Biology subjects

Miller, R. L.

Publications and source records attributed to Miller, R. L..

At least 19 recordsLinked to original sources

bioRxiv2026

The development of ToF-SIMS for in-situ glycosaminoglycan analysis

Glycosaminoglycans (GAGs) are linear polysaccharides with essential roles in a myriad of biological processes. Despite their biological importance, methods to determine both spatial and compositional information is limited. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) provides spatially resolved compositional information of biological molecules without enzymatic digestion or label incorporation, enabling unbiased analysis independent of enzyme or label selectivity, overcoming many current limitations in GAG analysis. Here, we present the identification and validation of GAG discriminatory ions from biological samples by comparison of spectra from purified GAGs and cells with genetically modified GAG biosynthetic pathways. Ions discriminatory of specific GAG sub-families are identified and related to GAG structural components. The analysis is applied to human induced pluripotent stem cells engineered to lack heparan sulphate (HS), where compensatory changes in GAG display that link to function were observed. Furthermore, the broad applicability and spatial resolution of the technique is highlighted through detection of a disease-induced reduction in HS within the individual glomeruli of diabetic mice.

biochemistry↗

bioRxiv2026

Label-free GAG disaccharide analysis by HILIC-MS/MS for studying diverse biological sample types

The extracellular matrix (ECM) and cell surface glycocalyx are key components of biology and play crucial roles in development and tissue function, as well as disease. Proteoglycans, and their glycosaminoglycan (GAG) side chains, are critical components of the ECM and the glycocalyx. GAGs can bind to many different proteins, such as chemokines, and form hydrated barriers around cells. Existing and new methods are helping us to uncover more about the roles of GAGs in biology. Here, we expand on existing technologies and provide streamlined, standardised and well-documented methods that can be easily adopted in standard analytical facilities. We provide extensive detailed step-by-step guides describing sample disruption, GAG disaccharide preparation from biological tissues and their analysis by HILIC-MS/MS. In addition, we demonstrate utility of this method when using a range of different samples as biological sources. This method will sit alongside existing and new techniques to help improve access to GAG analysis, and thereby further the field of understanding GAG function in complex biological contexts.

biochemistry↗

bioRxiv2026

When Randomness Becomes Rigid: Dynamic Connectivity Entropy and Symptom-Linked Network Dysfunction in Schizophrenia

High dimensionality of dynamic functional connectivity (dFNC) data representation complicates clinical interpretation and biomarker discovery. We propose a new complementary analytical framework based on dynamic inter-network connectivity entropy (DICE) and its potential use as a biomarker of mental illness. Our framework shows that DICE features extend beyond patient-control discrimination, revealing distinct pathophysiological signatures and differential associations with symptom dimensions. Using resting-state fMRI data from 311 participants, 160 controls, 151 schizophrenia (SZ) patients, we identified 53 intrinsic networks, computed DICE and derived three families of DICE-based metrics: (i) entropy level and range, (ii) distributional shape and temporal organization, (iii) entropy-state repertoire and occupancy. These measures revealed a multidimensional signature of altered entropy dynamics in SZ: (1) elevated baseline entropy with reduced fluctuation magnitude and reduced entropy acceleration; (2) reduced temporal persistence of entropy excursions and entropy distributions closer to Gaussian; and (3) a narrowed repertoire of entropy states, prolonged time in near-baseline entropy configurations. The DICE-based metrics within the SZ group show different associations with symptom dimensions. Reduced fluctuation magnitude and acceleration were associated with greater PANSS general symptom severity (disturbance of volition and preoccupation). Reduced deviation from Gaussianity was associated with higher PANSS positive severity (delusions and hallucinations). Reduced temporal persistence was associated with multiple PANSS positive, negative, and general symptoms. Reduced entropy-state diversity and prolonged dwell time in near-baseline states were associated with depression and PANSS positive/general severity, respectively. The multidimensional pathophysiology revealed through the different entropy patterns may potentially guide biomarker development and personalized treatments.

neuroscience↗

bioRxiv2026

Siglec-7 and -15 recognize repeated clustered sulfo-sialo O-glycan motifs on select O-glycoproteins

Siglecs are key immunoreceptors in immune homeostasis and cancer immunosuppression. Diverse endogenous and exogenous sialoglycan ligands induce Siglec-mediated immunoregulation, but the nature of endogenous Siglec ligands and how seemingly ubiquitous sialoglycans achieve selectivity is unclear. Here, we employed a HEK293 cell-based array and glycoprotein reporters designed from human mucins and mucin-like proteins with O-glycans in natural clusters and repeat motifs to dissect specificities of Siglec-7 and -15. We first used precomplexed Siglec-Fc chimera to demonstrate that CHST1-mediated 6-O-sulfation markedly enhanced binding to select O-glycoproteins, namely MAdCAM1, CD43, and PSGL-1. Probing binding properties of Siglec-7 expressed exogenously on CHO cells or endogenously on human monocytes by reverse assays employing fluorophore-tagged O-glycoprotein reporters revealed specific binding to the sulfo-sialyl core1 O-glycoform of the same O-glycoproteins. Our study indicates that Siglec-7 on immune cells employs multivalent interactions with repeated motifs of clustered sulfo-sialyl O-glycans to drive selectivity in ligand interactions and Siglec-induced immunosuppression.

molecular biology↗

bioRxiv2025

Genetic and preclinical evidence implicating chondroitin sulfate as a matritherapeutic target for the treatment of type 2 diabetes

Peptide-based treatments for type 2 diabetes (T2D) are often limited by variable patient responses, frequent discontinuation, and substantial costs. Emerging lines of evidence link the extracellular matrix (ECM) to the pathophysiology of T2D, highlighting a largely unexplored modality for managing this heterogenous disease. Chondroitin sulfate (CS), a major glycosaminoglycan in the ECM, has been suggested to improve cardiometabolic health in preclinical research. However, the human genetic and pharmacological basis for CS as an anti-diabetic target is largely unexplored. Here, we uncover novel and robust links between 12 CS-related genes and both glycemic traits and the risk of T2D in hu-mans. Complementing this, administration of CS leads to a profound lowering of blood glucose levels in severely diabetic mice and improves glucose tolerance and cardiac clearance of circulating glucose in diet-induced obese mice without causing hypoglyce-mia or other adverse effects. The improvement in glycemic control is accompanied by increased glucose-stimulated insulin secretion and enhanced insulin action, effects which seem to occur independent of the incretin system. The combination of human genetic evidence and appealing pharmacodynamic features highlights CS as a promising ECM-target for developing novel pharmacotherapies that complement current treatments for T2D.

physiology↗

bioRxiv2025

The Ligand Preference of LRP1 is Regulated by O-glycans

Low-density lipoprotein receptor (LDLR) and LDLR-related proteins (LRPs) are endocytic receptors serving as essential physiological regulators of multiple processes including cholesterol clearance, protein reabsorption and neuronal protein trafficking. We originally discovered O-glycans in linkers of the ligand-binding domains of LDLR/LRP receptors and showed that these play critical roles for uptake of LDL by LDLR and albumin by LRP2. Remarkably, these linker O-glycans are introduced exclusively by GALNT11, one out of 20 polypeptide GalNAc-transferase isoenzymes. Here, we investigate the role of linker O-glycans on the large ([~]600 kDa) and widely expressed multiligand LRP1 receptor implicated in diseases including neuropathies. In genetically engineered cell models we activated endogenous full-coding LRP1 with and without O-glycans and demonstrate that while the uptake of certain ligands, such as RAP and ApoE, was unaffected by O-glycans, the uptake of the neurotoxic molecules tau and amyloid beta was altered and in opposite directions. This demonstrates that O-glycans in ligand-binding domains can differentially modulate ligand affinity and specificity of LRP1. Our findings highlight an overlooked regulatory mechanism of endocytic receptors and identify the ligand repertoire of LRP1 as being influenced by O-glycans, with potential implications for neurodegenerative disease.

molecular biology↗

bioRxiv2025

Frequency-Aware Interpretable Deep Learning Framework for Alzheimer's Disease Classification Using rs-fMRI

Gaining insight into the spectral and temporal alterations in brain connectivity associated with Alzheimers disease (AD) may offer pathways toward more informative biomarkers and a deeper understanding of disease mechanisms. We propose FINE (Frequency-aware Interpretable Neural Encoder), a novel deep learning model designed to capture multi-scale temporal and frequency-specific patterns in dynamic functional network connectivity (dFNC) derived from resting-state fMRI. FINE integrates multiple expert branches, including convolutional layers, learnable wavelet layers, transformers, and static encoders, enabling the joint modeling of temporal evolution and spectral content of brain networks in an end-to-end framework. Beyond classification, FINE supports frequency-wise interpretability by aligning gradient-based saliency maps with statistical group differences, revealing potential robust, biologically meaningful biomarkers of AD. Evaluated on the large OASIS-3 dataset (856 subjects), FINE achieves AD classification performance (ROC-AUC 0.769) and provides insights into frequency-specific connectivity disruptions, particularly within subcortical, sensorimotor, and cerebellar networks. Our results demonstrate that incorporating frequency-aware modeling and interpretable architectures can advance both disease classification and underlying functional disruption of AD-related brain dynamics.

neuroscience↗

bioRxiv2025

Precision functional mapping reveals less inter-individual variability in the child vs. adult human brain

Human brain organization shares a common underlying structure, though recent studies have shown that features of this organization also differ significantly across individual adults. Understanding the developmental pathways that lead to individually unique brains is important for advancing models of cognitive development and neurodevelopmental disorders. Here we use highly personalized precision neuroimaging methods to map brain networks within 12 individual children, ages 8-12 years. We demonstrate fMRI functional connectivity maps that substantially exceed the reliability of traditional techniques, allowing us to measure individual differences after overcoming biases from measurement noise. Children share core functional network topography, with greatest inter-individual variability in association regions, consistent with adult findings. However, children show less between-subject variability than adults, suggesting increasing individual differentiation in brain networks with development. This pediatric precision neuroimaging dataset is publicly available to support future brain development research and provides a high-fidelity foundation for studying individual variation in atypical development.

neuroscience↗

bioRxiv2025

Topologically Optimized Intrinsic Brain Networks

The estimation of brain networks is instrumental in quantifying and evaluating brain function. Nevertheless, achieving precise estimations of subject-level networks has proven to be a formidable task. In response to this challenge, researchers have developed group-inference frameworks that leverage robust group-level estimations as a common reference point to infer corresponding subject-level networks. Generally, existing approaches either leverage the common reference as a strict, voxel-wise spatial constraint (i.e., strong constraints at the voxel level) or impose no constraints. Here, we propose a targeted approach that harnesses the topological information of group-level networks to encode a high-level representation of spatial properties to be used as constraints, which we refer to as Topologically Optimized Intrinsic Brain Networks (TOIBN). Consequently, our method inherits the significant advantages of constraint-based approaches, such as enhancing estimation efficacy in noisy data or small sample sizes. On the other hand, our method provides a softer constraint than voxel-wise penalties, which can result in the loss of individual variation, increased susceptibility to model biases, and potentially missing important subject-specific information. Our analyses show that the subject maps from our method are less noisy and true to the group networks while promoting subject variability that can be lost from strict constraints. We also find that the topological properties resulting from the TOIBN maps are more expressive of differences between individuals with schizophrenia and controls in the default mode, subcortical, and visual networks.

neuroscience↗

bioRxiv2024

A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia

Over the past decade and a half, dynamic functional imaging has revolutionized the neuroimaging field. Since 2009, it has revealed low dimensional brain connectivity measures, has identified potential common human spatial connectivity states, has tracked the transition patterns of these states, and has demonstrated meaningful alterations in these transition and spatial patterns in neurological disorders, psychiatric disorders, and over the course of development. More recently, researchers have begun to analyze this data from the perspective of dynamic system and information theory in the hopes of understanding the constraints within which these dynamics occur and how they may support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Progress has begun to accelerate in this area, particularly around consciousness, which appears to be strongly linked to entropy production in the brain. Outside of disorders of consciousness, however, little attention has been paid to the effects of psychiatric disease on entropy production in the human brain. Even disorders characterized by substantial changes in conscious experience have not been widely analyzed from this perspective. Here, we begin to rectify this gap by examining the complexity of subject trajectories in this state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this state space over the course of the scan. The dynamic complexity of these trajectories is estimated using a Kozachenko-Leonenko entropy estimator, which assesses the rate of Shannon entropy production along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls, and that this drop in complexity concentrates along specific dimensions of projected basis space. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients brain function.

neuroscience↗

bioRxiv2024

Uncovering Effects of Schizophrenia upon a Maximally Significant, Minimally Complex Subset of Default Mode Network Connectivity Features

A common analysis approach for resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data involves clustering windowed correlation time-series and assigning time windows to clusters (i.e., states) that can be quantified to summarize aspects of the dFNC dynamics. However, those methods can be dominated by a select few features and obscure key dynamics related to less dominant features. This study presents an iterative feature learning approach to identify a maximally significant and minimally complex subset of dFNC features within the default mode network (DMN) in schizophrenia (SZ). Utilizing dFNC data from individuals with SZ and healthy controls (HC), our approach uncovers a subset of features that has a greater number of dFNC states with disorder-related dynamics than is found when all features are present in the clustering. We find that anterior cingulate cortex/posterior cingulate cortex (ACC/PCC) interactions are consistently related to SZ across the most significant iterations of the feature learning analysis and that individuals with SZ tend to spend more time in states with greater intra-ACC anticorrelation and almost no time in a state of high intra-ACC correlation that HCs periodically enter. Our findings highlight the need for nuanced analyses to reveal disorder-related dynamics and advance our understanding of neuropsychiatric disorders.

neuroscience↗

bioRxiv2024

Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures

Deep learning methods are increasingly being applied to raw electro-encephalogram (EEG) data. However, if these models are to be used in clinical or research contexts, methods to explain them must be developed, and if these models are to be used in research contexts, methods for combining explanations across large numbers of models must be developed to counteract the inherent randomness of existing training approaches. Model visualization-based explainability methods for EEG involve structuring a model architecture such that its extracted features can be characterized and have the potential to offer highly useful insights into the patterns that they uncover. Nevertheless, model visualization-based explainability methods have been underexplored within the context of multichannel EEG, and methods to combine their explanations across folds have not yet been developed. In this study, we present two novel convolutional neural network-based architectures and apply them for automated major depressive disorder diagnosis. Our models obtain slightly lower classification performance than a baseline architecture. However, across 50 training folds, they find that individuals with MDD exhibit higher {beta} power, potentially higher {delta} power, and higher brain-wide correlation that is most strongly represented within the right hemisphere. This study provides multiple key insights into MDD and represents a significant step forward for the domain of explainable deep learning applied to raw EEG. We hope that it will inspire future efforts that will eventually enable the development of explainable EEG deep learning models that can contribute both to clinical care and novel medical research discoveries.

neuroscience↗

bioRxiv2024

Identifying Reproducibly Important EEG Markers of Schizophrenia with an Explainable Multi-Model Deep Learning Approach

The diagnosis of schizophrenia (SZ) can be challenging due to its diverse symptom presentation. As such, many studies have sought to identify diagnostic biomarkers of SZ using explainable machine learning methods. However, the generalizability of identified biomarkers in many machine learning-based studies is highly questionable given that most studies only analyze explanations from a small number of models. In this study, we present (1) a novel feature interaction-based explainability approach and (2) several new approaches for summarizing multi-model explanations. We implement our approach within the context of electroencephalogram (EEG) spectral power data. We further analyze both training and test set explanations with the goal of extracting generalizable insights from the models. Importantly, our analyses identify effects of SZ upon the , {beta}, and {theta} frequency bands, the left hemisphere of the brain, and interhemispheric interactions across a majority of folds. We hope that our analysis will provide helpful insights into SZ and inspire the development of robust approaches for identifying neuropsychiatric disorder biomarkers from explainable machine learning models.

neuroscience↗

bioRxiv2023

Xylosyltransferase Bump-and-hole Engineering to Chemically Manipulate Proteoglycans in Mammalian Cells

Mammalian cells orchestrate signalling through interaction events on their surfaces. Proteoglycans are an intricate part of these interactions, carrying large glycosaminoglycan polysaccharides that recruit signalling molecules. Despite their importance in development, cancer and neurobiology, a relatively small number of proteoglycans have been identified. In addition to the complexity of glycan extension, biosynthetic redundancy in the first protein glycosylation step by two xylosyltransferase isoenzymes XT1 and XT2 complicates annotation of proteoglycans. Here, we develop a chemical genetic strategy that manipulates the glycan attachment site of cellular proteoglycans. By employing a tactic termed bump- and-hole engineering, we engineer the two isoenzymes XT1 and XT2 to specifically transfer a chemically modified xylose analogue to target proteins. The chemical modification contains a bioorthogonal tag, allowing the ability to visualise and profile target proteins modified by both transferases in mammalian cells. The versatility of our approach allows pinpointing glycosylation sites by tandem mass spectrometry, and exploiting the chemical handle to manufacture proteoglycans with defined GAG chains for cellular applications. Engineered XT enzymes permit a view into proteoglycan biology that is orthogonal to conventional techniques in biochemistry.

biochemistry↗

bioRxiv2023

Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram Data

While deep learning methods are increasingly applied in research contexts for neuropsychiatric disorder diagnosis, small dataset size limits their potential for clinical translation. Data augmentation (DA) could address this limitation, but the utility of EEG DA methods remains relatively underexplored in neuropsychiatric disorder diagnosis. In this study, we train a model for major depressive disorder diagnosis. We then evaluate the utility of 6 EEG DA approaches. Importantly, to remove the bias that could be introduced by comparing performance for models trained on larger augmented training sets to models trained on smaller baseline sets, we also introduce a new baseline trained on duplicate training data to better. We lastly examine the effects of the DA approaches upon representations learned by the model with a pair of explainability analyses. We find that while most approaches boost model performance, they do not improve model performance beyond that of simply using a duplicate training set without DA. The exception to this is channel dropout augmentation, which does improve model performance. These findings suggest the importance of comparing EEG DA methods to a baseline with a duplicate training set of equal size to the augmented training set. We also found that some DA methods increased model robustness to frequency (Fourier transform surrogates) and channel (channel dropout) perturbation. While our findings on EEG DA efficacy are restricted to our dataset and model, we hope that future studies on deep learning for small EEG datasets and on new EEG DA methods will find our findings helpful.

neuroscience↗

bioRxiv2023

Markov Spatial Flows in BOLD fMRI: a Novel Lens on the BOLD Signal Reveals Attracting Patterns of Signal Intensity

While the analysis of temporal signal fluctuations and cofluctuations has long been a fixture of blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI) research, the role and implications of spatial propagation within the 4D neurovascular BOLD signal has been almost entirely neglected. As part of a larger research program aimed at capturing and analyzing spatially propagative dynamics in BOLD fMRI, we report here a method that exposes large-scale functional attractors of spatial flows formulated as Markov processes defined at the voxel level. The brainwide stationary distributions of these voxel-level Markov processes represent patterns of signal accumulation toward which we find evidence that the brain exerts a probabilistic propagative undertow. These probabilistic propagative attractors are spatially structured and organized interpretably over functional regions. They also differ significantly between schizophrenia patients and controls.

neuroscience↗

bioRxiv2023

Cross-Sampling Rate Transfer Learning for Enhanced Raw EEG Deep Learning Classifier Performance in Major Depressive Disorder Diagnosis

Transfer learning offers a route for developing robust deep learning models on small raw electroencephalography (EEG) datasets. Nevertheless, the utility of applying representations learned from large datasets with a lower sampling rate to smaller datasets with higher sampling rates remains relatively unexplored. In this study, we transfer representations learned by a convolutional neural network on a large, publicly available sleep dataset with a 100 Hertz sampling rate to a major depressive disorder (MDD) diagnosis task at a sampling rate of 200 Hertz. Importantly, we find that the early convolutional layers contain representations that are generalizable across tasks. Moreover, our approach significantly increases mean model accuracy from 82.33% to 86.99%, increases the models use of lower frequencies, ({theta}-band), and increases its robustness to channel loss. We expect this analysis to provide useful guidance and enable more widespread use of transfer learning in EEG deep learning studies.

neuroscience↗

bioRxiv2023

Disuse-driven plasticity in the human thalamus and putamen

Motor adaptation in cortico-striato-thalamo-cortical loops has been studied mainly in animals using invasive electrophysiology. Here, we leverage functional neuroimaging in humans to study motor circuit plasticity in the human subcortex. We employed an experimental paradigm that combined two weeks of upper-extremity immobilization with daily resting-state and motor task fMRI before, during, and after the casting period. We previously showed that limb disuse leads to decreased functional connectivity (FC) of the contralateral somatomotor cortex (SM1) with the ipsilateral somatomotor cortex, increased FC with the cingulo-opercular network (CON) as well as the emergence of high amplitude, fMRI signal pulses localized in the contralateral SM1, supplementary motor area and the cerebellum. From our prior observations, it remains unclear whether the disuse plasticity affects the thalamus and striatum. We extended our analysis to include these subcortical regions and found that both exhibit strengthened cortical FC and spontaneous fMRI signal pulses induced by limb disuse. The dorsal posterior putamen and the central thalamus, mainly CM, VLP and VIM nuclei, showed disuse pulses and FC changes that lined up with fmri task activations from the Human connectome project motor system localizer, acquired before casting for each participant. Our findings provide a novel understanding of the role of the cortico-striato-thalamo-cortical loops in human motor plasticity and a potential link with the physiology of sleep regulation. Additionally, similarities with FC observation from Parkinson Disease (PD) questions a pathophysiological link with limb disuse.

neuroscience↗