empirical data

Borrelia burgdorferi genotypic diversity, pathogenesis, and host cellular responses

PROJECT SUMMARY Lyme disease is the most common tick-borne illness in the United States, with an estimated 476,000 cases annually, and Pennsylvania (PA) consistently reports one of the highest case numbers nationwide. Borrelia burgdorferi sensu stricto (Bb) is a causative agent of Lyme disease in the US and is transmitted by Ixodes spp. ticks. Bb produces various outer surface proteins (Osp) and other mechanisms to survive in vectors, evade host immune systems, and to propagate infection within a host. Over 35 OspC genotypes have been characterized, which fluctuate in abundance in natural vector and host populations, suggesting host adaptation. While many Lyme-infected patients recover following antibiotic treatment, some may experience neurological symptoms, Lyme neuroborreliosis (LNB), which may be associated with specific genotypes. While previous studies focused on clinical manifestations, pathogenicity, genetic variations, and host immune responses using mouse models or patient samples, the genotype-specific immune responses that contribute to disease progression in humans remain poorly understood. Our central hypothesis is that certain Bb OspC genotypes, maintained in natural populations, are associated with distinct host immune responses that influence disease severity, progression, and persistence. Aim 1 will define the dynamics of OspC genotypes in tick and small mammal populations over time in Western PA to establish a 16-year longitudinal tick study and an 8-year longitudinal small mammal study. Using deep amplicon sequencing, we will quantify genotype diversity, detect low-abundance genotypes, and identify potential host-adapted genotypes. These empirical data will inform a compartmental mathematical model to evaluate OspC genotype prevalence, distribution, and public health risks, including LNB, across space and time. Aim 2 will assess how distinct Bb OspC genotypes affect the host immune landscape and cellular responses using human samples. To determine how Bb genotype contributes to disease phenotype, we will perform immune profiling studies which will include microscopy-based assessment of infected cell cultures, flow cytometric analysis of immune cell phenotypes, and measurement of genotype-specific cytokine, chemokine, and antigen production (sub-Aim2a). We will also employ multi-omics approaches that integrate single cell RNA sequencing with antibody-based protein profiling (scRNA-seq/Ab-seq) to characterize transcriptional and functional changes in immune cell populations exposed to different Bb genotypes (sub-Aim2b). This work is innovative in its integration of long-term ecological data with advanced immune profiling and single cell multi- omics to uncover genotype-specific mechanisms of Bb pathogenicity and human immune response—an approach not previously applied in Lyme disease research. These studies will clarify how specific genotypes influence immune responses and disease severity. Together, the proposed aims will identify critical genetic and immunological mechanisms that drive Bb pathogenicity and human susceptibility, informing the development of improved diagnostics, targeted therapies, and public health interventions to reduce the burden of Lyme disease.

The Brain Prize winners' webinar

This webinar brings together three leaders in theoretical and computational neuroscience—Larry Abbott, Haim Sompolinsky, and Terry Sejnowski—to discuss how neural circuits generate fundamental aspects of the mind. Abbott illustrates mechanisms in electric fish that differentiate self-generated electric signals from external sensory cues, showing how predictive plasticity and two-stage signal cancellation mediate a sense of self. Sompolinsky explores attractor networks, revealing how discrete and continuous attractors can stabilize activity patterns, enable working memory, and incorporate chaotic dynamics underlying spontaneous behaviors. He further highlights the concept of object manifolds in high-level sensory representations and raises open questions on integrating connectomics with theoretical frameworks. Sejnowski bridges these motifs with modern artificial intelligence, demonstrating how large-scale neural networks capture language structures through distributed representations that parallel biological coding. Together, their presentations emphasize the synergy between empirical data, computational modeling, and connectomics in explaining the neural basis of cognition—offering insights into perception, memory, language, and the emergence of mind-like processes.

Diffuse coupling in the brain - A temperature dial for computation

The neurobiological mechanisms of arousal and anesthesia remain poorly understood. Recent evidence highlights the key role of interactions between the cerebral cortex and the diffusely projecting matrix thalamic nuclei. Here, we interrogate these processes in a whole-brain corticothalamic neural mass model endowed with targeted and diffusely projecting thalamocortical nuclei inferred from empirical data. This model captures key features seen in propofol anesthesia, including diminished network integration, lowered state diversity, impaired susceptibility to perturbation, and decreased corticocortical coherence. Collectively, these signatures reflect a suppression of information transfer across the cerebral cortex. We recover these signatures of conscious arousal by selectively stimulating the matrix thalamus, recapitulating empirical results in macaque, as well as wake-like information processing states that reflect the thalamic modulation of largescale cortical attractor dynamics. Our results highlight the role of matrix thalamocortical projections in shaping many features of complex cortical dynamics to facilitate the unique communication states supporting conscious awareness.

A new experimental paradigm to study analogy transfer

Analogical reasoning is one of the most complex cognitive functions in humans that allows abstract thinking, high-level reasoning, and learning. Based on analogical reasoning, one can extract an abstract and general concept (i.e., an analogy schema) from a familiar situation and apply it to a new context or domain (i.e., analogy transfer). These processes allow us to solve problems we never encountered before and generate new ideas. However, the place of analogy transfer in problem solving mechanisms is unclear. This presentation will describe several experiments with three main findings. First, we show how analogy transfer facilitates problem-solving, replicating existing empirical data largely based on the radiation/fortress problems with four new riddles. Second, we propose a new experimental task that allows us to quantify analogy transfer. Finally, using science network methodology, we show how restructuring the mental representation of a problem can predict successful solving of an analogous problem. These results shed new light on the cognitive mechanism underlying solution transfer by analogy and provide a new tool to quantify individual abilities.

NMC4 Panel: The Contribution of Models vs Data

Theory-driven probabilistic modeling of language use: a case study on quantifiers, logic and typicality

Theoretical linguistics postulates abstract structures that successfully explain key aspects of language. However, the precise relation between abstract theoretical ideas and empirical data from language use is not always apparent. Here, we propose to empirically test abstract semantic theories through the lens of probabilistic pragmatic modelling. We consider the historically important case of quantity words (e.g., `some', `all'). Data from a large-scale production study seem to suggest that quantity words are understood via prototypes. But based on statistical and empirical model comparison, we show that a probabilistic pragmatic model that embeds a strict truth-conditional notion of meaning explains the data just as well as a model that encodes prototypes into the meaning of quantity words.

How to simulate and analyze drift-diffusion models of timing and decision making

My talk will discuss the use of some of these four, simple Matlab functions to simulate models of timing, and to fit models to empirical data. Feel free to examine the code and the relatively brief book chapter that explains the code before the talk if you would like to learn more about computational/mathematical modeling.