We found that GNTI (0.03-3 mg/kg, s.c., behind the neck) induces compulsive and vigorous scratching behavior in a dose-dependent manner. A standard submaximal dose (0.3 mg/kg) of GNTI caused animals to scratch 500-600 times in a 30 min observation period. Intrathecal (i.t.) or intraperitoneal (i.p.) administration of GNTI did not elicit scratching behavior. Duration of action of GNTI was 60-70 min and tolerance to the repetitive behavior did not develop. C-fos expressing neurons, in response to GNTI injection, were localized on the lateral side of the superficial layers of the dorsal horn of the cervical spinal cord. Compound 48/80, a chemically different pruritogen, evoked c-fos expression in neurons which are located on the lateral side of the superficial layer of the dorsal horn. These data suggest that both GNTI and compound 48/80 activate a group of sensory neurons located on the lateral side of lamina I and II. Pretreating (at -20 min) and posttreating (at +5 min) mice with the kappa opioid receptor agonist, nalfurafine (0.001-0.03 mg/kg, s.c.), significantly attenuated scratching induced by GNTI (0.3 mg/kg). These effects were not a consequence of behavioral depression. Tolerance did not develop to the anti-scratch activity of nalfurafine. Pretreating mice with nalfurafine (0.02 mg/kg) prevented both GNTI- and compound 48/80-provoked c-fos expression. Our c-fos results suggest that the preclinical antipruritic activity of nalfurafine occurs at the spinal level. Moreover, our results reinforce the need to evaluate nalfurafine as a potentially useful antipruritic in human conditions involving itch. GNTI still elicited excessive scratching in mice lacking mu, delta or kappa opioid receptors, respectively, as well as in mice pretreated with either naloxone or norbinaltorphimine. The H1 receptor antagonist, fexofenadine, or the H4 receptor antagonist, JNJ 10191584, did not attenuate GNTI-induced scratching. Also, pretreating mice with the peptide GRPR antagonist, [D-Phe6]bombesin(6-13) methyl ester, or the non-peptide GRPR antagonist, RC-3095, did not antagonize scratching induced by GNTI. Furthermore, GRPR mRNA levels did not change in response to GNTI injection. Telenzepine, a standard M1 receptor antagonist, had no marked effect against GNTI-elicited scratching, however (unexpectedly) McN-A-343, an M1 receptor agonist, attenuated this behavior in a dose-dependent manner. In the second part of our studies, we found that pretreating mice with lidocaine (i.d., behind the neck) inhibits GNTI-induced scratching and prevents GNTI-provoked c-fos expression in the dorsal horn of the spinal cord. Similarly, lidocaine (i.d., hind leg) inhibits formalin-induced nociception as well as formalin-provoked c-fos expression. While injection (s.c.) of formalin to the face of mice induced only wiping (indicating pain) by forepaws of the injection side, injection (s.c.) of GNTI to the face elicited grooming and scratching (indicating itch). In contrast to formalin, GNTI did not induce c-fos expression in the trigeminal nucleus suggesting that pain and itch sensations are projected differently along the sensory trigeminal pathway. In short, our main results indicate that a) the scratch-inducing activity of GNTI is not mediated by opioid, histamine or GRP receptors; b) kappa opioid receptors are involved, at least in part, in the inhibition of itch sensation and thus, on the basis of our results, nalfurafine holds promise as a potentially useful antipruritic in human conditions involving itch; and c) agonism at M1 receptors inhibits GNTI-induced scratching therefore the M1 receptor may be a key target for antipruritic drug development. / Pharmacology

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Self-injurious behavior (SIB) is a behavioral pathology seen in a small percentage of humans and non-human primates. In one previous study, macaques with SIB had more sleep disruption than controls, but observations were limited. Two studies were conducted: a baseline study to investigate nighttime activity in rhesus macaques (Macaca mulatta) displaying SIB and controls, and a probiotic study to assess probiotic Bifidobacterium infantis 35624 for high nighttime activity. Subjects were 13 rhesus macaques, 5 with SIB (3 females; 1 SIB). Videocapture of Nighttime Activity (VNRA) was developed to record in complete darkness. IR-receptive webcams were connected to a laptop running ISPYCONNECT, software which recorded movement. Subjects were observed during the entire lights-off period (8pm-7am). Measures included total movement time (TMT), movement in hour 1 (HR1) and hour 11 (HR11), and number of videos. In the baseline, SIB subjects had higher TMT (pBifidobacterium infantis 35624 had no effect on sleep disruption, and also that increased nighttime activity seems to be a persistent characteristic of SIB subjects. It is unknown if increased nighttime activity affects SIB subjects; it may result in elevated SIB, or the SIB pathology could result in sleep disruption.

The dorsal CA2 subregion (dCA2) of the hippocampus exerts a critical role in social novelty recognition (SNR) memory and in the promotion of social aggression. Whether the SNR memory and social aggression functions of dCA2 are related or represent independent processes is unknown. Here I investigated the hypothesis that an animal is more likely to attack a novel compared to familiar animal and that dCA2 promotes social aggression through its ability to distinguish between novel and familiar animals. To test this hypothesis, I conducted a multi-day resident intruder (R-I) test to assess aggression towards familiarized and novel conspecifics. I found that residents were indeed more likely to attack a novel intruder, and that silencing of dCA2 caused a more profound suppression of aggression towards a novel than a familiarized intruder. To explore whether and how dCA2 pyramidal neurons encode aggression, I recorded calcium signals from resident dCA2 pyramidal neurons using microendoscopy during the R-I test. I found that a fraction of dCA2 neurons were selectively activated or inhibited during exploration, dominance, and attack behaviors and that the responses varied with conspecific novelty. Based on dCA2 population activity, a set of binary linear classifiers could accurately predict whether an animal was engaged in each of these forms of social behavior. Notably, the accuracy of decoding aggression was greater for novel compared to familiar intruders. Moreover, calcium signals were more highly correlated during R-I tests with the same familiarized intruder on successive days compared to R-I tests with a familiar and novel intruder on successive days. Similarly, I found significant cross-day decoding results during attack-related behaviors towards familiar-familiar but not for familiar-novel intruder pairs. Together, these findings demonstrate that dCA2 integrates social experience to guide future behavior and provides insight into how SNR memory adaptively influences aggressive behavior. Encounters with novel intruders generally promote aggression while familiarization leads to its stabilization. Moreover, my results are consistent with the hypothesis that dCA2 promotes aggression by computing social novelty.

This study explores the impact of missed workout sessions on cognitive functioning, focusing on stress, emotional well-being, attention and memory. Using a mixed-methods approach, survey data from 164 participants were analyzed through descriptive statistics, Pearson correlation and One-way ANOVA tests and a qualitative thematic analysis of open-ended responses. Results show a significant positive correlation between stress levels and negative mood impacts when workouts are missed (r = .681). One-way ANOVA indicated significant differences in stress levels and compensatory behaviors based on workout frequency, main goals and for how long the participants had been working out on a regular basis. Frequent exercisers reported higher stress and more compensatory behaviors. Thematic analysis identified themes like emotional responses, cognitive impacts and coping strategies. Findings highlight the importance of consistent exercise for cognitive and emotional stability. Further research should focus on interventions to support maintaining exercise routines and coping mechanisms for missed sessions.

Neuroscience as a field is collecting more data than at any other time in history. The scale of this data allows us to ask fundamental questions about the mechanisms of brain function, the basis of behavior, and the development of disorders. Our ambitious goals as well as the abundance of data being recorded call for reproducible, reliable, and accessible systems to push the field forward. While we have made great strides in building reproducible and accessible machine learning (ML) systems for neuroscience, reliability remains a major issue. In this dissertation, we show that we can leverage existing data and domain expert knowledge to build more reliable ML systems to study animal behavior. First, we consider animal pose estimation, a crucial component in many scientific investigations. Typical transfer learning ML methods for behavioral tracking treat each video frame and object to be tracked independently. We improve on this by leveraging the rich spatial and temporal structures pervasive in behavioral videos. Our resulting weakly supervised models achieve significantly more robust tracking. Our tools allow us to achieve improved results when we have imperfect, limited data while requiring users to label fewer training frames and speeding up training. We can more accurately process raw video data and learn interpretable units of behavior. In turn, these improvements enhance performance on downstream applications. Next, we consider a ubiquitous approach to (attempt to) improve the reliability of ML methods, namely combining the predictions of multiple models, also known as deep ensembling. Ensembles of classical ML predictors, such as random forests, improve metrics such as accuracy by well-understood mechanisms such as improving diversity. However, in the case of deep ensembles, there is an open methodological question as to whether, given the choice between a deep ensemble and a single neural network with similar accuracy, one model is truly preferable over the other. Via careful experiments across a range of benchmark datasets and deep learning models, we demonstrate limitations to the purported benefits of deep ensembles. Our results challenge common assumptions regarding the effectiveness of deep ensembles and the “diversity” principles underpinning their success, especially with regards to important metrics for reliability, such as out-of-distribution (OOD) performance and effective robustness. We conduct additional studies of the effects of using deep ensembles when certain groups in the dataset are underrepresented (so-called “long tail” data), a setting whose importance in neuroscience applications is revealed by our aforementioned work. Altogether, our results demonstrate the essential importance of both holistic systems work and fundamental methodological work to understand the best ways to apply the benefits of modern machine learning to the unique challenges of neuroscience data analysis pipelines. To conclude the dissertation, we outline challenges and opportunities in building next-generation ML systems.

Humans possess the fascinating ability to communicate the most complex of ideas through spoken language, without requiring any external tools. This process has two sides—a speaker producing speech, and a listener comprehending it. While the two actions are intertwined in many ways, they entail differential activation of neural circuits in the brains of the speaker and the listener. Both processes are the active subject of artificial intelligence research, under the names of speech synthesis and automatic speech recognition, respectively. While the capabilities of these artificial models are approaching human levels, there are still many unanswered questions about how our brains do this task effortlessly. But the advances in these artificial models allow us the opportunity to study human speech recognition through a computational lens that we did not have before. This dissertation explores the intricate processes of speech perception and comprehension by drawing parallels between artificial and biological neural networks, through the use of computational frameworks that attempt to model either the brain circuits involved in speech recognition, or the process of speech recognition itself. There are two general types of analyses in this dissertation. The first type involves studying neural responses recorded directly through invasive electrophysiology from human participants listening to speech excerpts. The second type involves analyzing artificial neural networks trained to perform the same task of speech recognition, as a potential model for our brains. The first study introduces a novel framework leveraging deep neural networks (DNNs) for interpretable modeling of nonlinear sensory receptive fields, offering an enhanced understanding of auditory neural responses in humans. This approach not only predicts auditory neural responses with increased accuracy but also deciphers distinct nonlinear encoding properties, revealing new insights into the computational principles underlying sensory processing in the auditory cortex. The second study delves into the dynamics of temporal processing of speech in automatic speech recognition networks, elucidating how these systems learn to integrate information across various timescales, mirroring certain aspects of biological temporal processing. The third study presents a rigorous examination of the neural encoding of linguistic information of speech in the auditory cortex during speech comprehension. By analyzing neural responses to natural speech, we identify explicit, distributed neural encoding across multiple levels of linguistic processing, from phonetic features to semantic meaning. This multilevel linguistic analysis contributes to our understanding of the hierarchical and distributed nature of speech processing in the human brain. The final chapter of this dissertation compares linguistic encoding between an automatic speech recognition system and the human brain, elucidating their computational and representational similarities and differences. This comparison underscores the nuanced understanding of how linguistic information is processed and encoded across different systems, offering insights into both biological perception and artificial intelligence mechanisms in speech processing. Through this comprehensive examination, the dissertation advances our understanding of the computational and representational foundations of speech perception, demonstrating the potential of interdisciplinary approaches that bridge neuroscience and artificial intelligence to uncover the underlying mechanisms of speech processing in both artificial and biological systems.

The purpose of the study was to investigate the role of metabotropic glutamate receptors (mGluR) in the ontogeny of amphetamine-induced behavioral sensitization. Eleven-day-old rat pups were given five daily bilateral infusions of the mGluR antagonist, (RS)-methyl-4-carboxyphenylglycine (MCPG) followed by a systemic injection of amphetamine and locomotor activity was measured. It was hypothesized that rats receving amphetamine pretreatment and an amphetamine challenge would exhibit a significant increase in activity, indicating short-term behavioral sensitization. As predicted, repeated amphetamine administration during the pretreatment phase produced progressively enhanced locomotor activity, indicating the development of behavioral sensitization. The effect of MCPG on locomotor activity appears to be independent from the effects of amphetamine-induced locomotor activity and MCPG pretreatment failed to consistently block the expression of behavioral sensitization in rats pretreated with amphetamine and challenged with amphetamine. This study demonstrated that contrary to previous studies on adult rats, the mGluR system does not appear to consistently mediate the development of amphetamine-induced sensitization in neonatal rats.

Prenatal opioid exposure has been studied in relation to infants' medical outcomes. However, large gaps exist in the literature supporting early identification of atypical neurobehavior and motor development of infants with prenatal opioid exposure. The purpose of the study was to investigate whether prenatal opioid exposure has a negative influence on a newborn infant’s neurobehavior and motor development to aid in the early identification of potential delays. Using a prospective quasi experimental design, infants motor development using the Test of Infant Motor Performance (TIMP) and neurobehavior using the NICU Neonatal Network Scale (NNNS) was assessed on 58 infants in a hospital setting. Even after statistically controlling for covariates both the TIMP and the six out of twelve subscales of the NNNS: attention, handling, self-regulation, arousal, excitability, and stress were significantly different between the two groups of infants. Infants’ TIMP z-scores were significantly correlated with the NNNS subscales of attention, handing, self-regulation, arousal, excitability, hypertonicity, non-optimal reflexes, and stress. The findings highlight the similarities between the two groups and the outcome measures used for early identification of infants at-risk for delays following prenatal opioid exposure. The neonatal outcomes described here, including growth deficits, motor delays and altered neurobehavior are critical given their association with longer-term health and developmental impacts.

Small interfering RNAs (siRNAs) are a promising class of drugs for treating genetically-defined diseases. Therapeutic siRNAs enable specific modulation of gene expression, but require chemical architecture that facilitates efficient in vivodelivery. siRNAs are informational drugs, therefore specificity for a target gene is defined by nucleotide sequence. Thus, developing a chemical scaffold that efficiently delivers siRNA to a particular tissue provides an opportunity to target any disease-associated gene in that tissue. The goal of this project was to develop a chemical scaffold that supports efficient siRNA delivery to the brain for the treatment of neurodegenerative diseases, specifically Huntington’s disease (HD). HD is an autosomal dominant neurodegenerative disorder that affects 3 out of every 100,000 people worldwide. This disorder is caused by an expansion of CAG repeats in the huntingtin gene that results in significant atrophy in the striatum and cortex of the brain. Silencing of the huntingtin gene is considered a viable treatment option for HD. This project: 1) identified a hyper-functional sequence for siRNA targeting the huntingtin gene, 2) developed a fully chemically modified architecture for the siRNA sequence, and 3) identified a new structure for siRNA central nervous system (CNS) delivery—Divalent-siRNA (Di-siRNA). Di-siRNAs, which are composed of two fully chemically-stabilized, phosphorothioate-containing siRNAs connected by a linker, support potent and sustained gene modulation in the CNS of mice and non-human primates. In mice, Di-siRNAs induced potent silencing of huntingtin mRNA and protein throughout the brain one month after a single intracerebroventricular injection. Silencing persisted for at least six months, with the degree of gene silencing correlating to guide strand tissue accumulation levels. In Cynomolgus macaques, a bolus injection exhibited significant distribution and robust silencing throughout the brain and spinal cord without detectable toxicity. This new siRNA scaffold opens the CNS for RNAi-based gene modulation, creating a path towards developing treatments for genetically-defined neurological disorders.