Biology subjects

Fulcher, B. D.

Publications and source records attributed to Fulcher, B. D..

At least 19 recordsLinked to original sources

bioRxiv2026

Heart rate dynamics embed a shared representation of individual brain organization and cognitive function

Idiosyncratic brain functional organization shapes intricate cardiac dynamics through central-peripheral autonomic interactions, yet a comprehensive mapping between multi-scale heart rate dynamics and whole-brain functional architecture remains lacking. Here, combining highly comparative time-series analysis with multi-modal neuroimaging and intracranial electrophysiology, we establish heart rate dynamics as a physiological fingerprint that maps onto whole-brain functional architecture. Partial least squares analysis revealed a generalizable latent axis linking reduced heart rate temporal complexity and elevated micro-scale predictability to heightened resting-state functional connectivity across default mode, salience, and sensorimotor networks. This covariance aligns spatially with serotonergic, noradrenergic, and cholinergic neuromodulatory gradients, persists across physiological confound controls and cross-session validations, derives support from human intracranial electrophysiological recordings, and extends to active cognitive states. Furthermore, brain-covarying heart rate signatures underpin the predictive capacity of heart rate dynamics for individual fluid and crystallized intelligence, demonstrating a shared representational substrate. Our findings demonstrate a robust neurovisceral coupling architecture, establishing well-characterized heart rate dynamics as a scalable, neurobiologically anchored window into human brain functional organization and cognitive traits.

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bioRxiv2026

Dynamic Routing on a Static Structure: Noradrenergic Gatingof Cortical Wave Propagation

Stimulus-evoked cortical responses range from tightly localized activations to spatially extended traveling waves, yet the mechanisms through which these distinct regimes are flexibly recruited remain unclear. Based on neuroanatomical and neuroimaging evidence, we hypothesize that the noradrenergic (NA) arousal system, originating in the locus coeruleus (LC), dynamically modulates cortical gain to shape spatiotemporal propagation under a fixed anatomical backbone. To test this hypothesis, we extend an empirically grounded corticothalamic neural field model to incorporate an LC which delivers modulatory (gain-changing) rather than driving inputs to cortex. We then examine evoked responses under phasic versus tonic NA modulation, with geometry given by distance-dependent connectivity plus additional long-range fast-conducting non-local projections (FNPs). Phasic LC activation produces spatiotemporally sustained traveling waves of cortical activity, whereas tonic activation yields temporally prolonged but spatially localized responses; without modulation, waves are weak and dissipative. Timing and spatial pattern of LC engagement gate wave-onset latency and impose anisotropy, restricting propagation to modulated territories and halting it across unmodulated gaps. In the presence of long-range FNPs, modulation enables rapid non-local activation of distant cortical regions and sharply lowers the gain required for long-distance routing. These results identify NA phasic gain control as a parsimonious mechanism for variability in evoked dynamics and for context-dependent privileging of specific communication pathways over a largely static connectome. Author summaryThe brain doesnt always react to a stimulus in the same way: sometimes the response is local, while at other times it sweeps across the cortex as a wave. We propose that this flexibility can be controlled by the noradrenergic arousal signaling from the locus coeruleus (LC), which adjusts the gain of cortical circuits rather than directly driving them. Using an empirically grounded corticothalamic model augmented with an LC, we contrasted brief phasic bursts versus sustained tonic modulation. Phasic LC activity promoted robust traveling waves, while tonic activity produced longer-lasting but spatially confined responses. By further specifying when and where it engages, the LC can gate where waves start, which directions they travel, and where they stop--even across unmodulated gaps. When long-range connections are present, LC modulation also enables rapid, selective activation of distant targets and dramatically lowers the gain needed for long-distance routing. This neuromodulatory mechanism offers a simple, testable account of trial-to-trial variability in evoked dynamics, capturing a mechanism through which the brain can transiently privilege specific communication pathways through a static structural connectome.

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bioRxiv2025

Mapping functional, structural, and transcriptomic correlates of homotopic connectivity

Homotopic functional connectivity (HoFC)--the synchronous activity between homologous regions of the left and right cortical hemispheres--is a hallmark of inter-hemispheric brain architecture, yet its biological underpinnings remain incompletely understood. Here, we characterize spatial variation in resting-state HoFC and its relation to dominant natural axes of anatomical, functional, and transcriptomic organization across the human cerebral cortex. We show that the regional distribution of HoFC, from the lowest in association areas (especially limbic) to the highest in primary sensorimotor regions, is preserved in neuropsychiatric disorders in a separate neuroimaging cohort (including schizophrenia, bipolar I disorder, and attention-deficit hyperactivity disorder) despite disease-associated perturbations in magnitude. Regional variation in HoFC is not fully explained by properties of physical embedding or vascular innervation in the brain, suggesting involvement of alternative physiological processes. Furthermore, we find that regions with stronger HoFC are more functionally connected within and across hemispheres at rest, suggesting that homotopic functional coupling is positively associated with brain-wide functional synchrony. Our results collectively suggest that HoFC is inherently aligned with a fundamental natural axis of macroscopic cortical organization and shared global signal--advancing our understanding of the biological correlates of cortical homotopy and suggesting potential mechanisms to investigate in the future.

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bioRxiv2025

Comprehensive profiling of anaesthetised brain dynamics across phylogeny

Intrinsic dynamics of neuronal circuits shape information processing. Combining neuroimaging with causal perturbation offers the opportunity to understand how local dynamics mediate the link between neurobiology and functional repertoire. We compile a unique dataset of multi-scale neural activity during wakefulness and anaesthetic-induced suppression of information processing encompassing human, macaque, marmoset, mouse, zebrafish and nematode. Applying massive feature extraction, we comprehensively characterise local neural dynamics across >6,000 time-series features. Using dynamics as a common space for cross-species comparison reveals a conserved dynamical profile of anaesthesia across species, characterised by shorter intrinsic timescales of neural activity and dampened interregional synchrony. This dynamical regime is experimentally reversed in vivo by deep-brain stimulation of the macaque centromedian thalamus, restoring behavioural responsiveness. Spatially, this conserved dynamical phenotype covaries with conserved transcriptional profiles of excitatory and inhibitory neurotransmission across human, macaque, marmoset and mouse cortex. Biophysical modelling provides a mechanistic link between the macroscale dynamical phenotype of anaesthesia, and microscale effects of key molecular targets on the timescales of synaptic excitation and inhibition. Altogether, comprehensive dynamical phenotyping reveals a shared neural endpoint of anaesthesia: across species and scales, anaesthetics induce spatio-temporal isolation of local neural activity.

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bioRxiv2025

Benchmarking overlapping community detection methods for applications in human connectomics

Brain networks exhibit non-trivial modular organization, with groups of densely connected areas participating in specialized functions. Traditional community detection algorithms assign each node to one module, but this representation cannot capture integrative, multi-functional nodes that span multiple communities. Despite the increasing availability of overlapping community detection algorithms (OCDAs) to capture such integrative nodes, there is no objective procedure for selecting the most appropriate method and its parameters for a given problem. Here we overcome this limitation by introducing a datadriven method for selecting an OCDA and its parameters from performance on a tailored ensemble of generated benchmark networks, assessing 22 unique algorithms and parameter settings. Applied to the human structural connectome, we find that the Order Statistics Local Optimization Method (OSLOM) best identifies ground-truth overlapping structure in the benchmark ensemble and yields a seven-network decomposition of the human cortex. These modules are bridged by fifteen overlapping regions that generally sit at the apex of the putative cortical hierarchy--suggesting integrative, higher-order function-- with network participation increasing along the cortical hierarchy, a finding not supported using a non-overlapping modular decomposition. This data-driven approach to selecting OCDAs is applicable across domains, opening new avenues to detecting and quantifying informative structures in complex real-world networks.

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bioRxiv2025

Synaptome architecture shapes regional dynamics in the mouse brain

Synapses are the connections that transform neurons from simple electrically charged cells into complex circuits that support perception, cognition and action. Recent advances in single-punctum synapse mapping in mice have made it possible to study the diversity of synapses and how these synapse types are differentially expressed across the brain. A salient question is how synapse diversity shapes the spatial patterning of whole-brain dynamics. Here we derive > 6 000 time-series features from fMRI recordings in awake mice to construct a comprehensive macroscale dynamical phenotype of each synapse type. We find that spatial variation in synapse types colocalizes with spatial variation in regional dynamics. Time-series in regions enriched for SAP102-expressing synapses display high-amplitude events while time-series in regions enriched for PSD95-expressing synapses display low stationarity. These regional variations in synapse types and dynamics are associated with patterns of structural and functional connectivity and the placement of hubs. Finally, using two additional fMRI datasets in anaesthetized mice, we show that synapses expressing short- and long-lifetime proteins are differentially engaged across behavioural states. Collectively, this work demonstrates that the spatial organization of microscale synapse types fundamentally shapes whole-brain dynamics.

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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

Canonical time-series features for characterizing biologically informative dynamical patterns in fMRI

The interdisciplinary time-series analysis literature encompasses thousands of statistical features for quantifying interpretable properties of dynamical data. But for any given application, it is likely that just a small subset of informative time-series features is required to capture the dynamical quantities of interest. So, while comprehensive libraries of time-series features have been developed, it is useful to construct reduced and computationally efficient subsets for specific applications. In this work, we demonstrate a systematic process to deduce such a reduced set, focused on the problem of distinguishing changes to functional Magnetic Resonance Imaging (fMRI) time series caused by a range of experimental manipulations of excitatory and inhibitory neural activity in mouse cortical circuits. We reduce a comprehensive library of over 7000 candidate time-series features down to a subset of 16 features, which we call catchaMouse16, that aims to both: (i) accurately characterize biologically relevant properties of fMRI time series; and (ii) minimize inter-feature redundancy. The catchaMouse16 feature set accurately classifies experimental perturbations of neuronal activity from fMRI recordings, and also shows strong generalization performance on an unseen mouse and human resting-state fMRI data where it tracks spatial variations in excitatory and inhibitory cortical cell densities, often with greater statistical power than the full hctsa feature set. We provide an efficient, open-source implementation of the catchaMouse16 feature set in C (achieving an approximately 60 times speed-up relative to the native Matlab code of the same features), with wrappers for Python and Matlab. This work demonstrates a procedure to reduce a large candidate time-series feature set down to the key statistical properties of mouse fMRI dynamics that can be used to efficiently quantify and interpret informative dynamical patterns in neural time series.

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bioRxiv2024

Thalamic Control Over Laminar Cortical Dynamics Across Conscious States

The human brain must support both stable and flexible neural dynamics in order to adapt to changing contexts that are inherently non-linear. The thalamus has been linked to the coordination of these opposing dynamical regimens in the cerebral cortex, however existing methodological approaches have not integrated sufficient neurobiological details with a sensitive measure of neural dynamics that permits sensitivity to time-series non-linearities. Inspired by the field of fluid dynamics, we use a novel approach to show that spontaneous fMRI data exhibits non-trivial fluctuations in predictability over time, akin to a river that has sections of smooth and predictable (laminar) versus rough and unpredictable (non-laminar) fluid flow. We use a combination of pharmacological fMRI, macaque electrophysiology and a large-scale biophysical model of the thalamocortical system to provide robust evidence that the thalamus provides versatile control over globally linear dynamics in the cerebral cortex that characterize conscious states.

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bioRxiv2024

Benchmarking methods for mapping functional connectivity in the brain

The networked architecture of the brain promotes synchrony among neuronal populations and the emergence of coherent dynamics. These communication patterns can be comprehensively mapped using noninvasive functional imaging, resulting in functional connectivity (FC) networks. Despite its popularity, FC is a statistical construct and its operational definition is arbitrary. While most studies use zero-lag Pearsons correlations by default, there exist hundreds of pairwise interaction statistics in the broader scientific literature that can be used to estimate FC. How the organization of the FC matrix varies with the choice of pairwise statistic is a fundamental methodological question that affects all studies in this rapidly growing field. Here we comprehensively benchmark the topological and geometric organization, neurobiological associations, and cognitive-behavioral relevance of FC matrices computed using a large library of 239 pairwise interaction statistics. We comprehensively investigate how canonical features of FC networks vary with the choice of pairwise statistic, including (1) hub mapping, (2) weight-distance trade-offs, (3) structure-function coupling, (4) correspondence with other neurophysiological networks, (5) individual fingerprinting, and (6) brain-behavior prediction. We find substantial quantitative and qualitative variation across FC methods. Throughout, we observe that measures such as covariance (full correlation), precision (partial correlation) and distance display multiple desirable properties, including close correspondence with structural connectivity, the capacity to differentiate individuals and to predict individual differences in behavior. Using information flow decomposition, we find that differences among FC methods may arise from differential sensitivity to the underlying mechanisms of inter-regional communication, with some more sensitive to redundant and some to synergistic information flow. In summary, our report highlights the importance of tailoring a pairwise statistic to a specific neurophysiological mechanism and research question, providing a blueprint for future studies to optimize their choice of FC method.

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bioRxiv2024

Synchrony, oscillations, and phase relationships in collective neuronal activity: a highly comparative overview of methods

Neuronal activity is organized in collective patterns that are critical for information coding, generation, and communication between brain areas. These patterns are often described in terms of synchrony, oscillations, and phase relationships. Many methods have been proposed for the quantification of these collective states of dynamic neuronal organization. However, it is difficult to determine which method is best suited for which experimental setting and research question. This choice is further complicated by the fact that most methods are sensitive to a combination of synchrony, oscillations, and other factors; in addition, some of them display systematic biases that can complicate their interpretation. To address these challenges, we adopt a highly comparative approach, whereby spike trains are represented by a diverse library of measures. This enables unsupervised or supervised classification in the space of measures, or in that of spike trains. We compile a battery of 122 measures of synchrony, oscillations, and phase relationships, complemented with 9 measures of spiking intensity and variability. We first apply them to sets of synthetic spike trains with known statistical properties, and show that all measures are confounded by extraneous factors such as firing rate or population frequency, but to different extents. Then, we analyze spike trains recorded in different species-rat, mouse, and monkey-and brain areas- primary sensory cortices and hippocampus-and show that our highly comparative approach provides a high-dimensional quantification of collective network activity that can be leveraged for both unsupervised and supervised classification of firing patterns. Overall, the highly comparative approach provides a detailed description of the empirical properties of multineuron spike train analysis methods, including practical guidelines for their use in experimental settings, and advances our understanding of neuronal coordination and coding. Author summaryCognition and brain-body regulation rely on collective patterns of neural activity, which are typically described in terms of synchrony, oscillations and phase relationships. Many methods have been proposed for measuring these properties, and selecting the most appropriate method for a given research question can be a daunting task. To address this issue, we assembled a broad range of statistical measures and tested them on both synthetic and biological spike trains. Our analyses indicate that there is not an overall "best" measure, and inform on the relative advantages and drawbacks of a broad range of measures with respect to several criteria of interest for their empirical application, including their modulation by firing rate or spike failures, population frequency, sequentialness and rhythmicity, as well as their bias and precision resulting from finite time window length and number of neurons. Our results provide a comprehensive picture of the range of available methods for the quantification of collective patterns of neural activity, enabling researchers to make better informed decisions and avoid interpretational pitfalls.

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bioRxiv2024

Extracting interpretable signatures of whole-brain dynamics through systematic comparison

The brains complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.

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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

Uncovering a stability signature of brain dynamics associated with meditation experience using massive time-series feature extraction

Previous research has examined resting electroencephalographic (EEG) data to explore brain activity related to meditation. However, previous research has mostly examined power in different frequency bands. Here we compared >7000 time-series features of the EEG signal to comprehensively characterize brain activity differences in meditators, using many measures that are novel in meditation research. Eyes-closed resting-state EEG data from 49 meditators and 46 non-meditators was decomposed into the top eight principal components (PCs). We extracted 7381 time-series features from each PC and each participant and used them to train classification algorithms to identify meditators. Highly differentiating individual features from successful classifiers were analysed in detail. Only the third PC (which had a central-parietal maximum) showed above-chance classification accuracy (67%, pFDR = 0.007), for which 405 features significantly distinguished meditators (all pFDR < 0.05). Top-performing features indicated that meditators exhibited more consistent statistical properties across shorter subsegments of their EEG time-series (higher stationarity) and displayed an altered distributional shape of values about the mean. By contrast, classifiers trained with traditional band-power measures did not distinguish the groups (pFDR > 0.05). Our novel analysis approach suggests the key signatures of meditators brain activity are higher temporal stability and a distribution of time-series values suggestive of longer, larger, or more frequent non-outlying voltage deviations from the mean within the third PC of their EEG data. The higher temporal stability observed in this EEG component might underpin the higher attentional stability associated with meditation. The novel time-series properties identified here have considerable potential for future exploration in meditation research and the analysis of neural dynamics more broadly.

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bioRxiv2023

Neurophysiological signatures of cortical micro-architecture

Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6 800 timeseries features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is colocalized with multiple micro-architectural features, including genomic gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.

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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

Neuromodulation of striatal D1 cells shapes BOLD fluctuations in anatomically connected thalamic and cortical regions

Understanding how the brains macroscale dynamics are shaped by underlying microscale mechanisms is a key problem in neuroscience. In animal models, we can now investigate this relationship in unprecedented detail by directly manipulating cellular-level properties while measuring the whole-brain response using resting-state fMRI. Here we focused on understanding how blood-oxygen-level-dependent (BOLD) dynamics, measured within a structurally well-defined striato-thalamo-cortical circuit, are shaped by chemogenetically exciting or inhibiting D1 medium spiny neurons (MSNs) of the right dorsomedial striatum (CPdm). We characterize changes in both the BOLD dynamics of individual cortical and subcortical brain areas, and patterns of inter-regional coupling (functional connectivity) between pairs of areas. Using a classification approach based on a large and diverse set of time-series properties, we found that CPdm neuromodulation alters BOLD dynamics within thalamic subregions that project back to dorsomedial striatum. In the cortex, the strongest changes in local dynamics were observed in unimodal regions, i.e., regions that process information from a single sensory modality, while changes in the local dynamics weakened along a putative cortical hierarchical gradient towards transmodal regions. In contrast, a decrease in functional connectivity was observed only for cortico-striatal connections after D1 excitation. Our results provide a comprehensive understanding of how targeted cellular-level manipulations affect local BOLD dynamics at the macroscale, including the role of a circuits structural characteristics and hierarchical cortical level in shaping those dynamics. These findings contribute to ongoing attempts to understand the influence of structure-function relationships in shaping inter-regional communication at subcortical and cortical levels.

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bioRxiv2021

Extracting dynamical understanding from neural-mass models of mouse cortex

New brain atlases with high spatial resolution and whole-brain coverage have rapidly advanced our knowledge of the brains neural architecture, including the systematic variation of excitatory and inhibitory cell densities across the mammalian cortex. But understanding how the brains microscale physiology shapes brain dynamics at the macroscale has remained a challenge. While physiologically based mathematical models of brain dynamics are well placed to bridge this explanatory gap, their complexity can form a barrier to providing clear mechanistic interpretation of the dynamics they generate. In this work we develop a neural-mass model of the mouse cortex and show how bifurcation diagrams, which capture local dynamical responses to inputs and their variation across brain regions, can be used to understand the resulting whole-brain dynamics. We show that strong fits to resting-state functional magnetic resonance imaging (fMRI) data can be found in surprisingly simple dynamical regimes--including where all brain regions are confined to a stable fixed point--in which regions are able to respond strongly to variations in their inputs, consistent with direct structural connections providing a strong constraint on functional connectivity in the anesthetized mouse. We also use bifurcation diagrams to show how perturbations to local excitatory and inhibitory coupling strengths across the cortex, constrained by cell-density data, provide spatially dependent constraints on resulting cortical activity, and support a greater diversity of coincident dynamical regimes. Our work illustrates methods for visualizing and interpreting model performance in terms of underlying dynamical mechanisms, an approach that is crucial for building explanatory and physiologically grounded models of the dynamical principles that underpin large-scale brain activity.

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