Biology subjects

Duff, E. P.

Publications and source records attributed to Duff, E. P..

8 recordsLinked to original sources

bioRxiv2026

Intrinsic molecular susceptibility underlies selective neuronal vulnerability in the Alzheimer's disease entorhinal cortex

Entorhinal cortex (EC) excitatory neurons are lost early in Alzheimers disease (AD), yet the specific subtype and characteristics contributing to this vulnerability are poorly understood. Combining imaging mass cytometry (206,913 cells; 62 donors) and single nucleus RNA sequencing (42,780 nuclei; 36 donors) of post-mortem EC, we found that calbindin-expressing layer 2-3 excitatory neurons accumulate high phospho-tau burden and are preferentially lost in AD. In non-diseased brains, these neurons exhibit elevated tau-modifying kinase expression (ERK1/2, FYN, ROCK), reduced phosphatase expression (PP2A/B, PP5) and low mitochondrial respiratory capacity which together are predicted to promote high vulnerability to tau pathology. Trajectory analysis resolved progression from homeostasis through DNA damage and proteostatic stress to developmental re-entry and death priming. In silico screening suggested histone deacetylase inhibitors and cyclooxygenase inhibitors as candidate resilience-promoting therapeutics. Our work thus reframes intrinsic features of neuronal identity promoting phospho-tau formation as modifiable determinants of the selective vulnerability of EC calbindin neurons.

neuroscience↗

bioRxiv2025

Precon_all: A species-agnostic automated pipeline for non-human cortical surface reconstruction

Cortical surface reconstruction has changed how we study brain morphology and geometry. However, extending these methods to non-human species has been limited by the lack of standardized pipelines, anatomical templates, and variability in imaging protocols. To address these challenges, we present Precon_all, an open-source, species-agnostic pipeline that automates cortical surface reconstruction for non-human neuroimaging. It runs reliably across a wide range of anatomical structures and imaging conditions and has been successfully applied to datasets from primates, carnivores, and artiodactyls. Its modular framework mirrors human neuroimaging workflows, supports manual quality control, and produces outputs compatible with FreeSurfer and Connectome Workbench. In doing so, it substantially reduces technical barriers to non-human neuroimaging. As data-sharing initiatives continue to expand access to non-human imaging datasets, Precon_all provides a scalable and standardized solution that supports the broader adoption of surface-based methods for studying cortical evolution through comparative neuroscience.

neuroscience↗

bioRxiv2020

A White Matter Atlas and Common Connectivity Space Facilitate the Pig as a Translational Model in Neuroscience

Neuroimagings capability to quickly and rapidly phenotype the cortical organization of the whole brain brings with it the possibility to extend our understanding of cortical organization across the mammalian lineage. However, neuroimaging has thus far generally limited itself to a small number of species, with most animal studies being performed in either rodents or Non-Human Primates. Here we perform a first pass characterization of an animal which has recently seen its stock rise in the neuroscience community with the development of new models of neurological disease; the domestic pig. Characterizing the structural connectome of the pig, we create a white matter atlas, and an anatomical template which we use to build a horizontal translation between the pig and human based on a connectivity blueprint approach. We find that conserved trends of structural connectivity across species enabled spatial prediction of regions of interest between the pig and human, showing the potential horizontal translations have as a tool to assess the translational validity of porcine models of neurological disease. Releasing the anatomical template, white matter atlas, and connectivity blueprints, we hope to ease and promote the acceptance of the pig as an alternative large-animal model by the neuroimaging community.

neuroscience↗

bioRxiv2020

Integrating large-scale neuroimaging research datasets: harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets

Large scale neuroimaging datasets present the possibility of providing normative distributions for a wide variety of neuroimaging markers, which would vastly improve the clinical utility of these measures. However, a major challenge is our current poor ability to integrate measures across different large-scale datasets, due to inconsistencies in imaging and non-imaging measures across the different protocols and populations. Here we explore the harmonisation of white matter hyperintensity (WMH) measures across two major studies of healthy elderly populations, the Whitehall II imaging sub-study and the UK Biobank. We identify pre-processing strategies that maximise the consistency across datasets and utilise multivariate regression to characterise sample differences contributing to study-level differences in WMH variations. We also present a parser to harmonise WMH-relevant non-imaging variables across the two datasets. We show that we can provide highly calibrated WMH measures from these datasets with: (1) the inclusion of a number of specific standardised processing steps; and (2) appropriate modelling of sample differences through the alignment of demographic, cognitive and physiological variables. These results open up a wide range of applications for the study of WMHs and other neuroimaging markers across extensive databases of clinical data. HIGHLIGHTSO_LIWe harmonised measures of WMHs across two studies on healthy ageing C_LIO_LISpecific pre-processing strategies can increase comparability across studies C_LIO_LIModelling of biological differences is crucial to provide calibrated measures C_LI

neuroscience↗

bioRxiv2020

Inferring the infant pain experience: a translational fMRI-based signature approach

BackgroundIn the absence of verbal communication it is challenging to infer an individuals sensory and emotional experience. In adults, fMRI has been used to develop multivariate brain activity signatures, which reliably capture elements of human pain experience. We translate whole-brain fMRI signatures that encode pain perception in adults to the newborn infant brain, to advance understanding of functional brain development and pain perception in early life. MethodsA cohort of adults (n=10; mean age=28.3 years) and 2 cohorts of healthy infants (Cohort A: n=15; Cohort B: n=22; mean postnatal age=3 days) were stimulated with low intensity nociceptive stimuli (64-512mN) during acquisition of functional MRI data. fMRI pain signatures were applied directly to the adult data and transformed such that they could be applied to the infant brain. In each cohort, we assessed the concordance of the signatures with the brain responses using cosine-similarity scores, and we assessed stimulus intensity encoding of the signature responses using Spearman rank correlation. Brain activity in pro-pain and anti-pain brain regions were also examined. FindingsThe Neurologic Pain Signature (NPS), which reflects aspects of nociceptive pain experience, was activated in both the adults and infants, and reliably encoded stimulus intensity. However, the Stimulus Intensity Independent Pain Signature (SIIPS1), which reflects higher-level cognitive modulation of nociceptive input, was only expressed in adults. Pro-pain brain regions showed similar activation patterns in adults and infants, whereas, anti-pain brain regions exhibited divergent responses. InterpretationBasic intensity encoding of nociceptive information is similar in adults and infants. However, translation of adult brain signatures into infants reveals significant differences in infant cerebral processing of nociceptive information, which may reflect their lack of expectation, motivation and contextualisation. This study expands the use of brain activity pain signatures to non-verbal patients and provides a potential approach to assess analgesic interventions in infancy. FundingThis work was funded by Wellcome (Senior Research Fellowship awarded to Prof. Rebeccah Slater) and SSNAP "Support for Sick and Newborn Infants and their Parents" Medical Research Fund (University of Oxford Excellence Fellowship awarded to Dr Eugene Duff). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for research articles published prior to March 2020 using terms including fMRI, infant or neonate, and pain or nociception in the title or abstract. Due to the relatively new emergence of this field, and the experimental and analytical challenges involved in studying cerebral processing of pain in the MRI environment in healthy newborn infants, only five fMRI studies have examined infant brain responses to nociceptive input. In a foundational pilot study, Williams et al., applied an experimental noxious stimulus to a single infant, evoking widespread brain activity that included several brain regions involved in pain processing in adults. Goksan et al., subsequently performed an observational cohort study and used regional analyses to compare active brain regions in infants (n=10) and adults (n=10), concluding that the evoked patterns of brain activity were broadly similar in infants and adults. Further follow-up analysis in the infant cohort revealed that the functional connectivity of brain regions involved in descending pain modulation influences the magnitude of pain-related brain activity. Two further studies focused on methodological advances, providing evidence-based recommendations for fMRI acquisition parameters and image processing in order to maximise the quality of infant data, and these methods have been implemented in this study. Added value of this studyThis study translates validated adult pain fMRI brain signatures to a nonverbal patient population in which the assessment and management of pain presents a significant clinical challenge. Application of fMRI brain signatures to newborn infants expands on previous fMRI studies that provided only qualitative evidence that noxious stimulation commonly activates brain regions in the adult and infant brain. Here we demonstrate that the basic encoding of the sensory discriminative aspects of pain, as represented by the Neurologic Pain Signature (NPS), occurs in both adults and infants, whereas higher-level cognitive modulation of pain, represented by the Stimulus Intensity Independent Pain Signature (SIIPS1) is only present in adults and not observed in infants. The differences in how the immature infant brain processes pain, relative to the mature adult brain, are likely to reflect differences in their expectation, motivation and contextualisation of external events rather than differences in their core nociceptive cerebral processing of pain. This work allows us to use quantitative fMRI observations to make stronger inferences related to pain experience in nonverbal infants. Implications of all the available evidenceBehavioural pain scores used in neonatal clinical care offer limited sensitivity and specificity to pain. Neonatal clinical trials that use these scores as outcome measures frequently report a lack of efficacy of common analgesic interventions, resulting in few evidence-based drugs for treating pain. The value of using brain-based neuroimaging markers of pain as a means of providing objective evidence of analgesic efficacy in early proof of concept studies is well recognised in adults, even in the absence of behavioural pain modulation. Similarly, in infants EEG-based measures of noxious-evoked brain activity have been used as outcome measures in clinical trials of analgesics to overcome some of the inherent limitations of using behavioural observations to quantify analgesic efficacy. Considering the successful translation of the Neurologic Pain Signature (NPS) and its sensitivity to analgesic modulation in adults, this novel methodology represents an objective brain-based fMRI approach that could be used to advance the discovery and assessment of analgesic interventions in infancy.

neuroscience↗

bioRxiv2020

The Developing Human Connectome Project: typical and disrupted functional connectivity across the perinatal period

The Developing Human Connectome Project (dHCP) is an Open Science project which provides the first large sample of neonatal functional MRI (fMRI) data with high temporal and spatial resolution. This data enables mapping of intrinsic functional connectivity between spatially distributed brain regions under normal and adverse perinatal circumstances, offering a framework to study the ontogeny of large-scale brain organisation in humans. Here, we characterise in unprecedented detail the maturation and integrity of resting-state networks (RSNs) at normal term age in 337 infants (including 65 born preterm). First, we applied group independent component analysis (ICA) to define 11 RSNs in term-born infants scanned at 43.5-44.5 weeks postmenstrual age (PMA). Adult-like topography was observed in RSNs encompassing primary sensorimotor, visual and auditory cortices. Among six higher-order, association RSNs, analogues of the adult networks for language and ocular control were identified, but a complete default mode network precursor was not. Next, we regressed the subject-level datasets from an independent cohort of infants scanned at 37-43.5 weeks PMA against the group-level RSNs to test for the effects of age, sex and preterm birth. Brain mapping in term-born infants revealed areas of positive association with age across four of six association RSNs, indicating active maturation in functional connectivity from 37 to 43.5 weeks PMA. Female infants showed increased connectivity in inferotemporal regions of the visual association network. Preterm birth was associated with striking impairments of functional connectivity across all RSNs in a dose-dependent manner; conversely, connectivity of the superior parietal lobules within the lateral motor network was abnormally increased in preterm infants, suggesting a possible mechanism for specific difficulties such as developmental coordination disorder which occur frequently in preterm children. Overall, we find a robust, modular, symmetrical functional brain organisation at normal term age. A complete set of adult-equivalent primary RSNs is already instated, alongside emerging connectivity in immature association RSNs, consistent with a primary-to-higher-order ontogenetic sequence of brain development. The early developmental disruption imposed by preterm birth is associated with extensive alterations in functional connectivity.

neuroscience↗

bioRxiv2018

Large-scale intrinsic connectivity is consistent across varying task demands

Measuring whole-brain functional connectivity patterns based on task-free ( restingstate) spontaneous fluctuations in the functional MRI (fMRI) signal is a standard approach to probing habitual brain states, independent of task-specific context. This view is supported by spatial correspondence between task- and rest-derived connectivity networks. Yet, it remains unclear whether intrinsic connectivity observed in a resting-state acquisitions is persistent during task. Here, we sought to determine how changes in ongoing brain activation, elicited by task performance, impact the integrity of whole-brain functional connectivity patterns. We employed a steadystates paradigm, in which participants continuously executed a specific task (without baseline periods). Participants underwent separate task-based (visual, motor and visuomotor) or task-free (resting) steady-state scans, each performed over a 5-minute period. This unique design allowed us to apply a set of traditional resting-state analyses to various task-states. In addition, a classical fMRI block-design was employed to identify individualized brain activation patterns for each task, allowing to characterize how differing activation patterns across the steady-states impact whole-brain intrinsic connectivity patterns. By examining correlations across segregated brain regions (nodes) and the whole brain (using independent component analysis), we show that the whole-brain network architecture characteristic of the resting-state is robustly preserved across different steady-task states, despite striking inter-task changes in brain activation (signal amplitude). Subtler changes in functional connectivity were detected locally, within the active networks. Together, we show that intrinsic connectivity underlying the canonical resting-state networks is relatively stable even when participants are engaged in different tasks and is not limited to the resting-state.\n\nNew and Noteworthy\n\nDoes intrinsic functional connectivity (FC) reflect the canonical or transient state of the brain? We tested the consistency of the intrinsic connectivity networks across different task-conditions. We show that despite local changes in connectivity, at the whole-brain level there is little modulation in FC patterns, despite profound and large-scale activation changes. We therefore conclude that intrinsic FC largely reflects the a priori habitual state of the brain, independent of the specific cognitive context.

neuroscience↗

bioRxiv2017

The Developing Human Connectome Project: a Minimal Processing Pipeline for Neonatal Cortical Surface Reconstruction

The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.

neuroscience↗