Integrated Calendar

In living systems, the tissue micro-architecture consists of myriad cellular and subcellular elements whose density, size/shape distributions, composition, and permeability, endow the tissue with its biological functionality. Dynamic transport mechanisms are further critical for maintaining homeostasis and supporting diverse physiological functions such as action potentials and biochemical signaling. Still, how these biophysical properties change over time and how they couple to activity, remains largely unknown. This is mainly due to the difficulty in mapping these properties in-vivo, longitudinally, and with sufficient specificity. Magnetic Resonance Imaging (MRI), with its capacity for longitudinal studies and wealth of microscopic information leading to multiple contrast mechanisms, provides an outstanding opportunity to decipher these phenomena. In this talk I will discuss our recent advances in diffusion and functional MRI, including novel pulse sequences and biophysical modeling of diffusion processes in the microscopic tissue milieu, which provide, for the first time, the sought-after specificity for density, size, and permeability of particular (sub)cellular elements in tissues. I will show new experiments in rodents proving unique power-laws predicted from biophysical models, revealing axon density and size, as well as cell body density and size, along with validations against ground-truth histology and applications in animal models of disease. Evidence for exchange between the intracellular and extracellular space will also be given, along with a first approach for quantitatively mapping permeability in tissue. I will also introduce correlation tensor MRI (CTI), a new approach for source-separation in diffusional kurtosis, that offers surrogate markers of neurite beading effects, thereby further enhancing specificity, especially in stroke. Finally, I will touch upon dynamic modulations of neural tissue microstructure upon neural activity, and provide evidence for the existence of a neuro-morphological coupling in diffusion-weighted functional MRI signals. Future vistas and potential applications will be discussed.

To function as an integrated entity, the organism must synchronize between behavior and physiology. Our research focuses on probing this synchronization through the lens of the brain-immune system interface. The immune system, pivotal in preserving the organism's integrity, is also a sensitive barometer of its overall state. I will discuss the emerging understanding of how the brain represents the state of the immune system and the specific neural mechanisms that enable the brain to orchestrate immune responses.

I will present a theoretical framework for analyzing learning algorithms which rely on dependent, rather than independent, observations. While a common assumption is that the learning algorithm receives independent datapoints, such as unrelated images or texts, this assumption often does not hold. An example is data on opinions across a social network, where opinions of related people are often correlated, for example as a consequence of their interactions. I will present a line of work that models the dependence between such related datapoints using a probabilistic framework in which the observed datapoints are assumed to be sampled from some joint distribution, rather than sampled i.i.d. The joint distribution is modeled via the Ising model, which originated in the theory of Spin Glasses in statistical physics and was used in various research areas. We frame the problem of learning from dependent data as the problem of learning parameters of the Ising model, given a training set that consists of only a single sample from the joint distribution over all datapoints. We then propose using the Pseudo-MLE algorithm, and provide a corresponding analysis, improving upon the prior literature which necessitated multiple samples from this joint distribution. Our proof benefits from sparsifying a model's interaction network, conditioning on subsets of variables that make the dependencies in the resulting conditional distribution sufficiently weak. We use this sparsification technique to prove generic concentration and anti-concentration results for the Ising model, which have found applications beyond the scope of our work.

Based on joint work with Constantinos Daskalakis, Anthimos Vardis Kandiros, Nishanth Dikkala, Siddhartha Jayanti, Surbhi Goel and Davin Choo.

We propose a generalization of the Minkowski average of two subsets of a Riemannian manifold, in which geodesics are replaced by an arbitrary family of parametrized curves.

Under certain assumptions, we characterize families of curves on a Riemannian surface for which a Brunn-Minkowski inequality holds with respect to a given volume form.

The kinetics of protein–DNA recognition, along with its thermodynamic properties, including affinity and specificity, play a central role in shaping biological function. Protein–DNA recognition kinetics are characterized by two key elements: the time taken to locate the target site amid various nonspecific alternatives; and the kinetics involved in the recognition process, which may necessitate overcoming an energetic barrier. In my presentation, I will describe the complexity of protein-DNA kinetics obtained from molecular coarse-grained simulations of various protein systems. The kinetics of protein-DNA recognition are influenced by various molecular characteristics, frequently necessitating a balance between kinetics and stability. Furthermore, protein-DNA recognition may undergo evolutionary optimization to accomplish optimal kinetics for ensuring proper cellular function.

The skeletal muscle tissue that allows our bodies to move, is comprised of enormous muscle fibers, termed myofibers. Myofibers must grow with our body and adapt to its needs throughout life. This is accomplished by adding nuclei via cell-to-cell fusion. However, the fusion mechanism is poorly understood. To gain a better understanding of the fusion and repair mechanisms I recapitulated myoblast-to-myofiber fusion in culture, which allowed me for the first time to visualize the fusion and regeneration processes at high resolution, generating the seminal observations that form the central hypothesis for my PhD.

: I’ll describe two projects:
In the first, we examined the circuitry that underlies olfaction in a mouse model with severe developmental degeneration of the OB. The olfactory bulb (OB) is a critical component of mammalian olfactory neuroanatomy. Beyond being the first and sole relay station for olfactory information to the rest of the brain, it also contains elaborate stereotypical circuitry that is considered essential for olfaction. In our mouse model, a developmental collapse of local blood vessels leads to degeneration of the OB. Mice with degenerated OBs could perform odor-guided tasks and even responded normally to innate olfactory cues. I will describe the aberrant circuitry that supports functional olfaction in these mice.
The second project focusses on the nucleus of the lateral olfactory tract. This amygdaloid nucleus is typically considered part of the olfactory cortex, yet almost nothing is known about its function, connectivity, and physiology. I will describe our approach to studying this intriguing structure and will present some of its cellular and synaptic properties that may guide hypotheses about its function.