Biology subjects

Leech, R.

Publications and source records attributed to Leech, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Brain-thought states during movie-watching reflect shared sensory anchors and film- and person- specific processes in association cortex

Naturalistic paradigms such as movie-watching have transformed functional neuroimaging by capturing cognitive dynamics during rich, real-world experiences. A fundamental challenge, however, is determining whether the neural signatures identified during these experiences reflect general principles of cognition or the specific contexts in which they are measured. To address this question, we combined fMRI data from participants watching eight diverse films with continuous reports of ongoing thought. We found that the generalizability of brain-thought relationships depends on the cognitive process being recruited. Neural signatures linked to sensory processing remained robust across films, whereas those involving memory-related association networks depended on how we modelled between-film variability, indicating that cognition becomes increasingly context-dependent along the cortical hierarchy from sensory to association cortex. Within default and limbic networks, we identified two contrasting memory-related states with opposite consequences for comprehension. One, reflecting the integration of prior knowledge with ongoing experience, predicted better narrative comprehension and was revealed only after accounting for between-film variability. The other, associated with stimulus-independent thought, predicted poorer comprehension and emerged only when between-film variability was not modelled. Together, these findings establish when brain-experience relationships measured during naturalistic paradigms can generalize across contexts and when they instead reflect interactions between cognition and the specific environments in which cognition unfolds. More broadly, the consistency of a given brain-experiential association may depend less on where it occurs in the brain and more on the viewer's contextual experience: perceiving the film, drawing on memory to understand it, or drifting away.

neuroscience↗

bioRxiv2026

Thoughts and cortical responses are shaped by the type of change at event boundaries

Continuous experience is divided into discrete and meaningful events, but different kinds of event transitions may shape thought and brain activity in different ways. We examined how perceived event boundaries relate to visual, auditory, and semantic features, ongoing thought, and large-scale cortical states during movie-watching. We combined three independent samples who viewed the same eight movie clips, aligning these measures to a shared stimulus timeline: One marked event boundaries, one reported ongoing thought, and one was scanned with fMRI. Whole-brain spatial correlations showed that event boundaries correlated positively with visual change and negatively with auditory change, but not with any thought pattern. Time-resolved analyses showed that boundaries were associated with a reduction in distracted thoughts and brain states positioned toward visual processing. Clustering boundaries by movie feature change profile identified visual, semantic, interruption, and narrative boundary types. All four types showed a shift toward visual processing in the brain, but their other associations differed. Visual boundaries were associated with less distraction, semantic boundaries with thoughts related to past experience and prior knowledge, and narrative boundaries with more detailed and deliberate thoughts. Visual and semantic boundaries also showed distinct shifts in brain state that were not apparent in the other two types. These findings suggest that event boundaries mark a set of state shifts in ongoing experience, with a shared reorientation toward visual processing and changes in thought and brain state that vary with the type of transition.

neuroscience↗

bioRxiv2026

RADIAL AND TANGENTIAL FIBER COMPONENTS IN HUMAN SUPERFICIAL WHITE MATTER AND THEIR ASSOCIATIONS WITH MACROSCALE FUNCTION

The superficial white matter (SWM), a thin layer of white matter beneath the cortex, contains a complex arrangement of short- and long-range fiber systems that support communication between cortical regions. These fiber populations include subcortical U-shaped fibers (U-fibers) that predominantly follow a tangential trajectory along the gray matter-white matter interface and long-range fibers that extend more radially through the deeper white matter to connect distant cortical regions. Here, we developed a surface-based framework to dissociate radial and tangential fiber components within the human SWM and investigated their microstructural properties and functional relevance using in-vivo high-field MRI. These components were quantified using apparent fiber density (AFD), fractional anisotropy (FA), and mean diffusivity (MD). We further contextualized SWM fiber architecture by examining depth-dependent microstructural profiles and their relationship with normative patterns of cortical organization and hierarchical specialization. The anatomical assessment of the proposed fiber orientation separation was supported by tractography-based bundle analyses, while the anatomical accuracy of tractography reconstructions was confirmed through comparison with post-mortem white matter dissection data. To assess their functional relevance, the fiber predominance map derived from radial and tangential AFD was related to spatial patterns of functional variability across the cortex. Regions with greater radially oriented fiber contribution exhibited more gradual changes in functional similarity across geodesic distance, whereas tangentially oriented fiber-enriched regions showed more rapid local functional differentiation. These findings reveal distinct structural and functional signatures of radial and tangential SWM fiber components and provide a framework for investigating their contributions to cortical organization and functional specialization. SIGNIFICANCE STATEMENTThe human cerebral cortex is organized into specialized regions that support diverse functions, but the structural principles underlying this organization remain incompletely understood. The superficial white matter (SWM), a thin layer of white matter beneath the cortex, contains intermingled short- and long-range fiber systems that support communication between cortical regions. However, these fiber populations have remained difficult to characterize because they are closely intertwined within the SWM. By developing a reproducible framework for mapping and quantifying distinct SWM fiber systems, this work provides a foundation for systematic investigation of this complex compartment. Integration of multimodal datasets, together with open sharing of analytical tools and standardized features, promotes transparency, reproducibility, enabling future studies of how superficial connectivity supports brain organization.

neuroscience↗

bioRxiv2026

HippoGenes: A robust workflow for subregional transcriptomic profiling of the human hippocampus

The human hippocampus is a unique cortical structure central to brain function, plasticity, and disease. Unravelling its complex organization requires the integration of multiscale data, linking molecular features to mesoscale anatomy and macroscale functional patterns. Gene expression is a fundamental microscale phenotype, and its profiling can provide a reference description of how molecular features are distributed across the brain. Capitalizing on recent imaging-transcriptomic analyses, we introduce HippoGenes, a repository of fine-grained gene expression patterns across human hippocampal subregions. We leveraged spatial statistical models and hippocampal surface mapping to reconstruct dense transcriptomic maps from sparse post-mortem tissue samples of the Allen Human Brain Atlas, generating continuous expression estimates for thousands of genes aligned to a common surface-based coordinate system. We illustrate the utility of HippoGenes to (i) map medial-lateral and anterior-posterior transcriptomic gradients that align with subfield and tripartite subdivisions of the hippocampal formation, (ii) examine associations between gene expression and canonical microstructural and functional features of the hippocampus, and (iii) perform a molecular decoding of subregional alterations in neurological patients with hippocampal pathology. HippoGenes provides a framework for exploring the molecular organization of the hippocampus, opening avenues for multiscale integration in health and disease, and is openly available on https://hippogenes.readthedocs.io/.

neuroscience↗

bioRxiv2026

Neural feature spaces: characterizing the geometry of brain activity across development

The human brain undergoes profound changes from childhood to adulthood. These changes are foundational to cognitive, affective and social development, and measuring them is essential for identifying atypical development. A common approach consists of analyzing metrics derived from brain activity, such as spectral power, aperiodic activity and signal complexity. However, individual metrics are sensitive to external factors unrelated to development, contributing to inconsistent findings in the literature. Moreover, these metrics are not independent; they show some degree of intrinsic redundancy, which itself evolves across development. Here, we propose a novel approach that focuses on the relationships between neural features rather than their values, defining a "neural feature space" that captures the geometry of brain activity. We analyzed the neural feature spaces of children (4-12 years) and adults (30-45 years), based on 128-channel EEG recordings during naturalistic movie viewing. Specifically, we computed a range of spectral, aperiodic and complexity features and quantified pairwise distances between them using latent variable modeling. Neural feature spaces were significantly different between age groups. Notably, high frequency bands were less differentiated in children and distances between spectral and complexity features differed. Crucially, distances were highly stable across tasks, in contrast to feature values, which varied substantially. These findings suggest that the geometry of neural feature spaces provides robust, interpretable markers of neurodevelopment, offering a complementary approach to feature-based analyses.

neuroscience↗

bioRxiv2026

Reorganisation of Functional Connectivity Gradients in Post-Stroke Aphasia

Aphasia after stroke arises from focal damage to language-relevant cortex but is accompanied by widespread alterations in functional connectivity. In 60 stroke survivors, we related principal component-derived language scores to both lesion location and stroke-related changes in resting-state functional organisation, combining structural lesion-symptom mapping with a gradient-based approach. Structural lesion-symptom mapping identifies which damaged regions are associated with specific language impairments but does not capture how surviving cortex is reconfigured within the brains large-scale architecture. In contrast, lesion-gradient mapping characterises whole-brain connectivity gradients - data-driven axes that summarise the principal patterns of variation in cortical connectivity - and quantifies how behavioural variation relates to displacement of structurally intact regions along these intrinsic axes. Stroke was associated with altered positioning of cortical regions along the gradient separating default mode and control networks. Notably, the position of the left inferior frontal gyrus along this axis predicted dissociable language outcomes: displacement toward default mode connectivity patterns was associated with better speech but poorer writing performance. This opposing behavioural profile suggests that inferior frontal cortex contributes to language through flexible large-scale coupling, with distinct coupling regimes differentially supporting spoken and written production. These findings indicate that gradient-based mapping provides a mechanistic link between focal tissue damage and distributed behavioural consequences by revealing systematic reorganisation of macroscale functional architecture beyond the lesion site.

neuroscience↗

bioRxiv2026

AutoNeuro: An Open-Source fMRI Toolbox for Real-Time Neuroadaptive Task Design

Real-time functional magnetic resonance imaging (fMRI) offers a powerful means of studying brain function adaptively, enabling experimental parameters to be updated dynamically in response to ongoing neural activity. However, current approaches remain limited by complex infrastructure requirements, bespoke implementations, and a lack of flexible frameworks for closed-loop neuroimaging, with many primarily focusing on neurofeedback experimental designs. Here we present AutoNeuro, an open-source framework for real-time fMRI acquisition, preprocessing, feature extraction, and adaptive experimental control. AutoNeuro connects directly to the MRI scanner, receiving reconstructed slices as soon as they become available, and streams them into a modular analysis pipeline designed for low-latency processing. Neural features are estimated at the temporal resolution of acquisition and are passed to a Bayesian optimisation agent that selects task conditions to maximise a user-defined objective function. Experimental conditions are represented within a bounded "experiment space", allowing heterogeneous conditions to be explored within a common coordinate system. We demonstrate AutoNeuro in a real-time fMRI experiment in which the system adaptively sampled task conditions to obtain a continuous map of brain response to the range of conditions contained within the experimental space. The system operated within the temporal constraints of real-time preprocessing and analysis, maintaining stable model estimates across iterations, converging on experimental conditions most relevant to the measured brain metric. These results establish AutoNeuro as a flexible platform for closed-loop neuroimaging, supporting hypothesis-driven optimisation as well as exploratory mapping of brain metrics across large experimental spaces.

neuroscience↗

bioRxiv2026

The stability of thought: using experience sampling and brain imaging to determine the contextually bound nature of human cognition.

Human behavior is highly flexible, allowing efficient performance across a wide range of task contexts. A distributed set of frontal and parietal regions, commonly termed the multiple-demand network (MDN), is consistently engaged during diverse cognitively demanding tasks and is thought to support this flexibility. However, it remains unclear how patterns of MDN engagement relate to the qualitative features of ongoing cognition experienced during task performance. To address this issue, we examined the reliability of self-reported experiential features sampled during performance of a broad range of tasks. Across tasks, we found little evidence that particular patterns of thought were intrinsically more reliable than others, nor that individual tasks were associated with stable, characteristic thought profiles. Instead, the reliability of specific experiential features varied systematically across task contexts, with the same patterns showing high stability in some tasks and low stability in others. We next asked whether stable patterns of thought were associated with distinct neural signatures. We found that patterns of brain activity resembling the MDN tended to be present for tasks in which deliberate task focus was high, and when distraction was lower, adding to an emerging body of research suggesting that coordinated activity within frontal and parietal regions helps to establish a stable goal-focused mode of thoughts and actions.

neuroscience↗

bioRxiv2025

Auditory Cortical Gradients Integrate Bottom-Up and Top-Down Structure During Natural Sound Categorisation

Understanding how the brain organises natural categories is a central challenge in neuroscience. While prior work has shown that categories can be decoded from distributed activity patterns in auditory cortex, it remains unclear how these categories are globally arranged relative to one another, and how low-level acoustic and higher-level semantic structure jointly shape this organisation. Here, we addressed these questions by deriving low-dimensional functional gradients from high-depth functional magnetic resonance imaging (fMRI) data (three participants, [~]4.7 hours each) acquired during a category-specific one-back task. These gradients captured the principal axes of population activity in auditory cortex. Gradient-based models of the auditory cortex explained category structure more accurately than region-of-interest or whole-brain approaches, revealing that category information is distributed across multiple continuous axes rather than aligned with any single organisational dimension. Projecting acoustic (gammatone filter-bank) and behavioural similarity spaces directly into a shared framework with the fMRI functional axes showed that both contribute to the brains category geometry, with acoustic structure exerting a somewhat stronger influence. However, representational relationships varied across category pairs: some reflected primarily acoustic similarity, others semantic distinctions, and many a combination of both. This pairwise heterogeneity shows how auditory cortex may integrate multiple representational dimensions that define higher-level categories. Significance StatementCategorising natural sounds requires the brain to transform diverse acoustic signals into meaningful concepts. How this is achieved remains unclear: prior work has shown distributed activation patterns in auditory cortex, but not how category relationships are organised within its functional architecture. We show that natural sound categories are embedded across continuous cortical gradients that integrate bottom-up acoustic structure with top-down semantic information. Different categories emerge as flexible combinations of these spectral and behavioural dimensions. These findings reveal that natural sound categorisation arises from the geometry of cortical organisation, moving beyond localist accounts and offering a new framework for understanding how perception and cognition are linked in the human auditory system.

neuroscience↗

bioRxiv2025

Spatial layout of visual specialization is shaped by competing default mode and sensory networks

Understanding how the brain encodes information started with a map but turned into a maze: paths multiplied; boundaries blurred. Neurons tuned to specific features are not confined to single regions, but distributed across the cortex. Retinotopy, once thought limited to early visual areas, now appears in over 20 cortical regions--from the tip of the occipital cortex to the shore of the lateral frontal cortex. To describe and understand these complex mosaics of functional specialization, we focus on the spatial influences that shape their emergence across the cortical sheet. To this end, we developed Spatial Component Decomposition (SCD), a sparse dictionary learning framework that locates sources of spatial influence without relying on prior assumptions from systems neuroscience. Applied to MRI data capturing retinotopic maps, SCD reveals a dominant linear gradient extending over 60 mm from V1 and covering all the known posterior visual areas. Yet, it also revealed systematic competition from other primary sensory areas and default mode transmodal hubs. These suppressive influences shape the cortical embedding of visual information, even during purely visual tasks. Our results suggest that functional specialization emerges from spatial competition between representational systems--not just from feedforward inputs.

neuroscience↗

bioRxiv2025

The intrinsic cortical geometry of reading

How does the brain support the complex processes that allow us to read? Using predictive modeling we establish that visual and association cortex are closer together in individuals with stronger oral reading ability. These findings indicate that large-scale cortical geometry provides a scaffold that supports the coordinated processing required to read.

neuroscience↗

bioRxiv2025

In toto imaging of germ plasm dynamics reveals an essential role for early distribution of germ granules in germline development

A fundamental question in developmental biology is how the fertilized egg gives rise to all the different cell types of an organism. The traditional view is that the different cell types are specified either by intrinsic factors such as cell fate determinants or via intercellular signaling. In some animals a cytoplasmic determinant-like substance called germplasm specifies the germline. In zebrafish eggs, germplasm is dispersed in form of mRNP complexes called germ granules, which are enriched at the animal pole. After fertilization the distribution of germ granules changes dramatically. The germ granules accumulate in the corners of the first two cleavage furrows of the embryo, to form four large masses that are essential for germline development. Although germ granule movement has been linked to the network dynamics of the microtubular and actin cytoskeleton, a clear mechanistic understanding of the process is currently lacking. Fundamental questions about germplasm dynamics, including "What is the main driving force?" have not been answered yet. To address this gap, we performed careful quantitative analysis of germ granule dynamics relative to dynamic cytoskeletal reorganization in early zebrafish embryos by live-imaging. We identified stereotypic signatures of germ granule dynamics across different regions of the early embryo. Interestingly, we find that the timing of large-scale germ granule movements contrasts prevailing models for the mechanism of germ granule aggregation during cleavage divisions, and rather points to cytokinetic apparatus itself. Using zebrafish mutants affecting the RNA-binding protein Ybx1 (Y-box binding-protein 1), we show that the timing and dynamics of germ granule accumulation in the blastodisc is a crucial factor for appropriate later aggregation into cleavage furrows and eventual distribution to PGCs. Germplasm accumulation in the cleavage furrows is reduced and ectopic aggregates form at the blastoderm margin of ybx1 mutant embryos. Our work establishes Ybx1 as a novel factor with crucial functions in germplasm distribution and suggests that additional factors drive normal germplasm dynamics.

developmental biology↗

bioRxiv2025

Human cortical dynamics reflect graded contributions of local geometry and network topography

The brain is a physically embedded and heavily interconnected system that expresses neural rhythms across multiple time scales. While these dynamics result from the complex interplay of local and inter-regional factors, the relative contribution of such mechanisms across the cortex remains unclear. Our study explores geometric, microstructural, and connectome-level constraints on cortex-wide neural activity. We leverage intracranial electroencephalography recordings to derive a coordinate system of human cortical dynamics. Using multimodal neuroimaging, we could then demonstrate that these patterns are largely explainable by geometric properties indexed by inter-regional distance. However, dynamics in transmodal association regions are additionally explainable by incorporation of inter-regional microstructural similarity and connectivity information. Our findings are generally consistent when cross-referencing electroencephalography and imaging data from large-scale atlases and when using data obtained in the same individuals, suggesting subject-specificity and population-level generalizability. Together, our results suggest that the relative contribution of local and macroscale constraints on cortical dynamics varies systematically across the cortical sheet, specifically highlighting the role of transmodal networks in inter-regional cortical coordination.

neuroscience↗

bioRxiv2024

Individual brain activity patterns during task are predicted by distinct resting-state networks that may reflect local neurobiological features

Understanding how individual cortical features shape functional brain organization offers a promising framework for examining the principles of cognitive specialization in the human brain. This study explores the relationship between various cortical characteristics--i.e resting-state functional connectivity, structural connectivity, microstructure, morphology, and geometry--and the layout of task-specific functional activations. We employ linear models to predict the functional layout of the cortex at the individual level from each of these feature modalities. Our findings demonstrate that resting-state component loadings predict individual task activations, consistently across hemispheres and independent datasets. Whereas the first few components provide a common space for functional activations across tasks, predictive higher-order component loadings demonstrated task-specificity. Cortical microstructure/morphology was notably predictive of activation strength in the occipital cortex, highlighting its relevance for cortical functional specialization. By relating resting state components to a set of reference maps of cortical organization, we identify associations that suggest possible neurobiological underpinnings of specific cognitive functions. The remaining feature modalities were only predictive of group-level functional activations. These results advance our understanding of how distinct cortical features may contribute to functional specialization, guiding future inquiry into the organization of cognitive functions on the cortex.

neuroscience↗

bioRxiv2024

Ultra-low-field brain MRI morphometry: test-retest reliability and correspondence to high-field MRI

Magnetic resonance imaging (MRI) enables non-invasive monitoring of healthy brain development and disease. Widely used higher field (>1.5 T) MRI systems are associated with high energy and infrastructure requirements, and high costs. Recent ultra-low-field (<0.1T) systems provide a more accessible and cost-effective alternative. However, it remains uncertain whether anatomical ultra-low-field neuroimaging can be used to reliably extract quantitative measures of brain morphometry, and to what extent such measures correspond to high-field MRI. Here we scanned 23 healthy adults aged 20-69 years on two identical 64 mT systems and a 3 T system, using T1w and T2w scans across a range of (64 mT) resolutions. We segmented brain images into 4 global tissue types and 98 local structures, and systematically evaluated between-scanner reliability of 64 mT morphometry and correspondence to 3 T measurements, using correlations of tissue volume and Dice spatial overlap of segmentations. We report high 64 mT reliability and correspondence to 3 T across 64 mT scan contrasts and resolutions, with highest performance shown by combining three T2w scans with low through-plane resolution into a single higher-resolution scan using multi-resolution registration. Larger structures show higher 64 mT reliability and correspondence to 3 T. Finally, we showcase the potential of ultra-low-field MRI for mapping neuroanatomical changes across the lifespan, and monitoring brain structures relevant to neurological disorders. Raw images are publicly available, enabling systematic validation of pre-processing and analysis approaches for ultra-low-field neuroimaging.

neuroscience↗

bioRxiv2024

Patterns of Ongoing Thought in the Real World and Their Link to Mental Health and Well-Being

The thoughts we experience in daily life have implications for our mental health and well-being. However, it is often difficult to measure thought patterns outside of laboratory conditions due to concerns about the voracity of measurements taken in daily life. To address this gap in the literature, our study set out to measure patterns of thought as they occur in daily life and assess the robustness of these measures and their associations with trait measurements of mental health and well-being. A sample of undergraduate participants completed multi-dimensional experience sampling surveys eight times per day for five days as they went around their normal lives. Principal component analysis reduced these data to identify the dimensions that explained the patterns of thought reported by our participants. We used linear modelling to map how these thought patterns related to the activities taking place at the time of the probe, highlighting good consistency within the sample, as well as substantial overlap with prior work. Multiple regression was used to examine associations between patterns of ongoing thought and aspects of mental health and well-being, highlighting a pattern of "Intrusive Distraction" that had a positive association with anxiety, and a negative association with social well-being. Notably, this thought pattern tended to be most prevalent in solo activities and was relatively suppressed when interacting with other people (either in person or virtually). Our study, therefore, highlights the use of multi-dimensional experience sampling as a tool to understand how ongoing thoughts in daily life impact on our mental health and well-being, and establishes the important role social connectedness plays in the etiology of intrusive thinking.

neuroscience↗

bioRxiv2024

The spatial layout of antagonistic brain regions are explicable based on geometric principles

Brain activity emerges in a dynamic landscape of regional increases and decreases that span the cortex. Increases in activity during a cognitive task are often assumed to reflect the processing of task-relevant information, while reductions can be interpreted as suppression of irrelevant activity to facilitate task goals. Here, we explore the relationship between task-induced increases and decreases in activity from a geometric perspective. Using a technique known as kriging, developed in earth sciences, we examined whether the spatial organisation of brain regions showing positive activity could be predicted based on the spatial layout of regions showing activity decreases (and vice versa). Consistent with this hypothesis we established the spatial distribution of regions showing reductions in activity could predict (i) regions showing task-relevant increases in activity in both groups of humans and single individuals; (ii) patterns of neural activity captured by calcium imaging in mice; and, (iii) showed a high degree of generalisability across task contexts. Our analysis, therefore, establishes that antagonistic relationships between brain regions are topographically determined, a spatial analog for the well documented anti-correlation between brain systems over time. Significance StatementIt is well documented that brain activity changes in response to the demands of different situations, although what gives rise to the observed cortical activity patterns remains poorly understood. Using analytic tools from earth sciences, we examined whether the landscape of regional changes in activity emerge from a set of common topographical causes. Using only regions showing decreases in activity, we could predict the landscape of regions showing increases in activity using fMRI in humans and calcium imaging in mice. Our results suggest topographical principles determine the landscape of peaks and valleys in brain activity -- a spatial analog for the well documented anti-correlation between sets of brain regions over time.

neuroscience↗

bioRxiv2024

The impact of heterogeneous spatial autocorrelation on comparisons of brain maps

It is increasingly common to statistically compare brain maps to assess how spatially similar they are. However, statistical inference can be challenging due to the presence of spatial autocorrelation. Therefore, random permutation approaches based on null models are widely used to address this concern. Here, we show how that the presence of heterogeneity in the spatial autocorrelation across brain maps impacts the validity of statistical inference in common approaches for spatially correlated maps. Furthermore, we illustrate how a Bayesian spatial regression approach can be applied to compare functional and structural cortical brain maps, yielding valid statistical inferences even in the presence of heterogeneity. Explicitly modelling spatial properties provides more valid inferences about whole brain spatial maps allowing a wider and more sophisticated range of neurobiological questions to be answered about the relationship between brain maps than are possible with current approaches.

neuroscience↗