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Binocular Population Responses in the Early Visual System
No full textBinocular vision emerges from the convergence of monocular inputs in the primary visual cortex (V1), yet the specific mechanisms governing this integration remain unresolved. This dissertation addresses three fundamental questions: (1) How do subcortical and cortical circuits contribute to binocular vision, particularly in the transition from the lateral geniculate nucleus (LGN) to V1? (2) Does neural suppression in V1 serve as the initiating mechanism for binocular rivalry? (3) What laminar mechanisms underlie interocular conflict? The first chapter explores the pathway of binocular vision from the LGN to cortex. Neurophysiological recordings from the LGN examine how magnocellular, parvocellular, and koniocellular streams shape binocular processing in V1. The second chapter evaluates whether response suppression is a prerequisite for initiating binocular rivalry. Recordings from V1 while macaque subjects viewed binocular rivalry flash suppression (BRFS) reveal that response facilitation—not suppression—dominates at the onset of BRFS, suggesting that adaptation mechanisms drive rivalry initiation in this context. The third chapter investigates how V1 resolves interocular conflict across its laminar profile. Using laminar electrodes, we observed coherence-based mechanisms across the columnar microcircuit that occur with interocular conflict. Conflict between the eyes reduces interlaminar coherence, particularly between deep and superficial layers, offering a novel account of how V1 dynamically regulates binocular competition beyond changes in spiking activity. Together, these studies provide new insight into how binocular vision emerges from neural population activity across multiple processing stages, emphasizing the role of adaptation, population-level coherence, and cortical micro circuitry in shaping visual perception
Advances in High-Resolution Functional Mri of Squirrel Monkey Brain at 9.4t Mri
No full textHigh-resolution functional MRI (fMRI) has significantly advanced our understanding of brain networks, providing unique insights into neural dynamics at a laminar scale. This dissertation focuses on developing and optimizing high-resolution fMRI techniques for studying the squirrel monkey brain at 9.4T, including advancements in RF coil design and comparative evaluations of imaging sequences, as well as validating the functional relevance of resting-state functional connectivity (rsFC) through comparisons with optogenetic and tactile stimulation paradigms. Firstly, an optimized quadrature birdcage RF coil was specifically designed for imaging squirrel monkey brain at 9.4T with refinements in coil length and shielding, which improved signal-to-noise ratio (SNR) and temporal SNR (tSNR), crucial for capturing fine-scale neural activity. Secondly, Gradient-echo (GE) and spin-echo (SE) sequences were comparatively evaluated for their sensitivity to layer-specific blood oxygen level-dependent (BOLD) effects during tactile stimulation and resting state. Additionally, Anderson-Weiss mean field theory was employed to provide insights into vessel size distributions, further enhancing our understanding of the physiological mechanisms underlying BOLD effects across cortical layers. Finally, the neural underpinnings and functional relevance of rsFC were investigated by directly comparing rsFC networks derived from resting-state fMRI BOLD signals with activation patterns elicited by neuron-type-specific optogenetic stimulation of excitatory neurons in the S2 cortex and natural tactile stimulation of the fingers. These results suggest that rsFC can serve as a reliable tool for assessing functional brain organization and mapping intrinsic connectivity patterns. Overall, this dissertation advances the field of high-resolution functional MRI by optimizing RF coils associated with preclinical imaging at 9.4T, comparing imaging sequences, and validating the functional relevance of resting-state functional connectivity
Bridging the Psychological Gap: The Role of Future Self-Continuity, Consideration of Future Consequences, and Motivation in Goal-Directed Behavior
No full textFuture-oriented thinking is crucial for motivation and decision-making, yet individuals often struggle to maintain a psychological connection with their future selves. This study examines how consideration of future consequences influences future self-continuity and motivation within self-determination theory. It also investigates whether an empathy-based intervention—guided visualization and future-self letter writing—enhances future self-continuity and motivation. Using a randomized controlled design, participants were assigned to an empathy-induction or control group and completed measures of consideration of future consequences, future self-continuity, and motivation. Results showed consideration of future consequences significantly predicted future self-continuity, introjected regulation, external regulation, and amotivation, suggesting individuals who consider long-term consequences perceive a stronger connection with their future selves and rely less on external motivation. However, it did not predict intrinsic motivation, nor did empathy intervention affect any dependent variables. These findings highlight the complexity of future-oriented thinking and motivation, suggesting cognitive awareness of future consequences does not necessarily enhance intrinsic motivation or emotional identification with the future self. Future research should further explore the interaction between cognitive and emotional components of future self-continuity and motivation. Additionally, studies should examine alternative intervention strategies that might more effectively strengthen both the psychological connection to one’s future self and self-determined motivation. Understanding these relationships could inform the development of interventions that help individuals maintain motivation and long-term goal commitment by integrating cognitive and emotional aspects of future-oriented thinking
Bicyclo[1.1.1]pentyl Substituents in Phosphorus and Aluminum Organometallics
No full textThe use of organophosphines as ligands in organometallic chemistry has been an enabling technology in catalysis with applications spanning from hydroformylation to cross-coupling chemistry. However, few homoleptic tri-tert-alkylphosphines have been reported despite the importance of the archetype PtBu3. Tris(bicyclo[1.1.1]pentyl)phosphine PBcp3 can be prepared by radical addition of PH3 to [1.1.1]propellane, giving the smallest tri-tert-alkylphosphine known. It gives a bis-ligated Pd(0) complex Pd(PBcp3)2 that is exceptionally reactive toward alkyl halide oxidative addition and functions as a general ligand for palladium-catalyzed cross-coupling of sp3 electrophiles. Additionally, the primary phosphine reagent iPr2NPH2·BH3 can serve as a doubly protected PH2Cl proxy, enabling the synthesis of bis(bicyclo[1.1.1]pentyl)chlorophosphine (Bcp2PCl) for the first time. Bcp2PCl serves as a general reagent for the preparation of a family of bis(bicyclo[1.1.1]pentyl) alkyl- and arylphosphines, including new members of privileged phosphine ligand scaffolds. Organoaluminum compounds are another useful class of organometallic reagents which allow transfer of substituents to various substrates. Addition of [1.1.1]propellane to aluminum hydrides provides access to bicyclo[1.1.1]pentyl aluminum compounds, and these reagents can provide synthetic routes to organic bicyclo[1.1.1]pentyl products through carbonyl addition and cross coupling methodologies
Boris Yeltsin's Russia: Constitutional Conflicts, NATO, and the Dawn of Putin
No full textHistory Department Honors ThesisCollege of Arts and ScienceDepartment of Histor
Adeana McNicholl on How Preta Narratives Constructed Buddhist Cosmology and Shaped Buddhist Ethics
No full textIn this podcast, Chris Benda, religious studies and theology librarian at Vanderbilt Divinity Library, interviews Professor Adeana McNicholl about her book Of Ancestors and Ghosts: How Preta Narratives Constructed Buddhist Cosmology and Shaped Buddhist Ethics
A Variational Autoencoder-Reinforcement Learning Framework for C13 NMR-Based Natural Product Structure Elucidation
No full textWith almost two-thirds of all approved small-molecule drugs having natural products (NPs) and their derivatives as part of their development process, NPs undoubtedly play an important role in drug development. Nuclear magnetic resonance (NMR) spectroscopy is one of the most useful tools for analyzing the NP isolates and determining which are suitable for therapeutic applications. From a molecule’s NMR spectral data alone, chemists can often elucidate the molecule’s structure and determine its key properties. However, the NMR spectra of complex and large molecules, which are often the case for NPs, are typically hard to interpret because of overlapping signals and complex splitting patterns. Researchers have found ways to automate the process of elucidating structures from NMR spectral data. These methods include computing the structure through computational methods, predicting the structure from predicted substructures, and predicting the structure from the NMR spectral data directly. However, recent research has not attempted to elucidate structures that are as large and complex as NPs. This work focuses on the current progress of implementing and evaluating a machine learning framework that utilizes variational autoencoders (VAEs) and reinforcement learning (RL) to predict the structure of a potential NP from the NMR chemical shifts. A key advantage of this approach is addressing a limitation of existing generative models, which often struggle to generate complex molecules like NPs. Experienced researchers can often propose reasonable initial structural guesses based on NMR data, and this framework can automate the tedious optimization of their guesses to achieve the best match. Progress so far includes fine-tuning a VAE model for encoding a researcher's structural guess and decoding optimized structures, as well as developing an RL reward function that scores the quality of a guess based on predicted C13 NMR chemical shifts. Ongoing work involves integrating these components into a full RL framework and conducting comprehensive evaluations
Common Cause versus Dynamic Mutualism: Empirical Insights into ADHD’s Placement Within the Psychopathology Hierarchy
No full textContemporary psychopathology classification systems disagree on how ADHD should be conceptualized. One system regards ADHD as an externalizing condition because it argues that it is primarily comorbid with conditions characterized by poor impulse control. Another system regards ADHD as a neurodevelopmental condition because it is primarily comorbid with conditions characterized by early onset and cognitive deficits. Despite these disagreements, both perspectives assume that ADHD’s comorbidity arises because ADHD shares a common set of causes, or shared etiology, with other forms of psychopathology. Still, emerging models posit that comorbidity arises from dynamic interactions among forms of psychopathology, a theory known as dynamic mutualism. We pitted the common cause theory against the dynamic mutualism theory in explaining the developmental ties between ADHD dimensions (i.e., ADHD, inattention, hyperactivity/impulsivity, cognitive disengagement) and three psychopathology spectra (i.e., externalizing, neurodevelopmental, internalizing). Results support the common cause theory, with the strongest evidence for the neurodevelopmental and externalizing spectra. Taken together, our findings support conceptualizing ADHD as a condition that spans both the externalizing and neurodevelopmental spectra as opposed to fitting exclusively within either category
“We are Music City”: Examining the Labor Process of Tipped Workers on Nashville’s Honky-Tonk Row
No full textThis three-paper dissertation investigates the experiences of independent tipped workers in downtown Nashville’s local gig economy through a 22-month ethnographic study of musicians and bartenders laboring in the city’s iconic honky-tonk bars. Despite facing a lack of formal protections and a complex labor market, these workers developed occupational and symbolic strategies to navigate the risks and rewards of independent and gig-ish work in an intensely competitive tourism economy. The first paper examines how independent musicians coordinated complex performances without managerial oversight or professional self-determination. It introduces the concept of narrative coordination to describe how occupational communities synchronized project-based work through interaction with shared group culture rather than formal organizational structures. While these informal systems enhanced flexibility, their fragility is revealed in moments of failure, or "trainwrecks," which paradoxically reinforced occupational norms and community boundaries. The second paper focuses on organizational control, examining how bars managed tipped labor in a context where the wage relation is informal and contingent. Through the comparative analysis of three honky-tonk bars, I develop an inductive typology of “tipping regimes” that structure workers’ access to income and recognition. While larger regimes mediated control through technical and bureaucratic means, smaller regimes relied on embedded relational frames that shaped competition, solidarity, and inequality among workers. The third paper investigates how independent workers resisted marginalization in deregulated labor markets by leveraging symbolic capital. Musicians and bartenders responded to precarity through distinct forms of symbolic resistance. Across both groups, senior workers emerged as informal advocates, contributing to a form of occupational activism that sought to reclaim agency in the absence of formal labor protections. This dissertation illuminates the symbolic and organizational infrastructure of independent service work in one of America’s fastest-growing urban economies. It offers an account of how independent workers coordinated labor, negotiated control, and asserted value in the informal spaces of the new economy, contributing to broader debates on precarity, occupational community, and symbolic power in labor sociology and the sociology of work. This work also provides valuable insights for organizing service workers in the American South
Data-centric AI for Small Molecule Drug Discovery
No full textData-centric AI focuses on improving the quality and utility of data to enhance model performance rather than solely emphasizing innovations in model architecture. The shift from a model-driven to a data-driven paradigm has gained attention in drug discovery, where data quality issues and data quantity limitations present significant challenges for model training. Therefore, we introduced WelQrate, a high-quality benchmark dataset for small molecule drug discovery, supported by a professional curation pipeline and standardized evaluation framework aimed at bridging the gap between the biochemistry and AI communities. We found that dataset quality greatly influences model evaluation during benchmarking, stressing the importance of enhancing data quality. Our experiments revealed two significant challenges in real-world drug discovery data that must be carefully considered when developing new algorithms: extreme class imbalance due to low percentages of active compounds and structural distribution shift resulting from the unexplored chemical space compared to known drugs. Examining imbalance issues in WelQrate datasets, we developed ScaffAug, a novel data-driven augmentation framework that utilizes a diffusion model to generate novel training samples based on molecular scaffolds with under-represented structures and minor classes. We demonstrated that incorporating these generated molecules through a self-training strategy can significantly augment the performance of activity predictors