8,095 research outputs found

    Phase synchronization during the processing of taxonomic and thematic relations

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    Semantic relations include “taxonomic” relations based on shared features and “thematic” relations based on co-occurrence in events. The “dual-hub” account proposes that the anterior temporal lobe (ATL) is functionally specialized for taxonomic relations and the inferior parietal lobule (IPL) for thematic relations. This study examined this claim by analyzing the intra- and inter-region phase synchronization of intracranial EEG data from electrodes in the ATL, IPL, and two subregions of the semantic control network: left inferior frontal gyrus (IFG) and posterior middle temporal gyrus (pMTG). Ten participants with epilepsy completed a semantic relatedness judgment task during intracranial EEG recording and had electrodes in at least one hub and at least one semantic control region. Theta band phase synchronization was partially consistent with the dual-hub account: synchronization between the ATL and IFG/pMTG increased when processing taxonomic relations, and synchronization within the IPL and between IPL and pMTG increased when processing thematic relations

    Computational techniques to interpret the neural code underlying complex cognitive processes

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    Advances in large-scale neural recording technology have significantly improved the capacity to further elucidate the neural code underlying complex cognitive processes. This thesis aimed to investigate two research questions in rodent models. First, what is the role of the hippocampus in memory and specifically what is the underlying neural code that contributes to spatial memory and navigational decision-making. Second, how is social cognition represented in the medial prefrontal cortex at the level of individual neurons. To start, the thesis begins by investigating memory and social cognition in the context of healthy and diseased states that use non-invasive methods (i.e. fMRI and animal behavioural studies). The main body of the thesis then shifts to developing our fundamental understanding of the neural mechanisms underpinning these cognitive processes by applying computational techniques to ana lyse stable large-scale neural recordings. To achieve this, tailored calcium imaging and behaviour preprocessing computational pipelines were developed and optimised for use in social interaction and spatial navigation experimental analysis. In parallel, a review was conducted on methods for multivariate/neural population analysis. A comparison of multiple neural manifold learning (NML) algorithms identified that non linear algorithms such as UMAP are more adaptable across datasets of varying noise and behavioural complexity. Furthermore, the review visualises how NML can be applied to disease states in the brain and introduces the secondary analyses that can be used to enhance or characterise a neural manifold. Lastly, the preprocessing and analytical pipelines were combined to investigate the neural mechanisms in volved in social cognition and spatial memory. The social cognition study explored how neural firing in the medial Prefrontal cortex changed as a function of the social dominance paradigm, the "Tube Test". The univariate analysis identified an ensemble of behavioural-tuned neurons that fire preferentially during specific behaviours such as "pushing" or "retreating" for the animal’s own behaviour and/or the competitor’s behaviour. Furthermore, in dominant animals, the neural population exhibited greater average firing than that of subordinate animals. Next, to investigate spatial memory, a spatial recency task was used, where rats learnt to navigate towards one of three reward locations and then recall the rewarded location of the session. During the task, over 1000 neurons were recorded from the hippocampal CA1 region for five rats over multiple sessions. Multivariate analysis revealed that the sequence of neurons encoding an animal’s spatial position leading up to a rewarded location was also active in the decision period before the animal navigates to the rewarded location. The result posits that prospective replay of neural sequences in the hippocampal CA1 region could provide a mechanism by which decision-making is supported

    Development and assessment of learning-based vessel biomarkers from CTA in ischemic stroke

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    The Genetic and Neuronal Substrates of Melatonin Signaling in Zebrafish Sleep

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    Sleep is hypothesized to be regulated by two processes: a circadian drive, which communicates time of day to ensure that sleep is timed to the appropriate day/night phase, and a homeostatic drive, by which the propensity for sleep becomes stronger over the course of prolonged wakefulness. While studies suggest that adenosine and serotonin signaling in part mediate the homeostatic sleep drive, factors that act downstream of the circadian clock to promote sleep were unidentified until recently. Previous work in the Prober lab has shown that the nocturnal hormone melatonin acts downstream of the circadian rhythm to promote sleep in zebrafish. The downstream processes by which melatonin promotes sleep is poorly understood across all animal models. This is likely because melatonin research has been primarily conducted using nocturnal laboratory rodent models, in whom melatonin does not seem to play a role in sleep, and because of the widely held view that melatonin informs the circadian clock and does not promote sleep directly. In Chapter 1 of this thesis, I review some of the research conducted over the last 50 years that has informed our current understanding of melatonin and its role in sleep. In Chapter 2, I describe our efforts to use the zebrafish, in which melatonin is both potently sedating and essential for nightly sleep, to uncover some of the mechanisms by which melatonin might promote sleep. We found that melatonin acts through a particular melatonin receptor family called MT1, whereas melatonin receptors belonging to other families were dispensable for sleep. We show that MT1 receptors are expressed broadly throughout the zebrafish brain and are enriched in brain regions involved in sensory processing, particularly in those related to vision. We tested the hypothesis that melatonin promotes sleep, at least in part, by dampening visual responsiveness at night. We show that, separable from sleep, exogenous melatonin suppresses behavioral responses to light stimuli, and loss of endogenous melatonin results in day-like behavioral responses to light stimuli during the night. We are using whole brain imaging in live zebrafish to corroborate our behavioral results with neuronal GCaMP recordings. We hope that the findings presented here contribute to a greater understanding of melatonin’s role in sleep, which may help enhance its value as a natural therapeutic aid

    Auditory naming is impaired in posterior cortical atrophy and early-onset Alzheimer’s disease

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    IntroductionVisual naming ability reflects semantic memory retrieval and is a hallmark deficit of Alzheimer’s disease (AD). Naming impairment is most prominently observed in the late-onset amnestic and logopenic variant Primary Progressive Aphasia (lvPPA) syndromes. However, little is known about how other patients across the atypical AD syndromic spectrum perform on tests of auditory naming, particularly those with primary visuospatial deficits (Posterior Cortical Atrophy; PCA) and early onset (EOAD) syndromes. Auditory naming tests may be of particular relevance to more accurately measuring anomia in PCA syndrome and in others with visual perceptual deficits.MethodsForty-six patients with biomarker-confirmed AD (16 PCA, 12 lvPPA, 18 multi-domain EOAD), at the stage of mild cognitive impairment or mild dementia, were administered the Auditory Naming Test (ANT). Performance differences between groups were evaluated using one-way ANOVA and post-hoc t-tests. Correlation analyses were used to examine ANT performance in relation to measures of working memory and word retrieval to elucidate cognitive mechanisms underlying word retrieval deficits. Whole-cortex general linear models were generated to determine the relationship between ANT performance and cortical atrophy.ResultsBased on published cutoffs, out of a total possible score of 50 on the ANT, 56% of PCA patients (mean score = 45.3), 83% of EOAD patients (mean = 39.2), and 83% of lvPPA patients (mean = 29.8) were impaired. Total uncued ANT performance differed across groups, with lvPPA performing most poorly, followed by EOAD, and then PCA. ANT performance was still impaired in lvPPA and EOAD after cuing, while performance in PCA patients improved to the normal range with phonemic cues. ANT performance was also directly correlated with measures of verbal fluency and working memory, and was associated with cortical atrophy in a circumscribed semantic language network.DiscussionAuditory confrontation naming is impaired across the syndromic spectrum of AD including in PCA and EOAD, and is likely related to auditory-verbal working memory and verbal fluency which represent the nexus of language and executive functions. The left-lateralized semantic language network was implicated in ANT performance. Auditory naming, in the absence of a visual perceptual demand, may be particularly sensitive to measuring naming deficits in PCA

    Development and assessment of learning-based vessel biomarkers from CTA in ischemic stroke

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    A comparative study of GNN and MLP based machine learning for the diagnosis of Alzheimer’s Disease involving data synthesis

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    Alzheimer’s Disease (AD) is a neurodegenerative disease that commonly occurs in older people. It is characterized by both cognitive and functional impairment. However, as AD has an unclear pathological cause, it can be hard to diagnose with confidence. This is even more so in the early stage of Mild Cognitive Impairment (MCI). This paper proposes a U-Net based Generative Adversarial Network (GAN) to synthesize fluorodeoxyglucose -positron emission tomography (FDG-PET) from magnetic resonance imaging - T1 weighted imaging (MRI-T1WI) for further usage in AD diagnosis including its early-stage MCI. The experiments have displayed promising results with Structural Similarity Index Measure (SSIM) reaching 0.9714. Furthermore, three types of classifiers are developed, i.e., one Multi-Layer Perceptron (MLP) based classifier, two Graph Neural Network (GNN) based classifiers where one is for graph classification and the other is for node classification. 10-fold cross-validation has been conducted on all trials of experiments for classifier comparison. The performance of these three types of classifiers has been compared with the different input modalities setting and data fusion strategies. The results have shown that GNN based node classifier surpasses the other two types of classifiers, and has achieved the state-of-the-art (SOTA) performance with the best accuracy at 90.18% for 3-class classification, namely AD, MCI and normal control (NC) with the synthesized fluorodeoxyglucose - positron emission tomography (FDG-PET) features fused at the input level. Moreover, involving synthesized FDG-PET as part of the input with proper data fusion strategies has also proved to enhance all three types of classifiers’ performance. This work provides support for the notion that machine learning-derived image analysis may be a useful approach to improving the diagnosis of AD

    Enabling neighbour-labelling: using synthetic biology to explore how cells influence their neighbours

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    Cell-cell interactions are central to development, but exploring how a change in any given cell relates to changes in the neighbour of that cell can be technically challenging. Here, we review recent developments in synthetic biology and image analysis that are helping overcome this problem. We highlight the opportunities presented by these advances and discuss opportunities and limitations in applying them to developmental model systems

    Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

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    Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly
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