Biology subjects

Ching, S.

Publications and source records attributed to Ching, S..

17 recordsLinked to original sources

bioRxiv2026

Task-induced topological and geometrical changes in whole-brain dynamics predict cognitive individual differences

Across the last three decades, functional magnetic resonance imaging (fMRI) research - through both resting-state (rsfMRI) and task-based (tfMRI) studies - has greatly advanced our understanding regarding the neural basis of cognition. Yet the mechanistic relationship between rsfMRI and tfMRI is still poorly understood. In particular, it remains unclear how and why the brain activation patterns observed during the resting state are linked to cognitive functioning and individual differences present during task performance. Here, we test a unifying computational account which postulates that task contexts modulate the nonlinear attractor landscape and associated dynamical properties of the brain present under resting conditions, and further that the nature of this modulation is impacted by meaningful cognitive individual differences. To test this account, we develop a joint rsfMRI-tfMRI modeling and analysis framework called Mesoscale Individualized NeuroDynamics with eXogenous inputs (MINDy-X) and apply it to resting and N-back working memory task data from the Human Connectome Project. We first validated that the joint model can simulate and predict both rsfMRI and tfMRI data accurately, consistent with a common underlying dynamical system. Analyses of this joint model revealed that task-related modulation bifurcated the predominantly multistable attractor dynamics present during the resting state towards a predominantly monostable dynamics observed during N-back task states. This topological shift was also accompanied by a geometric reconfiguration, with the task state characterized by an enrichment of dynamical attractor "motifs" clustered around the frontoparietal (FPN) and default mode (DMN) networks. Task-related modulations of this attractor landscape were further subject to clear individual differences, such that individuals who did not exhibit a shift in attractor topology were more error-prone and less cautious in responding, while closer geometric proximity to the FPN and DMN motifs explained additional aspects of task performance. N-back behavior was best characterized by the combination of topological and geometric properties present in both task and rest states, suggesting that they each account for unique aspects of individual variability. The current work supports a novel computational framework for understanding the whole-brain neural activity patterns observed during rsfMRI and tfMRI as reflecting different states within a common non-linear dynamical system. This framework provides a new vocabulary for characterizing cognitive functioning in terms of the unique geometric and topological configuration of the associated attractor landscapes, with the potential for wide application in many domains of basic and clinical neuroscience research.

neuroscience↗

bioRxiv2026

Multitimescale Computation by Astrocytes

The basic computational unit of the brain has long been defined as the neuron. However, mounting evidence suggests that other cells, especially astrocytes, also perform computations. Here, we demonstrate that cerebellar astrocytes decompose norepinephrine input into slow and fast calcium ion activities through differential adrenergic receptor engagement. During reward learning in mice, these slow and fast activities selectively target and modulate distinct synaptic pathways. Causal manipulations reveal that fast 2-adrenergic signals govern event-triggered responses and reinforcement learning, whereas slow 1-adrenergic signals maintain behavioral states and coordinate transitions. Remarkably, an actor-critic neural network trained on a similar sequence task spontaneously recapitulates these multitemporal dynamics, suggesting that astrocytes implement critic-like computations that evaluate states and modulate neuronal learning. These findings establish astrocytes as multilevel processors that transform univariate neuromodulatory inputs into multivariate, pathway-specific circuit controls operating in parallel with neuronal processing.

neuroscience↗

bioRxiv2025

Two views of the brain are reconciled by a unifying principle of maximal information processing

There is selective pressure on brains to maximize computational capacity and adaptability in an unpredictable world. Prior work suggests that this demand is satisfied by a regime called criticality, which has emerged as a powerful, unifying framework for understanding how computation can arise in biological systems. However, this framework has been confined to high-dimensional network models. At first glance, this appears irreconcilable with many of the foundational, low dimensional dynamical models that have driven progress in theoretical and computational neuroscience for a century. If criticality is a universal principle, then all models that accurately capture significant aspects of brain function should be constrained by the same fact. Lacking a definition of criticality in low-dimensional dynamical systems, this has been impossible to evaluate. Here, we develop a mathematical definition of criticality that transcends dimensionality by recognizing temporal scale invariance as analogous to spatial scale invariance that defines criticality in large systems. We demonstrate that there are two mechanistically distinct sources of criticality at bifurcations, one deterministic and one that emerges from noise fluctuations. Further, we show that some but not all canonical bifurcations in neural models exhibit criticality, and only a subset of these are biologically plausible. We conduct numerical analyses demonstrating that information processing capacity peaks at critical bifurcations, and evaluate which historically influential neural models contain these bifurcations. Our results establish criticality as a universal neurobiological principle that is accessible to systems of any dimensionality. This unifies disparate modeling approaches under a single computational framework and suggests that optimal information processing emerges not from model-specific mechanisms but from fundamental properties of critical dynamics themselves.

neuroscience↗

bioRxiv2025

Ventral striatal astrocytes contribute to reinforcement learning

Astrocytes influence synaptic plasticity and neuronal function through astrocytic calcium dynamics (ACD). However, astrocytes contribution to cognitive operations like reinforcement learning (RL) remains unclear. To examine this, we trained mice on a RL dependent probabilistic decision-making task. We attenuated ACD across distinct striatal regions, and found ACD attenuation specifically in ventral striatum (VS) increased decision noisiness and impaired reward-guided choice performance. This effect was largely due to a reduction in "win-stay" behavior. Using in-vivo calcium imaging, we found that VS ACD correlated with reward prediction errors (RPEs). Furthermore, these trial-by-trial ACD fluctuations predicted trial-by-trial choice variability. In-silico lesions of a biologically constrained circuit model suggest that astrocytes could regulate behavioral variability by sharing RPE signals across populations of striatal neurons. Together, these results suggest that VS astrocytes contribute to cortico-striatal functions to mediate decision noisiness.

neuroscience↗

bioRxiv2025

Bone Resists Fatigue Through Crack Deceleration at the Fibril Scale

Bone endures millions of cycles throughout its lifetime by accumulating damage at a rate slow enough to allow for cell-mediated repair, but the mechanisms that delay this fatigue failure remain poorly understood. While prior studies have focused on the fatigue response of macroscale architecture of bone, the role of its nanoscale structure in resisting fatigue has been experimentally inaccessible. Here, we combine in-situ fatigue loading with synchrotron X-ray tomography and radiography to directly observe crack propagation in human bone with [~] 21 nm spatial and 100 ms temporal resolution. We find that mineralized collagen fibrils decelerate crack growth through branching along the fibril axes, while orthogonal cracks are intermittently decelerated by nanoscale interfibrillar interfaces. These mechanisms suppress damage accumulation under physiological loads by an order of magnitude. Our findings uncover a previously unobserved toughening strategy at the nanoscale, providing insight as to how the hierarchical structure of bone bridges the timescale gap between mechanical damage and biological repair.

bioengineering↗

bioRxiv2025

A neural network with episodic memory learns causal relationships between narrative events

Humans reflect on memories to make sense of ongoing events. Past work has shown that people retrieve causally related memories during comprehension, but the mechanisms underlying this process remain unclear. Here, we used a recurrent neural network augmented with a key-value episodic memory buffer and trained it to predict upcoming scenes while watching a television episode. At each time step, the model transformed the current scene into a value representing memory content and a key representing memory address, both stored as episodic memory. The model retrieved selective past values by applying self-attention over stored keys and integrated these memories with the current scene representation to generate predictions. The model retrieved memories similar to those retrieved by human participants watching the same episode during fMRI. Importantly, this similarity disappeared when causal relationships between events were controlled for. The model also represented causally related events with similar patterns, similar to how the human brain represents these events. These findings suggest that using two distinct memory representations allows the model to retrieve memories and organize events based on causal relationships, beyond semantic or perceptual similarities. Together, this work proposes a key-value episodic memory system as a candidate computational mechanism for how humans retrieve causally related memories to comprehend naturalistic events.

neuroscience↗

bioRxiv2025

Short-term sensory memory mediates adaptation, habituation, and a paradoxical neural-behavioral transformation in C. elegans

Repeated exposure to stimuli elicits decreasing sensory neural responses over time (adaptation). However, resulting behavioral responses can either weaken over time (habituation) or remain invariant, indicating that the neural-behavioral link is not fixed. To investigate neural adaptation and its flexible translation into behavioral decision making, we created a mathematical framework hypothesizing (1) that sensory networks optimize the speed and accuracy of encoding exogenous stimuli, and (2) that representations form along two time scales, one embedding immediate information and the other stimulus history. Using experimental recordings of the nematode C. elegans, we validated normative model predictions of this optimal encoding strategy, specifically how neural dynamics and adaptation levels vary with stimulus timing. A parametric Bayesian decoder architecture predicted conditions leading to behavioral habituation or invariance, but also paradoxical inversion, whereby appetitive stimuli elicit aversive responses. Experiments with food odors validated that inversion behavior occurred after several repetitions with a long stimulation time and low odor concentrations. Mechanistically, during sensory neural adaptation, weaker immediate stimulus representations can be compensated by secondary processes through memory effects, with biological origins that remain to be studied.

neuroscience↗

bioRxiv2025

Geometry of neural dynamics along the cortical attractor landscape reflects changes in attention

The brain is a complex dynamical system whose activity reflects changes in internal states, such as attention. While prior work has shown that large-scale brain activity reflects attention, the mechanism governing this association in a time-varying and task-dependent manner remains unknown. Here, we tested a hypothesis that the geometry of neural dynamics on the attractor landscape, or the movement along the "hills and valleys", reflects changes in attentional states over time and variations across controlled and naturalistic contexts. We fit a parametric dynamical systems model to fMRI data collected during rest, task performance, and naturalistic movie-watching. The model decomposes neural dynamics into components that are intrinsic versus extrinsically driven by stimuli. Model parameters were biologically meaningful, reflecting both cognitive states and individual differences. Model simulations revealed a set of attractors that mirrored functional brain networks, spanning the canonical gradient from sensorimotor to default mode network regions. The speed and direction of neural trajectories toward these attractors systematically varied across attentional states in a context-dependent manner. When participants were paying attention to effortful tasks, neural dynamics converged directly toward a task-relevant attractor, suggesting that it occupied a steeper region of the attractor landscape. In contrast, when participants were engaged in sitcom episodes, neural dynamics were in a flattened region of the landscape, directed away from the attractors. These findings demonstrate that while the positions of the attractors are largely determined by the cortical organization, the geometry of neural dynamics on the attractor landscape changes systematically across attentional states and situational contexts.

neuroscience↗

bioRxiv2025

Identification of modulated whole-brain dynamical models from nonstationary electrophysiological data

ObjectiveUnderstanding the mechanisms underlying brain dynamics is a long-held goal in neuroscience. However, these dynamics are both individualized and nonstationary, making modeling challenging. Here, we present a data-driven approach to modeling nonstationary dynamics based on principles of neuromodulation, at the level of individual subjects. ApproachPreviously, we developed the mesoscale individualized neural dynamics (MINDy) modeling approach to capture individualized brain dynamics which do not change over time. Here, we extend the MINDy approach by adding a modulatory component which is multiplied by a set of baseline, stationary connectivity weights. We validate this model on both synthetic data and publicly available EEG data in the context of anesthesia, a known modulator of neural dynamics. Main ResultsWe find that our modulated MINDy approach is accurate, individualized, and reliable. Additionally, we find that our models yield biologically interpretable inferences regarding the effects of propofol anesthesia on mesoscale cortical networks, consistent with previous literature on the neuromodulatory effects of propofol. SignificanceUltimately, our data-driven modeling approach is reliable and scalable, and provides insight into mechanisms underlying observed brain dynamics. Our modeling methodology can be used to infer insights about modulation dynamics in the brain in a number of different contexts.

neuroscience↗

bioRxiv2024

Dynamical models reveal anatomically reliable attractor landscapes embedded in resting state brain networks

Analyses of functional connectivity (FC) in resting-state brain networks (RSNs) have generated many insights into cognition. However, the mechanistic underpinnings of FC and RSNs are still not well-understood. It remains debated whether resting state activity is best characterized as noise-driven fluctuations around a single stable state, or instead, as a nonlinear dynamical system with nontrivial attractors embedded in the RSNs. Here, we provide evidence for the latter, by constructing whole-brain dynamical systems models from individual resting-state fMRI (rfMRI) recordings, using the Mesoscale Individualized NeuroDynamic (MINDy) platform. The MINDy models consist of hundreds of neural masses representing brain parcels, connected by fully trainable, individualized weights. We found that our models manifested a diverse taxonomy of nontrivial attractor landscapes including multiple equilibria and limit cycles. However, when projected into anatomical space, these attractors mapped onto a limited set of canonical RSNs, including the default mode network (DMN) and frontoparietal control network (FPN), which were reliable at the individual level. Further, by creating convex combinations of models, bifurcations were induced that recapitulated the full spectrum of dynamics found via fitting. These findings suggest that the resting brain traverses a diverse set of dynamics, which generates several distinct but anatomically overlapping attractor landscapes. Treating rfMRI as a unimodal stationary process (i.e., conventional FC) may miss critical attractor properties and structure within the resting brain. Instead, these may be better captured through neural dynamical modeling and analytic approaches. The results provide new insights into the generative mechanisms and intrinsic spatiotemporal organization of brain networks.

neuroscience↗

bioRxiv2023

Precision data-driven modeling of cortical dynamics reveals idiosyncratic mechanisms of canonical oscillations

Task-free brain activity affords unique insight into the functional structure of brain network dynamics and is a strong marker of individual differences. In this work, we present an algorithmic optimization framework that makes it possible to directly invert and parameterize brain-wide dynamical-systems models involving hundreds of interacting brain areas, from single-subject time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions. We extensively validate the models performance in forecasting future brain activity and predicting individual variability in key M/EEG markers. Lastly, we demonstrate the power of our technique in resolving individual differences in the generation of alpha and beta-frequency oscillations. We characterize subjects based upon model attractor topology and a dynamical-systems mechanism by which these topologies generate individual variation in the expression of alpha vs. beta rhythms. We trace these phenomena back to global variation in excitation-inhibition balance, highlighting the explanatory power of our framework in generating mechanistic insights.

neuroscience↗

bioRxiv2020

Enhancing Task fMRI Preprocessing via Individualized Model-Based Filtering of Intrinsic Activity Dynamics

Brain responses recorded during fMRI are thought to reflect both rapid, stimulus-evoked activity and the propagation of spontaneous activity through brain networks. In the current work we describe a method to improve the estimation of task-evoked brain activity by first "filtering-out" the intrinsic propagation of pre-event activity from the BOLD signal. We do so using Mesoscale Individualized NeuroDynamic (MINDy; [1]) models built from individualized resting-state data (MINDy-based Filtering). After filtering, time-series are analyzed using conventional techniques. Results demonstrate that this simple operation significantly improves the statistical power and temporal precision of estimated group-level effects. Moreover, use of MINDy-based filtering increased the similarity of neural activation profiles and prediction of individual differences in behavior across tasks measuring the same construct (cognitive control).Thus, by subtracting the propagation of previous activity, we obtain better estimates of task-related neural activity.

neuroscience↗

bioRxiv2020

Scalable Surrogate Deconvolution for Identification of Partially-Observable Systems and Brain Modeling

For many biophysical systems, direct measurement of all state-variables, in - vivo is not-feasible. Thus, a key challenge in biological modeling and signal processing is to reconstruct the activity and structure of interesting biological systems from indirect measurements. These measurements are often generated by approximately linear time-invariant (LTI) dynamical interactions with the hidden system and may therefore be described as a convolution of hidden state-variables with an unknown kernel. In the current work, we present an approach termed surrogate deconvolution, to directly identify such coupled systems (i.e. parameterize models). Surrogate deconvolution reframes certain nonlinear partially-observable identification problems, which are common in neuroscience/biology, as analytical objectives that are compatible with almost any user-chosen optimization procedure. We show that the proposed technique is highly scalable, low in computational complexity, and performs competitively with the current gold-standard in partially-observable system estimation: the joint Kalman Filters (Unscented and Extended). We show the benefits of surrogate deconvolution for model identification when applied to simulations of the Local Field Potential and blood oxygen level dependent (BOLD) signal. Lastly, we demonstrate the empirical stability of Hemodynamic Response Function (HRF) kernel estimates for Mesoscale Individualized NeuroDynamic (MINDy) models of individual human brains. The recovered HRF parameters demonstrate reliable individual variation as well as a stereotyped spatial distribution, on average. These results demonstrate that surrogate deconvolution promises to enhance brain-modeling approaches by simultaneously and rapidly fitting large-scale models of brain networks and the physiological processes which generate neuroscientific measurements (e.g. hemodynamics for BOLD fMRI).

neuroscience↗

bioRxiv2020

Computing and Optimizing Over All Fixed-Points of Discrete Systems on Large Networks

Equilibria, or fixed points, play an important role in dynamical systems across various domains, yet finding them can be computationally challenging. Here, we show how to efficiently compute all equilibrium points of discrete-valued, discrete-time systems on sparse networks. Using graph partitioning, we recursively decompose the original problem into a set of smaller, simpler problems that are easy to compute, and whose solutions combine to yield the full equilibrium set. This makes it possible to find the fixed points of systems on arbitrarily large networks meeting certain criteria. This approach can also be used without computing the full equilibrium set, which may grow very large in some cases. For example, one can use this method to check the existence and total number of equilibria, or to find equilibria that are optimal with respect to a given cost function. We demonstrate the potential capabilities of this approach with examples in two scientific domains: computing the number of fixed points in brain networks and finding the minimal energy conformations of lattice-based protein folding models.

neuroscience↗

bioRxiv2020

Neural Circuit Dynamics for Sensory Detection

We consider the question of how sensory networks enable the detection of sensory stimuli in a combinatorial coding space. We are specifically interested in the olfactory system, wherein recent experimental studies have reported the existence of rich, enigmatic response patterns associated with stimulus onset and offset. This study aims to identify the functional relevance of such response patterns, i.e., what benefits does such neural activity provide in the context of detecting stimuli in a natural environment. We study this problem through the lens of normative, optimization-based modeling. Here, we define the notion of a low dimensional latent representation of stimulus identity, which is generated through action of the sensory network. The objective of our optimization framework is to ensure high fidelity tracking of a nominal representation in this latent space in an energy efficient manner. It turns out that the optimal motifs emerging from this framework possess morphological similarity with prototypical onset and offset responses observed in vivo. Furthermore, this objective can be exactly achieved by a network with reciprocal excitatory-inhibitory competitive dynamics, similar to interactions between principal neurons (PNs) and local neurons (LNs) in the early olfactory system of insects. The derived model also makes several predictions regarding maintenance of robust latent representations in the presence of confounding background information and tradeoffs between the energy of sensory activity and resultant behavioral measures such as speed and accuracy of stimulus detection. Significance StatementA key area of study in olfactory coding involves understanding the transformation from high-dimensional sensory stimulus to low-dimensional decoded representation. Here, we treat not only the dimensionality reduction of this mapping but also its temporal dynamics, with specific focus on stimuli that are temporally continuous. We examine through optimization-based synthesis how sensory networks can track representations without prior assumption of discrete trial structure. We show that such tracking can be achieved by canonical network architectures and dynamics, and that the resulting responses resemble observations from neurons in the insect olfactory system. Thus, our results provide hypotheses regarding the functional role of olfactory circuit activity at both single neuronal and population scales.

systems biology↗

bioRxiv2019

Individualized Dynamic Brain Models: Estimation and Validation with Resting-State fMRI

A key challenge for neuroscience is to develop generative, causal models of the human nervous system in an individualized, data-driven manner. Previous initiatives have either constructed biologically-plausible models that are not constrained by individual-level human brain activity or used data-driven statistical characterizations of individuals that are not mechanistic. We aim to bridge this gap through the development of a new modeling approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling, wherein we fit nonlinear dynamical systems models directly to human brain imaging data. The MINDy framework is able to produce these data-driven network models for hundreds to thousands of interacting brain regions in just 1-3 minutes per subject. We demonstrate that the models are valid, reliable, and robust. We show that MINDy models are predictive of individualized patterns of resting-state brain dynamical activity. Furthermore, MINDy is better able to uncover the mechanisms underlying individual differences in resting state activity than functional connectivity methods.

neuroscience↗

bioRxiv2017

Geometric Classification of Brain Network Dynamics via Donic Derivative Discriminants

Over the past decade, pattern decoding techniques have granted neuroscientists improved anatomical specificity in mapping neural representations associated with function and cognition. Dynamical patterns are of particular interest, as evidenced by the proliferation and success of frequency domain methods that reveal structured spatiotemporal rhythmic brain activity. One drawback of such approaches, however, is the need to estimate spectral power, which limits the temporal resolution of classification. We propose an alternative method that enables classification of dynamical patterns with high temporal fidelity. The key feature of the method is a conversion of time-series into their temporal derivatives. By doing so, dynamically-coded information may be revealed in terms of geometric patterns in the phase space of the derivative signal. We derive a geometric classifier for this problem which simplifies into a straightforward calculation in terms of covariances. We demonstrate the relative advantages and disadvantages of the technique with simulated data and benchmark its performance with an EEG dataset of covert spatial attention. By mapping the weights anatomically we reveal a retinotopic organization of covert spatial attention. We especially highlight the ability of the method to provide strong group-level classification performance compared to existing benchmarks, while providing information that is synergistic to classical spectral-based techniques. The robustness and sensitivity of the method to noise is also examined relative to spectral-based techniques. The proposed classification technique enables decoding of dynamic patterns with high temporal resolution, performs favorably to benchmark methods, and facilitates anatomical inference.

neuroscience↗