Biology subjects

Sharp, D. J.

Publications and source records attributed to Sharp, D. J..

10 recordsLinked to original sources

bioRxiv2026

PARS: an automated, open-source pipeline for subject-specific finite element head modelling from MRI

Converting medical images into anatomically detailed, subject-specific finite element (FE) models is a long-standing bottleneck in brain computational modelling. These models are used to predict brain tissue deformation, e.g. in traumatic brain injury, particle diffusion in brain drug delivery, and other biophysical phenomena across neurological disorders. However, existing model creation workflows depend on manual image segmentation, proprietary meshing software, and labourintensive repair of meningeal and interface structures, limiting reproducibility and cohort analysis. Here we present PARS, a fully automated, open-source pipeline that converts a T1-weighted MRI scan into a simulation-ready FE head model. PARS combines anatomical parcellation with tissue maps and uses iterative neighbourhood-based reclassification, yielding a gap-free whole-head label volume. The volume is directly converted into a hexahedral mesh, augmented with algorithmically reconstructed falx, tentorium, pia and dura mater, and refined by Laplacian smoothing under a node-locking scheme that controls element quality and the explicit-solver stable timestep. We evaluated PARS on 23 subjects spanning cranial volumes of 832-1,329 cm3, at 1.0, 1.5 and 2.0 mm MRI resolutions. At 1 mm, meshes achieved a median Scaled Jacobian of 0.976{+/-}0.012, and total intracranial volume error of 0.54{+/-}0.19%; quality remained high at 1.5 mm (SJ: 0.933{+/-}0.018) and 2 mm (SJ: 0.921{+/-}0.016). Model creation runtime ranged from 9 to 38 minutes per subject. Models generated by PARS have been validated against cadaveric brain displacement data and demonstrated utility across traumatic brain injury and normal pressure hydrocephalus research. PARS provides an open-access, reproducible resource that substantially lowers the barriers to subject-specific brain modelling.

bioengineering↗

bioRxiv2025

A universal dynamic routing architecture governs the flow of human cortical activity

Behaviour requires distributed neural processing to be flexibly integrated and segregated, which in turn demands that information be dynamically routed across the brain. However, whether a unifying principle governs the routing of neural activity remains unknown. Here, we report that the flow of cortical activity is directed through canonical routing modes that are conserved across individuals, robust to variations in age, frequency band, brain state, and even the presence of neurodegeneration. These modes are constructed from the divergence and vorticity of the time-varying unit-phase vector field derived from electroencephalogram (EEG) recordings. We show that the canonical routing modes are flexibly combined to generate distinct, state- and function-dependent flow architectures that remain consistent across individuals. Disruption to the routing mode dynamics mechanistically links structural abnormalities and cognitive impairment in Alzheimers disease. Together, these canonical routing modes provide a unified framework for describing, modelling, and predicting the dynamic integration and segregation of macroscopic brain activity underpinning behaviour.

neuroscience↗

bioRxiv2024

High-dimensional proteomic analysis for pathophysiological classification of Traumatic Brain Injury

Pathophysiology and outcomes after Traumatic Brain Injury (TBI) are complex and highly heterogenous. Current classifications are uninformative about pathophysiology, which limits prognostication and treatment. Fluid-based biomarkers can identify pathways and proteins relevant to TBI pathophysiology. Proteomic approaches are well suited to exploring complex mechanisms of disease, as they enable sensitive assessment of an expansive range of proteins. We used novel high-dimensional, multiplex proteomic assays to study changes in plasma protein expression in acute moderate-severe TBI. We analysed samples from 88 participants in the longitudinal BIO-AX-TBI cohort (n=38 TBI within 10 days of injury, n=22 non-TBI trauma, n=28 non-injured controls) on two platforms: Alamar NULISA CNS Diseases and OLINK(R) Target 96 Inflammation. Participants also had data available from Simoa(R) (neurofilament light, GFAP, total tau, UCHL1) and Millipore (S100B). The Alamar panel assesses 120 proteins, most of which have not been investigated before in TBI, as well as proteins, such as GFAP, which differentiate TBI from non-injured and non-TBI trauma controls. A subset (n=29 TBI, n=24 non-injured controls) also had subacute 3T MRI measures of lesion volume and white matter injury (fractional anisotropy, scanned 10 days to 6 weeks after injury). Differential Expression analysis identified 16 proteins with TBI-specific significantly different plasma expression. These were neuronal markers (calbindin2, UCHL1, visinin-like protein1), astroglial markers (S100B, GFAP), tau and other neurodegenerative disease proteins (total tau, pTau231, PSEN1, amyloid beta42, 14-3-3{gamma}), inflammatory cytokines (IL16, CCL2, ficolin2), cell signalling (SFRP1), cell metabolism (MDH1) and autophagy related (sequestome1) proteins. Acute plasma levels of UCHL1, PSEN1, total tau and pTau231 correlated with subacute lesion volume, while sequestome1 was correlated with whole white matter skeleton fractional anisotropy and CCL2 was inversely correlated with corpus callosum FA. Neuronal, astroglial, tau and neurodegenerative proteins correlated with each other, and IL16, MDH1 and sequestome1. Clustering (k means) by acute protein expression identified 3 TBI subgroups which had differential injury patterns, but did not differ in age or outcome. Proteins that overlapped on two platforms had excellent (r>0.8) correlations between values. We identified TBI-specific changes in acute plasma levels of proteins involved in amyloid processing, inflammatory and cellular processes such as autophagy. These changes were related to patterns of injury, thus demonstrating that processes previously only studied in animal models are also relevant in human TBI pathophysiology. Our study highlights the potential of proteomic analysis to improve the classification and understanding of TBI pathophysiology, with implications for prognostication and treatment development.

neuroscience↗

bioRxiv2023

Mild blast TBI raises gamma connectivity, EEG power, and reduces GABA interneuron density

At least one traumatic brain injury (TBI) will be experienced by approximately 50-60 million of the worlds population in their lifetime and is the biggest cause of death and disability in those under 40. Mild traumatic brain injury (mTBI) can induce subtle changes but have long-lasting effects that may be difficult to detect through conventional neurological assessment, including standard clinical imaging techniques. These changes can lead to an increased risk of future neurodegeneration and emphasises the need to use more sensitive diagnostic tools such as EEG in order to identify injury and opportunities for therapeutic intervention. In this study, we investigated electrophysiological and histopathological changes in a rat model of mild blast-induced TBI. We used a 32-channel EEG electrode array to detect global and local changes in neural activity and functional connectivity in acute (3 to 4-hours) as well as chronic phases (1 and 3-months) post-injury. GABAergic inhibitory interneurons, crucial for maintaining an excitatory/inhibitory balance, were quantified using immunohistochemistry. Mild blast-induced TBI had minimal effects on resting power and connectivity at the acute timepoint but resulted in resting-state global power increases at all frequencies as well as a relative power increase in slow-wave frequencies in the chronic phase post-injury. Functional connectivity increases in the gamma frequency along with increases in power in the chronic phase pointed towards an alteration in the excitatory/inhibitory balance. Indeed, electrophysiological changes were associated with reduced density of GABAergic interneurons at 7-days, 1-month, and 3months post-injury, with a decrease in somatostatin-positive cell density in the 5th layer of all cortical regions of interest, and a parvalbumin decrease in the 5th layer of the primary auditory cortex. In contrast, the total number of neurons, measured by NeuN did not change significantly, thus demonstrating a biased impact on inhibitory interneuron populations. Our work demonstrates that the techniques and metrics of injury assessment employed in this study are sensitive enough to reflect the subtle changes present in mTBI and therefore hold potential clinical relevance. By using non-invasive EEG assessments and histopathology, we were able to reveal direct correlates and potential sources of the abnormalities caused by mild blast-induced TBI.

neuroscience↗

bioRxiv2023

Just a phase? Causal probing reveals spurious phasic dependence of sensory perception

For over a decade, electrophysiological studies have reported correlations between sensory perception and the phase of spontaneous pre-stimulus brain oscillations. To date, these findings have been interpreted as evidence that the brain uses neural oscillations to sample and predict upcoming stimuli. Yet, evidence from simulations have shown that analysis artefacts could also lead to spurious pre-stimulus oscillations that appear to predict future brain responses. To address this discrepancy, we conducted an experiment in which visual stimuli were presented timed to specific phases of spontaneous alpha and theta oscillations. This allowed us to causally probe the role of ongoing neural activity in visual processing independent of the stimulus-evoked dynamics. Our findings did not support a causal link between spontaneous alpha / theta rhythms and behaviour. However, spurious correlations between theta phase and behaviour emerged offline using gold-standard acausal time-frequency analyses. These findings demonstrate that care should be taken when inferring causal relationships between neural activity and behaviour using acausal analyses. HIGHLIGHTSO_LIWe causally probed the role of spontaneous EEG rhythms in visual attention via phase-locking. C_LIO_LITheta phase predicted behaviour offline, but cues presented in real-time with theta had no effect on behaviour. C_LIO_LISpurious theta rhythmic sampling is an artefact of the evoked potential and acausal filtering. C_LIO_LIecHT accurately computes the phase in real-time and mitigates erroneous phase-behaviour correlations. C_LI

neuroscience↗

bioRxiv2023

The neuron mixer and its impact on human brain dynamics

A signal mixer made of a transistor or a diode facilitates rich computation, which has been the building block of modern telecommunications. The mixing produces new signals at the sum and difference frequencies of the input, thereby enabling powerful operations such as frequency conversion (aka heterodyning), phase detection, and multiplexing. Here, we report that a neural cell is also a signal mixer. We found through ex-vivo and in-vivo whole-cell measurements that neurons mix exogenous (controlled) and endogenous (spontaneous) subthreshold membrane potential oscillations, producing new oscillation frequencies. We show, using pharmacological manipulation, that the neural mixing originates in the voltage-gated ion channels. Furthermore, we demonstrate that the neural mixing dynamic is evident in human brain activity and is associated with cognitive functions. We found that the human electroencephalogram (EEG) displays distinct clusters of local and inter-region mixing interactions. By quantifying the strength of these interactions before a task, we show that converting the salient posterior alpha-beta oscillations into gamma-band oscillations regulates the visual attention state. Neural circuit oscillations have been observed in nearly every cognitive domain and species, and abnormal spectra of neural oscillations have been found in almost all brain disorders. Signal mixing enables individual neurons to actively sculpt the spectrum of their circuit oscillations and utilize them for computational operations, which have only been seen in modern telecommunication until now.

neuroscience↗

bioRxiv2021

Vestibular Agnosia is linked to abnormal functional brain networks

Vestibular Agnosia - where peripheral vestibular activation triggers the usual reflex nystagmus response but with attenuated or no self-motion perception - is found in brain disease with disrupted cortical network functioning, e.g. traumatic brain injury (TBI) or neurodegeneration (Parkinsons Disease). Patients with acute focal hemispheric lesions (e.g. stroke) do not manifest vestibular agnosia. Thus brain network mapping techniques, e.g. resting state functional MRI (rsfMRI), are needed to interrogate functional brain networks mediating vestibular agnosia. Whole-brain rsfMRI was acquired from 39 prospectively recruited acute TBI patients with preserved peripheral vestibular function, along with self-motion perceptual thresholds during passive yaw rotations in the dark. Following quality-control checks, 25 patient scans were analyzed. TBI patients were classified as having vestibular agnosia (n = 11) or not (n = 14) via laboratory testing of self-motion perception. Using independent component analysis, we found altered functional connectivity in the right superior longitudinal fasciculus and left rostral prefrontal cortex in vestibular agnosia. Moreover, regions of interest analyses showed both inter-hemispheric and intra-hemispheric network disruption in vestibular agnosia. In conclusion, our results show that vestibular agnosia is mediated by bilateral anterior and posterior network dysfunction and reveal the distributed brain mechanisms mediating vestibular self-motion perception.

neuroscience↗

bioRxiv2021

A U-Net model of local brain-age

AO_SCPLOWBSTRACTC_SCPLOWWe propose a new framework for estimating neuroimaging-derived "brain-age" at a local level within the brain, using deep learning. The local approach, contrary to existing global methods, provides spatial information on anatomical patterns of brain ageing. We trained a U-Net model using brain MRI scans from n=3463 healthy people (aged 18-90 years) to produce individualised 3D maps of brain-predicted age. When testing on n=692 healthy people, we found a median (across participant) mean absolute error (within participant) of 9.5 years. Performance was more accurate (MAE around 7 years) in the prefrontal cortex and periventricular areas. We also introduce a new voxelwise method to reduce the age-bias when predicting local brain-age "gaps". To validate local brain-age predictions, we tested the model in people with mild cognitive impairment or dementia using data from OASIS3 (n=267). Different local brain-age patterns were evident between healthy controls and people with mild cognitive impairment or dementia, particularly in subcortical regions such as the accumbens, putamen, pallidum, hippocampus and amygdala. Comparing groups based on mean local brain-age over regions-of-interest resulted in large effects sizes, with Cohens d values >1.5, for example when comparing people with stable and progressive mild cognitive impairment. Our local brain-age framework has the potential to provide spatial information leading to a more mechanistic understanding of individual differences in patterns of brain ageing in health and disease.

neuroscience↗

bioRxiv2020

Investigating the interaction between white matter and brain state on tDCS-induced changes in brain network activity

BackgroundTranscranial direct current stimulation (tDCS) is a form of noninvasive brain stimulation whose potential as a cognitive therapy is hindered by our limited understanding of how participant and experimental factors influence its effects. Using functional MRI to study brain networks, we have previously shown in healthy controls that the physiological effects of tDCS are strongly influenced by brain state. We have additionally shown, in both healthy and traumatic brain injury (TBI) populations, that the behavioral effects of tDCS are positively correlated with white matter (WM) structure. ObjectivesIn this study we investigate how these two factors, WM structure and brain state, interact to shape the effect of tDCS on brain network activity. MethodsWe applied anodal, cathodal and sham tDCS to the right inferior frontal gyrus (rIFG) of healthy (n=22) and TBI participants (n=34). We used the Choice Reaction Task (CRT) performance to manipulate brain state during tDCS. We acquired simultaneous fMRI to assess activity of cognitive brain networks and used Fractional Anisotropy (FA) as a measure of WM structure. ResultsWe find that the effects of tDCS on brain network activity in TBI participants are highly dependent on brain state, replicating findings from our previous healthy control study in a separate, patient cohort. We then show that WM structure further modulates the brain-state dependent effects of tDCS on brain network activity. These effects are not unidirectional - in the absence of task with anodal and cathodal tDCS, FA is positively correlated with brain activity in several regions of the default mode network. Conversely, with cathodal tDCS during CRT performance, FA is negatively correlated with brain activity in a salience network region. ConclusionsOur results show that experimental and participant factors interact to have unexpected effects on brain network activity, and that these effects are not fully predictable by studying the factors in isolation.

neuroscience↗

bioRxiv2017

Brain state and polarity dependent modulation of brain networks by transcranial direct current stimulation

Transcranial direct current stimulation (TDCS) has been widely used to improve cognitive function. However, current deficiencies in mechanistic understanding hinders wider applicability. To clarify its physiological effects, we acquired fMRI whilst simultaneously acquiring TDCS to the right inferior frontal gyrus (rIFG) of healthy human participants, a region involved in coordinating activity within brain networks. TDCS caused widespread modulation of network activity depending on brain state ( rest or choice reaction time task) and polarity (anodal or cathodal). During task, TDCS increased salience network activation and default mode network deactivation, but had the opposite effect during rest. Furthermore, there was an interaction between brain state and TDCS polarity, with cathodal effects more pronounced during task performance and anodal effects more pronounced during rest. Overall, we show that rIFG TDCS produces brain state and polarity dependent effects within large-scale cognitive networks, in a manner that goes beyond predictions from the current literature.

neuroscience↗