Biology subjects

Pang, J. C.

Publications and source records attributed to Pang, J. C..

At least 19 recordsLinked to original sources

bioRxiv2026

From Geometry to Hierarchy: Charting the Dominant Modes of Human Cortical Function Through Development

Cortical function in the human cortex is spatially patterned along a macroscale information processing hierarchy. This hierarchy tracks a sensory-association axis anchored at one end by unimodal sensory regions and at the other by transmodal association areas. Biophysical models of brain dynamics suggest that this axis should align with the dominant, resonant modes of cortical anatomy and geometry, but they do not. Instead, the dominant anatomical modes correspond to simple spatial gradients extending along rostro-caudal, medio-lateral, and dorso-ventral axes. How does this divergence between anatomical and functional modes of the cortex arise? Here, using four independent functional magnetic resonance imaging datasets spanning infancy to young adulthood (n=1,512, aged 0-21 years), we show that this spatial divergence is attributable to the maturation of long-range cortico-cortical inter-regional functional coupling. Specifically, we find that the dominant functional mode of the human cortex during the first 3 to 4 years of life closely resembles the rostro-caudal mode of cortical geometry, then shifts to a rudimentary sensory-association-like mode between 4 and 6 years of age and ultimately matures into a prototypical sensory-association mode by age 13. In infancy and early childhood, a linear model predicting the topography of the dominant functional mode from modes of cortical geometry substantially outperforms a model relying on the adult sensory-association mode, whereas the reverse is true in later stages of development. Furthermore, targeted removal of long-range connections causes the adult functional mode to regress from a sensory-association topography to an infant-like rostro-caudal mode. Our findings indicate that the maturation of long-range cortico-cortical coupling supports the gradual emergence of hierarchical modes of cortical function from an organization initially dominated by geometric constraints.

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bioRxiv2026

Cortical geometry constrains the unimodal anchors of sensory integration

The human cerebral cortex is organized along a unimodal-to-transmodal hierarchy, which provides a putative substrate for the integration of sensory signals from primary cortical fields with information from other modalities. Diverse structural and functional properties of the cortex, including myelination, gene expression, neurodevelopmental timing, and inter-regional functional connectivity are patterned along this hierarchy. One exception to this hierarchy are the dominant eigenmodes of cortical geometry, which are instead patterned along rostrocaudal, mediolateral, and dorsoventral axes, each anchored by a primary sensory area at one extreme. Recent work has reconstructed the unimodal-to-transmodal hierarchy by integrating seed-based functional connectivity from three primary sensory areas, suggesting hierarchical organization may be driven by converging sensory input. Although geometric eigenmodes do not directly express the unimodal-to-transmodal hierarchy, they may encode modality-specific sensory organization originating from primary areas. Using MRI data from the Human Connectome Project, we tested whether geometric eigenmodes encode sensory function by modelling multisensory integration directly from cortical geometry. Specifically, we substituted functional connectivity maps from each primary sensory area with the rostrocaudal, mediolateral, and dorsoventral geometric eigenmodes, following a previously validated sensory integration mapping framework. Each geometric eigenmode corresponded to a distinct sensory domain (rostrocaudal-visual: |r| = 0.516; mediolateral-somatosensory: |r| = 0.551; dorsoventral-auditory: |r| = 0.342). Together, the three geometric eigenmodes created a mapping space that differentiated between unimodal brain regions with similar accuracy as fMRI-based models ({delta} = 64.74{degrees}; p < .001); however, differences between geometric and functional maps were largest within the transmodal association cortex. Reproducing the full unimodal-to-transmodal hierarchy required additional higher-frequency geometric eigenmodes (15 eigenmodes: r2 = 0.64). These findings suggest that the unimodal anchors of sensory integration are shaped by cortical geometry, with low-frequency geometric eigenmodes providing a structural basis for sensory organization, while the transmodal apex requires additional structural information to emerge.

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bioRxiv2026

Regional heterogeneity shapes macroscopic wave dynamics of the human and non-human primate cortex

Growing evidence indicates that macroscopic cortical activity is dominated by propagating waves of excitation. However, many computational models of such wave dynamics assume that the cortex is a spatially homogeneous medium, ignoring the rich regional variations in cellular, molecular, and physiological properties that are known to shape how brain activity evolves through both space and time. Here, we develop a general framework grounded in neural field theory to model how regional heterogeneities in diverse cortical properties shape spatiotemporal brain activity evolving on cortical surface meshes. This enables high resolution, vertex-level simulations without requiring predefined parcellations. The model requires only a standard mesh model of the cortical manifold and a spatial heterogeneity map, providing a biologically grounded and computationally efficient framework that can be generalised to human and non-human species. Using multiple cellular, molecular, and physiological maps in humans--and analogous maps in macaques and marmosets--we show that our model can consistently recapitulate known relationships between regional heterogeneities and variations in cortical wave speed. In particular, we find that models parameterised by heterogeneities in intracortical myelin and excitation-inhibition balance yield the largest performance improvements relative to spatially homogeneous models. Our results identify a key role for regional variations in myelin, receptor, and genetic architecture in shaping the spatial patterning of macroscale cortex-wide activity that is conserved across primate species with diverse cortical geometries.

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bioRxiv2025

Geometric constraints on the architecture of mammalian cortical connectomes

The intricate network of axonal fibres that forms the mammalian cortical connectome has a complex topology, being organized in a way that is neither completely regular nor random, as well as a characteristic topography, in which specific anatomical locations are imbued with distinctive connectivity profiles. The mechanisms that give rise to such properties remain a mystery. Here, we formulate a simple analytic model derived from neural field theory that prioritizes physical constraints on connectome architecture by assuming that connectivity is preferentially concentrated between pairs of cortical locations that facilitate the excitation of resonant geometric modes of the cortex. We show that the model outperforms existing approaches in reproducing multiple topological and topographical properties of cortical connectomes mapped at spatial scales spanning orders of magnitude in humans, chimpanzees, macaques, marmosets, and mice, as mapped with either non-invasive diffusion magnetic resonance imaging or invasive viral tract-tracing. Our findings thus point to a fundamental role of geometry in shaping the multiscale architecture of cortical connectomes that has been conserved across 90 million years of evolution.

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bioRxiv2025

A unified model of cortico-hippocampal interactions through neural field theory

Numerous physical systems evolve on curved manifolds whose geometry constrains their dynamics. The human brain provides a canonical example, with large-scale neural activity evolving on distinct anatomical surfaces such as the cortex and hippocampus. Within each structure, intrinsic feedback loops and manifold geometry shape characteristic neural rhythms. However, core cognitive functions of the brain arise from reciprocal interactions between these spatially distinct neural structures. Here, we introduce a general framework for geometry-constrained coupling between spatially extended dynamical systems evolving on separate manifolds. Using quasi-conformal mapping, we construct neighbourhood-preserving interactions that enable continuous neural activity on distinct geometries to interact while preserving local neighbourhood structure. Applying this framework to neural activity evolving on cortical and hippocampal surfaces, we show that increasing inter-manifold coupling reorganises system dynamics, producing frequency shifts, mode interactions, and coupling-driven instabilities consistent with critical transitions. These effects reproduce key features of large-scale brain activity, including topographically organised synchronisation between cortex and hippocampus during healthy cognition and critical transitions to seizure-like spectral dynamics. These results identify inter-manifold coupling as a core influence on dynamics in the human brain and highlight the broader role of geometry-constrained coupling on emergent dynamics in complex spatially extended systems.

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bioRxiv2025

The Motion Sensitivity and Predictive Utility of Different Estimates of Inter-regional Functional Coupling in Resting-state Functional MRI.

Numerous methods exist for quantifying statistical dependencies, termed functional coupling (FC), between regional brain activity recorded with resting-state functional magnetic resonance imaging (rs-fMRI). However, their efficacy in mitigating the effects of known sources of noise, such as those induced by participant head motion, and in augmenting effect sizes for brain-wide association studies (BWAS), remains unclear. Here we compared 10 different measures of FC, including correlations, partial correlations, coherence, mutual information, and partial information decomposition, and one measure of effective connectivity (EC; regression dynamic causal modelling), across two independent datasets comprising a total of 1,797 participants (867 males). Each method was evaluated for its ability to mitigate motion-related confounds in FC/EC estimates and for its utility in predicting 94 behavioural measures, as assessed via cross-validated kernel ridge regression. Our analyses showed that EC was most resistant to motion artifacts but had the weakest behavioral predictions. Conversely, traditional correlation-based methods showed the highest sensitivity to motion, but offered the strongest behavioral prediction across most domains and datasets. Nonetheless, relative differences in predictive accuracies were small, indicating that the use of different FC or EC metrics in rs-fMRI does not significantly impact BWAS effect sizes.

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bioRxiv2025

Spectral imprint of structural embedding in effective connectivity

Neural fluctuations exhibit rich spectral profiles that reflects both local dynamics and structural (or anatomical) embedding. Yet, standard models of resting-state effective connectivity neglect structural embedding and assume uniformity in the timescales of regions endogenous fluctuations. We introduce a chromatic dynamic causal model (DCM) in which structural valency (or degree) modulates the spectral color of endogenous fluctuations. Specifically, we assume a linear mapping between regional structural valency and the spectral exponent of scale-free auto-spectra. Simulations show this mapping can emerge as a generic consequence of structural embedding under minimal coupling in a non-equilibrium regime. We show chromatic DCM reliably recovers ground-truth parameters across network sizes and noise conditions, outperforming standard spectral DCM. Applied to empirical data, chromatic DCM reveals that valency-exponent mappings vary across a cortical hierarchy, and that its parameters are conserved across a homologous network in humans, macaques, marmosets, and mice. These findings advance a generative account of structure-function coupling and expand the repertoire of biophysical mechanisms available for inference in effective connectivity modeling.

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bioRxiv2025

Geometric influences on the regional organization of the mammalian brain

The mammalian brain comprises anatomically and functionally distinct regions, yet the principles governing their organization across scales remain unclear. Efforts to map regional boundaries use expert judgement or data-driven clustering of functional, connectional, and/or architectonic properties, but these approaches are often descriptive, have limited generalizability, and do not elucidate generative mechanisms. Here, we develop a geometrically constrained framework for the hierarchical, multiscale regional organization of neocortical and non-neocortical structures. The framework yields parcels at any resolution with high internal homogeneity across hundreds of anatomical, functional, cellular, and molecular properties in humans, macaques, marmosets, and mice, and it generalizes to understudied mammalian species. Finally, we demonstrate that the framework captures the essence of a reaction-diffusion mechanism in which brain geometry shapes the spatial expression of putative patterning molecules that establish a blueprint of regional identity during development. Our findings point to a conserved, universal influence of geometry on mammalian brain organization.

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bioRxiv2024

Multiscale Quantification of Hemispheric Asymmetry in Cortical Maps Using Geometric Eigenmodes

Hemispheric asymmetry is a universal property of brain organization with wide implications into brain function and structure, and diseases. This study presents a laterality index for characterizing hemispheric asymmetries that underlie cortical maps using geometric eigenmodes derived from human cortical surfaces.We develop a generalized design to quantify asymmetries across various cortical spatial scales. While the design is individual-specific, we implement normalization steps to enable unbiased comparisons across individuals. As a proof of concept, we validated the method on cortical maps of 545 subjects across two datasets, using fMRI maps of healthy individuals and tau-PET maps of patients across the Alzheimers disease continuum. Our results reveal that cortical regions in different canonical functional networks have connectivity patterns that entail different degrees of hemispheric asymmetry. Moreover, aggregates of the pathological tau protein manifest subtle asymmetries at varying spatial scales along the disease continuum.

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bioRxiv2024

On reconstruction of cortical functional maps using subject-specific geometric and connectome eigenmodes

Understanding the interplay between human brain structure and function is crucial to discern neural dynamics. This study explores the relation between brain structure and macroscale functional activity using subject-specific structural connectome eigenmodes, complementing prior work that focused on group-level models and geometry. Leveraging data from the Human Connectome Project, we assess accuracy in reconstructing various functional MRI-based cortical maps using individualised eigenmodes, specifically, across a range of connectome construction parameters. Our results show only minor differences in performance between surface geometric eigenmodes, a local neighborhood graph, a highly smoothed null model, and individual and group-level connectomes at modest smoothing and density levels. Furthermore, our results suggest that spatially smooth eigenmodes best explain functional data. The absence of improvement of individual connectomes and surface geometry over smoothed null models calls for further methodological innovation to better quantify and understand the degree to which brain structure constrains brain function.

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bioRxiv2024

Reply to: Eigenmodes of the brain: revisiting connectomics and geometry

In Pang, Aquino et al. (2023)1, we presented multiple lines of evidence to indicate that brain geometry plays a previously under-appreciated role in shaping dynamics. Mansour et al. raise concerns about one specific analysis, in which we showed that eigenmodes derived from the geometry of the human cortex can reconstruct diverse activity maps generated with functional magnetic resonance imaging (fMRI) better than eigenmodes derived from connectomes estimated with diffusion MRI (dMRI). Here, we address their concerns and show how our findings and conclusions remain valid.

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bioRxiv2024

Geometric eigenmode brain fingerprinting and its longitudinal associations with adolescent mental health and wellbeing

Background Brain fingerprinting research posits that individual uniqueness can be identified by structural and functional features that may also be linked to mental health outcomes. Global structural features of the brain can be succinctly and directly captured from magnetic resonance imaging (MRI) via the eigenmodes of the cortical surface - known as geometric eigenmodes. This research investigates how the uniqueness of geometric eigenmodes changes across adolescence and their longitudinal relation to mental health and wellbeing. MethodsThe current study utilised n=613 MRI, self-report and demographic datasets from N=116 community-recruited adolescents enrolled in the Longitudinal Adolescent Brain Study (LABS), between the ages of 12-17 years. MPRAGE scans at each participants visit were used to derive 225 left-hemisphere geometric eigenmodes. Eigenmodes were clustered into 14 eigengroups and developmental trajectories of their uniqueness and longitudinal associations with mental wellbeing and psychological distress were examined. ResultsAll eigengroups become significantly more unique longitudinally, and higher mode (shorter wavelength) eigengroups were more unique than lower mode groups in adolescence. Less uniqueness in eigengroup 6 was significantly associated with higher psychological distress and lower mental wellbeing at concurrent and future timepoints. ConclusionGeometric eigengroup brain fingerprinting offers a novel way to examine neurodevelopment. This study provides evidence that eigengroups have distinct trajectories from adolescence to adulthood, consistent with other imaging studies demonstrating increasing uniqueness in this period. Importantly, they are associated with mental health state and thus may represent neurobiological markers for mental illness onset, building on previous LABS research demonstrating that the functional uniqueness of the cognitive control network predicts psychological distress four months later.

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bioRxiv2024

Generation of surrogate brain maps preserving spatial autocorrelation through random rotation of geometric eigenmodes

The brain expresses activity in complex spatiotemporal patterns, reflected in the influence of spatially distributed cytoarchitectural, biochemical, and genetic properties. The correspondence between these multimodal "brain maps" may reflect underlying causal pathways and is hence a topic of substantial interest. However, these maps possess intrinsic smoothness (spatial autocorrelation, SA) which can inflate spurious cross-correlations, leading to false positive associations. Identifying true associations requires knowledge about the distribution of correlations that arise by chance in the presence of SA. This null distribution can be generated from an ensemble of surrogate brain maps that preserve the intrinsic SA but break the correlations between maps. The present work introduces the "eigenstrapping" method, which uses a spectral decomposition of cortical and subcortical surfaces in terms of geometric eigenmodes, and then randomly rotating these modes to produce SA-preserving surrogate brain maps. It is shown that these surrogates appropriately represent the null distribution of chance pairwise correlations, with similar or superior false positive control to current state-of-the-art procedures. Eigenstrapping is fast, eschews the need for parametric assumptions about the nature of a maps SA, and works with maps defined on smooth surfaces with or without a boundary. Moreover, it generalizes to broader classes of null models than existing techniques, offering a unified approach for inference on cortical and subcortical maps, spatiotemporal processes, and complex patterns possessing higher-order correlations.

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bioRxiv2023

Reply to: Commentary on Pang et al. (2023) Nature

In Pang et al. (2023)1, we identified a close link between the geometry and function of the human brain by showing that: (1) eigenmodes derived from cortical geometry parsimoniously reconstruct activity patterns recorded with functional magnetic resonance imaging (fMRI); (2) task-evoked cortical activity results from excitations of brain-wide modes with long wavelengths; (3) wave dynamics, constrained by geometry and distance-dependent connectivity, can account for diverse aspects of spontaneous and evoked brain activity; and (4) geometry and function are strongly coupled in the subcortex. Faskowitz et al. (2023)2 raise concerns about the framing of our paper and the specificity of the eigenmode reconstructions in result (1). Here, we address these concerns and show how specificity is established by using appropriate benchmarks.

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bioRxiv2023

Mode-based morphometry: A multiscale approach to mapping human neuroanatomy

Voxel-based morphometry (VBM) and surface-based morphometry (SBM) are two widely used neuroimaging techniques for investigating brain anatomy. These techniques rely on statistical inferences at individual points (voxels or vertices), clusters of points, or a priori regions-of-interest. They are powerful tools for describing brain anatomy, but offer little insights into the generative processes that shape a particular set of findings. Moreover, they are restricted to a single spatial resolution scale, precluding the opportunity to distinguish anatomical variations that are expressed across multiple scales. Drawing on concepts from classical physics, here we develop an approach, called mode-based morphometry (MBM), that can describe any empirical map of anatomical variations in terms of the fundamental, resonant modes--eigenmodes--of brain anatomy, each tied to a specific spatial scale. Hence, MBM naturally yields a multiscale characterization of the empirical map, affording new opportunities for investigating the spatial frequency content of neuroanatomical variability. Using simulated and empirical data, we show that the validity and reliability of MBM are either comparable or superior to classical vertex-based SBM for capturing differences in cortical thickness maps between two experimental groups. Our approach thus offers a robust, accurate, and informative method for characterizing empirical maps of neuroanatomical variability that can be directly linked to a generative physical process.

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bioRxiv2022

Can hubs of the human connectome be identified consistently with diffusion MRI?

Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population (n=294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome; its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines ({rho}>0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different processing choices can influence connectome organization. Author SummaryThe increasing use of diffusion MRI for mapping white matter connectivity has been matched by a similar increase in the number of ways to process the diffusion data. Here, we assess how diffusion processing affects hubs across 1760 pipeline variations. Many processing pipelines do not show a high concentration of connectivity within hubs. When present, hub location and distribution vary based on processing choices. The choice of probabilistic or deterministic tractography has a major impact on hub location and strength. Finally, node strength in weighted networks can correlate highly with node size. Overall, our results illustrate the need for prudent decision-making when processing and interpreting diffusion MRI data. Code and data availabilityAll the data used in this study is openly available on Figshare at https://doi.org/10.26180/c.6352886.v1. Scripts to analyze these data are available on GitHub at https://github.com/BMHLab/DegreeVariability. Competing InterestsThe authors declare that they have no competing interests.

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bioRxiv2022

Geometric constraints on human brain function

The brains anatomy constrains its function, but precisely how remains unclear. Here, we show that human cortical and subcortical activity, measured with magnetic resonance imaging under spontaneous and diverse task-evoked conditions, can be parsimoniously understood as resulting from excitations of fundamental, resonant modes of the brains geometry (i.e., its shape) rather than modes from complex inter-regional connectivity, as classically assumed. We then use these modes to show that task-evoked activations across >10,000 brain maps are not confined to focal areas, as widely believed, but instead excite brain-wide modes with wavelengths spanning >60 mm. Finally, we confirm theoretical predictions that the close link between geometry and function is explained by a dominant role for wave-like dynamics, showing that such dynamics can reproduce numerous canonical spatiotemporal properties of spontaneous and evoked recordings. Our findings challenge prevailing views of brain function and identify a previously under-appreciated role of brain geometry that is predicted by a unifying and physically principled approach.

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bioRxiv2022

Evolutionary shaping of human brain dynamics

The human brain is distinct from those of other species in terms of size, organization, and connectivity. How do these structural evolutionary differences drive patterns of neural activity enabling brain function? Here, we combine brain imaging and biophysical modeling to show that the anatomical wiring of the human brain distinctly shapes neural dynamics. This shaping is characterized by a narrower distribution of dynamic ranges across brain regions compared with that of chimpanzees, our closest living primate relatives. We find that such a sharp dynamic range distribution supports faster integration between regions, particularly in transmodal systems. Conversely, a broad dynamic range distribution as seen in chimpanzees facilitates brain processes relying more on neural interactions within specialized local brain systems. These findings suggest that human brain dynamics have evolved to foster rapid associative processes in service of complex cognitive functions and behavior.

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