Biology subjects

Rees, G.

Publications and source records attributed to Rees, G..

12 recordsLinked to original sources

bioRxiv2024

Hierarchical cortical plasticity in congenital sight impairment

A robust learning system balances adaptability to new experiences with stability of its foundational architecture. To investigate how the human brain implements this we used a new approach to study plasticity and stability across hierarchical processing stages in visual cortex. We compare the rod system of individuals born with rod-only photoreceptor inputs (achromatopsia) to the typically developed rod system, allowing us to dissociate impacts of life-long versus transient responses to altered input. Cortical input stages (V1) exhibited high stability, with input-deprived cortex showing no retinotopic remapping and exhibiting structural hallmarks of deprivation. However, plasticity manifested as reorganised read-out of these inputs by higher-order cortex, in a pattern that could compensate for the lower resolution of a rod-only system and its lack of high-density foveal input. We propose that these hierarchical dynamics robustly optimize processing of available input and could reflect a broader principle of brain organisation with important implications for emerging sight-rescue therapies.

neuroscience↗

bioRxiv2022

Uncertain Precision: Neurobiological and Physiological Correlates of Conservative Confidence Bias

Correctly estimating the influence of uncertainty on our decisions is a critical metacognitive faculty. However, the relationship between sensory uncertainty (or its inverse, precision), decision accuracy, and subjective confidence is currently unclear. Although some findings indicate that healthy adults exhibit an illusion of over-confidence, under-confidence in response to sensory uncertainty has also been reported. One reason for this ambiguity is that stimulus intensity and precision are typically confounded with one another, limiting the ability to assess their independent contribution to metacognitive biases. Here we report four psychophysical experiments controlling these factors, finding that healthy human participants are systematically under-confident when discriminating low-precision stimuli. This bias remains even when decision accuracy and reaction time are accounted for, indicating that a performance-independent computation partially underpins the influence of sensory precision on confidence. We further show that this influence is linked to fluctuations in arousal and individual differences in the neuroanatomy of the left superior parietal lobe and middle insula. These results illuminate the neural and physiological correlates of precision misperception in metacognition. Significance StatementThe ability to recognize the influence of sensory uncertainty on our decisions underpins the veracity of self-monitoring, or metacognition. In the extreme, a systematic confidence bias can undermine decision accuracy and potentially underpin disordered self-insight in neuropsychiatric illness. Previously it was unclear if metacognition accurately reflects changes in sensory precision, in part due to confounding effects of stimulus intensity and precision. Here we overcome these limitations to repeatedly demonstrate a robust precision-related confidence bias. Further, we reveal novel neuroanatomical and physiological markers underlying this metacognitive bias. These results suggest a unique state-based computational mechanism may drive subjective confidence biases and further provide new avenues for investigating maladaptive awareness of uncertainty in neuropsychiatric disorders.

neuroscience↗

bioRxiv2021

Timing of selective basal ganglia white matter loss in Huntington's disease

ObjectivesTo investigate the timeframe prior to symptom onset when cortico-basal ganglia white matter (WM) loss begins in premanifest Huntingtons disease (preHD), and which striatal and thalamic sub-region WM tracts are most vulnerable. MethodsWe performed fixel-based analysis, which allows resolution of crossing WM fibres at the voxel level, on diffusion tractography derived WM tracts of striatal and thalamic sub-regions in two independent cohorts; TrackON-HD, which included 72 preHD (approx. 11 years before disease onset) and 85 controls imaged at three time points over two years; and the HD young adult study (HD-YAS), which included 54 preHD (approx. 25 years before disease onset) and 53 controls, imaged at one time point. Group differences in fibre density and cross section (FDC) were investigated. ResultsWe found no significant group differences in cortico-basal ganglia sub-region FDC in preHD gene carriers 25 years before onset. In gene carriers 11 years before onset, there were reductions in striatal (limbic and caudal motor) and thalamic (premotor, motor and sensory) FDC at baseline, with no significant change over 2 years. Caudal motor-striatal, pre-motor-thalamic, and primary motor-thalamic FDC at baseline, showed significant correlations with the Unified Huntingtons disease rating scale (UHDRS) total motor score (TMS). Limbic cortico-striatal FDC and apathy were also significantly correlated. ConclusionsOur findings suggest that the initiation of disease modifying therapies 25 years before onset could protect these important brain networks from undergoing neurodegeneration and highlight selectively vulnerable sub-regions of the striatum and thalamus that may be important targets for future therapies.

neuroscience↗

bioRxiv2020

Relating quantitative 7T MRI across cortical depths to cytoarchitectonics, gene expression and connectomics: a framework for tracking neurodegenerative disease

Cortical layer-specific ultra-high field MRI has the potential to provide anatomically precise biomarkers and mechanistic insights into neurodegenerative disease. Here we compare cortical layer-specificity for a 7T multi-parametric mapping (MPM) 500m whole brain acquisition to the von Economo and Big Brain post-mortem histology atlases. We also investigate the relationship between 7T MPMs, layer-specific gene expression and Huntingtons disease related genes, using the Allen Human Brain atlas. Finally we link MPM cortical depth measures with white matter connections using high-fidelity diffusion tractography from a 300mT/m Connectom MRI system. We show that R2* across cortical depths is highly correlated with layer-specific cell number, cell staining intensity and gene expression. Furthermore white matter connections were highly correlated with grey matter R1 and R2* across cortical depths. These findings demonstrate the potential of combining 7T MPMs, gene expression and white matter connections to provide an anatomically precise framework for tracking neurodegenerative disease.

neuroscience↗

bioRxiv2019

Improving the diagnosis and classification of Ph-negative myeloproliferative neoplasms through deep phenotyping

Myeloproliferative neoplasms (MPNs) are clonal disorders characterized by excessive proliferation of myeloid lineages. Accurate classification and appropriate management of MPNs requires integration of clinical, morphological and genetic findings. Despite major advances in understanding the molecular and genetic basis, morphological assessment of the bone marrow trephine (BMT) remains paramount in differentiating between MPN subtypes and reactive conditions. However, morphological assessment is heavily constrained by a reliance on subjective, qualitative and poorly reproducible criteria. To address this, we have developed a machine-learning strategy for the automated identification and quantitative analysis of megakaryocyte morphology using clinical BMT samples. Using a sample cohort of recently diagnosed or established ET (n = 48) and reactive control cases (n = 42) we demonstrated a high predictive accuracy (AUC = 0.95) of automated tissue ET diagnosis based upon these specific megakaryocyte phenotypes. These separate morphological phenotypes showed evidence of specific genotype associations, which offers promise that an automated cell phenotyping approach may be of clinical diagnostic utility as an adjunct to standard genetic and molecular tests. This has great potential to assist in the routine assessment of newly diagnosed or suspected MPN patients and those undergoing treatment / clinical follow-up. The extraction of quantitative morphological data from BMT sections will also have value in the assessment of new therapeutic strategies directed towards the bone marrow microenvironment and can provide clinicians and researchers with objective, quantitative data without significant demands upon current routine specimen workflows.

pathology↗

bioRxiv2019

Dynamics of cortical degeneration over a decade in Huntington's Disease

The neurodegenerative process is typically slowly progressive and complex. While simple models of neurodegeneration suggest that brain changes progress at a near constant rate, previous research shows regional variation within the temporal progression of atrophy, indicating that over the course of neurodegeneration, different regions may undergo changing rates of atrophy. Characterization of long-term dynamic brain changes in neurodegeneration requires both extensive longitudinal MRI datasets and an advanced modeling framework. Until recently, both of these elements were not available. Here, we implement a novel dynamic systems approach to infer patterns of regional progression spatially and temporally in a unique longitudinal dataset with up to seven annual individual brain scans per participant from 49 Huntingtons Disease (HD) gene-carriers. We map participant-and group-level trajectories of cortical atrophy in HD using a decade of data that encompasses motor symptom onset and, for the first time, show that neurodegenerative brain changes exhibit complex temporal dynamics of atrophy with substantial regional variation in progressive cortical atrophy. Some fronto-occipital cortical areas show an almost constant rate of atrophy, while medial-inferior temporal areas undergo only minor change. Interestingly, cortical sensory-motor areas were found to show a noticeable acceleration of atrophy following HD diagnosis. Furthermore, we establish links between individual atrophy and genetic markers of HD (CAG repeat length), as well as showing that cortical motor network changes predict subsequent decline in task-based motor performance, demonstrating face-validity of the model. Our findings highlight the complex pattern of dynamic cortical change occurring in HD that can help to resolve the biological underpinnings of HD progression.

neuroscience↗

bioRxiv2018

Activity-is better than connectivity-neurofeedback training in Huntington’s disease

Non-invasive methods, such as neurofeedback training (NFT), could support cognitive symptom management in Huntingtons disease (HD) by targeting brain regions whose function is impaired. The aim of our single-blind, sham-controlled study was to collect rigorous evidence regarding the feasibility of NFT in HD by examining two different methods, activity and connectivity real-time fMRI NFT. Thirty-two HD gene-carriers completed 16 runs of NFT training, using an optimized real-time fMRI protocol. Participants were randomized into four groups, two treatment groups, one receiving neurofeedback derived from the activity of the Supplementary Motor Area (SMA), and another receiving neurofeedback based on the correlation of SMA and left striatum activity (connectivity NFT), and two sham control groups, matched to each of the treatment groups. We examined differences between the groups during NFT training sessions and after training at follow-up sessions. Transfer of training was measured by measuring the participants ability to upregulate NFT target levels without feedback (near transfer), as well as by examining change in objective, a-priori defined, behavioural measures of cognitive and psychomotor function (far transfer) before and at 2 months after training. We found that the treatment group had significantly higher NFT target levels during the training sessions compared to the control group. However, we did not find robust evidence of better transfer in the treatment group compared to controls, or a difference between the two NFT methods. We also did not find evidence in support of a relationship between change in cognitive and psychomotor function and NFT learning success. We conclude that although there is evidence that NFT can be used to guide participants to regulate the activity and connectivity of specific regions in the brain, evidence regarding transfer of learning and clinical benefit was not robust. Although the intervention is non-invasive, given the costs and absence of reliable evidence of clinical benefit, we cannot recommend real-time fMRI NFT as a potential intervention in HD.

neuroscience↗

bioRxiv2018

Stability and sensitivity of structural connectomes: effect of thresholding and filtering and demonstration in neurodegeneration

Structural connectomes derived using diffusion tractography are increasingly used to investigate white matter connectivity in neurological diseases. However inherent biases in diffusion tractography algorithms may lead to both false negatives and false positives in connectome construction. A range of graph thresholding approaches and more recently several streamline filtering algorithms have been developed to address these issues. However there is no consensus in the literature regarding the best available approach. Using a cohort of Huntingtons disease patients and healthy controls we compared the effect of several graph thresholding strategies: proportional, absolute, consensus and consistency thresholding, with and without streamline filtering, using Spherical Deconvolution Informed Filtering of tractograms (SIFT2) algorithm. We examined the effect of thresholding strategies on the stability of graph theory metrics and the sensitivity of these measures in neurodegeneration. We show that while a number of graph thresholding procedures result in stable metrics across thresholds, the detection of group differences is highly variable. We also showed that the application of streamline filtering using SIFT2 resultes in better detection of group differences and stronger clinical correlations. We therefore conclude that the application of SIFT2 streamline filtering without graph thresholding may be sufficient for structural connectome construction.

neuroscience↗

bioRxiv2018

Assessment of Functional Connectome Construction Strategies in Neurodegeneration

Connectomics can be used to investigate functional brain networks in neurodegenerative diseases including Huntingtons disease (HD). In this developing field, different connectome construction strategies have emerged in parallel. However, there is a need to understand the influences of different strategies on subsequent analyses when constructing a connectome. This study systematically compares connectome construction strategies based on their biological relevance to functional networks in neurodegeneration.\n\nWe asked which functional connectome construction strategy was best able to discriminate HD gene carriers from healthy controls, and how such a strategy affected modular organization of the network. The major factors compared were principal component-based correction versus wavelet decomposition for physiological noise correction, the type of parcellation atlas (functional, structural and multi-modal), weighted versus binarized networks, and unthresholded versus proportionally thresholded networks. We found that principal component-based correction generated the most discriminatory connectomes, while binarization and proportional thresholding did not increase discrimination between HD gene carriers and healthy controls. When a functional parcellation atlas was used, the highest discrimination rates were obtained. We observed that the group differences in modular organization of the functional connectome were greatly affected by binarization and thresholding, showing no consistent pattern of modularity.\n\nThis study suggests that functional connectome construction strategies using principal component-based correction and weighted unthresholded connectivity matrices may outperform other strategies.

neuroscience↗

bioRxiv2017

Evaluation of Metrics for Benchmarking Antimicrobial Use in the United Kingdom Dairy Industry

The issue of antimicrobial resistance is of global concern across human and animal health. In 2016 the UK government committed to new targets for reducing antimicrobial use (AMU) in livestock. However, though a number of metrics for quantifying AMU are defined in the literature, all give slightly different interpretations.\n\nThis paper reviews a selection of metrics for AMU in the dairy industry: total mg, total mg/kg, daily dose and daily course metrics. Although the focus is on their application to the dairy industry, the metrics and issues discussed are relevant across livestock sectors.\n\nIn order to be used widely, a metric should be understandable and relevant to the veterinarians and farmers who are prescribing and using antimicrobials. This means that clear methods, assumptions (and possible biases), standardised values and exceptions should be published for all metrics. Particularly relevant are assumptions around the number and weight of cattle at risk of treatment and definitions of dose rates and course lengths; incorrect assumptions can mean metrics over- or under-represent AMU.\n\nThe authors recommend that the UK dairy industry work towards UK-specific metrics using UK-specific medicine dose and course regimens as well as cattle weights in order to monitor trends nationally.

epidemiology↗

bioRxiv2017

Quantitative MRI Provides Markers Of Intra-, Inter-Regional, And Age-Related Differences In Young Adult Cortical Microstructure

Measuring the structural composition of the cortex is critical to understanding typical development, yet few investigations in humans have charted markers in vivo that are sensitive to tissue microstructural attributes. Here, we used a well-validated quantitative MR protocol to measure four parameters (R1, MT, R2*, PD*) that differ in their sensitivity to facets of the tissue microstructural environment (R1, MT: myelin, macromolecular content; R2*: paramagnetic ions, i.e., iron; PD*: free water content). Mapping these parameters across cortical regions in a young adult cohort (18-30 years, N=93) revealed expected patterns of increased macromolecular content as well as reduced tissue water content in primary and primary adjacent cortical regions. Mapping across cortical depth within regions showed decreased expression of myelin and related processes - but increased tissue water content - when progressing from the grey/white to the grey/pial boundary, in all regions. Charting developmental change in cortical microstructure, we found that parameters with the greatest sensitivity to tissue myelin (R1 & MT) showed linear increases with age across frontal and parietal cortex (change 0.5-1.0% per year). Overlap of robust age effects for both parameters emerged in left inferior frontal, right parietal and bilateral pre-central regions. Our findings afford an improved understanding of ontogeny in early adulthood and offer normative quantitative MR data for inter- and intra-cortical composition, which may be used as benchmarks in further studies.\n\nHighlights O_LIWe mapped multi-parameter maps (MPMs) across and within cortical regions\nC_LIO_LIWe charted age effects on myelin and related processes at mid-cortical depth\nC_LIO_LIInter- and intra-regional differences in MPMs emerged at primary and association cortex\nC_LIO_LIIron-sensitive R2* map foci tended to overlap MPMs sensitive to myelin (R1, MT)\nC_LIO_LIR1 and MT increased with age (0.5-1.0% per year) in frontal and parietal cortex\nC_LI

neuroscience↗

bioRxiv2017

Metacognitive impairments extend perceptual decision making weaknesses in compulsivity

Awareness of ones own abilities is of paramount importance in adaptive decision making. Psychotherapeutic theories assume such metacognitive insight is impaired in compulsivity, though this is supported by scant empirical evidence. In this study, we investigate metacognitive abilities in compulsive participants using computational models, where these enable a segregation between metacognitive and perceptual decision making impairments. We examined twenty low-compulsive and twenty high-compulsive participants, recruited from a large population-based sample, and matched for other psychiatric and cognitive dimensions. Hierarchical computational modelling of the participants metacognitive abilities on a visual global motion detection paradigm revealed that high-compulsive participants had a reduced metacognitive ability. This impairment was accompanied by a perceptual decision making deficit whereby motion-related evidence was accumulated more slowly in high compulsive participants. Our study shows that the compulsivity spectrum is associated with a reduced ability to monitor ones own performance, over and above any perceptual decision making difficulty.

neuroscience↗