arXiv subjects

Changsong Zhou

Publications and source records attributed to Changsong Zhou.

At least 19 recordsLinked to original sources

arXiv2026

When is vaccine prioritization worth optimizing?

Optimizing vaccine prioritization is often treated as the default policy response when vaccine supply is limited. Yet optimized prioritization carries administrative, ethical and communication costs, motivating an upstream question: whether differences among vaccine allocations can alter epidemic outcomes enough to make optimization epidemiologically necessary. We show that optimization is not always worth pursuing: in some regimes, vaccination markedly reduces epidemic burden, but many feasible allocation rules perform almost equally well, making the necessity of optimization low. We quantify this necessity as the range of epidemic outcomes generated by different allocations under fixed supply and show that it is governed by competition between vaccinating high-contact groups to slow transmission and vaccinating groups that benefit most directly: necessity is low when these protection routes are balanced and high when one dominates. Increasing transmission intensity changes this balance and drives a transition in the optimal allocation from transmission-focused prioritization toward direct protection. Different prevention objectives exhibit distinct transition thresholds, creating regimes in which optimizing one objective substantially compromises another, thereby revealing when the choice of prevention target matters most. This framework reframes vaccine prioritization as a prior decision problem, identifying when optimization is warranted, when simpler rules suffice, and when prevention goals conflict.

physics.bio-ph↗

arXiv2026

Data-driven inference of brain dynamical states from the r-spectrum of correlation matrices

We present a data-driven framework to characterize large-scale brain dynamical states directly from correlation matrices at the single-subject level. By treating correlation thresholding as a percolation-like probe of connectivity, the approach tracks multiple cluster- and network-level observables and identifies a characteristic percolation threshold, rc, at which these signatures converge. We use $r_c$ as an operational and physically interpretable descriptor of large-scale brain dynamical state. Applied to resting-state fMRI data from a large cohort of healthy individuals (N = 996), the method yields stable, subject-specific estimates that covary systematically with established dynamical indicators such as temporal autocorrelations. Numerical simulations of a whole-brain model with a known critical regime further show that $r_c$ tracks changes in collective dynamics under controlled variations of excitability. By replacing arbitrary threshold selection with a criterion intrinsic to correlation structure, the r-spectra provides a physically grounded approach for comparing brain dynamical states across individuals.

q-bio.NC↗

arXiv2025

Optimal Griffiths Phase in Heterogeneous Human Brain Networks: Brain Criticality Embracing Stability and Flexibility across Individuals

A prominent hypothesis in neuroscience proposes that brains achieve optimal performance by operating near a critical point. However, this framework, which often assumes a universal critical point, fails to account for the extensive individual variability observed in neural dynamics and cognitive functions. These variabilities are not noise but rather an inherent manifestation of a fundamental systems-biology principle: the necessary trade-off between robustness and flexibility in human populations. Here, we propose that the Griffiths phase (GP), an extended critical regime synergically induced by two kinds of heterogeneities in brain network region and connectivity, offers a unified framework for brain criticality that better reconciles robustness and flexibility and accounts for individual variability. Using Human Connectome Project data and whole-brain modeling, we demonstrated that the synergic interplay between structural network modularity and regional heterogeneity in local excitability yields biologically viable GP featured with widely extended global excitability ranges, with an embedded optimal point that balances global/local information transmission. Crucially, an individua's position within the GP gives rise to unique global network dynamics, which in turn confer a distinctive cognitive profile via flexible configuration of functional connectivity for segregation, integration, and balance between them. These results establish GP as an evolved adaptive mechanism resolving the robustness-flexibility trade-off, fulfilling diverse cognitive demands through individualized criticality landscapes, providing a new framework of brain criticality.

q-bio.NC↗

arXiv2025

Dynamic Vaccine Prioritization via Non-Markovian Final-state Optimization

Effective vaccine prioritization is critical for epidemic control, yet real outbreaks exhibit memory effects that inflate state space and make long-term prediction and optimization challenging. As a result, many strategies are tuned to short-term objectives and overlook how vaccinating certain individuals indirectly protects others. We develop a general age-stratified non-Markovian epidemic model that captures memory dynamics and accommodates diverse epidemic models within one framework via state aggregation. Building on this, we map non-Markovian final states to an equivalent Markovian representation, enabling real-time fast direct prediction of long-term epidemic outcomes under vaccination. Leveraging this mapping, we design a dynamic prioritization strategy that continually allocates doses to minimize the predicted long-term final epidemic burden, explicitly balancing indirect transmission blocking with the direct protection of important groups and outperforming static policies and those short-term heuristics that target only immediate direct effects. We further uncover the underlying mechanism that drives shifts in vaccine prioritization as the epidemic progresses and coverage accumulates, underscoring the importance of adaptive allocations. This study renders long-term prediction tractable in systems with memory and provides actionable guidance for optimal vaccine deployment.

physics.bio-ph↗

arXiv2025

Simultaneously Determining Regional Heterogeneity and Connection Directionality from Neural Activity and Symmetric Connection

The spatiotemporal patterns of neural dynamics are jointly shaped by directed structural interactions and heterogeneous intrinsic features of the neural components. Despite well-developed methods for estimating directionality in network connections from network of homogeneous nodes, how local heterogeneity impacts on directionality estimation remains poorly understood. In particular, the role of excitatory-inhibitory interactions in shaping network directionality and how these interactions should be incorporated into reconstruction frameworks remain largely unexplored. Here, we present a novel reconstruction framework that simultaneously estimates effective heterogeneity across network nodes and asymmetric network connections from neural activity and symmetric connection, both are assessible in experimental data, validated using macaque cortical connectivity data and several circuit models. We found that the estimated local heterogeneity remains consistent across various forms of parameterized local circuit heterogeneity. Furthermore, we demonstrated and quantified how hidden local inhibitory populations only modify within-region connection strengths, elucidating the functional equivalence between dynamics of excitatory-inhibitory networks and purely observing excitatory networks when estimating effective heterogeneity and asymmetry. Finally, we demonstrated the sampling interval effect in estimating network interactions with respect to the sampling resolution. Together, our results not only provide a unified framework for evaluating relative functional contributions of local heterogeneity and asymmetry to overall system dynamics but also reveal the fundamental limitations and scaling principles in reconstructing neural circuit connectivity from experimental observations.

q-bio.NC↗

arXiv2025

Representational drift under spontaneous activity -- self-organized criticality enhances representational reliability

Neural systems face the challenge of maintaining reliable representations amid variations from plasticity and spontaneous activity. In particular, the spontaneous dynamics in neuronal circuit is known to operate near a highly variable critical state, which intuitively contrasts with the requirement of reliable representation. It is intriguing to understand how reliable representation could be maintained or even enhanced by critical spontaneous states. We firstly examined the co-existence of the scale-free avalanche in the spontaneous activity of mouse visual cortex with restricted representational geometry manifesting representational reliability amid the representational drift with respect to the visual stimulus. To explore how critical spontaneous state influences the neural representation, we built an excitation-inhibition network with homeostatic plasticity, which self-organizes to the critical spontaneous state. This model successfully reproduced both representational drift and restricted representational geometry observed experimentally, in contrast with randomly shuffled plasticity which causes accumulated drift of representational geometry. We further showed that the self-organized critical state enhances the cross-session low-dimensional representation, comparing to the non-critical state, by restricting the synapse weight into a low variation space. Our findings suggest that spontaneous self-organized criticality serves not only as a ubiquitous property of neural systems but also as a functional mechanism for maintaining reliable information representation under continuously changing networks, providing a potential explanation how the brain maintains consistent perception and behavior despite ongoing synaptic rewiring.

q-bio.NC↗

arXiv2025

Overlooked weak structural connections support human cognition under nonlinear connectome scaling

Human cognition depends on large scale communication constrained by white matter architecture. Although weak connections are abundant in mammalian connectomes, they have long been treated as noise and downweighted because of tractography uncertainty in the human brain, and their relevance to human cognition and large scale functional organization remains unresolved. Across multiple datasets and tractography pipelines, we show that, when tractography derived connectivity weights are interpreted through a nonlinear weighting framework, weak connections make measurable contributions to cognitive prediction, functional connectivity simulation, and structure-function coupling. These effects are selective: nonlinear weighting improves the prediction of general cognitive ability and memory more than that of crystallized intelligence or processing speed, consistent with the notion that weak connections preferentially expand the modal repertoire of brain networks to enhance both large scale integration and fine grained segregation, thereby supporting the functional balance essential for diverse cognitive abilities. Importantly, these effects are replicated in a reliability aware connectome generated by integrating two post tractography filtering methods, in which preserving weak links consistently outperforms conventional thresholding strategies. Finally, we show that weak connections contain functionally informative subsets organized along systems level and transcriptomic gradients. In particular, a specific class of weak connections, predominantly linking visual and motor systems with limbic regions and characterized by negative gene coexpression, exerts a disproportionately large influence on brain function.

q-bio.NC↗

arXiv2025

Beyond variance: sensitivity-based dimensions in brain networks underlie individual differences in cognitive ability

Explaining individual differences in cognitive abilities requires both identifying brain parameters that vary across individuals and understanding how brain networks are recruited for specific tasks. Typically, task performance relies on the integration and segregation of functional subnetworks, often captured by parameters like regional excitability and connectivity. Yet, the high dimensionality of these parameters hinders pinpointing their functional relevance. Here, we apply stiff-sloppy analysis to human brain data, revealing that certain subtle parameter combinations ("stiff dimensions") powerfully influence neural activity during task processing, whereas others ("sloppy dimensions") vary more extensively but exert minimal impact. Using a pairwise maximum entropy model of task fMRI, we show that even small deviations in stiff dimensions-derived through Fisher Information Matrix analysis-govern the dynamic interplay of segregation and integration between the default mode network (DMN) and a working memory network (WMN). Crucially, separating a 0-back task (vigilant attention) from a 2-back task (working memory updating) uncovers partially distinct stiff dimensions predicting performance in each condition, along with a global DMN-WMN segregation shared across both tasks. Altogether, stiff-sloppy analysis challenges the conventional focus on large parameter variability by highlighting these subtle yet functionally decisive parameter combinations.

q-bio.NC↗

arXiv2023

Validity of Markovian modeling for transient memory-dependent epidemic dynamics

The initial transient phase of an emerging epidemic is of critical importance for data-driven model building, model-based prediction of the epidemic trend, and articulation of control/prevention strategies. In principle, quantitative models for real-world epidemics need to be memory-dependent or non-Markovian, but this presents difficulties for data collection, parameter estimation, computation and analyses. In contrast, the difficulties do not arise in the traditional Markovian models. To uncover the conditions under which Markovian and non-Markovian models are equivalent for transient epidemic dynamics is outstanding and of significant current interest. We develop a comprehensive computational and analytic framework to establish that the transient-state equivalence holds when the average generation time matches and average removal time, resulting in minimal Markovian estimation errors in the basic reproduction number, epidemic forecasting, and evaluation of control strategy. Strikingly, the errors depend on the generation-to-removal time ratio but not on the specific values and distributions of these times, and this universality will further facilitate estimation rectification. Overall, our study provides a general criterion for modeling memory-dependent processes using the Markovian frameworks.

physics.soc-ph↗

arXiv2022

Mapping effective connectivity by virtually perturbing a surrogate brain

Effective connectivity (EC), indicative of the causal interactions between brain regions, is fundamental to understanding information processing in the brain. Traditional approaches, which infer EC from neural responses to stimulations, are not suited for mapping whole-brain EC in humans due to being invasive and having limited spatial coverage of stimulations. To address this gap, we present Neural Perturbational Inference (NPI), a data-driven framework designed to map EC across the entire brain. NPI employs an artificial neural network trained to learn large-scale neural dynamics as a computational surrogate of the brain. NPI maps EC by perturbing each region of the surrogate brain and observing the resulting responses in all other regions. NPI captures the directionality, strength, and excitatory/inhibitory properties of brain-wide EC. Our validation of NPI, using models having ground-truth EC, shows its superiority over Granger causality and dynamic causal modeling. Applying NPI to resting-state fMRI data from diverse datasets reveals consistent and structurally supported EC. Further validation using a cortico-cortical evoked potentials dataset reveals a significant correlation between NPI-inferred EC and real stimulation propagation pathways. By transitioning from correlational to causal understandings of brain functionality, NPI marks a stride in decoding the brain's functional architecture and facilitating both neuroscience research and clinical applications.

q-bio.NC↗

arXiv2022

Lifespan associations of resting-state brain functional networks with ADHD symptoms

Attention-deficit/hyperactivity disorder (ADHD) is increasingly being diagnosed in both children and adults, but the neural mechanisms that underlie its distinct symptoms and whether children and adults share the same mechanism remain poorly understood. Here, we used a nested-spectral partition (NSP) approach to study the resting-state brain functional networks of ADHD patients (n=97) and healthy controls (HCs, n=97) across the lifespan (7-50 years). Compared to the linear lifespan associations of brain functional segregation and integration with age in HCs, ADHD patients have a quadratic association in the whole brain and in most functional systems, whereas the limbic system dominantly affected by ADHD has a linear association. Furthermore, the limbic system better predicts hyperactivity, and the salient attention system better predicts inattention. These predictions are shared in children and adults with ADHD. Our findings reveal a lifespan association of brain networks with ADHD symptoms and provide potential shared neural bases of distinct ADHD symptoms in children and adults.

q-bio.NC↗

arXiv2021

What do neuroanatomical networks reveal about the ontology of human cognitive abilities?

Over the last decades, cognitive psychology has come to fair consensus about the ontological structure of human intelligence. However, it remains an open question, whether anatomical properties of the brain support the same ontology. The present study explored the ontological structure derived from neuroanatomical networks associated with performance in 15 cognitive tasks indicating various abilities. Results suggest that the brain-derived (neurometric) ontology is partly consistent with the cognitive performance-derived (psychometric) ontology. However, there are interpretable and complementary differences as well. Moreover, the cortical areas associated with different inferred abilities are segregated, with little or no overlap. Nevertheless, these spatially segregated cortical areas are integrated via denser white matter structural connections as compared with the general brain connectome. The integration of ability-related cortical networks constitutes a neural counterpart to the psychometric construct of general intelligence, while the consistency and differences between psychometric and neurometric ontologies represent crucial pieces of knowledge for theory building, clinical diagnostics and treatment.

q-bio.NC↗

arXiv2021

Segregation, integration and balance of large-scale resting brain networks configure different cognitive abilities

Diverse cognitive processes set different demands on locally segregated and globally integrated brain activity. However, it remains unclear how resting brains configure their functional organization to balance the demands on network segregation and integration to best serve cognition. Here, we use an eigenmode-based approach to identify hierarchical modules in functional brain networks, and quantify the functional balance between network segregation and integration. In a large sample of healthy young adults (n=991), we combine the whole-brain resting state functional magnetic resonance imaging (fMRI) data with a mean-filed model on the structural network derived from diffusion tensor imaging and demonstrate that resting brain networks are on average close to a balanced state. This state allows for a balanced time dwelling at segregated and integrated configurations, and highly flexible switching between them. Furthermore, we employ structural equation modelling to estimate general and domain-specific cognitive phenotypes from nine tasks, and demonstrate that network segregation, integration and their balance in resting brains predict individual differences in diverse cognitive phenotypes. More specifically, stronger integration is associated with better general cognitive ability, stronger segregation fosters crystallized intelligence and processing speed, and individual's tendency towards balance supports better memory. Our findings provide a comprehensive and deep understanding of the brain's functioning principles in supporting diverse functional demands and cognitive abilities, and advance modern network neuroscience theories of human cognition.

q-bio.NC↗

arXiv2021

Less is More: Wiring-Economical Modular Networks Support Self-Sustained Firing-Economical Neural Avalanches for Efficient Processing

Brain network is remarkably cost-efficient while the fundamental physical and dynamical mechanisms underlying its economical optimization in network structure and activity are not clear. Here we study intricate cost-efficient interplay between structure and dynamics in biologically plausible spatial modular neuronal network models. We find that critical avalanche states from excitation-inhibition balance, under modular network topology with less wiring cost, can also achieve less costs in firing, but with strongly enhanced response sensitivity to stimuli. We derived mean-field equations that govern the macroscopic network dynamics through a novel approximate theory. The mechanism of low firing cost and stronger response in the form of critical avalanche is explained as a proximity to a Hopf bifurcation of the modules when increasing their connection density. Our work reveals the generic mechanism underlying the cost-efficient modular organization and critical dynamics widely observed in neural systems, providing insights to brain-inspired efficient computational designs.

physics.bio-ph↗

arXiv2020

Understanding the computational difficulty of a binary-weight perceptron and the advantage of input sparseness

Limited precision of synaptic weights is a key aspect of both biological and hardware implementation of neural networks. To assign low-precise weights during learning is a non-trivial task, but may benefit from representing to-be-learned items using sparse code. However, the computational difficulty resulting from low weight precision and the advantage of sparse coding remain not fully understood. Here, we study a perceptron model, which associates binary (0 or 1) input patterns with outputs using binary (0 or 1) weights, modeling a single neuron receiving excitatory inputs. We considered a decimation process, where every time step, marginal probabilities of unfixed weights were evaluated, then the most polarized weight was fixed at its preferred value. We showed that decimation is a process approaching the dense solution region in weight configuration space. In two efficient algorithms (SBPI and rBP) for solving binary-weight perceptron, most time steps are spent on determining values of the weights fixed late in decimation. This algorithmic difficult point may result from strong cross-correlation between late-decimation-fixed weights in the solution subspace where early-decimation-fixed weights take their fixed values, and is related to solution condensation in this subspace during decimation. Input sparseness reduces the time steps that SBPI and rBP need to find solutions, by reducing time steps used to assign values to late-decimation-fixed weights, due to the reduction of cross-correlation between late-decimation-fixed weights. Our work suggests that the computational difficulty of constraint satisfaction problem originates from the subspace of late-decimation-fixed variables. Our work highlights the heterogeneity of learning dynamics of weights, which may help understand axonal pruning in brain development, and inspire more efficient algorithms to train artificial neural networks.

cond-mat.dis-nn↗

arXiv2020

Understanding the computation of time using neural network models

To maximize future rewards in this ever-changing world, animals must be able to discover the temporal structure of stimuli and then anticipate or act correctly at the right time. How the animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory? How temporal information is processed concurrently with spatial information and decision making? Why there are strong neuronal temporal signals in tasks in which temporal information is not required? A systematic understanding of the underlying neural mechanisms is still lacking. Here, we addressed these problems using supervised training of recurrent neural network models. We revealed that neural networks perceive elapsed time through state evolution along stereotypical trajectory, maintain time intervals in working memory in the monotonic increase or decrease of the firing rates of interval-tuned neurons, and compare or produce time intervals by scaling state evolution speed. Temporal and non-temporal information are coded in subspaces orthogonal with each other, and the state trajectories with time at different non-temporal information are quasi-parallel and isomorphic. Such coding geometry facilitates the decoding generalizability of temporal and non-temporal information across each other. The network structure exhibits multiple feedforward sequences that mutually excite or inhibit depending on whether their preferences of non-temporal information are similar or not. We identified four factors that facilitate strong temporal signals in non-timing tasks, including the anticipation of coming events. Our work discloses fundamental computational principles of temporal processing, and is supported by and gives predictions to a number of experimental phenomena.

q-bio.NC↗

arXiv2020

Hopf Bifurcation in Mean Field Explains Critical Avalanches in Excitation-Inhibition Balanced Neuronal Networks: A Mechanism for Multiscale Variability

Cortical neural circuits display highly irregular spiking in individual neurons but variably sized collective firing, oscillations and critical avalanches at the population level, all of which have functional importance for information processing. Theoretically, the balance of excitation and inhibition inputs is thought to account for spiking irregularity and critical avalanches may originate from an underlying phase transition. However, the theoretical reconciliation of these multilevel dynamic aspects in neural circuits remains an open question. Herein, we study excitation-inhibition (E-I) balanced neuronal network with biologically realistic synaptic kinetics. It can maintain irregular spiking dynamics with different levels of synchrony and critical avalanches emerge near the synchronous transition point. We propose a novel semi-analytical mean-field theory to derive the field equations governing the network macroscopic dynamics. It reveals that the E-I balanced state of the network manifesting irregular individual spiking is characterized by a macroscopic stable state, which can be either a fixed point or a periodic motion and the transition is predicted by a Hopf bifurcation in the macroscopic field. Furthermore, by analyzing public data, we find the coexistence of irregular spiking and critical avalanches in the spontaneous spiking activities of mouse cortical slice in vitro, indicating the universality of the observed phenomena. Our theory unveils the mechanism that permits complex neural activities in different spatiotemporal scales to coexist and elucidates a possible origin of the criticality of neural systems. It also provides a novel tool for analyzing the macroscopic dynamics of E-I balanced networks and its relationship to the microscopic counterparts, which can be useful for large-scale modeling and computation of cortical dynamics.

cond-mat.dis-nn↗

arXiv2016

Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs

The large-scale structural ingredients of the brain and neural connectomes have been identified in recent years. These are, similar to the features found in many other real networks: the arrangement of brain regions into modules and the presence of highly connected regions (hubs) forming rich-clubs. Here, we examine how modules and hubs shape the collective dynamics on networks and we find that both ingredients lead to the emergence of complex dynamics. Comparing the connectomes of C. elegans, cats, macaques and humans to surrogate networks in which either modules or hubs are destroyed, we find that functional complexity always decreases in the perturbed networks. A comparison between simulated and empirically obtained resting-state functional connectivity indicates that the human brain, at rest, lies in a dynamical state that reflects the largest complexity its anatomical connectome can host. Last, we generalise the topology of neural connectomes into a new hierarchical network model that successfully combines modular organisation with rich-club forming hubs. This is achieved by centralising the cross-modular connections through a preferential attachment rule. Our network model hosts more complex dynamics than other hierarchical models widely used as benchmarks.

q-bio.NC↗