Biology subjects

Holmes, A.

Publications and source records attributed to Holmes, A..

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.

neuroscience↗

bioRxiv2026

Early sensory deprivation drives local reorganization of sensory integration within a conserved global hierarchy

The human brain processes sensory information through a hierarchical system, from primary to higher-level regions, integrating inputs across modalities to support perception and cognition. While early sensory loss triggers widespread neuroplastic changes, its impact on integration across the cortical hierarchy remains unclear. Here, we examined the cortical reorganization of individuals with early blindness and deafness using a sensory integration framework that quantifies how brain regions prioritize different sensory inputs across the hierarchy. We found that early sensory deprivation drives highly localized reorganization adjacent to the deprived primary cortical areas: extrastriate cortex in early blindness and the superior temporal cortex in early deafness. These findings were further corroborated by analysis of the functional gradients, which found reorganization within these sensory regions. Notably, the hierarchy was largely preserved across groups. However, the sensory integration framework uniquely detected reorganization in language-related regions in deaf individuals with knowledge of a visual communication system known as cued speech. The specific differences between early deaf and hearing individuals remained restricted to superior temporal cortex. Together, our findings demonstrate that early sensory deprivation drives targeted reorganization adjacent to the affected primary sensory cortex, while preserving the overall hierarchy of cortical integration.

neuroscience↗

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.

neuroscience↗

bioRxiv2026

Selective Activation of Nerve Fiber Subpopulations with Intrafascicular Stimulation

BackgroundPeripheral nerve stimulation (PNS) is most effective when specific nerve fiber subpopulations are activated, while minimizing off-target activation, which may cause undesirable side effects. This selectivity depends primarily on electrode design and charge delivery. We hypothesized that selective PNS could be achieved through electrode placement and intrafascicular electric field steering using Longitudinal Intrafascicular Electrodes (LIFEs). MethodsLIFEs were implanted into the tibial fascicle of the sciatic nerve of 17 anesthetized adult rats. We tested whether electrodes positioned at different cross-sectional and longitudinal locations within the same fascicle, together with different electric field-steering approaches produced distinct activation patterns in the gastrocnemius lateralis muscle. Muscle responses were measured using high-density epimysial electromyography (HD-eEMG). ResultsElectrodes placed at different locations within the same fascicle activated distinct muscle regions, demonstrating intrafascicular selectivity. Bipolar stimulation recruited nerve fibers differently than monopolar stimulation, showing that electric field steering can further shape the selective recruitment. In both configurations, increasing the stimulation amplitude produced a graded increase in muscle activation. Furthermore, our findings demonstrated that HD-eEMG is an effective tool for evaluating intrafascicular selectivity. ConclusionThese findings suggest that improving on-target selectivity may support next-generation bioelectronic therapies with better outcomes and fewer side effects, potentially enabling more precise, organ-specific neuromodulation. Using multiple intrafascicular electrodes may provide two complementary strategies for enhancing selectivity: strategic intrafascicular placement to access different fiber subpopulations and bipolar configurations to steer recruitment beyond what a single electrode can achieve.

bioengineering↗

bioRxiv2026

Widespread use of invalid statistical tests in biomedical machine learning

Cross-validation is routinely used to compare performance in biomedical artificial intelligence. Standard tests ignore correlation across cross-validation folds, inflating false-positive rates. In a PRISMA-guided meta-analysis of 184 studies (impact factor [≥] 15) across 30 biomedical fields, 97% use invalid tests. Among studies with abstract-level claims supported by invalid tests, 59% rely on a spurious comparison: one that loses significance after correcting for this correlation. On average, regaining significance requires a 43% larger effect. Extending these findings to the broader literature, we estimate that spurious comparisons occur in nearly two in three studies and support abstract-level claims in nearly one in three studies. Simulations confirm false-positive rates paradoxically approach 100% when cross-validation is repeated to improve stability. We introduce SHARP, a redesign of cross-validation, which best balances false-positive control and power among 13 benchmarked tests. These results reveal widespread fragility in biomedical artificial intelligence and offer a practical route to valid comparisons.

bioinformatics↗

bioRxiv2026

Synthetic Immunological Niche Reveals Early Immune Dysregulationand Stratifies Therapeutic Response in Type 1 Diabetes

In Type 1 Diabetes (T1D), disease onset and response to immunotherapy vary widely among individuals, reflecting heterogeneous stage-specific immune dysregulation that remains undefined. To investigate this heterogeneity, we engineered a microporous polycaprolactone scaffold that forms a synthetic immunological niche (IN) upon subcutaneous implantation, enabling in vivo capture of systemic immune dysregulation. In non-obese diabetic (NOD) mice, longitudinal transcriptomic profiling of IN-infiltrating cells identified early-stage genes relatively enriched for myeloid cells, followed by progressive increases in T cell-associated dysregulation at later stages that distinguished T1D progressors from non-progressors. We derived an early-stage IN-based T1D gene signature capturing immune alterations. The signature stratified NOD progressors from non-progressors as early as 6 weeks of age and was conserved across human T1D datasets, distinguishing T1D from non-diabetic individuals in spleen and pancreatic lymph node samples, but not peripheral blood. Signature-based stratification further revealed enrichment of macrophage-associated TNF- pathways in NOD progressors, validated in human T1D islets. Given heterogeneous response to anti-TNF- therapy, IN profiling identified resistance-associated mechanisms and enabled derivation of a pathway score that prospectively distinguished treatment-sensitive from resistant mice prior to therapy, establishing the IN as a minimally invasive platform for detecting stage-wise immune dysregulation and stratifying immunotherapy response in T1D.

bioengineering↗

bioRxiv2026

A citywide metagenomic analysis reveals surface-specific microbiome and resistome patterns in outdoor urban environments across Liverpool, UK.

Urbanisation is rapidly increasing worldwide, with increasing attention focused on its consequences for human populations and the environment. Despite the importance of outdoor urban environments for biodiversity and human wellbeing, their microbial ecology remains poorly characterised, particularly in relation to emerging microbial threats including antimicrobial resistance (AMR). Here, we present a citywide metagenomic study of outdoor public surfaces across Liverpool, United Kingdom, examining microbial community composition, diversity, and antimicrobial resistance gene (ARG) distribution across five distinct surface types. We show that patterns of human activity and surface use strongly influence both microbial community structure and AMR signatures in outdoor urban environments. Touchpoints were enriched for human-associated taxa and exhibited the highest overall resistome burdens, whereas Pathway and Waterside niches showed no strong taxonomic enrichment and exhibited low ARG prevalence. Refuse surfaces showed mixed patterns, characterised by sporadic but occasionally high-abundance ARG detections. Soil harboured the most distinct microbial communities but showed minimal ARG prevalence, which may partly reflect the limited representation of environmental taxa in current ARG databases. This study provides a baseline for understanding how urban infrastructure and behaviour shape microbial and resistance landscapes, and highlights the value of outdoor metagenomic surveillance for future environmental and public health research.

microbiology↗

bioRxiv2025

Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotype

Popular methods for analyzing the brains functional connectome examine statistical associations between pairs of atlas-defined brain regions, viewing the strength of these links as independent values. However, edges within a standard connectivity matrix, i.e., correlations between individual regions or nodes, are not independent. They are part of an interconnected system. Here, we propose that consideration of both independent, linear relationships (as in standard approaches such as linear kernel ridge regression and connectome-based predictive modeling) as well as higher order statistical associations - such as tertiary interactions between matrix components and global features of the matrix space - will enhance identification of meaningful individual differences. To test this, we adopt a geometrically grounded measure of similarity that accounts for higher-order local statistical relationships and global interactions, the Wasserstein metric. Results indicate that considering connectivity matrices as representations of their associated Gaussian distributions significantly improves both identification of individuals based on their connectivity matrices (aka, fingerprinting) and prediction of individual differences in phenotypes such as fluid intelligence and openness to experience. Thus, both pairwise local and global brain connectivity properties encode for meaningful individual differences that relate to phenotypic expressions and should be considered in brain-behavior predictive models.

neuroscience↗

bioRxiv2025

Risk reshapes amygdala representation of choice

Modifying behavior in response to changing environmental conditions is a crucial adaptive function. This capacity is exemplified when animals curtail pursuit of a valued outcome that risks being punished by aversive consequences, but the mediating brain mechanisms remain poorly understood. Here, using in vivo cellular-resolution calcium (Ca2+) imaging, optogenetics and chemogenetics, we show that risk of punishment dramatically alters animals choice between a large/risky and small- /safe reward and produces novel, causally necessary, patterns of activity in basolateral amygdala (BLA) neurons. We find that experience of a punished outcome generates a BLA representation that is selectively replayed when animals subsequently abort choice of the large/risky reward option. Additionally, we show that risk leads to the incorporation of newly encoding BLA neurons into the pre-choice representation, which predicts shifting away from the large/risky option. These findings reveal how dynamic reshaping of BLA representations underpins behavioral flexibility in the face of risk.

neuroscience↗

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.

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

A Tissue Proteolysis Activity Mapping and Substrate Discovery Platform for Identifying Novel Tumor-Activated Biosensors

Dysregulated extracellular proteolytic activity is a prominent hallmark of cancer and can thus be exploited for tumor detection and therapeutic development. However, the discovery of tumor-responsive probes has been hindered by the lack of methods capable of capturing proteolytic events directly in tissue samples. Here, we report PSurf, a platform that enables the identification of tissue-specific protease sensors with tissue specimens. Through differential selection of tumor-specific sequences over healthy tissue, PSurf identified context-specific tumor-activated probes that precisely distinguish metastatic lesions in lung tissue slices. Using these substrates, we engineered nanobody-targeted biosensors that release urinary reporters upon tumor-specific cleavage in vivo, enabling precise noninvasive tumor detection in a murine lung metastasis model. PSurf provides a foundation for developing conditionally activated agents through tissue-specific activity mapping and probe discovery.

bioengineering↗

bioRxiv2025

Inferring macroscopic intrinsic neural timescales using optimal control theory

The temporal evolution of brain activity relies on complex interactions within and between brain regions that are mediated by neurobiology and connectivity. To understand these interactions, many large-scale efforts have measured structural connectivity, neural activity, gene expression, and cognition across multiple modalities and species. However, data-driven discovery of large-scale activity models remains difficult owing to the lack of flexible quantitative frameworks for estimating the interplay between brain structure and function while preserving biophysical realism. Here, we provide such a framework by integrating network control theory (NCT) with automatic differentiation to estimate model parameters with greater biophysical realism from data. Specifically, we estimate the structural form of regional self-inhibition--a quantity that is experimentally difficult to measure--from MRI data. Next, we demonstrate that the resulting model-based self-inhibition parameters correlate significantly with regions intrinsic neural timescales (INTs), neurobiological measures of gene expression and cell-type densities, as well as behavioral measures of cognition. We demonstrate consistent results across multiple datasets and species. Finally, we demonstrate that our self-inhibition parameters enable the efficient control of brain dynamics from fewer brain regions. Taken together, our results provide a simple and flexible quantitative framework that more accurately captures the interplay between brain structure, function, and dynamics with greater biophysical realism.

neuroscience↗

bioRxiv2025

Neuroanatomy Reflects Individual Variability In Impulsivity in Youth

Individual differences in neural circuits underlying emotional regulation, motivation, and decision-making are implicated in many psychiatric illnesses. Interindividual variability in these circuits may manifest, at least in part, as individual differences in impulsivity at both normative and clinically significant levels. Impulsivity reflects a tendency towards rapid, unplanned reactions to internal or external stimuli without considering potential negative consequences coupled with difficulty inhibiting responses. Here, we use multivariate brain-based predictive models to explore the neural bases of impulsivity across multiple behavioral scales, neuroanatomical features (cortical thickness, surface area, and gray matter volume), and sexes (females and males) in a large sample of youth from the Adolescent Brain Cognitive Development (ABCD) Study at baseline (n=9,099) and two-year follow-up (n=6,432). Impulsivity is significantly associated with neuroanatomical variability, and these associations vary across behavioral scales and neuroanatomical features. Impulsivity broadly maps onto cortical thickness in dispersed regions (e.g., inferior frontal, lateral occipital, superior frontal, entorhinal), as well as surface area and gray matter volume in specific medial (e.g., parahippocampal, cingulate) and polar (e.g., frontal and temporal) territories. Importantly, while many relationships are stable across sexes and time points, others are sex-specific and dynamic. These results highlight the complexity of the relationships between neuroanatomy and impulsivity across scales, features, sexes, and time points in youth. These findings suggest that neuroanatomy, in combination with other biological and environmental factors, reflects a key driver of individual differences in impulsivity in youth. As such, neuroanatomical markers may help identify youth at increased risk for developing impulsivity-related illnesses. Furthermore, this work emphasizes the importance of adopting a multidimensional and sex-specific approach in neuroimaging and behavioral research.

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

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.

neuroscience↗

bioRxiv2024

The efficacy of resting-state fMRI denoising pipelines for motion correction and behavioural prediction.

Resting-state functional magnetic resonance imaging (rs-fMRI) is a pivotal tool for mapping the functional organization of the brain and its relation to individual differences in behaviour. One challenge for the field is that rs-fMRI signals are contaminated by multiple sources of noise that can contaminate these rs-fMRI signals, affecting the reliability and validity of any derivative phenotypes and attenuating their correlations with behaviour. Here, we investigate the efficacy of different noise mitigation pipelines, including white-matter and cerebrospinal fluid regression, independent component analysis (ICA) - based artefact removal, volume censoring, global signal regression (GSR), and diffuse cluster estimation and regression (DiCER), in simultaneously achieving two objectives: mitigating motion-related artifacts and augmenting brain-behaviour associations. Our analysis, which employed three distinct quality control metrics to evaluate motion influence and a kernel ridge regression for behavioural predictions of 81 different behavioural variables across two independent datasets, revealed that no single pipeline universally excels at achieving both objectives consistently across different cohorts. Pipelines combining ICA-FIX and GSR demonstrate a reasonable trade-off between motion reduction and behavioural prediction performance, but inter-pipeline variations in predictive performance are modest.

neuroscience↗

bioRxiv2024

Metapipeline-DNA: A Comprehensive Germline & Somatic Genomics Nextflow Pipeline

SummaryThe price, quality and throughout of DNA sequencing continue to improve. Algorithmic innovations have allowed inference of a growing range of features from DNA sequencing data, quantifying nuclear, mitochondrial and evolutionary aspects of both germline and somatic genomes. To automate analyses of the full range of genomic characteristics, we created an extensible Nextflow meta-pipeline called metapipeline-DNA. Metapipeline-DNA analyzes targeted and whole-genome sequencing data from raw reads through pre-processing, feature detection by multiple algorithms, quality-control and data- visualization. Each step can be run independently and is supported robust software engineering including automated failure-recovery, robust testing and consistent verifications of inputs, outputs and parameters. Metapipeline-DNA is cloud-compatible and highly configurable, with options to subset and optimize each analysis. Metapipeline-DNA facilitates high-scale, comprehensive analysis of DNA sequencing data. AvailabilityMetapipeline-DNA is an open-source Nextflow pipeline under the GPLv2 license and is available at https://github.com/uclahs-cds/metapipeline-DNA.

bioinformatics↗