Topic: Emory University

Spatio-temporal Regulation of Gene Expression in Neurons: Insights from Imaging mRNAs Live in Action

Probing neural population dynamics with recurrent neural networks

Large-scale recordings of neural activity are providing new opportunities to study network-level dynamics with unprecedented detail. However, the sheer volume of data and its dynamical complexity are major barriers to uncovering and interpreting these dynamics. I will present latent factor analysis via dynamical systems, a sequential autoencoding approach that enables inference of dynamics from neuronal population spiking activity on single trials and millisecond timescales. I will also discuss recent adaptations of the method to uncover dynamics from neural activity recorded via 2P Calcium imaging. Finally, time permitting, I will mention recent efforts to improve the interpretability of deep-learning based dynamical systems models.

Dysfunctional translation in disease

In the fifth of this year’s Brain Prize webinars, Emily Osterweil (Harvard Medical School, USA), Gary Bassell (Emory University, USA) and Giovanna Mallucci (Altos Labs, UK) will present their work on dysfunctional translation in disease. Each speaker will present for 25 minutes, and the webinar will conclude with an open discussion. The webinar will be moderated by two of the winners of the 2023 Brain Prize, Michael Greenberg and Erin Schuman.

Computational and mathematical approaches to myopigenesis

Myopia is predicted to affect 50% of all people worldwide by 2050, and is a risk factor for significant, potentially blinding ocular pathologies, such as retinal detachment and glaucoma. Thus, there is significant motivation to better understand the process of myopigenesis and to develop effective anti-myopigenic treatments. In nearly all cases of human myopia, scleral remodeling is an obligate step in the axial elongation that characterizes the condition. Here I will describe the development of a biomechanical assay based on transient unconfined compression of scleral samples. By treating the scleral as a poroelastic material, one can determine scleral biomechanical properties from extremely small samples, such as obtained from the mouse eye. These properties provide proxy measures of scleral remodeling, and have allowed us to identify all-trans retinoic acid (atRA) as a myopigenic stimulus in mice. I will also describe nascent collaborative work on modeling the transport of atRA in the eye.

September webinar

A parsimonious description of global functional brain organization in three spatiotemporal patterns

Resting-state functional magnetic resonance imaging (MRI) has yielded seemingly disparate insights into large-scale organization of the human brain. The brain’s large-scale organization can be divided into two broad categories: zero-lag representations of functional connectivity structure and time-lag representations of traveling wave or propagation structure. In this study, we sought to unify observed phenomena across these two categories in the form of three low-frequency spatiotemporal patterns composed of a mixture of standing and traveling wave dynamics. We showed that a range of empirical phenomena, including functional connectivity gradients, the task-positive/task-negative anti-correlation pattern, the global signal, time-lag propagation patterns, the quasiperiodic pattern and the functional connectome network structure, are manifestations of these three spatiotemporal patterns. These patterns account for much of the global spatial structure that underlies functional connectivity analyses and unifies phenomena in resting-state functional MRI previously thought distinct.

Non-regular behavior during the coalescence of liquid-like cellular aggregates

The fusion of cell aggregates widely exists during biological processes such as development, tissue regeneration, and tumor invasion. Cellular spheroids (spherical cell aggregates) are commonly used to study this phenomenon. In previous studies, with approximated assumptions and measurements, researchers found that the fusion of two spheroids with some cell type is similar to the coalescence of two liquid droplets. However, with more accurate measurements focusing on the overall shape evolution in this process, we find that even in the previously-regarded liquid-like regime, the fusion process of spheroids can be very different from regular liquid coalescence. We conduct numerical simulations using both standard particulate models and vertex models with both Molecular Dynamics and Brownian Dynamics. The simulation results show that the difference between spheroids and regular liquid droplets is caused by the microscopic overdamped dynamics of each cell rather than the topological cell-cell interactions in the vertex model. Our research reveals the necessity of a new continuum theory for “liquid” with microscopically overdamped components, such as cellular and colloidal systems. Detailed analysis of our simulation results of different system sizes provides the basis for developing the new theory.

The GluN2A Subunit of the NMDA Receptor and Parvalbumin Interneurons: A Possible Role in Interneuron Development

N-methyl-D-aspartate receptors (NMDARs) are excitatory glutamate-gated ion channels that are expressed throughout the central nervous system. NMDARs mediate calcium entry into cells, and are involved in a host of neurological functions. The GluN2A subunit, encoded by the GRIN2A gene, is expressed by both excitatory and inhibitory neurons, with well described roles in pyramidal cells. By using Grin2a knockout mice, we show that the loss of GluN2A signaling impacts parvalbumin-positive (PV) GABAergic interneuron function in hippocampus. Grin2a knockout mice have 33% more PV cells in CA1 compared to wild type but similar cholecystokinin-positive cell density. Immunohistochemistry and electrophysiological recordings show that excess PV cells do eventually incorporate into the hippocampal network and participate in phasic inhibition. Although the morphology of Grin2a knockout PV cells is unaffected, excitability and action-potential firing properties show age-dependent alterations. Preadolescent (P20-25) PV cells have an increased input resistance, longer membrane time constant, longer action-potential half-width, a lower current threshold for depolarization-induced block of action-potential firing, and a decrease in peak action-potential firing rate. Each of these measures are corrected in adulthood, reaching wild type levels, suggesting a potential delay of electrophysiological maturation. The circuit and behavioral implications of this age-dependent PV interneuron malfunction are unknown. However, neonatal Grin2a knockout mice are more susceptible to lipopolysaccharide and febrile-induced seizures, consistent with a critical role for early GluN2A signaling in development and maintenance of excitatory-inhibitory balance. These results could provide insights into how loss-of-function GRIN2A human variants generate an epileptic phenotypes.

NMC4 Keynote: Latent variable modeling of neural population dynamics - where do we go from here?

Large-scale recordings of neural activity are providing new opportunities to study network-level dynamics with unprecedented detail. However, the sheer volume of data and its dynamical complexity are major barriers to uncovering and interpreting these dynamics. I will present machine learning frameworks that enable inference of dynamics from neuronal population spiking activity on single trials and millisecond timescales, from diverse brain areas, and without regard to behavior. I will then demonstrate extensions that allow recovery of dynamics from two-photon calcium imaging data with surprising precision. Finally, I will discuss our efforts to facilitate comparisons within our field by curating datasets and standardizing model evaluation, including a currently active modeling challenge, the 2021 Neural Latents Benchmark [neurallatents.github.io].

The Impact of Racism-related Stress on Neurobiological Systems in Black Americans”

Black Americans experience diverse racism-related stressors throughout the lifespan. Disproportionately high trauma exposure, economic disadvantage, explicit racism and inequitable treatment are stressors faced by many Black Americans. These experiences have a cumulative negative impact on psychological and physical health. However, little is understood about how experiences of racism, such as discrimination, can mediate health outcomes via their effects on neurobiology. I will present clinical, behavioral, physiological and neurobiological data from Black American participants in the Grady Trauma Project, a longstanding study of trauma conducted in inner-city Atlanta. These data will be discussed in the context of both risk and resilience/adaptation perspectives. Finally, recommendations for future clinical neuroscience research and targets for intervention in marginalized populations will be discussed.

Functional consequences of microscopic skin features on snake locomotion

Neural circuit parameter variability, robustness, and homeostasis

Neurons and neural circuits can produce stereotyped and reliable output activity on the basis of highly variable cellular, synaptic, and circuit properties. This is crucial for proper nervous system function throughout an animal’s life in the face of growth, perturbations, and molecular turnover. But how can reliable output arise from neurons and synapses whose parameter vary between individuals in a population, and within an individual over time? I will review how a combination of experimental and computational methods can be used to examine how neuron and network function depends on the underlying parameters, such as neuronal membrane conductances and synaptic strengths. Within the high-dimensional parameter space of a neural system, the subset of parameter combinations that produce biologically functional neuron or circuit activity is captured by the notion of a ‘solution space’. I will describe solution space structures determined from electrophysiology data, ion channel expression levels across populations of neurons and animals, and computational parameter space explorations. A key finding centers on experimental and computational evidence for parameter correlations that give structure to solution spaces. Computational modeling suggests that such parameter correlations can be beneficial for constraining neuron and circuit properties to functional regimes, while experimental results indicate that neural circuits may have evolved to implement some of these beneficial parameter correlations at the cellular level. Finally, I will review modeling work and experiments that seek to illuminate how neural systems can homeostatically navigate their parameter spaces to stably remain within their solution space and reliably produce functional output, or to return to their solution space after perturbations that temporarily disrupt proper neuron or network function.

SARC-CoV-2 modeling: What have we learned from this pandemic about how (not) to model disease spread?

The SARS-CoV-2 pandemic is awash in data, including daily, spatially-resolved COVID case data, virus sequence data, patients `omics data, and mobility data. Journals are now also awash in studies that make use of quantitative modeling approaches to gain insight into the geographic spread of SARS-CoV-2 and its temporal dynamics, as well as studies that predict the impact of control strategies on SARS-CoV-2 circulation. Some, but by no means all, of these studies are informed by the massive amounts of available data. Some, but by no means all, of these studies have been useful — in that their predictions revealed something beyond simple back of the envelope calculations. To summarize some of these findings, in this symposium, we will address questions such as: What do we want from models of disease spread? What can and should be predicted? Which data are the most useful for predictions? When do we need mechanistic models? What have we learned about how to model disease spread from unmet and/or conflicting predictions? The workshop speakers will explore these questions from different perspectives on what data need to be considered and how models can be evaluated. As at other TMLS workshops, each speaker will deliver a 10-minute talk with ample time set aside for moderated questions/discussion. We expect the talks to be provocative and bold, while respecting different perspectives.

Simons-Emory Workshop on Neural Dynamics: What could neural dynamics have to say about neural computation, and do we know how to listen?

Speakers will deliver focused 10-minute talks, with periods reserved for broader discussion on topics at the intersection of neural dynamics and computation. Organizer & Moderator: Chethan Pandarinath - Emory University and Georgia Tech Speakers & Discussants: Adrienne Fairhall - U Washington Mehrdad Jazayeri - MIT John Krakauer - John Hopkins Francesca Mastrogiuseppe - Gatsby / UCL Abigail Person - U Colorado Abigail Russo - Princeton Krishna Shenoy - Stanford Saurabh Vyas - Columbia

Measuring the hidden dynamics of animal behavior

On being the right size: Is the search for underlying physical principles a wild-goose chase?

When was the last time you ran into a giant? Chances are never. Almost 100 years ago, JBS Haldane posed an outwardly simple yet complex question – what is the most optimal size (for a biological system)? The living world around us contains a huge diversity of organisms, each with its own characteristic size. Even the size of subcellular organelles is tightly controlled. In absence of physical rulers, how do cells and organisms truly “know” how large is large enough? What are the mechanisms in place to enforce size control? Many of these questions have motivated generations of scientists to look for physical principles underlying size control in biological systems. In the next edition of Emory's Theory and Modeling of Living Systems (TMLS) workshop series, our panel of speakers will take a close look at these questions, across the entire scale - from the molecular, all the way to the ecosystem.

Towards generalized inference of single-trial neural population dynamics

Biology is “messy”. So how can we take theory in biology seriously and plot predictions and experiments on the same axes?

Many of us came to biology from physics. There we have been trained on such classic examples as muon g-2, where experimental data and theoretical predictions agree to many significant digits. Now, working in biology, we routinely hear that it is messy, most details matter, and that the best hope for theory in biology is to be semi-qualitative, predict general trends, and to forgo the hope of ever making quantitative predictions with the precision that we are used to in physics. Colloquially, we should be satisfied even if data and models differ so much that plotting them on the same plot makes little sense. However, some of us won’t be satisfied by this. So can we take theory in biology seriously and predict experimental outcomes within (small) error bars? Certainly, we won’t be able to predict everything, but this is never required, even in traditional physics. But we should be able to choose some features of data that are nontrivial and interesting, and focus on them. We also should be able to find different classes of models --- maybe even null models --- that match biology better, and thus allow for a better agreement. It is even possible that large-dimensional datasets of modern high-throughput experiments, and the ensuing “more is different” statistical physics style models will make quantitative, precise theory easier. To explore the role of quantitative theory in biology, in this workshop, eight speakers will address some of the following general questions based on their specific work in different corners of biology: Which features of biological data are predictable? Which types of models are best suited to making quantitative predictions in different fields? Should theorists interested in quantitative predictions focus on different questions, not typically asked by biologists? Do large, multidimensional datasets make theories (and which theories?) more or less likely to succeed? This will be an unapologetically theoretical physics workshop — we won’t focus on a specific subfield of biology, but will explore these questions across the fields, hoping that the underlying theoretical frameworks will help us find the missing connections.

Can we predict the diversity of real populations? Part II: What determines microbial diversity?

Microbes make up the vast majority of the tree of life. While we know very little about most microbial species, large-scale sequencing is giving us glimpses of the diversity that exists both within species and in ecosystems. The challenge now is to find the patterns in this diversity and understand them. This session features provocative talks on attempts to meet that challenge.

Can we predict the diversity of real populations? Part I: What is linked selection doing to populations?

Natural selection affects not only selected alleles, but also indirectly affects all genes near selected sites on the genome. An increasing body of evidence suggests that this linked selection is an important driver of evolutionary dynamics throughout the genomes of many species, implying that we need to substantially revise our basic understanding of molecular evolution. This session brings together early-career researchers working towards a quantitative understanding of the prevalence and effects of linked selection.

(What) can soft matter physics teach us about biological function?

The “soft, active, and living matter” community has grown tremendously in recent years, conducting exciting research at the interface between soft matter and biological systems. But are all living systems also soft matter systems? Do the ideas of function (or purpose) in biological systems require us to introduce deep new ideas into the framework of soft matter theories? Does the (often) qualitatively different character of data in biological experiments require us to change the types of experiments we conduct and the goals of our theoretical treatments? Eight speakers will anchor the workshop, exploring these questions across a range of biological system scales. Each speaker will deliver a 10-minute talk with another 10 minutes set aside for moderated questions/discussion. We expect the talks to be broad, bold, and provocative, discussing both the nature of the theoretical tools and experimental techniques we have at present and also those we think we will ultimately need to answer deep questions at the interface of soft matter and biology.

Inaugural Simons-Emory Symposium On Motor Control: "What tools are we missing to understand motor control? What could we learn if we had them

This is the inaugural symposium of the Simons-Emory International Consortium on Motor Control, and speakers will deliver 10 minute talks (each followed by 10 minutes of discussion) addressing this question: "What tools are we missing to understand motor control, and what could we learn if we had them?”

Can machine learning learn new physics, or do we need to put it in by hand?"\

There has been a surge of publications on using machine learning (ML) on experimental data from physical systems: social, biological, statistical, and quantum. However, can these methods discover fundamentally new physics? It can be that their biggest impact is in better data preprocessing, while inferring new physics is unrealistic without specifically adapting the learning machine to find what we are looking for — that is, without the “intuition” — and hence without having a good a priori guess about what we will find. Is machine learning a useful tool for physics discovery? Which minimal knowledge should we endow the machines with to make them useful in such tasks? How do we do this? Eight speakers below will anchor the workshop, exploring these questions in contexts of diverse systems (from quantum to biological), and from general theoretical advances to specific applications. Each speaker will deliver a 10 min talk with another 10 minutes set aside for moderated questions/discussion. We expect the talks to be broad, bold, and provocative, discussing where the field is heading, and what is needed to get us there.

Physics of Behavior: Now that we can track (most) everything, what can we do with the data?

We will organize the workshop around one question: “Now that we can track (most) everything, what can we do with the data?” Given the recent dramatic advances in technology, we now have behavioral data sets with orders of magnitude more accuracy, dimensionality, diversity, and size than we had even a few years ago. That being said, there is still little agreement as to what theoretical frameworks can inform our understanding of these data sets and suggest new experiments we can perform. We hope that after this workshop we’ll see a variety of new ideas and perhaps gain some inspiration. We have invited eight speakers, each studying different systems, scales, and topics, to provide 10 minute presentations focused on the above question, with another 10 minutes set aside for questions/discussions (moderated by the two of us). Although we naturally expect speakers to include aspects of their own work, we have encouraged all of them to think broadly and provocatively. We are also hoping to organize some breakout sessions after the talks so that we can have some more expanded discussions about topics arising during the meeting.