Biology subjects

Breakspear, M.

Publications and source records attributed to Breakspear, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Dopamine Compensates for Amyloid-Induced Default Mode Network Dysfunction to Support Learning

Throughout the preclinical phase of Alzheimers disease (AD) {beta}-amyloid (A{beta}) accumulates preferentially within the default mode network (DMN), yet the functional and behavioural consequences of this pathological burden remain poorly understood. Using task-based fMRI combined with A{beta}, tau, and dopamine PET in cognitively normal older adults, we show that A{beta} burden impairs learning independent of tau, but this learning performance is recovered with higher dorsolateral striatal dopamine synthesis capacity. Investigating the neural mechanisms that support this learning, we observe that A{beta} positive individuals show attenuated DMN activity to error related feedback, a metric that relates to poorer learning. When estimating the effective connectivity during feedback, computational modelling reveals that A{beta} induces dis-inhibition of the DMN during error processing. Critically, dopamine synthesis capacity in the dorsolateral striatum rebalances effective connectivity between the DMN and frontostriatal network, thereby opposing A{beta} related disruption. These findings establish a systems-level framework in which A{beta} impairs learning by disrupting dynamic DMN modulation during feedback, a disruption for which dopaminergic function can partially compensate. This suggests that learning in the presence of A{beta} may be subserved by dopamine-dependent network rebalancing, a candidate mechanism of cognitive resilience to support learning in preclinical AD.

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bioRxiv2026

Gradients in excitability generate hippocampal waves and shape their interactions with cortex

Travelling waves are a prominent feature of hippocampal activity, but the mechanisms determining their propagation and influence on the cerebral cortex remain unclear. Using a biophysically-grounded model of neural activity evolving across hippocampal and cortical surfaces, we show that spatial gradients in external in-put or neural excitability are a sufficient mechanism for the emergence of travelling waves along the long axis of the hippocampus. These waves emerge only above a critical gradient threshold, propagate with biologically-plausible velocities and exhibit frequency-dependent reversals in direction across slow and fast theta regimes. When coupled to the cerebral cortex, anterior-to-posterior hippocampal waves selectively reorganise globally synchronous cortical activity into metastable travelling waves, whereas posterior-to-anterior hippocampal waves do not, revealing a directional asymmetry in hippocampal-cortical communication. Conversely, cortical waves aligned with large-scale functional gradients induce structured hippocampal waves, revealing complementary direction specific effects across the two systems. These analyses identify structured excitability gradients as a principle governing wave propagation in the hippocampus and suggest how wave-to-wave interactions may coordinate complex cortical and hippocampal interactions during mnemonic and perceptual processes.

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bioRxiv2026

What Is a Generative Model? Definitions, Disagreements, and Evaluation in Human Neuroimaging

The term generative model is widely used in human neuroimaging; however, its meaning is often left implicit. Prompted by observations and discussions at the 2025 Organization for Human Brain Mapping (OHBM) Annual Meeting, we surveyed members of the neuroimaging community to examine how generative models are defined, used, and evaluated in practice. Responses revealed some agreement on functional criteria-- such as a models ability to simulate data--alongside marked disagreement about whether specific, widely used methods should be considered generative models. Evaluative priorities also varied across respondents, though out-of-sample generalization and interpretability were consistently emphasized. Rather than proposing a single definition, this perspective highlights the diversity of current usage and argues for greater clarity when the term is invoked.

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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

Brain-heart coupling shapes large scale brain dynamics

Functional brain networks reconfigure to support adaptive behaviour, balancing integration and segregation. These dynamics are influenced by neuromodulatory arousal, reflected in heart rate (HR) fluctuations. We leverage intracranial EEG (iEEG), functional MRI (fMRI), and HR recordings during emotional movie viewing to probe neurocardiac contributions to large-scale network dynamics. Network dynamics using high-frequency iEEG activity (60-140 Hz), an established marker of neuronal firing, reveal that greater network integration tracks elevated HR, while segregation aligns with lower HR. However, we observe an inverted relationship between HR and network dynamics in a separate fMRI dataset utilising the same movie, and replicate this in another independent fMRI dataset. Biophysical modelling of BOLD from iEEG reproduces the iEEG-HR findings, suggesting neurovascular transformation may obscure neural-cardiac links in fMRI. These results link large-scale network dynamics to physiological arousal through HR driven adrenergic-cholinergic neuromodulation and highlight the complex interplay between neural activity, autonomic regulation, and haemodynamics.

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bioRxiv2025

Differential effects of neurodegenerative disorders on verbal creative idea generation and its cognitive cornerstones

Clinical research has documented a heterogeneous effect of neurodegenerative disorders on creativity. Some disorders are associated with impaired creative performance, while others may preserve, or even enhance, it. However, the underlying cognitive mechanisms that give rise to the heterogeneous behavioural manifestations remain poorly understood. From a theory-driven approach, we conducted a neuropsychological investigation to examine how frontotemporal lobar degeneration (FTLD), and Alzheimer's Disease (AD) differentially affected the cognitive mechanisms underlying creative thought (semantic cognition, episodic memory, executive control functions). In parallel, we assessed participants' verbal creative idea generation performance using the ideational fluency task (also known as the Alternate Uses Task). We analysed data from 24 individuals with FTLD [clinical consensus diagnosis corticobasal syndrome (CBS: n=20) or progressive supranuclear palsy (PSP: n=4)], 24 individuals with clinical consensus diagnosis AD, and 24 healthy individuals. We reached four major conclusions. First, while both disease groups exhibited deficits in controlled semantic retrieval and executive control functions, only participants with AD exhibited degradation in semantic representations and episodic memory. Second, both disease groups generated fewer responses in both typical and creative conditions across objects that were context-free - i.e., associated with multiple contexts (e.g., brick) and context-bound -i.e., associated with a dominant context (e.g., table knife). Third, when given a context-free object prompt, the ability to generate novel responses appeared to be preserved in participants with FTLD, which contrasted with an impaired ability when prompted to give creative uses for the context-bound object. Fourth, participants with AD produced similar levels of response novelty for both context-free and context-bound objects. From a clinical-cognitive neuroscience perspective, this study demonstrates that distinct cognitive profiles resulting from different neurodegenerative conditions yield differential effects on creative thought. As the first study to show that the contextual information of semantic stimuli can mediate the novelty of verbal responses, we suggest that future research should carefully examine how this factor influences creative task performance.

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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

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

Spurious correlations in surface-based functional brain imaging

The study of functional MRI data is increasingly performed after mapping from volumetric voxels to surface vertices. Processing pipelines commonly used to achieve this mapping produce meshes with uneven vertex spacing, with closer neighbours in sulci compared to gyri. Consequently, correlations between the fMRI time series of neighbouring sulcal vertices are stronger than expected. However, the causes, extent, and impacts of this bias are not well understood or widely appreciated. We explain the origins of these biases, and using in-silico models of fMRI data, illustrate how they lead to spurious results. The bias leads to leakage of anatomical cortical folding information into fMRI time series. We show that many common analyses can be affected by this "gyral bias", including test-retest reliability, fingerprinting, functional parcellations, regional homogeneity, and brain-behaviour associations. Finally, we provide recommendations to avoid or remedy this spatial bias.

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bioRxiv2024

Phylogenetically-Preserved Multiscale Neuronal Activity: Iterative Coarse-Graining Reconciles Scale-Dependent Theories of Brain Function

Brain recordings collected at different resolutions support statistically distinct signatures of information processing, leading to scale-dependent theories of brain function. Here, we demonstrate that these disparate neural-coding signatures emerge from the same multiscale functional organisation of neuronal activity across calcium-imaging recordings collected from the whole brains of zebrafish and nematode, as well as sensory regions of the fly, mouse, and macaque brain. Network simulations show that hierarchical-modular structural connectivity facilitates multiscale functional coordination, enhancing information processing benefits such as a maximal dynamic range. Finally, we demonstrate that this cross-scale organisation supports distinct behavioural states across species by reconfiguring functional affiliation and temporal dynamics at the mesoscale. Our findings suggest that self-similar scaling of neuronal activity is a universal principle that reconciles scale-dependent theories of brain function, facilitating both efficiency and resiliency while enabling significant reconfiguration of mesoscale cellular ensembles to accommodate behavioural demands.

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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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bioRxiv2023

Low-dimensional dynamics of brain-muscle networks during gait

Walking is a complex motor activity that requires coordinated interactions between sensory and motor systems. We used mobile EEG and EMG to investigate the brain-muscle networks involved in gait control during overground walking in young, older and individuals with Parkinsons Disease. Dynamic interactions between the sensorimotor cortices and eight leg muscles within a gait cycle were assessed using multivariate analysis. We identified three distinct brain-muscle networks during a gait cycle. These networks include a bilateral network, a left-lateralised network activated during the left swing phase, and a right-lateralised network active during right swing. The trajectories of these networks are contracted in older adults, indicating a reduction in neuromuscular connectivity with age. Individuals with impaired tactile sensitivity of the foot showed a selective enhancement of the bilateral network, possibly reflecting a compensation strategy to maintain gait stability. These findings provide a parsimonious description of interindividual differences in neuromuscular connectivity during gait. TeaserDynamic network analysis shows how brain-muscle connectivity during gait varies with age and somatosensory function.

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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

Metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks

Although BOLD signal decreases in the default mode network (DMN) are commonly observed during attention-demanding tasks, their neurobiological underpinnings are not fully understood. Previous work has shown decreases but also increases in glucose metabolism that match with or dissociate from these BOLD signal decreases, respectively. To resolve this discrepancy, we analyzed functional PET/MRI data from 50 healthy subjects during the performance of the visuo-spatial processing game Tetris(R) and combined this with previously published data sets of working memory as well as visual and motor stimulation. Our findings show that the glucose metabolism of the posteromedial DMN is dependent on the metabolic demands of the correspondingly engaged task-positive brain networks. Specifically, the dorsal attention (involved in Tetris(R)) and frontoparietal networks (engaged during working memory) shape the glucose metabolism of the posteromedial DMN in opposing directions. External attention-demanding tasks lead to a downregulation of the posteromedial DMN with consistent decreases in the BOLD signal and glucose metabolism, whereas working memory is subject to metabolically expensive mechanisms of BOLD signal suppression. We suggest that the former finding is mediated by decreased glutamate signaling, while the latter results from active GABAergic inhibition, regulating the competition between self-generated and task-driven internal demands. The results demonstrate that the DMN relates to cognitive processing in a flexible manner and does not always act as a cohesive task-negative network in isolation.

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bioRxiv2022

Reproducibility of neuroimaging studies of brain disorders with hundreds -not thousands- of participants

An important current question in neuroimaging concerns the sample sizes required for producing reliable and reproducible results. Recent findings suggest that brain-wide association studies (BWAS) linking neuroimaging features with behavioural phenotypes in the general population are characterised by (very) weak effects and consequently need large samples sizes of 3000+ to lead to reproducible findings. A second, important goal in neuroimaging is to study brain structure and function under disease conditions, where effects are likely much larger. This difference in effect size is important. We show by means of power calculations and empirical analysis that neuroimaging studies in clinical populations need hundreds -and not necessarily thousands-of participants to lead to reproducible findings.

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bioRxiv2022

Quantifying dynamic facial expressions under naturalistic conditions

Facial affect is expressed dynamically - a giggle, grimace, or an agitated frown. However, the characterization of human affect has relied almost exclusively on static images. This approach cannot capture the nuances of human communication or support the naturalistic assessment of affective disorders. Using the latest in machine vision and systems modelling, we studied dynamic facial expressions of people viewing emotionally salient film clips. We found that the apparent complexity of dynamic facial expressions can be captured by a small number of simple spatiotemporal states - composites of distinct facial actions, each expressed with a unique spectral fingerprint. Sequential expression of these states is common across individuals viewing the same film stimuli but varies in those with the melancholic subtype of major depressive disorder. This approach provides a platform for translational research, capturing dynamic facial expressions under naturalistic conditions and enabling new quantitative tools for the study of affective disorders and related mental illnesses.

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