Biology subjects

Jackson, G.

Publications and source records attributed to Jackson, G..

7 recordsLinked to original sources

bioRxiv2025

Beyond the Resection: Surgical White Matter Disruption alters Non-Resected Brain Anatomy

Resective neurosurgery is a cornerstone treatment for many neurological conditions. Although traditionally viewed as localised procedure, increasing evidence from advanced magnetic resonance imaging (MRI) shows that also non-resected anatomy can degenerate following surgery. The relationship between local tissue removal and these postoperative changes remains thus far speculative. Here, we investigate the hypothesis that degenerative changes to surgically preserved grey and white matter are mediated by transneuronal degeneration, a deterioration of intact neuronal populations due to lost axonal input. Using a robust structural and diffusion MRI framework, we first identify widespread postoperative atrophy: pronounced cortical thickness decreases near the resection, and extensive white matter impairments across the ipsilateral hemisphere. Importantly, we then link these alterations to surgical white matter disruption, revealing a sequential network atrophy following neurosurgery. Beyond degenerative effects, we also demonstrate often reported structural network reorganisations as an artefact of image processing, indicating limited capacity for macroscale plasticity post-resection. Key PointsO_LIResective neurosurgery can lead to postoperative atrophy of non-resected grey and white matter. Utilising a robust longitudinal neuroimaging framework, we evaluate these changes as consequences of transneuronal degeneration, demonstrating a sequential atrophy of brain anatomy following the loss of surgically disrupted white matter. C_LIO_LIThe majority of severed connections projected to atrophic cortices near the resection, indicating them to be short association fibers. Consecutive white matter atrophy however affected extensive ipsilateral pathways, highlighting a cascading network effect of resective brain surgery. C_LIO_LIWe did not find evidence to support often reported structural reorganisations of large-scale brain networks post-resection. Yet, we demonstrate that evidence typically interpreted as such can be replicated when using non-robust reconstruction methods. C_LI

neuroscience↗

bioRxiv2024

A worldwide ENIGMA study on epilepsy-related gray and white matter compromise across the adult lifespan

ObjectivesTemporal lobe epilepsy (TLE) is commonly associated with mesiotemporal pathology and widespread alterations of grey and white matter structures. Evidence supports a progressive condition although the temporal evolution of TLE is poorly defined. This ENIGMA-Epilepsy study utilized multimodal magnetic resonance imaging (MRI) data to investigate structural alterations in TLE patients across the adult lifespan. We charted both grey and white matter changes and explored the covariance of age-related alterations in both compartments. MethodsWe studied 769 TLE patients and 885 healthy controls across an age range of 17-73 years, from multiple international sites. To assess potentially non-linear lifespan changes in TLE, we harmonized data and combined median split assessments with cross-sectional sliding window analyses of grey and white matter age-related changes. Covariance analyses examined the coupling of grey and white matter lifespan curves. ResultsIn TLE, age was associated with a robust grey matter thickness/volume decline across a broad cortico-subcortical territory, extending beyond the mesiotemporal disease epicentre. White matter changes were also widespread across multiple tracts with peak effects in temporo-limbic fibers. While changes spanned the adult time window, changes accelerated in cortical thickness, subcortical volume, and fractional anisotropy (all decreased), and mean diffusivity (increased) after age 55 years. Covariance analyses revealed strong limbic associations between white matter tracts and subcortical structures with cortical regions. ConclusionsThis study highlights the profound impact of TLE on lifespan changes in grey and white matter structures, with an acceleration of aging-related processes in later decades of life. Our findings motivate future longitudinal studies across the lifespan and emphasize the importance of prompt diagnosis as well as intervention in patients.

neuroscience↗

bioRxiv2021

Improved accuracy for automated counting of a fish in baited underwater videos for stock assessment

The ongoing need to sustainably manage fishery resources necessitates fishery-independent monitoring of the status of fish stocks. Camera systems, particularly baited remote underwater video stations (BRUVS), are a widely-used and repeatable method for monitoring relative abundance, required for building stock assessment models. The potential for BRUVS-based monitoring is restricted, however, by the substantial costs of manual data extraction from videos. Computer vision, in particular deep learning models, are increasingly being used to automatically detect and count fish at low abundances in videos. One of the advantages of BRUVS is that bait attractants help to reliably detect species in relatively short deployments (e.g. 1 hr). The high abundances of fish attracted to BRUVS, however, make computer vision more difficult, because fish often occlude other fish. We build upon existing deep learning methods for identifying and counting a target fisheries species across a wide range of fish abundances. Using BRUVS imagery targeting a recovering fishery species, Australian snapper (Chrysophrys auratus), we tested combinations of three further mathematical steps likely to generate accurate, efficient automation: 1) varying confidence thresholds (CTs), 2) on/off use of sequential non-maximum suppression (Seq-NMS), and 3) statistical correction equations. Output from the deep learning model was accurate at very low abundances of snapper; at higher abundances, however, the model over-predicted counts by as much as 50%. The procedure providing the most accurate counts across all fish abundances, with counts either correct or within 1 to 2 of manual counts (R2 = 93.4%), used Seq-NMS, a 55% confidence threshold, and a cubic polynomial corrective equation. The optimised modelling provides an automated procedure offering an effective and efficient method for accurately identifying and counting snapper in BRUV footage. Further testing is required to ensure that automated counts of snapper remain accurate in the survey region over time, and to determine the applicability to other regions within the distributional range of this species. For monitoring stocks of fishery species more generally, the specific equations will differ but the procedure demonstrated here would help to increase the usefulness of BRUVS, while decreasing costs.

ecology↗

bioRxiv2020

Grain aphids (Sitobion avenae) with knockdown resistance (kdr) to insecticide exhibit fitness trade-offs, including increased vulnerability to the natural enemy Aphidius ervi.

The development of insecticide-resistance mechanisms in aphids has been associated with inhibitory, pleiotropic fitness costs. Such fitness costs have not yet been examined in the UKs most damaging cereal aphid, Sitobion avenae (grain aphid) (Hemiptera: Aphididae). This study aimed to evaluate the fitness trade-offs of the insecticide-resistant S. avenae clone versus an insecticide-susceptible S. avenae clone. Additionally, the parasitoid, Aphidius ervi (Hymenoptera: Braconidae), was introduced to examine its potential as a biological control agent. This study found that insecticide-resistant clones had significantly lower population growth and individual relative growth rate. Furthermore, insecticide-resistant clones suffered from a significantly greater rate of parasitisation (mummification) compared to their insecticide-susceptible counterparts. The successfulness of the parasitoid as a biological control agent could prevent the spread of the insecticide-resistant genotype. However, for this to be possible, insecticide spraying regimes need to be moderated, and habitat modification and parasitoid manipulation must be considered.

ecology↗

bioRxiv2019

Donor monocyte-derived macrophages promote human acute graft versus host disease

Myelopoiesis is invariably present, and contributes to pathology, in animal models of graft versus host disease (GVHD). In humans, a rich inflammatory infiltrate bearing macrophage markers has also been described in histological studies. In order to determine the origin, functional properties and role in pathogenesis of these cells, we isolated single cell suspensions from acute cutaneous GVHD and subjected them to genotype, transcriptome and in vitro functional analysis. A donor-derived population of CD11c+CD14+ cells was the dominant population of all leukocytes in GVHD. Surface phenotype and nanostring gene expression profiling indicated the closest steady-state counterpart of these cells to be monocyte-derived macrophages. In GVHD, however, there was upregulation of monocyte antigens SIRP and S100A8/9, and transcripts associated with leukocyte trafficking, pattern recognition, antigen presentation, and co-stimulation. Isolated GVHD macrophages stimulated greater proliferation and activation of allogeneic T cells, and secreted higher levels of inflammatory cytokines than their steady-state counterparts. In HLA-matched mixed leukocyte reactions, we also observed differentiation of activated macrophages with a similar phenotype. These exhibited cytopathicity to a cell line and mediated pathological damage to skin explants, independently of T cells. Together, these results define the origin, functional properties and potential pathogenic roles of human GVHD macrophages.

immunology↗

bioRxiv2019

Temporal complexity of fMRI is reproducible and correlates with higher order cognition

It has been hypothesized that resting state networks (RSNs) likely display unique temporal complexity fingerprints, quantified by their multi-scale entropy patterns [1]. This is a hypothesis with a potential capacity for developing digital biomarkers of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of [1] was that resting state functional magnetic resonance imaging (rsfMRI) data from only 20 healthy individuals was used for the analysis. To validate this hypothesis in a larger cohort, we used rsfMRI datasets of 1000 healthy young adults from the Human Connectome Project (HCP), aged 22-35, each with four 14.4-minute rsfMRI recordings and parcellated into 379 brain regions. We quantified multi-scale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We performed effect-size analysis on the data in 8 RSNs. Given that the morphology of multi-scale entropy is affected by the choice of its tolerance parameter (r) and embedding dimension (m), we repeated the analyses at multiple values of r and m including the values used in [1]. Our results reinforced high temporal complexity in the default mode and frontoparietal networks. Lowest temporal complexity was observed in the sub-cortical areas and limbic system. We investigated the effect of temporal resolution (determined by the repetition time TR) after downsampling of rsfMRI time series at two rates. At a low temporal resolution, we observed increased entropy and variance across datasets. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially when the tolerance parameter r is equal to 0.5. A strong relationship was observed between temporal complexity of RSNs and fluid intelligence (peoples capacity to reason and think flexibly) through step-wise regression analysis suggesting that complex dynamics of the human brain is an important attribute of high-level brain function. Finally, the results confirmed that the relationship between functional brain connectivity strengths and rsfMRI temporal complexity changes over time scales, likely due to the regulation of neural synchrony at local and global network levels.

neuroscience↗

bioRxiv2018

Towards fast and reliable simultaneous EEG-fMRI analysis of epilepsy with automatic spike detection

ObjectiveThe process of manually marking up epileptic spikes for simultaneous electroencephalogram (EEG) and resting state functional MRI (rsfMRI) analysis in epilepsy studies is a tedious and subjective task for a human expert. The aim of this study was to evaluate whether automatic EEG spike detection can facilitate EEG-rsfMRI analysis, and to assess its potential as a clinical tool in epilepsy.\n\nMethodsWe implemented a fast algorithm for detection of uniform interictal epileptiform discharges (IEDs) in one-hour scalp EEG recordings of 19 refractory focal epilepsy datasets (from 16 patients) who underwent a simultaneous EEG-rsfMRI recording. Our method was based on matched filtering of an IED template (derived from human markup) used to automatically detect other similar EEG events. We comprehensively compared simultaneous EEG-rsfMRI results between automatic IED detection and standard analysis with human EEG markup only.\n\nResultsIn contrast to human markup, automatic IED detection takes a much shorter time to detect IEDs and export an output text file containing spike timings. In 13/19 focal epilepsy cases, statistical EEG-rsfMRI maps based on automatic spike detection method were comparable with human markup, and in 6/19 focal epilepsy cases it revealed additional brain regions not seen with human EEG markup. Additional events detected by our automated method independently revealed similar patterns of activation to a human markup. Overall, automatic IED detection provides greater statistical power in EEG-rsfMRI analysis compared to human markup in a short timeframe.\n\nConclusionsAutomatic spike detection is a simple and fast method that can reproduce comparable and, in some cases, even superior results compared to the common practice of manual EEG markup in EEG-rsfMRI analysis of epilepsy.\n\nSignificanceOur study shows that IED detection algorithms can be effectively used in epilepsy clinical settings. This work further helps in translating EEG-rsfMRI research into a fast, reliable and easy-to-use clinical tool for epileptologists. Our IED detection approach will be publicly available as a MATLAB package at: https://github.com/omidvarnia/Automatic_focal_spike_detection.\n\nHighlightsO_LIAutomatic spike detection increases the number of detected uniform epileptic interictal discharges and enhances statistical power of EEG-rsfMRI inter-subject variability maps,\nC_LIO_LIAutomatic spike detection can identify additional activated brain regions with presumed epileptogenic focus not seen in standard analysis based on human markup,\nC_LIO_LIAutomatic spike detection can shorten the IED identification process.\nC_LI

neuroscience↗