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Translational Cognitive Neuroscience Lab

 
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A superlist combining individual seminars and series from other lists on talks.cam. These Neuroscience-themed seminars will be advertised throughout the relevant interest group in Cambridge.
Updated: 48 min 45 sec ago

Wed 20 Jul 12:30: Taking heterogeneity seriously: Towards new research and support strategies ***PLEASE NOTE NEW TIME OF ARCLUB TALKS***

Tue, 19/07/2022 - 11:27
Taking heterogeneity seriously: Towards new research and support strategies

A commonly used quote used when talking about autism is: “If you’ve met one autistic person, you’ve met one autistic person”. This quote is used to illustrate the point that autism is an extremely heterogeneous condition. Yet, variability is rarely acknowledged in research, nor investigated. If high heterogeneity is part and parcel to autism, we really need to start understanding, not only what is driving it and how it may impact on developmental outcomes, but more importantly how we can use this knowledge to develop effective support. In this talk I will present preliminary data providing rather surprising results regarding variability in autism. I will also briefly discus how variability can be embraced in employment support.

***PLEASE NOTE NEW TIME OF ARCLUB TALKS***

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Tue 19 Jul 15:30: Taking heterogeneity seriously: Towards new research and support strategies

Tue, 19/07/2022 - 09:10
Taking heterogeneity seriously: Towards new research and support strategies

A commonly used quote used when talking about autism is: “If you’ve met one autistic person, you’ve met one autistic person”. This quote is used to illustrate the point that autism is an extremely heterogeneous condition. Yet, variability is rarely acknowledged in research, nor investigated. If high heterogeneity is part and parcel to autism, we really need to start understanding, not only what is driving it and how it may impact on developmental outcomes, but more importantly how we can use this knowledge to develop effective support. In this talk I will present preliminary data providing rather surprising results regarding variability in autism. I will also briefly discus how variability can be embraced in employment support.

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Tue 28 Jun 11:00: Noise-Aware Differentially Private Synthetic Data

Tue, 28/06/2022 - 09:32
Noise-Aware Differentially Private Synthetic Data

Synthetic data generated under differential privacy (DP) promises to significantly simplify analysis of sensitive personal data. Existing work has shown that simply analysing DP synthetic data as if it were real does not produce valid inferences of population-level quantities, leading to too narrow confidence intervals and thereby risking false discoveries. We propose using multiple imputation techniques to avoid these problems. This requires simulating multiple synthetic data sets from the Bayesian posterior predictive distribution over data sets. We propose a novel noise-aware Bayesian DP synthetic data generation mechanism for discrete data that enables generating such a distribution of data sets. Our experiments demonstrate that the method is able to produce accurate confidence intervals from DP synthetic data.

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Mon 04 Jul 16:00: Spatial heterogeneity of microglia in CNS disease

Mon, 27/06/2022 - 10:44
Spatial heterogeneity of microglia in CNS disease

Microglia are the tissue resident macrophages of the CNS with typical macrophage properties such as innate immune functions and phagocytic activity. They also possess specific CNS -tailored functions such as synaptic pruning and neuronal support. Microglia express a specific transcriptional profile reflective of their CNS macrophage function. In recent years, several studies have elucidated the changes that occur in microglia in the context of development and CNS disease. With the emergence of single cell sequencing to deep phenotype individual cells, novel findings regarding the role of microglia in human CNS development, homeostasis, and disease will be presented.

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Thu 30 Jun 15:00: Network function in human cerebral organoids as a platform for mechanistic and therapeutic advances in cognitive disorders

Sun, 26/06/2022 - 15:04
Network function in human cerebral organoids as a platform for mechanistic and therapeutic advances in cognitive disorders

Human cerebral organoids offer an extraordinary in vitro cellular model for studying human brain development and early disturbances in neurologic disease. Microelectrode array (MEA) recordings are commonly used to compare neuronal activity in 2D and 3D cultures. Yet, MEA recordings can also reveal cellular-scale network activity (Schroeter et al., 2017), including patterns or motifs in network function seen across spatial scales from cellular to whole brain networks. We have used MEA recordings from human air-liquid interface cerebral organoids (ALI-COs; Giandomenico et al., 2019) to study network function and maturation. We have also demonstrated intact neuronal network function development with MEA recordings in a human cerebral organoid model of amyotrophic lateral sclerosis with frontotemporal dementia (ALS/FTD; Szenbenyi et al., 2021). To facilitate investigations of network development in ALI0C Os and the impact of disease-causing perturbations, we created a MATLAB network analysis pipeline (MEA-NAP) for batch analysis of MEA experiments to compare network function over time and conditions (e.g., genetic mutation or drug treatment). This user-friendly, open source diagnostic tool can process raw voltage time-series acquired from single- (Multichannel System) or multi-well MEAs (Axion) and automatically infer key network properties from organoids or 2D human (or murine) neuronal cultures. Our pipeline enables users to perform MEA analysis beyond standard measures of activity or correlation alone to identify differences in network topology and roles of individual nodes in network activity. Our analyses of network function in ALI -COs demonstrate that they can serve as a platform for investigating disease mechanisms and screening new therapies.

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Mon 27 Jun 16:00: Somewhere over the Brainbow: super-multicolour imaging to automate neural circuit reconstructions

Wed, 22/06/2022 - 16:31
Somewhere over the Brainbow: super-multicolour imaging to automate neural circuit reconstructions

Ectopic Neuroscience Seminar in person and on Zoom

Hosted in person by Elisa Galliano and on Zoom by Cambridge Neuroscience

The brain is made up of dense networks of interconnected neurons. Mapping the anatomy of these dense networks is one of the biggest challenges in neuroscience. Electron microscopy provides the highest resolution and is used as a gold standard in connectomics; however, its data size hampers large-scale circuit reconstruction at the millimetre scale. Light microscopy combined with tissue clearing is a new emerging approach for mesoscopic circuit mapping. However, the reconstruction of densely labelled circuits is challenging as its limited resolution hinders the discrimination of different neurons. Stochastic multicolour labelling strategies, such as Brainbow, utilise a combination of 3 fluorescent proteins (XFPs) to create different colour hues. Allowing for the reconstruction of densely labelled circuits. However, these tools only produce 20 colour hues, which is not enough to reconstruct neuronal circuits at sufficient density. Moreover, manual circuit tracing based on the colour hue is a rate limiting step in this strategy. We aimed to solve these issues by increasing the number of colour hues available, then use machine learning to automatically reconstruct neurons based on their colour hue alone. Firstly, we increased the number of colour hues by stochastically expressing a combination of 7 different fluorescent proteins, then separating the spectral overlap through linear unmixing. Our modelling suggests that this can generate 1,200 different colour hues. Secondly, as our eyes are limited to trichromatic vision, we developed a pipeline to automatically recognize the combination of >3 colours. This pipeline includes a newly developed unsupervised clustering algorithm, named the “Euclidean Crawler”, which classifies data points in N-dimensional space purely based on the threshold Euclidean distance. It holds an advantage over other distance-based clustering algorithms as we do not need to specify the number of clusters (like K-means), nor the density of clusters (like mean-shift clustering). As proof of concept, first, we successfully reconstructed densely labelled layer 2/3 neurons in S1 (~300 neurons). Secondly, we automatically reconstructed long range (>2 mm) axonal terminals of mitral and tufted cell axons in the olfactory cortex. Finally, we used our automatic circuit reconstruction pipeline to register neighbouring brain sections: this was done by identifying neurites that go between sections by their colour hue, then performing piece-wise linear mapping for registration. Thus, super multi-colour labelling is a powerful tool for highly multiplexed circuit reconstruction on the mesoscopic scale.

https://us02web.zoom.us/j/84601255206?pwd=S1UzMVBSbFJOQnlPUmZZV1IzSmhlQT09

Meeting ID: 84601255206 / Passcode: 844907

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Mon 27 Jun 15:30: Perception and learning in autism

Tue, 21/06/2022 - 21:49
Perception and learning in autism

The ability to evaluate sensory information, build and update expectations is a critical part of daily functioning. When this goes awry, it may lead to perceptual disturbances as seen in autism spectrum conditions. In this talk, I present behavioural and neural correlates of perception and learning in autistic adults through findings from experimental psychology, functional MRI , and high-resolution 7Tesla Magnetic Resonance Spectroscopy (MRS). I discuss how these findings relate to classic cognitive theories (such as Weak Central Coherence, enhanced perceptual Function, and hyper-systemizing) and more recent Bayesian explanations of autistic perception.

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Tue 28 Jun 11:00: Noise-Aware Differentially Private Synthetic Data

Mon, 20/06/2022 - 16:22
Noise-Aware Differentially Private Synthetic Data

Synthetic data generated under differential privacy (DP) promises to significantly simplify analysis of sensitive personal data. Existing work has shown that simply analysing DP synthetic data as if it were real does not produce valid inferences of population-level quantities, leading to too narrow confidence intervals and thereby risking false discoveries. We propose using multiple imputation techniques to avoid these problems. This requires simulating multiple synthetic data sets from the Bayesian posterior predictive distribution over data sets. We propose a novel noise-aware Bayesian DP synthetic data generation mechanism for discrete data that enables generating such a distribution of data sets. Our experiments demonstrate that the method is able to produce accurate confidence intervals from DP synthetic data.

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Mon 20 Jun 15:30: Theory of mind, gender, and human progress | Empathy, psychopathy, and brain structure

Thu, 16/06/2022 - 13:44
Theory of mind, gender, and human progress | Empathy, psychopathy, and brain structure

Abstract:

In the first part of my talk, I will present a registered study where we relate a female advantage in theory of mind (as measured by the Eyes Test) to gender equality (e.g. gender employment gap) and socioeconomic development (e.g. gross domestic product) across the 20 regions of Italy. In the second part of my talk, I will present a study where we relate both cognitive and affective empathy (as measured by the Interpersonal Reactivity Index) as well as psychopathic traits and diagnosis (as measured by the Psychopathy Checklist) to brain structure (as measured by MRI ) in an American sample of incarcerated males with a history of both violent and non-violent crime.

Bio:

Marcin is a Visiting PhD Student at the Autism Research Centre at the University of Cambridge, pursuing his degree in cognitive neuroscience at the IMT School for Advanced Studies Lucca. Mostly from a neuroimaging perspective, he is interested in sex/gender differences, theory of mind, empathy, personality, and mental conditions related to both sex/gender and social behaviour (including psychopathy and autism).

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Thu 16 Jun 11:00: Estimating RSV seasonality from pandemic disruptions: a modelling study

Tue, 14/06/2022 - 20:31
Estimating RSV seasonality from pandemic disruptions: a modelling study

Background: Respiratory syncytial virus (RSV) is a leading cause of respiratory tract infections and bronchiolitis in young children. The seasonal pattern of RSV is shaped by short-lived immunity, seasonally varying contact rates and pathogen viability. The magnitude of each of these parameters is not fully clear. The disruption of the regular seasonality of RSV during the COVID pandemic in 2020 due to control measures, and the ensuing delayed surge in RSV cases provides an opportunity to disentangle these factors and to understand the implication for vaccination strategies. A better understanding of the drivers of RSV seasonality is key for developing future vaccination strategies. Methods: We developed a mathematical model of RSV transmission, which simulates the sequential re-infection (SEIRRS4) and uses a flexible Von Mises function to model the seasonal forcing. Using MCMC we fit the model to laboratory confirmed RSV data from 2010-2022 from NSW while accounting for the reduced contact rates during the pandemic with Google mobility data. We estimated the baseline transmission rate, its amplitude and shape during RSV season as well as the duration of immunity. The resulting parameter estimates were compared to a fit to pre-pandemic data only, and to a fit with a cosine forcing function. We then simulated the expected shifts in peak timing and amplitude under two vaccination strategies: continuous and seasonal vaccination. Results: We estimate that RSV dynamics in NSW can be best explained by a high effective baseline transmission rate (2.94/d, 95% CrI 2.73-3.18) and a narrow peak with a maximum 13% increase compared to the baseline transmission rate. We also estimate the duration of post infection temporary but sterilizing immunity to be 412 days (95% CrI 391-435). Including data from the pandemic period in the fit reduced parameter correlation substantially and improved parameter identifiability. The continuous vaccination strategy led to more extreme seasonal incidence with a delay in the peak timing and a higher amplitude whereas seasonal vaccination flattened the incidence curves.

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Tue 14 Jun 11:00: Estimating RSV seasonality from pandemic disruptions: a modelling study

Tue, 14/06/2022 - 12:47
Estimating RSV seasonality from pandemic disruptions: a modelling study

Background: Respiratory syncytial virus (RSV) is a leading cause of respiratory tract infections and bronchiolitis in young children. The seasonal pattern of RSV is shaped by short-lived immunity, seasonally varying contact rates and pathogen viability. The magnitude of each of these parameters is not fully clear. The disruption of the regular seasonality of RSV during the COVID pandemic in 2020 due to control measures, and the ensuing delayed surge in RSV cases provides an opportunity to disentangle these factors and to understand the implication for vaccination strategies. A better understanding of the drivers of RSV seasonality is key for developing future vaccination strategies. Methods: We developed a mathematical model of RSV transmission, which simulates the sequential re-infection (SEIRRS4) and uses a flexible Von Mises function to model the seasonal forcing. Using MCMC we fit the model to laboratory confirmed RSV data from 2010-2022 from NSW while accounting for the reduced contact rates during the pandemic with Google mobility data. We estimated the baseline transmission rate, its amplitude and shape during RSV season as well as the duration of immunity. The resulting parameter estimates were compared to a fit to pre-pandemic data only, and to a fit with a cosine forcing function. We then simulated the expected shifts in peak timing and amplitude under two vaccination strategies: continuous and seasonal vaccination. Results: We estimate that RSV dynamics in NSW can be best explained by a high effective baseline transmission rate (2.94/d, 95% CrI 2.73-3.18) and a narrow peak with a maximum 13% increase compared to the baseline transmission rate. We also estimate the duration of post infection temporary but sterilizing immunity to be 412 days (95% CrI 391-435). Including data from the pandemic period in the fit reduced parameter correlation substantially and improved parameter identifiability. The continuous vaccination strategy led to more extreme seasonal incidence with a delay in the peak timing and a higher amplitude whereas seasonal vaccination flattened the incidence curves.

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Thu 16 Jun 15:00: Discovering and quantifying patterns in networks with coloured nodes

Tue, 14/06/2022 - 12:02
Discovering and quantifying patterns in networks with coloured nodes

Graphs with coloured nodes provide an informative model for complex systems whose units are associated to a discrete number of classes. Some relevant examples include social systems, geographic networks, and cell adjacency networks. Quite often, as in the case of geographical networks, the arrangement of those classes is an important aspect of the global organisation of the system. Hence, quantifying the existence of heterogeneity and correlations in the assignment of the nodes of a graph to classes is paramount to characterise the behaviour of a system. In this talk we will cover the basics of networks with coloured nodes, and we will show how a simple set of measures, based on random walks on the graph, can be effectively used to measure the existence of correlations and heterogeneity among classes. Interesting applications include the quantification of spatial segregations in cities, the identification of polarisation in social systems, the emergence of robust spatial organisation in plant tissues, and the incorporation of metadata in community detection tasks.

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Thu 14 Jul 15:00: Title to be confirmed

Mon, 13/06/2022 - 13:41
Title to be confirmed

Abstract not available

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Thu 30 Jun 15:00: Title to be confirmed

Mon, 13/06/2022 - 13:41
Title to be confirmed

Abstract not available

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Thu 16 Jun 15:00: Title to be confirmed

Mon, 13/06/2022 - 13:40
Title to be confirmed

Abstract not available

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Wed 15 Jun 16:00: Misinformation: Subjective beliefs, Source credibility, and Social Networks

Mon, 13/06/2022 - 10:28
Misinformation: Subjective beliefs, Source credibility, and Social Networks

Misinformation is a significant societal challenge with impact on health, elections, and more. To understand why and how misinformation spreads in society as well as gauge the impact of interventions, we have to consider multiple factors such as cognitive functions (e.g. belief revision, cognitive biases), social functions (e.g. the social networks people inhabit, socio-cultural norms), and structural elements (e.g. algorithms that promote information on social media, legislation). As such, it is hardly surprising that several strands of research explore the impact and spread of misinformation. In this talk, I focus on the interplay between belief revision and social structure by considering Bayesian approaches to source credibility and dependencies as well as how these models unfold in dynamic and complex information systems. I present research on cognitive models that test belief revision predictions and consider how these can be implemented in agent-based models to explore facets such as echo chamber formation, micro-targeting, and inoculation. While I provide some supportive evidence for the Bayesian models, I highlight key limitations around the complications of reliability as well as consider perspectives for the construction of larger information models that integrate insights from cognitive and social psychology as well as disciplines like anthropology and political science.

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Mon 20 Jun 15:30: Theory of mind, gender, and human progress | Empathy, psychopathy, and brain structure

Fri, 10/06/2022 - 15:12
Theory of mind, gender, and human progress | Empathy, psychopathy, and brain structure

In the first part of my talk, I will present a registered study where we relate a female advantage in theory of mind (as measured by the Eyes Test) to gender equality and socioeconomic development across the 20 regions of Italy. In the second part of my talk, I will present a study where we relate both cognitive and affective empathy (as measured by the Interpersonal Reactivity Index) as well as psychopathic traits and diagnosis (as measured by the Psychopathy Checklist) to brain structure (as measured by MRI ) in an American sample of incarcerated males with a history of both violent and non-violent crime.

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Cambridge Memory Meeting 2015

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