MathBio Seminar Spring 2023

Upcoming:

Prof. Kaiwen Kam, Cell Biology and Anatomy, Chicago Medical School, Rosalind Franklin University of Medicine and Science (Online)

Title: Emergent properties in the neural circuits controlling breathing 

Date: April 24, 2022

Abstract: Breathing is a vital rhythmic motor behavior controlled by neural circuits in the brainstem. The preBötzinger Complex (preBötC), a nucleus in the ventrolateral medulla, generates rhythmic population activity that drives inspiratory motor output. Despite the apparent simplicity of its function, the mechanisms generating inspiratory rhythmic activity in preBötC remain elusive. We hypothesize that inspiratory rhythmogenesis and other dynamic breathing patterns are emergent properties of the preBötC network. To characterize preBötC network properties that contribute to rhythmogenesis, we utilize graded perturbations and an advanced optical technique, holographic photostimulation, which allows patterning of light to excite multiple regions simultaneously. We find that rhythmic preBötC activity consists of two separable components: small amplitude burstlets, which we hypothesize are rhythmogenic, and larger inspiratory bursts, which initiate a pattern-generating process essential for motor output. Holographic photostimulation of just 4-9 preBötzinger Complex inspiratory neurons, <1% of the population, is capable of producing inspiratory-related bursts. Surprisingly, this evoked motor output occurs after a delay of ~250 ms, comparable to, and perhaps congruent with, the duration of burstlet activity. Beyond resting breathing, we suggest that other inspiratory rhythmic patterns, such as sighs and sniffs, that are interweaved with normal breaths are also emergent properties of the network. Our findings reveal that emergent properties in the preBötC expand the dynamic range of respiratory function and contribute to the robustness and lability of breathing.  

Past:

Prof. Nandakumar Narayanan, Neurology, University of Iowa (In person: 205 MLH)

Title: Timing and Dopamine

Date: January 30, 2022

Abstract: Timing, or deciding when to act, is critical to mammalian behavior.  Despite its importance, it is unclear how neurons encode time.  Here, we present neuroscientific evidence from humans and rodents about how the neurotransmitter dopamine affects timing behavior, and the neuronal encoding of time.  Our hope is that this might inspire new insights into computational principles of how neuronal networks encode time.

Prof. Nicholas Trapp, Department of Psychiatry, University of Iowa (In person: 205 MLH)

Title: What Lesions Can Teach Us About Complex Brain-Behavior Relationships

Date: February 06, 2022

Abstract: Understanding neural circuits that support mood is a central goal of affective neuroscience, and improved understanding of the anatomy could inform more targeted interventions in mood disorders. Lesion studies provide a method of inferring the anatomical sites causally related to specific functions, including mood. Here, we will discuss a large-scale study evaluating the location of acquired, focal brain lesions in relation to symptoms of depression.  Multivariate lesion-symptom mapping was performed to identify lesion sites associated with higher or lower depression symptom burden, which we refer to as “risk” versus “resilience” regions.  The brain networks and white matter tracts associated with peak regional findings were identified using functional and structural lesion network mapping, respectively.  Lesion-symptom mapping identified brain regions significantly associated with both higher and lower depression severity.  These results demonstrate that lesions to specific nodes of the salience network and default mode network are associated with greater risk versus resiliency for depression symptoms in the setting of focal brain lesions.

Prof. Cheng Ly, Department of Statistical Sciences and Operations Research, Virginia Commonwealth University (Online)

Title: Variable Neuronal Spiking in a Different Way

Date: February 13, 2022

Abstract: At the onset of sensory stimulation, the variability and co-variability of spiking activity is widely reported to decrease, especially in cortex. Considering the potential benefits of such decreased variability for coding, it has been suggested that this could be a general principle governing all sensory systems. We show this is not so. In rats we found increased variability of spiking with odor stimulation. How does this happen, and what is different here? Using models and analysis, we predicted that this is due to network interactions within the circuit, not from inherited variability from the input signal via the nose. We tested and confirmed this prediction in awake animals with direct optogenetic stimulation to circumvent the pathway through the nose. Our results establish increases in spiking variability at stimulus onset as a viable alternative coding strategy to the more commonly observed decreases in variability in many cortical systems.

Prof. Veronica Ciocanel, Departments of Mathematics and Department of Biology,  Duke University (Online)

Title: Modeling and topological data analysis for biological ring channel dynamics

Date: February 20, 2022

Abstract: Actin filaments are polymers that interact with motor proteins inside cells and play important roles in cell motility, shape, and development. Depending on its function, this dynamic network of interacting proteins reshapes and organizes in a variety of structures, including bundles, clusters, and contractile rings. Datasets that describe the interaction of actin filaments with motors through time can either be generated used stochastic agent-based models or can come directly from experiments, typically in the form of fluorescence videos. In studying the emergence and maintenance of ring channel structures in such complex time-series data, we develop tools based on persistent homology that can distinguish between distinct filament organizations and experimental conditions. This work raises interesting questions about assessing the significance of topological features in topological summaries such as persistence diagrams.

Prof. Amit Bose, Department of Mathematical Sciences, New Jersey Institute of Technology (Online)

Title:  Understanding the limits of entrainment of circadian oscillator models using one-dimensional maps

Date: February 27, 2022

Abstract: A central feature of circadian systems is their response to an external, pacemaking 24 hour light-dark drive which typically leads to entrainment of circadian oscillator.  There are, however, several naturally arising situations in which a circadian system is incapable of entrainment, either due to abnormal intrinsic properties of the oscillators or due to changes in the light-dark input that the oscillators  receive. In this talk, we will use entrainment maps to describe circumstances that fall outside the normal fixed phase relationship between LD forcing and oscillator such as during jet lag, shift work and non-24 hour sleep-wake disorder.  The mathematical and computational methods used to study these problems revolve around finding stable limit cycle solutions of the governing equations and it is the reduction of this study to a one-dimensional framework that will be the focus of the talk.

Prof. Na Yu, Department of Mathematics,  Toronto Metropolitan University (Online)

Title: Segmenting hyperspectral images of eye tissues

Date: March 6, 2022

Abstract: Hyperspectral imaging is a powerful tool that adds quantitative spectral information as the third dimension for each pixel, thus overcoming this limitation. However, there is a high demand for data analysis approaches to extract useful spectral-spatial information from the complex tissues of the eye and other tissues in 3D hyperspectral images. To address this need, we develop open-source 3D segmentation algorithms that can effectively extract spectral-spatial information from hyperspectral images of eye tissues. These algorithms can also be expanded to analyze hyperspectral images of other tissues.

Prof. Dae Wook Kim, Department of Mathematics, University of Michigan (Online)

Title: Circadian assessments from wearable data for precision medicine

Date: March 20, 2022

Abstract: The efficacy and toxicity of diverse drugs including more than 50 anticancer drugs largely depend on dosing time. Despite the potential benefits to patients from time-of-day treatment, current clinical practice guidelines have largely ignored it. This is mainly due to the lack of reliable and efficient methods to identify the patient’s internal time in the real world. Wearables (e.g., Apple Watch) provide an opportunity for non-invasive continuous monitoring of physiological signals, such as activity and heart rate. In this talk, I will present a Kalman filter approach that assimilates wearable data into the model of the human circadian (~24 hr) clock and estimates the internal circadian time. I will also introduce a Kalman filter-assisted neural network approach for early detection of aberrant changes in circadian physiology related to disease progression from wearable measurements. The mathematics of the wearables can pave the way toward precision medicine in the real world.

Prof. Yuri Dabaghian, Department of Neurology, UTHealth Houston (Online)

Title: Learning orientations in a topological map and synthetic geometry

Date: March 27, 2022

Abstract: Spatial cognition in mammals is based on an internalized representation of space, which incorporates relational, metric, angular and other types of information. A key component of this representation is a framework of qualitative spatiotemporal relationships—atopological map of the ambient space encoded by the hippocampus and complemented by more detailed metrical data provided by other brain regions. In particular, experimental studies have identified several parts of the brain where neuronal spiking explicitly represents the animal’s head orientation, which is believed to contribute directional information to the cognitive map. However, it remains unclear how these different types of spatial information may synthesize, i.e., combine into a single coherent spatial framework, and how the brain can intrinsically interpret different patterns of spiking activity as locations or directions. We propose a phenomenological model that combines the hippocampal map of locations with orientations and sheds light into how the animal can learn an affine map of the environment.

Prof. Sufyan Ashhad, National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India (Online)

Title: Heavy-tailed synaptic weight distribution augments preBötzinger Complex synchronization underlying inspiratory rhythmogenesis

Date: April 03, 2022

Abstract: Mammalian breathing is robust yet exceptionally labile, essential to adapt to rapid metabolic shifts, for airway reflexes, and to enable non-ventilatory behaviors, e.g., vocalization. Breathing movements are generated by an intricate system of interconnected neural circuits, the breathing central pattern generator (bCPG) in the brainstem. The anatomical core of bCPG is the preBötzinger Complex (preBötC) that generated inspiratory rhythm. Theoretical frameworks for breathing rhythmogenesis based on pacemaker neurons do not account for the observed robustness and flexibility of the breathing rhythm. In this talk, I will present results from experimental and modeling studies that establish that network synchronization is critical for breathing rhythmogenesis. Specifically, we discovered that preBötC rhythm is an emergent network property where neuronal synchronization at low network activity levels is necessary for the generation and propagation of inspiratory command. The synchronization and propagation of preBötC activity exhibits attractor dynamics where weak network synchrony fails to propagate to the (pre) motor neurons to drive inspiration. To understand the theoretical underpinnings of preBötC synchronization, we developed models of the preBötC microcircuit significantly constrained by the experimental parameters. Our analysis revealed that the experimentally observed lognormally (but not uniformly) distributed synaptic strengths augment coincidence detection of the convergent inputs and are essential for the network to synchronize under experimental constraints. Notably, the lognormally distributed synaptic strengths are pervasive in neuronal networks involved in such functions as memory and cognition. Thus, this mechanism of network assembly, exhibiting attractor dynamics, could underlie behaviorally relevant network computations throughout the mammalian nervous system.

Prof. Jan Wessel, Department of Neurology & Department of Psychological and Brain Sciences, University of Iowa (In person: 205 MLH)

Title: Elucidating the computational principles of inhibitory motor control using neural β burst recordings

Date: April 17, 2022

Abstract: The ability to exert control over motoric processes is paramount to safe and efficient everyday behaviors. For example, humans can readily cancel an already initiated movement (e.g., walking into the street) when environmental events (e.g., an overlooked car), prompt them to do so. The neural and behavioral principles underlying action-cancellation are investigated in the stop-signal task, a behavioral paradigm in which competing pro- and anti-kinetic processes can be readily quantified using simple computational models. In this talk, I will present the most prominent models of action-cancellation and how they relate to the neural dynamics in the stop-signal task. I will focus on the recent discovery of β frequency burst events in local field potential recordings from humans, which provide crucial insights into the computational principles and psychological variables that influence the ability to cancel an ongoing action.