Bulletin of the American Physical Society
APS March Meeting 2024
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session F37: Physics of Neural Systems IIFocus
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Sponsoring Units: DBIO GSNP DSOFT Chair: Jeff Tithof, University of Minnesota Room: 103C |
Tuesday, March 5, 2024 8:00AM - 8:36AM |
F37.00001: Using electric fields to control brain activity Invited Speaker: Alexander Opitz Neural oscillations reflect and organize brain functions. Brain Stimulation methods such as Transcranial Alternating Current stimulation (TACS) and Transcranial Magnetic Stimulation (TMS) use electric fields to control brain activity. In this talk I will present our recent research on TACS/TMS mechanisms and how to develop more effective stimulation protocols using concurrent brain measurements. I will show how non-invasive brain stimulation affects neural activity at the level of local field potentials and single-unit activity. I will demonstrate how local electric fields will affect spiking behavior and how this is affected by neuron morphology and orientation to the electric field. I will further discuss how findings from animal experiments can be translated to improve human brain stimulation protocols based on careful modeling and mapping of stimulation parameters. I will discuss how tracking brain oscillations in real-time to inform stimulation timing can improve the effectiveness of brain stimulation. |
Tuesday, March 5, 2024 8:36AM - 8:48AM |
F37.00002: Towards a comprehensive understanding of the neural circuits controlling Drosophila flight Itai Cohen, B. Kemper Ludlow, Han Kheng Teoh, Abby Leung, Samuel C Whitehead The robust navigation essential for animals' survival relies on the integration of sensory information from various modalities, to localize themselves and generate precise locomotive responses. With the recent unveiling of Drosophila's brain and ventral nerve cord connectomes, alongside the accessibility of genetic toolkits facilitating single-cell manipulation, Drosophila presents an unparalleled opportunity for comprehending the neural circuits responsible for creating a robust internal representation of the external world, which in turn guides their navigation and control. To unravel the fly's navigation program, we harness genetic fly lines, some of which we helped generate, to optogenetically target specific neuronal populations within the flight motor and sensory systems. By measuring their response to such neuronal perturbations in free flight, we shed light on the neuromuscular underpinning of the fly’s motor control. These investigations are a key step for our ongoing collaborative effort with multiple labs to understand how the neural network of Drosophila adapts to incorporate sensory modalities such as visual, gyroscopic and airflow sensors with varying temporal responses to control flight. In this talk, we will showcase paradigms for manipulating multiple sensors at once to tease out the principles underlying sensory processing and integration. |
Tuesday, March 5, 2024 8:48AM - 9:00AM |
F37.00003: Measuring the amount of computation done in C.elegans brain dynamics Junang Li, Matthew S Creamer, Andrew M Leifer, David H Wolpert Biological systems ranging from genetic circuits to the human brain perform computations. Although extensive research has been done on the functional and behavioral traits accompanying these computations, the equally crucial question of quantifying the precise computation done in biological systems, or at a minimum, the amount of computation done, is under-investigated. Here we introduce a novel approach, and use it to quantify the amount of computation done in the joint dynamics of the neurons in a C.elegans. Our approach uses recent breakthroughs in neural stochastic differential equations to estimate the requisite latent dimensionality of the joint dynamics of the neurons, based on time series data sets of that dynamics. In turn, our time series data are found using fluorescent microscopy to acquire the whole brain activity of C.elegans. Our approach finds that high and low amounts of computation are required in, respectively, (the joint neuronal dynamics of) freely moving and immobile worms. Our approach also provides insights into the amount of computation occurring during other behavioral states of the worm, e.g., when it is swimming backwards. |
Tuesday, March 5, 2024 9:00AM - 9:12AM |
F37.00004: Natural Visual Input Implies Estimation Biases in a Blowfly Motion Sensitive Neuron Charles Edelson, Robert de Ruyter, Sima Setayeshgar, William S Bialek Visual estimation of self-motion has been shown to be computationally similar across a wide variety of visual morphologies. Surprisingly, one of the conserved similarities is strong estimator biases in low signal-to-noise ratio regimes. Given the behavioral importance of motion estimation, the widespread existence of this bias suggests it arose as a feature of the statistics of natural scenes. Previously, we computationally investigated this phenomenon by using a specially designed "FlEye" camera made to mimic the fly visual system. We demonstrated that an optimal motion estimator constructed using scenes captured by the FlEye camera has naturally appearing correlator-like motion estimation properties at low signal-to-noise ratios. Additionally, we demonstrated that pitch acts as a source of noise during yaw estimation, effectively extending the range where correlator-like motion estimation is observed. These results suggest that the presence of "visual noise" drives biological motion estimation away from gradient-like and towards correlator-like estimators. Here, we connect these results back to the blowfly (Calliphora vicina) by comparing our computational motion estimator to H1, a wide field motion sensitive neuron involved in motion control in the fly: To do this, we performed playback experiments with FlEye videos using a high-intensity LED display while recording from H1. By comparing the behavior of the H1 neuron to our computational estimator, we characterize which features of the statistics of natural scenes are important for driving H1 toward correlator-like behaviors. Preliminary evidence shows that the biological motion sensor exhibits estimator biases that are qualitatively consistent with those predicted from measurements with the camera. |
Tuesday, March 5, 2024 9:12AM - 9:24AM |
F37.00005: State-dependent behavioral strategies in C. elegans olfactory navigation Kevin S Chen, Jonathan W Pillow, Andrew M Leifer Animals can dynamically adjust their behavioral response depending on the odor environment, their internal states, and learned experiences. To understand these behavioral dynamics, we study olfactory learning and navigation strategies in the roundworm C. elegans. We train worms to associate butanone odor with food (appetitive training) or starvation (aversive training) and record navigation in a controlled odor environment. We developed a mixture of Generalized Linear Models (MoGLM) that constitutes two known navigation strategies in worms: biased random walk that modulates turning probability and weathervaning that steers the direction of motion in response to change in odor concentration. By fitting MoGLM to navigation trajectories from different training conditions, we find that the worms can differentially alter strategies depending on the appetitive or aversive training. Given finite data, MoGLM can decode the learned experience with >90% accuracy and outperforms the classic chemotaxis metric. The MoGLM also correctly predicts behavioral responses to optogenetic perturbation in an olfactory neuron and captures behavioral variability across the population. Lastly, we extend MoGLM with a hidden Markov model and show that it better explains experimental observations. The recovered latent states last for seconds and consist of different navigation strategies. We discuss progress towards identifying the neural mechanisms underlying these state-dependent behavioral strategies. |
Tuesday, March 5, 2024 9:24AM - 9:36AM |
F37.00006: Behavioral variability of Drosophila larva under uni- and multi-sensory stimulation Yiming Xu, Mirna Mihovilovic Skanata Drosophila larva is a small crawling animal with about 10,000 neurons. Despite this simplicity, the larva carries out information-processing behaviors, including navigation which is enacted through a sequence of forward runs and reorienting turns. Larvae on average turn more often when facing increases in aversive stimuli, such as light or CO2. However, larva’s behaviors are variable and not all larvae respond to same stimulus presentations in the same way. Moreover, a single larva may not respond with the same fidelity to different sensory modalities, and this intrinsic variability may reflect the differences in the neural implementation of its navigational algorithms to a range of sensory modalities. To study the larva’s information-processing algorithms, we present blue light to stimulate larva’s visual system and red light to optogenetically activate CO2 receptor neurons. By studying responses to sensory stimuli alone and in combination, we categorize the variability of larva’s behavioral responses to light and fictive odors and determine the conditions under which their combination provides a more reliable response. These approaches allow us to hypothesize the basic underlying mechanisms that larvae engage in response to a combination of sensory cues. |
Tuesday, March 5, 2024 9:36AM - 10:12AM |
F37.00007: Internal States vs. External Cues in Crawling Insect Larvae Invited Speaker: Mason Klein Organisms that can move will seek out conditions that improve their chances of survival. Most prominently, they explore and search for food, but also respond to spatially or temporally varying external environmental stimuli. They will move up or down gradients of temperature or chemical concentration, for example. Simple animals, such as the Drosophila larva we deploy here, often move probabilistically using modified versions of the classic random walk, combining random exploration with directed navigation by modulating components of their behavior in response to stimuli. Our investigations here focus on the internal states of crawling animals at the individual and population levels, and how those states manifest as physical exploration and navigation. |
Tuesday, March 5, 2024 10:12AM - 10:24AM |
F37.00008: Lattice Boltzmann simulations reveal complementary function of different waste clearance mechanisms in the brain Reza Yousofvand, Jeff Tithof Cellular byproducts in the brain are removed through two pathways: passage through the blood-brain barrier (BBB) to be swept away via blood flow or direct drainage into the cerebrospinal fluid (CSF) enveloping the brain. Arteries bifurcate into arterioles that extend into the parenchyma, ultimately branching into capillaries. Surrounding these vessels are perivascular spaces (PVSs), annular channels formed between the vessel wall and brain tissue. CSF enters the brain through perivascular routes, mixing with interstitial fluid to facilitate waste elimination via bulk flow. The alternative route involves the BBB, allowing waste to pass directly through vascular walls into the bloodstream. Both of these mechanisms are found within the brain vasculature and they provide redundancy for eliminating waste with different efficacy at high and low concentrations. In this study, we have developed a Lattice Boltzmann code to simulate the brain tissue including its arteries, veins, and capillaries, permitting the selective activation or deactivation of each pathway. We then compare the results with limited existing experimental data to compute critical parameters that have not yet been measured experimentally to unveil distinct functionalities in these two parallel outflow routes. |
Tuesday, March 5, 2024 10:24AM - 10:36AM |
F37.00009: Unraveling the Influence of Geometry, Binding and Diffusion on Proteins in the Presynaptic Region Simon M Dannenberg, Sarah Mohammadinejad, Stefan Klumpp The mobility of a protein is a key factor determining its availability for chemical reactions in a cell. It is influenced by many factors including the diffusion coefficient, binding to membranes and the geometry of the environment. In the presynaptic region of neurons, the latter varies widely between different synapses. Combined with the tremendous biological importance of this region it becomes a compelling quest to investigate mobility here. In our work we use simulations to disentangle the interplay of the aforementioned factors. We demonstrate that the binding to synaptic vesicles and the cytoplasmic diffusion of the protein give rise to a specific length scale that determines whether the recovery of protein material is dominated by protein redistribution inside the synapse or via fluxes from the axon. This length scale is comparable to the size of the presynaptic region, which makes the interpretation of common experimental techniques for mobility measurements such as FRAP challenging. However, our simulations enable suggestions to circumvent pitfalls in Experiments. |
Tuesday, March 5, 2024 10:36AM - 10:48AM |
F37.00010: A flexible idealized model for investigating extracellular solute transport in the brain Saikat Mukherjee, Jeff Tithof The exchange between the cerebrospinal fluid (CSF) flowing through the perivascular spaces (PVSs; annular channels lining the brain vasculature) and interstitial fluid in the brain parenchyma is hypothesized to play a key role in clearing metabolic waste from the brain tissue. Impaired waste clearance has been linked to neurodegenerative diseases like Alzheimer's. We analytically formulate an axisymmetric model of fluid flow exchange between the PVS of a penetrating arteriole and the surrounding brain parenchyma modeled as a porous medium. We then numerically solve an advection-diffusion equation to model the solute transport in the domain. We find that the solute clearance leads to a gradient of concentration along the boundary between the PVS and the parenchyma and that a zero concentration boundary condition at the PVSs often used in parenchymal clearance models may not always be accurate. The model's simplicity offers flexibility in varying key parameters like PVS width, parenchyma thickness, permeability, inlet boundary conditions, and CSF flow velocity in the PVS to probe solute clearance conditions in both healthy conditions and in neurodegenerative disorders. |
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