Biology subjects

Harrison, S. J.

Publications and source records attributed to Harrison, S. J..

15 recordsLinked to original sources

bioRxiv2025

Age Associated Increase in Microglia Inflammation and Phagocytosis in the Adult Neural Stem Cell Niche.

Adult SVZ neurogenesis declines with age, but the niche mechanisms remain incompletely defined. Here, we show that SVZ microglia acquire activation-associated morphology with aging in male and female C57BL/6 mice. Reanalysis of published SVZ single-cell RNA-Seq data revealed that aged microglia upregulate inflammatory and lysosome/phagosome-related gene programs, including multiple phagocytosis-associated receptors and effectors. Functionally, young SVZ-derived microglia exhibit reduced basal phagocytic activity compared with non-SVZ microglia in vitro, whereas aged SVZ microglia show increased phagocytosis. In vivo SVZ wholemounts reveal increased progenitor-derived material within lysosomal compartments of microglia with age, without an accompanying increase in apoptosis. Inhibiting complement C3 activation partially restores SVZ proliferation in aged mice and coincides with a reduction of progenitor-derived material within lysosomal compartments of aged microglia. These data support a model in which microglial inflammatory signaling and engagement with neural progenitors contributes to age-related suppression of SVZ proliferation. Significance StatementAdult neural stem cells support plasticity and may aid repair after brain injury, but this capacity declines with age. We show that microglia in the aging subventricular zone acquire inflammatory and phagocytic programs, increase their lysosomal association with progenitor-derived material, and are linked to reduced progenitor proliferation. Complement inhibition partially restores proliferative output in aged mice, connecting inflammatory signaling to this niche defect. These findings identify age-dependent microglia-progenitor interactions as a mechanism that may limit neural stem cell activity in the adult brain and suggest immune pathways as possible targets for preserving or restoring neurogenic potential during aging.

cell biology↗

bioRxiv2025

Treatment with the ribosome biogenesis inhibitor CX-5461 increases platelet count in humans and enhances murine megakaryopoiesis

Thrombocytopenia is a common and serious complication of anticancer therapies. Here, we identify a novel thrombopoietic activity of the first-in-class ribosome biogenesis inhibitor CX-5461. In a phase I trial, 56% (9/16) of patients exhibited up to a 34% increase in platelet count following a single dose of CX-5461. In mice, CX-5461 elicited a rapid, reversible, and sustained [~]1.7-fold increase in platelet numbers without altering platelet function, lifespan, or inflammatory cytokines. Bone marrow analysis revealed a specific expansion of megakaryocytes (MKs), increased Sca1 MKs, and selective enrichment of MK-biased multipotent progenitor 2, independent of thrombopoietin (TPO) or c-mpl signalling. CX-5461 also mitigated carboplatin-induced thrombocytopenia, accelerating platelet recovery. Single-cell RNA sequencing and RNA velocity analysis confirmed enhanced differentiation of MK progenitors. These findings demonstrate that inhibition of ribosome biogenesis promotes TPO-independent megakaryopoiesis and identifies a previously unrecognised therapeutic opportunity to support platelet recovery in cancer treatment and potentially other thrombocytopenic states.

cell biology↗

bioRxiv2024

Multiscale Modes of Functional Brain Connectivity

Information processing in the brain spans from localised sensorimotor processes to higher-level cognition that integrates across multiple regions. Interactions between and within these subsystems enable multiscale information processing. Despite this multiscale characteristic, functional brain connectivity is often either estimated based on 10-30 distributed modes or parcellations with 100-1000 localised parcels, both missing across-scale functional interactions. We present Multiscale Probabilistic Functional Modes (mPFMs), a new mapping which comprises modes over various scales of granularity, thus enabling direct estimation of functional connectivity within- and across-scales. Crucially, mPFMs were not formulated, but emerged from data-driven multilevel Bayesian modelling of large functional MRI (fMRI) populations and every individual. We demonstrate that mPFMs capture both distributed brain modes and their co-existing subcomponents. In addition to validating mPFMs using simulations and real data, we show that mPFMs can predict [~]900 personalised traits from UK Biobank more accurately than current standard techniques. Therefore, mPFMs can offer a new basis for functional connectivity modelling and yield enhanced fMRI biomarkers for traits and diseases.

neuroscience↗

bioRxiv2024

Applying nutritional ecology to optimize diets of crickets raised for food and feed

Increasing yield is a primary goal of mass insect rearing for food and feed, and diet shapes insect life history traits important to yield, such as survival, development time, and body size at adulthood. Little is known about how developmental macronutrient intake impacts survival, growth, and adult body size of mass reared insects. Here, we applied the nutritional geometry framework and reared individual tropical house crickets (Gryllodes sigillatus) from hatch to adulthood on a wide range of protein:carbohydrate diets. We measured weekly food consumption, survival, development time to adulthood, and adult body size and mass, and calculated a yield metric to extrapolate our individual-level results and predict how diet influences yield at the mass rearing level. Yield was maximized on a 3P:1C diet, as crickets fed this diet were most likely to develop into adults and grew maximum mass and body size. When provided with a choice between diets, crickets selected a relatively balanced 1.05P:1C diet throughout development, but males consumed 17% more protein than females. Our results represent a crucial first step towards determining the optimal standard feed formulation required to maximize cricket farming yield.

ecology↗

bioRxiv2021

Interoception of breathing and its relationship with anxiety

Interoception, the perception of internal bodily states, is thought to be inextricably linked to affective qualities such as anxiety. While interoception spans sensory to metacognitive processing, it is not clear whether anxiety is differentially related to these processing levels. Here we investigated this question in the domain of breathing, using computational modelling and high-field (7 Tesla) fMRI to assess brain activity relating to dynamic changes in inspiratory resistance of varying predictability. Notably, the anterior insula was associated with both breathing-related prediction certainty and prediction errors, suggesting an important role in representing and updating models of the body. Individuals with low vs. moderate anxiety traits showed differential anterior insula activity for prediction certainty. Multimodal analyses of data from fMRI, computational assessments of breathing-related metacognition, and questionnaires demonstrated that anxiety-interoception links span all levels from perceptual sensitivity to metacognition, with strong effects seen at higher levels of interoceptive processes.

neuroscience↗

bioRxiv2021

TAPAS: an open-source software package for Translational Neuromodeling and Computational Psychiatry

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

neuroscience↗

bioRxiv2021

Hierarchical modelling of functional brain networks in population and individuals from big fMRI data

A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the models utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data. HighlightsO_LIWe introduce stochastic PROFUMO (sPROFUMO) for inferring functional brain networks from big data C_LIO_LIsPROFUMO hierarchically estimates fMRI networks for the population and every individual C_LIO_LIWe characterised high dimensional resting state fMRI networks from UK Biobank C_LIO_LIModel outperforms ICA and dual regression for estimation of individual-specific network topography C_LIO_LIWe demonstrate the models utility for predicting cognitive traits, and capturing subject variability in network topographies versus connectivity C_LI

neuroscience↗

bioRxiv2020

A Hilbert-based method for processing respiratory timeseries

In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators. Finally, we demonstrate that this leads to an increase in the amount of respiration-related variance removed from fMRI data when used as part of a typical preprocessing pipeline. Our implementation will be publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (translationalneuromodeling.org/tapas). HighlightsO_LIWe introduce a new estimator for respiratory volume per unit time from respiratory recordings. C_LIO_LIWe demonstrate how this is able to accurately characterise atypical breathing events. C_LIO_LIThis removes significantly more variance when used as a confound regressor for fMRI data. C_LIO_LIOur implementation will be included in PhysIO, released as part of TAPAS: translationalneuromodeling.org/tapas. C_LI

neuroscience↗

bioRxiv2020

Regression dynamic causal modeling for resting-state fMRI

AO_SCPLOWBSTRACTC_SCPLOW"Resting-state" functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks. Here, we show that a method recently developed for task-fMRI - regression dynamic causal modeling (rDCM) - extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.

neuroscience↗

bioRxiv2020

The Filter Detection Task for measurement of breathing-related interoception and metacognition

The study of the brains processing of sensory inputs from within the body ( interoception) has been gaining rapid popularity in neuroscience, where interoceptive disturbances are thought to exist across a wide range of chronic physiological and psychological conditions. Here we present a task and analysis procedure to quantify specific dimensions of breathing-related interoception, including interoceptive sensitivity (accuracy), decision bias, metacognitive bias, and metacognitive performance. Two major developments address some of the challenges presented by low trial numbers in interoceptive experiments: (i) a novel adaptive algorithm to maintain task performance at 70-75% accuracy; (ii) an extended hierarchical metacognitive model to estimate regression parameters linking metacognitive performance to relevant (e.g. clinical) variables. We demonstrate the utility of the task and analysis developments, using both simulated data and three empirical datasets. This methodology represents an important step towards accurately quantifying interoceptive dimensions from a simple experimental procedure that is compatible with clinical settings.

neuroscience↗

bioRxiv2019

The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants.

The developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20 to 45 weeks post-menstrual age. This is being achieved through the acquisition of multi-modal MRI data from over 1000 in- and ex-utero subjects combined with the development of optimised pre-processing pipelines. In this paper we present an automated and robust pipeline to minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, with low failure rates and high quality-assurance. The pipeline has been designed to specifically address the challenges that neonatal data presents including low and variable contrast and high levels of head motion. We provide a detailed description and evaluation of the pipeline which includes integrated slice-to-volume motion correction and dynamic susceptibility distortion correction, a robust multimodal registration approach, bespoke ICA-based denoising, and an automated QC framework. We assess these components on a large cohort of dHCP subjects and demonstrate that processing refinements integrated into the pipeline provide substantial reduction in movement related distortions, resulting in significant improvements in SNR, and detection of high quality RSNs from neonates. HighlightsO_LIAn automated and robust pipeline to minimally pre-process highly confounded neonatal fMRI data C_LIO_LIIncludes integrated dynamic distortion and slice-to-volume motion correction C_LIO_LIA robust multimodal registration approach which includes custom neonatal templates C_LIO_LIIncorporates an automated and self-reporting QC framework to quantify data quality and identify issues for further inspection C_LIO_LIData analysis of 538 infants imaged at 26-45 weeks post-menstrual age C_LI

neuroscience↗

bioRxiv2019

Modelling Subject Variability in the Spatial and Temporal Characteristics of Functional Modes

Recent work has highlighted the scale and ubiquity of subject variability in observations from functional MRI data (fMRI). Furthermore, it is highly likely that errors in the estimation of either the spatial presentation of, or the coupling between, functional regions can confound cross-subject analyses, making accurate and unbiased representations of functional data essential for interpreting any downstream analyses. Here, we extend the framework of probabilistic functional modes (PFMs) [Harrison et al. 2015] to capture cross-subject variability not only in the mode spatial maps, but also in the functional coupling between modes and in mode amplitudes. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git.fmrib.ox.ac.uk/samh/profumo. Using simulated data, resting-state data from 1,000 subjects collected as part of the Human Connectome Project [Van Essen et al. 2013], and an analysis of 14 subjects in a variety of continuous task-states [Kieliba et al. 2019], we demonstrate how PFMs are able to capture, within a single model, a rich description of how the spatio-temporal structure of resting-state fMRI activity varies across subjects. We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assumptions, much more of the (behaviorally relevant) cross-subject variability in fMRI activity should be attributed to the variability in spatial maps, and that, after accounting for this, functional coupling between modes primarily reflects current cognitive state. This has fundamental implications for the interpretation of cross-sectional studies of functional connectivity that do not capture cross-subject variability to the same extent as PFMs.

neuroscience↗

bioRxiv2019

The relationship between spatial configuration and functional connectivity of brain regions revisited

In our previous paper (Bijsterbosch et al., 2018), we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.\n\nImpact statementWe show that functional connectivity network matrices as estimated from resting state functional MRI are biased by spatially overlapping network structure.

neuroscience↗

bioRxiv2017

The relationship between spatial configuration and functional connectivity of brain regions

Brain connectivity is often considered in terms of the communication between functionally distinct brain regions. Many studies have investigated the extent to which patterns of coupling strength between multiple neural populations relates to behavior. For example, studies have used \"functional connectivity fingerprints\" to characterise individuals' brain activity. Here, we investigate the extent to which the exact spatial arrangement of cortical regions interacts with measures of brain connectivity. We find that the shape and exact location of brain regions interact strongly with the modelling of brain connectivity, and present evidence that the spatial arrangement of functional regions is strongly predictive of non-imaging measures of behaviour and lifestyle. We believe that, in many cases, cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Therefore, a better understanding of these effects is important when interpreting the relationship between functional imaging data and cognitive traits.

neuroscience↗

bioRxiv2017

Using Temporal ICA to Selectively Remove Global Noise While Preserving Global Signal in Functional MRI Data

Temporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal \"noise\" from a variety of sources, including subject motion, subject physiology, and the MRI equipment. Recently, methods have been developed to automatically and selectively remove spatially specific structured noise from fMRI data using spatial Independent Components Analysis (ICA) and machine learning classifiers. Spatial ICA is particularly effective at removing spatially specific structured noise from high temporal and spatial resolution fMRI data of the type acquired by the Human Connectome Project and similar studies. However, spatial ICA is mathematically, by design, unable to separate spatially widespread \"global\" structured noise from fMRI data (e.g., blood flow modulations from subject respiration). No methods currently exist to selectively and completely remove global structured noise while retaining the global signal from neural activity. This has left the field in a quandary--to do or not to do global signal regression--given that both choices have substantial downsides. Here we show that temporal ICA can selectively segregate and remove global structured noise while retaining global neural signal in both task-based and resting state fMRI data. We compare the results before and after temporal ICA cleanup to those from global signal regression and show that temporal ICA cleanup removes the global positive biases caused by global physiological noise without inducing the network-specific negative biases of global signal regression. We believe that temporal ICA cleanup provides a \"best of both worlds\" solution to the global signal and global noise dilemma and that temporal ICA itself unlocks interesting neurobiological insights from fMRI data.

neuroscience↗