arXiv subjects

Suman S. Kulkarni

Publications and source records attributed to Suman S. Kulkarni.

5 recordsLinked to original sources

arXiv2026

Activity-dependent epidemic spreading on multiscale brain networks predicts Alzheimer's disease progression

Neurodegenerative diseases can be viewed as spreading processes on brain networks, in which pathological proteins propagate between anatomically connected brain regions. Mathematical models have been used to study this process, but they generally ignore the influence of neuronal activity, even though experimental studies show that neuronal firing promotes protein transmission. Here, we couple a general node-activity process to susceptible--infected--susceptible dynamics. In this framework, an epidemic threshold determines whether small pathological seeds can grow, while a dominant network mode determines where growth begins. We derive approximations showing how neuronal activity shifts this threshold and redirects spreading by mixing structural network modes. For networks with multiscale structure, we decompose these changes into contributions from regional mean activity and within-region activity variation, allowing us to account for activity heterogeneity that is not resolved by brain imaging. Stochastic simulations validate the theoretical results across synthetic networks. We next use longitudinal human positron emission tomography to test whether neuronal activity predicts where and how broadly pathology spreads. Regional glucose metabolism serves as a proxy for neuronal activity, while tau accumulation measures disease progression. Adding neuronal activity to the network model captures spatial patterns of disease progression that are not explained by structural connectivity and established disease markers alone. Across individuals, predicted epidemic thresholds are also associated with how broadly pathology spreads through the brain. Together, these results connect epidemic theory to neurodegeneration, implicate neuronal activity as a driver of Alzheimer's disease progression, and motivate activity-modulating therapies to slow or prevent pathological spread.

q-bio.NC↗

arXiv2026

Quantifying the cost of network computations to unpack structure-function relationships in the brain

The brain supports computations through coordinated patterns of activity on an underlying network. These networks---from microscale navigational circuits in insects to macroscale brain areas in humans---are organized in structured ways that are thought to support their function. We seek a unifying quantitative framework to understand how network structure shapes the computations a network can readily support. To do so, we frame computation as a goal-directed transition of activity and quantify its cost on a given network using control theory. We then define the distribution of costs across all possible transitions as a $\textit{computational affordance landscape}$ that encodes which computations a network structure readily supports. We apply this framework to a circuit model for how insects maintain a sense of direction and show that updating orientation is the least costly computation, with predicted inputs consistent with known circuitry. In the human brain, we find that the affordance landscape varies systematically with the functional role of each network. Sensory networks display more heterogeneous landscapes (reflecting their role in specialized information processing), whereas association networks display more homogeneous landscapes (reflecting their role in generalized information processing). In recurrent neural networks trained on cognitive tasks, we show that learning progressively increases landscape heterogeneity, reshaping the distribution of affordable computations. Generally, we establish a quantitative framework for studying relationships between structure and computation in neural circuits, with future applications extending to other biological and physical networks.

q-bio.NC↗

arXiv2026

Topological and morphological signatures of disorder in a self-assembled, soft matter sponge network

Many soft matter systems exhibit ordered, polycontinuous network morphologies, such as the cubic (double) gyroid or diamond, as well as disordered network morphologies known generically as ``random sponges". While presumed to share similar local packing geometry, the structural relationship between these ordered and disordered network morphologies has remained obscure. We use slice and view scanning electron microscopy to analyze and compare multi-scale morphological features of an ordered double-gyroid morphology to the amorphous sponge morphology formed in the same block copolymer sample. We find that node valence of the minority component network of the sponge is mostly gyroidal (trivalent), with a small fraction of diamond-like (tetravalent) connections. We analyze mesoatoms -- space-filling volumes occupied by chains around each network node -- finding significant differences in shape and size between ordered and amorphous regions. Local block thickness and inter-domain curvature within mesoatomic units of the disordered sponge exhibits a surprisingly similar degree of dispersity to the ordered double-gyroid. The mean differences in local packing geometry derive from topological distinction: loops of the minority networks of the ordered double-gyroid are intercatenated, while loops of the disordered sponge are not. In this way, the sponge may be viewed as disordered variant of a single-gyroidal morphology. We exploit these topological differences to demarcate the boundary region between ordered and disordered networks and highlight modulations of the mesoatom motifs at the boundary. These observations point to new questions about potential metastability of disordered networks and their possible role as kinetic precursors to long-range ordered network morphologies.

cond-mat.soft↗

arXiv2025

Predictability and Statistical Memory in Classical Sonatas and Quartets

Statistical models and information theory have provided a useful set of tools for studying music from a quantitative perspective. These approaches have been employed to generate compositions, analyze structural patterns, and model cognitive processes that underlie musical perception. A common framework used in such studies is a Markov chain model, which models the probability of a musical event -- such as a note, chord, or rhythm -- based on a sequence of preceding events. While many studies focus on first-order models, relatively few have used more complex models to systematically compare across composers and compositional forms. In this study, we examine statistical dependencies in classical sonatas and quartets using higher-order Markov chains fit to sequences of top notes. Our data set of 605 MIDI files comprises piano sonatas and string quartets by Mozart, Haydn, Beethoven, and Schubert, from which we analyze sequences of top notes. We probe statistical dependencies using three distinct methods: Markov chain fits, time-delayed mutual information, and mixture transition distribution analysis. We find that, in general, the statistical dependencies in Mozart's music notably differ from that of the other three composers. Markov chain models of higher order provide significantly better fits than low-order models for Beethoven, Haydn, and Schubert, but not for Mozart. At the same time, we observe nuances across compositional forms and composers: for example, in the string quartets, certain metrics yield comparable results for Mozart and Beethoven. Broadly, our study extends the analysis of statistical dependencies in music, and highlights systematic distinctions in the predictability of sonatas and quartets from different classical composers. These findings motivate future work comparing across composers for other musical forms, or in other eras, cultures, or musical traditions.

physics.soc-ph↗

arXiv2025

Ising dynamics on multilayer networks with heterogeneous layers

Multilayer networks provide a framework to study complex systems with multiple types of interactions, multiple dynamical processes, and/or multiple subsystems. When studying a dynamical process on a multilayer network, it is important to consider how both layer structure and heterogeneity across layers impacts the overall dynamics. As a concrete example, we study Ising dynamics on multilayer networks and investigate how network structure affects its qualitative features. We focus primarily on multiplex networks, which are multilayer networks in which interlayer edges occur only between manifestations of the same entity on different layers, although we also consider one empirical example with a more general multilayer structure. We use numerical simulations and a mean-field approximation to examine the steady-state behavior of the Ising dynamics as a function of temperature (which is a key model parameter) for a variety of two-layer multilayer networks from both models and empirical data. We examine both the steady-state behavior and a metastable state in which the two layers are anti-aligned, and we explore the effects of interlayer coupling strength and structural heterogeneity. In synthetic multilayer networks with core--periphery structure, we show that interlayer edges that involve peripheral nodes can exert more influence than interlayer edges that involve only core nodes. Finally, we consider empirical multilayer networks from biological and social systems. Our work illustrates how heterogeneity across the layers of a multilayer network influences dynamics on the whole network.

physics.soc-ph↗