Biology subjects

Noble, S.

Publications and source records attributed to Noble, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Dynamic social envirotypes demonstrate adolescent brain dysconnectivity is contextual

The adolescent social environment and dynamics therein have been linked to divergent brain organization and greater risk of psychopathology later in life, suggesting the neural underpinnings of mental health may depend on social context. To examine this, we leveraged longitudinal ABCD Study data to identify dynamic social envirotypes, or distinct trajectories of social environment change over four years. Dynamic social envirotypes differed in mental health outcomes, changes in mental health, and whole-brain functional connectivity, with each dynamic social envirotype exhibiting a characteristic pattern of brain network organization. Crucially, functional connectivity associations with internalizing and externalizing problems diverged across dynamic social envirotypes, with the highest and lowest social resource envirotypes showing largely opposing associations between connectivity and symptom severity. These findings suggest that dysconnectivity is contextual: there is no single brain connectivity pattern that is universally associated with better mental health. Rather, adolescent brain connectivity relates to mental health in context-specific ways, challenging normative narratives of the neural underpinnings of psychopathology and centering the role of context for understanding and treating mental health.

neuroscience↗

bioRxiv2026

When Music Loses Its Pleasure: Hippocampal Cingulum White Matter as a Structural Mediator of Musical Reward Decline in Aging

Individuals ability to obtain pleasure from music, referred to as musical reward sensitivity, declines with age, yet the neural mechanisms underlying this decline remain unclear. In this study, we investigated musical reward sensitivity measured in 58 older adults and 131 young adults. Consistent with prior findings, young adults reported higher musical reward sensitivity than older adults (p < 0.05). To identify neuroanatomical predictors of musical reward sensitivity, we employed the elastic-net model to predict musical reward sensitivity using white matter microstructural properties and gray matter morphometric properties from the whole brain. In older adults, fractional anisotropy (FA) in the bilateral hippocampal cingulum (CGH) and external capsule (EC) reliably predicted individual differences in musical reward sensitivity. Moreover, FA in the right CGH significantly mediated the relationship between age and musical reward sensitivity in older adults. Notably, these associations were specific to musical reward sensitivity in older adults--that is, they did not replicate in young adults, and did not extend to general reward sensitivity. Together, these findings highlight the critical role of white matter integrity, particularly within the hippocampal-limbic pathways, in age-related changes in musical reward processing, and suggest a potential neurobiological target for interventions aimed at enhancing well-being in older adulthood. Key pointsO_LIWhole-brain machine learning identified the FA in the hippocampal cingulum and external capsule as robust predictors of musical reward sensitivity in older adults. C_LIO_LIFA in the right hippocampal cingulum mediated the association between age and musical reward sensitivity. C_LIO_LIThese findings extend previous association-based studies by identifying hippocampal-limbic white matter as a key neural substrate supporting musical reward sensitivity decline during aging. C_LI

neuroscience↗

bioRxiv2026

The Hidden Landscape of Missed Effects in Human Functional Neuroimaging

Functional neuroimaging aims to uncover brain processes underlying behavior and disease, yet studies are often underpowered to detect these effects. How this literature has shaped our understanding of brain function remains unknown, and little guidance exists for planning better powered studies. An underappreciated barrier is that commonly reported effect sizes across the brain are inflated, biasing study planning. Here, we introduce a correction for this inflation bias and show how more accurate studies can be planned using corrected effect size benchmarks from a mega-analysis of 63 typical studies across seven large datasets (52,979 participants). We find that common methods of planning studies based on uncorrected effects lead to roughly half the expected detections at typical sample sizes, with limited spatial overlap with original findings. These missed effects collectively explain meaningful additional variance in the desired outcome. We show how to recover missed effects by planning not only for power but also for a target number of detections via corrected benchmarks, or by taking a whole-brain approach with multivariate effects that individual research groups can detect (n < 50 compared to n > 1,000 for a typical univariate effect). These findings lay the groundwork for more informed study planning and a richer understanding of the widespread nature of brain effects, with implications for shared challenges (and solutions) across biomedicine.

neuroscience↗

bioRxiv2026

The Incremental Cluster Threshold-Free Cluster Enhancement Algorithm for Functional Connectivity Analysis

Threshold-free cluster enhancement (TFCE) is one of the most used statistical inference methods in neuroimaging, but its computational cost limits some of its applications. The current implementations recompute clusters at each threshold step, creating computation costs that poorly scale with precision increases. Furthermore, as larger samples and reduced noise increase maximum t-statistics, computational burden grows correspondingly. As the field moves towards finer parcellations, the number of FC edges grows quadratically with the number of ROIs, making TFCE computationally infeasible at the scales increasingly demanded by the field. We present Incremental Cluster TFCE (IC-TFCE), an algorithm that produces numerically equivalent results to standard TFCE while decoupling runtime from discretization precision. The IC-TFCE builds clusters incrementally from previous threshold steps rather than recomputing them, stores TFCE results on a region of interest (ROI) based structure instead of a functional connectivity (FC) edge structure for improved speed, and can be applied to voxel data through a novel graph transformation described and validated herein. This algorithm achieves a measured 3-93x speedup for FC TFCE depending on the precision parameter dh, making TFCE analyses with fine parcellations of 1000 or more ROIs computationally tractable for the first time. Finally, we validate correctness through mathematical proof and numerical comparison. The efficiency provided by IC-TFCE allowed a large-scale empirical power analysis across dh values to guide practitioners in parameter selection for their analyses.

neuroscience↗

bioRxiv2026

A low-variance subspace underlies individual differences in resting state fMRI

Non-invasive whole-brain recordings of human brain activity, such as those from resting state fMRI (rs-fMRI), contain reliable individual differences across thousands of features, but it remains unknown whether this reliability can be isolated in a small number of dimensions. Here, we directly optimize test-retest reliability across many of these features and identify a low-dimensional linear subspace with very high reliability. These dimensions form personal fingerprints, allowing participants to be identified with high accuracy despite fingerprints explaining only a fraction of the total variance. Several dimensions are strongly associated with a single anatomical, demographic, or behavioral variable, and most dimensions can be predicted from the anatomical layout of cortical regions. Together, our findings suggest that stable individual signatures can be isolated from rsfMRI. These signatures reflect persistent anatomical and physiological differences, and provide a principled low-dimensional basis for biomarker discovery.

neuroscience↗

bioRxiv2026

Traumatic Brain Injury Associated with Altered Corpus Callosum Microstructure in Females: Exploring the Roles of Menopause Timing and Hormone Therapy in UK Biobank

BackgroundTraumatic brain injury (TBI) has lasting effects on white matter, yet sex-specific factors such as menopause timing and hormone replacement therapy (HRT) may modulate these outcomes in females. ObjectivesTo investigate how TBI, menopause timing, HRT use, and reproductive history relate to corpus callosum white matter microstructure in female UK Biobank participants. DesignCross-sectional analysis of UK Biobank diffusion MRI data using propensity score matching to compare females with TBI to controls. MethodsWe analyzed diffusion MRI data from females with and without TBI. Corpus callosum fractional anisotropy (FA), mean diffusivity (MD), and isotropic volume fraction (ISOVF) were assessed. TBI effects were examined across pre- and post-menopausal groups, accounting for HRT use, duration, and reproductive factors. ResultsFemales with TBI (n=363) exhibited widespread corpus callosum alterations compared to propensity-matched controls (n=10,128), with reduced FA across all regions (genu: {beta}=-0.006, FDR p=0.027; body: {beta}=-0.006, FDR p=0.002; splenium: {beta}=-0.004, FDR p=0.009) and elevated MD in anterior regions (genu: FDR p=0.001; body: FDR p=0.002). TBI sustained before menopause was associated with significantly lower splenium FA ({beta}=-0.010, p=0.031) and higher body MD ({beta}=0.000019, p=0.021) compared with TBI sustained after menopause and controls. HRT use did not modify TBI-related alterations in primary analyses. However, among HRT users (n=3,108), a significant TBIxduration interaction emerged for genu MD ({beta}=2.00x10-6, p=0.0295), indicating that the effect of HRT duration on white matter microstructure differed between TBI cases and healthy females. Reproductive factors (parity, reproductive lifespan) independently predicted some white matter measures but did not confound TBI, menopause timing, or HRT associations. ConclusionsTBI-related white matter changes in females are influenced by menopause timing and hormonal exposure, with HRT effects dependent on duration and injury context. These findings highlight the importance of sex- and hormone-specific approaches in TBI research and the need for longitudinal studies to clarify mechanisms and potential interventions.

neuroscience↗

bioRxiv2026

PRISME: A MATLAB Toolbox For Large Data-Driven Multimodal Power Benchmarking

Low statistical power in neuroimaging often undermines research in the field, leading to missed effects, wasted resources, and reduced reproducibility. Performing power analyses during the study design phase is extremely important, but often prohibitively difficult due to a lack of analytical solutions and high computational costs. We present PRISME (Power Resampling Infrastructure for Statistical Method Evaluation), a MATLAB toolbox for neuroimaging power benchmarking. PRISME provides a computational framework for empirical power analysis independent of inference methods, enabling large scale power benchmarking and method comparison. The toolbox supports diverse neuroimaging data types, including both voxel-based activation and functional connectivity analyses, with a non-parametric, flexible algorithm and unified data representations. Furthermore, unlike previous empirical power approaches, PRISME supports multiple test types, such as association and difference tests with behavioral and clinical measures. Finally, PRISMEs 25x speedup from algorithmic optimizations enables larger-scale power benchmarking, including the first power analysis for the ABCD dataset. Overall, PRISME is the first method- and data-type-agnostic power benchmarking tool for neuroimaging, providing a single solution for power analysis across diverse study designs.

neuroscience↗

bioRxiv2025

Overlooked features lead to divergent neurobiological interpretations of brain-based machine learning biomarkers

A central objective in human neuroimaging is to understand the neurobiology underlying cognition and mental health. Machine learning models trained on brain connectivity data are increasingly used as tools for predicting behavioral phenotypes 1,2, enhancing precision medicine 3,4, and improving generalizability compared to traditional MRI studies 5. However, the high dimensionality of brain connectivity data makes model interpretation challenging 6. Prevailing practices within the field rely on sparsely selected brain connectivity features, implicitly interpreting identified feature networks as uniquely representative of a given phenotype while overlooking others. Here, we show that commonly overlooked brain connectivity features can achieve similar prediction accuracies while yielding markedly different neurobiological interpretations. Using four large-scale neuroimaging datasets spanning over 12,000 participants and 13 outcomes, we demonstrate that this phenomenon is widespread across cognitive, developmental, and psychiatric phenotypes. It extends to both functional connectivity (fMRI) and structural (DTI) connectomes and remains evident even in external validation. These findings suggest that common practices may lead to feature overinterpretation and a misrepresentation of the neurobiological bases of brain-behavior associations. Such interpretations present only the "tip of the iceberg" when certain disregarded features may be just as meaningful, potentially contributing to ongoing issues surrounding reproducibility within the field. More broadly, our results point to the possibility that multiple neurobiologically distinct models may exist for the same phenotype, with implications for identifying meaningful subtypes within clinical and research populations.

neuroscience↗

bioRxiv2024

Connectome caricatures: removing large-amplitude co-activation patterns in resting-state fMRI emphasizes individual differences

High-amplitude co-activation patterns are sparsely present during resting-state fMRI but drive functional connectivity1-5. Further, they resemble task activation patterns and are well-studied3,5-10. However, little research has characterized the remaining majority of the resting-state signal. In this work, we introduced caricaturing--a method to project resting-state data to a subspace orthogonal to a manifold of co-activation patterns estimated from the task fMRI data. Projecting to this subspace removes linear combinations of these co-activation patterns from the resting-state data to create Caricatured connectomes. We used rich task data from the Human Connectome Project (HCP)11 and the UCLA Consortium for Neuropsychiatric Phenomics12 to construct a manifold of task co-activation patterns. Caricatured connectomes were created by projecting resting-state data from the HCP and the Yale Test-Retest13 datasets away from this manifold. Like caricatures, these connectomes emphasized individual differences by reducing between-individual similarity and increasing individual identification14. They also improved predictive modeling of brain-phenotype associations. As caricaturing removes group-relevant task variance, it is an initial attempt to remove task-like co-activations from rest. Therefore, our results suggest that there is a useful signal beyond the dominating co-activations that drive resting-state functional connectivity, which may better characterize the brains intrinsic functional architecture.

neuroscience↗

bioRxiv2024

Brain-phenotype predictions can survive across diverse real-world data

Recent work suggests that machine learning models predicting psychiatric treatment outcomes based on clinical data may fail when applied to unharmonized samples. Neuroimaging predictive models offer the opportunity to incorporate neurobiological information, which may be more robust to dataset shifts. Yet, among the minority of neuroimaging studies that undertake any form of external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies. Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. We demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features with sample sizes in the hundreds. Results indicate the potential of functional connectivity-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of neuroimaging predictive models in real-world scenarios and clinical settings.

neuroscience↗

bioRxiv2023

Power and reproducibility in the external validation of brain-phenotype predictions

Identifying reproducible and generalizable brain-phenotype associations is a central goal of neuroimaging. Consistent with this goal, prediction frameworks evaluate brain-phenotype models in unseen data. Most prediction studies train and evaluate a model in the same dataset. However, external validation, or the evaluation of a model in an external dataset, provides a better assessment of robustness and generalizability. Despite the promise of external validation and calls for its usage, the statistical power of such studies has yet to be investigated. In this work, we ran over 60 million simulations across several datasets, phenotypes, and sample sizes to better understand how the sizes of the training and external datasets affect statistical power. We found that prior external validation studies used sample sizes prone to low power, which may lead to false negatives and effect size inflation. Furthermore, increases in the external sample size led to increased simulated power directly following theoretical power curves, whereas changes in the training dataset size offset the simulated power curves. Finally, we compared the performance of a model within a dataset to the external performance. The within-dataset performance was typically within r=0.2 of the cross-dataset performance, which could help decide how to power future external validation studies. Overall, our results illustrate the importance of considering the sample sizes of both the training and external datasets when performing external validation.

neuroscience↗

bioRxiv2023

The effects of data leakage on neuroimaging predictive models

Predictive modeling has now become a central technique in neuroimaging to identify complex brain-behavior relationships and test their generalizability to unseen data. However, data leakage, which unintentionally breaches the separation between data used to train and test the model, undermines the validity of predictive models. Previous literature suggests that leakage is generally pervasive in machine learning, but few studies have empirically evaluated the effects of leakage in neuroimaging data. Although leakage is always an incorrect practice, understanding the effects of leakage on neuroimaging predictive models provides insight into the extent to which leakage may affect the literature. Here, we investigated the effects of leakage on machine learning models in two common neuroimaging modalities, functional and structural connectomes. Using over 400 different pipelines spanning four large datasets and three phenotypes, we evaluated five forms of leakage fitting into three broad categories: feature selection, covariate correction, and lack of independence between subjects. As expected, leakage via feature selection and repeated subjects drastically inflated prediction performance. Notably, other forms of leakage had only minor effects (e.g., leaky site correction) or even decreased prediction performance (e.g., leaky covariate regression). In some cases, leakage affected not only prediction performance, but also model coefficients, and thus neurobiological interpretations. Finally, we found that predictive models using small datasets were more sensitive to leakage. Overall, our results illustrate the variable effects of leakage on prediction pipelines and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling.

neuroscience↗

bioRxiv2022

Trends in self-citation rates in neuroscience literature

Citation metrics influence academic reputation and career trajectories. Recent works have highlighted flaws in citation practices in the Neurosciences, such as the under-citation of women. However, self-citation rates--or how much authors cite themselves--have not yet been comprehensively investigated in the Neurosciences. This work characterizes self-citation rates in basic, translational, and clinical Neuroscience literature by collating 100,347 articles from 63 journals between the years 2000-2020. In analyzing over five million citations, we demonstrate four key findings: 1) increasing self-citation rates of Last Authors relative to First Authors, 2) lower self-citation rates in low- and middle-income countries, 3) gender differences in self-citation stemming from differences in the number of previously published papers, and 4) variations in self-citation rates by field. Our characterization of self-citation provides insight into citation practices that shape the perceived influence of authors in the Neurosciences, which in turn may impact what type of scientific research is done and who gets the opportunity to do it.

neuroscience↗

bioRxiv2021

Leveling up: improving power in fMRI by moving beyond cluster-level inference

Inference in neuroimaging commonly occurs at the level of "clusters" of neighboring voxels or connections, thought to reflect functionally specific brain areas. Yet increasingly large studies reveal effects that are shared throughout the brain, suggesting that reported clusters may only reflect the "tip of the iceberg" of underlying effects. Here, we empirically compare power of traditional levels of inference (edge and cluster) with broader levels of inference (network and whole-brain) by resampling functional connectivity data from the Human Connectome Project (n=40, 80, 120). Only network- and whole brain-level inference attained or surpassed "adequate" power ({beta}=80%) to detect an average effect, with almost double the power for network-compared with cluster-level procedures at more typical sample sizes. Likewise, effects tended to be widespread, and more widespread pooling resulted in stronger magnitude effects. Power also substantially increased when controlling FDR rather than FWER. Importantly, there may be similar implications for task-based activation analyses where effects are also increasingly understood to be widespread. However, increased power with broader levels of inference may diminish the specificity to localize effects, especially for non-task contexts. These findings underscore the benefit of shifting the scale of inference to better capture the underlying signal, which may unlock opportunities for discovery in human neuroimaging.

neuroscience↗

bioRxiv2021

Large-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity

Handedness influences differences in lateralization of language areas as well as dominance of motor and somatosensory cortices. However, differences in whole brain functional organization due to handedness have been relatively understudied beyond pre-specified networks of interest. Functional connectivity offers the ability to unravel differences in the functional organization of the whole brain. Here, we compared connectivity profiles of left- and right-handed individuals using data-driven parcellations of the whole brain. We explored differences in connectivity profiles of previously established regions of interest, and showed functional organization differences between primarily left- and primarily right-handed individuals in the motor, somatosensory, and language areas using functional connectivity. We then proceeded to investigate these differences in the whole brain and found that the functional organization of left- and right-handed individuals are not specific to regions of interest. In particular, we found that connections between and within-hemispheres and the cerebellum show distinct patterns of connectivity. Together these results shed light on regions of the brain beyond those traditionally explored that contribute to differences in the functional organization of left- and right-handed individuals.

neuroscience↗

bioRxiv2021

Spatial and temporal autocorrelation weave human brain networks

High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler low-dimensional statistics is largely unknown. To explore this question, we examine resting state fMRI (rs-fMRI) data using complex topology measures from network neuroscience. We show that spatial and temporal autocorrelation are reliable statistics which explain numerous measures of network topology. Surrogate timeseries with subject-matched spatial and temporal autocorrelation capture nearly all reliable individual and regional variation in these topology measures. Network topology changes during aging are driven by spatial autocorrelation, and multiple serotonergic drugs causally induce the same topographic change in temporal autocorrelation. This reductionistic interpretation of widely-used complexity measures may help link them to neurobiology.

neuroscience↗

bioRxiv2021

Low Infant Functional Connectome-based Identification Rates Across the First Year of Life

The uniqueness and stability of the adolescent and adult functional connectome has been demonstrated to be high (80-95% identification) using connectome-based identification (ID) or "fingerprinting". However, it is unclear to what extent individuals exhibit similar distinctiveness and stability in infancy, a developmental period of rapid and unparalleled brain development. In this study, we examined connectome-based ID rates within and across the first year of life using a longitudinal infant dataset at 1 month and 9 months of age. We also calculated the test-retest reliability of individual connections across the first year of life using the intraclass correlation coefficient (ICC). Overall, we found substantially lower infant ID rates than have been reported in adult and adolescent populations. Within-session ID rates were moderate and significant (ID = 48.94-70.83%). Between-session ID rates were very low and not significant, with task-to-task connectomes resulting in the highest between-session ID rate (ID = 26.6%). Similarly, average edge-level test-retest reliability was higher within-session than between-session (mean within-session ICC = 0.17, mean between-session ICC = 0.10). These findings suggest a lack of uniqueness and stability in functional connectomes across the first year of life consistent with the unparalleled changes in brain functional organization during this critical period.

neuroscience↗

bioRxiv2020

Recording of DNA binding events during gut commensalism reveals the action of a repurposed Candida albicans regulatory network

Candida albicans is a central fungal component of the human gut microbiota and an opportunistic pathogen. Two C. albicans transcription factors, Wor1 and Efg1, control its ability to colonize the mammalian gut. They are also master regulators of an epigenetic switch required for mating. Here, we show that six additional mating regulators influence gut commensalism. Using an adapted Calling Card-Seq protocol to record Candida transcription factor DNA binding events in the host, we validated these relationships during murine gut colonization. Finally, by comparing the in-host transcriptomes of regulatory mutants with enhanced vs. diminished commensal fitness, we identified a set of candidate commensalism effectors. These include Cht2, a GPI-linked chitinase whose gene is bound by Wor1, Czf1, and Efg1 in vivo and that we show to promote commensalism. We conclude that the network required for a C. albicans sexual switch is biochemically active in the host digestive tract, where it is repurposed to direct commensalism.

microbiology↗