Biology subjects

Schulz, E.

Publications and source records attributed to Schulz, E..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

PGC1α Regulates the Endothelial Response to Fluid Shear Stress via Telomerase Reverse Transcriptase Control of Heme Oxygenase-1

Fluid shear stress (FSS) is known to mediate multiple phenotypic changes in the endothelium. Laminar FSS (undisturbed flow) is known to promote endothelial alignment to flow that is key to stabilizing the endothelium and rendering it resistant to atherosclerosis and thrombosis. The molecular pathways responsible for endothelial responses to FSS are only partially understood. Here we have identified peroxisome proliferator gamma coactivator-1 (PGC-1) as a flow-responsive gene required for endothelial flow alignment in vitro and in vivo. Compared to oscillatory FSS (disturbed flow) or static conditions, laminar FSS (undisturbed flow) increased PGC-1 expression and its transcriptional co-activation. PGC-1 was required for laminar FSS-induced expression of telomerase reverse transcriptase (TERT) in vitro and in vivo via its association with ERR and KLF4 on the TERT promoter. We found that TERT inhibition attenuated endothelial flow alignment, elongation, and nuclear polarization in response to laminar FSS in vitro and in vivo. Among the flow-responsive genes sensitive to TERT status was heme oxygenase-1 (HMOX1), a gene required for endothelial alignment to laminar FSS. Thus, these data suggest an important role for a PGC-1-TERT-HMOX1 axis in the endothelial stabilization response to laminar FSS.

cell biology↗

bioRxiv2020

A Novel Tool for the Removal of Muscle Artefacts from EEG: Improving Data Quality in the Gamma Frequency Range

BackgroundThe past two decades have seen a particular focus towards high-frequency neural activity in the gamma band (>30Hz). However, gamma band activity shares frequency range with unwanted artefacts from muscular activity. New MethodWe developed a novel approach to remove muscle artefacts from neurophysiological data. We re-analysed existing EEG data that were decomposed by a blind source separation method (independent component analysis, ICA), which helped to better spatially and temporally separate single muscle spikes. We then applied an adapting algorithm that detects these singled-out muscle spikes. ResultsWe obtained data almost free from muscle artefacts; we needed to remove significantly fewer artefact components from the ICA and we included more trials for the statistical analysis compared to standard ICA artefact removal. All pain-related cortical effects in the gamma band have been preserved, which underlines the high efficacy and precision of this algorithm. ConclusionsOur results show a significant improvement of data quality by preserving task-relevant gamma oscillations of cortical origin. We were able to precisely detect, gauge, and carve out single muscle spikes from the time course of neurophysiological measures. We advocate the application of the tool for studies investigating gamma activity that contain a rather low number of trials, as well as for data that are highly contaminated with muscle artefacts. This validation of our tool allows for the application on event-free continuous EEG, for which the artefact removal is more challenging.

neuroscience↗

bioRxiv2020

Migraine Attacks as a Result of Hypothalamic Loss of Control

To assess the natural trajectory of brain activity over the migraine cycle, we assessed (1) the cerebral perfusion and (2) the hypothalamic connectivity during spontaneous headache attacks with follow-up recordings towards the next migraine attack. Using pseudo-continuous arterial spin labelling and resting-state functional magnetic resonance imaging, 12 migraine patients were examined in 82 sessions. We detected cyclic changes of brain perfusion in the limbic circuit (insula, hippocampus, and nucleus accumbens), with the highest perfusion during headache attacks. In addition, we found an increase of hypothalamic connectivity to the limbic system over the interictal interval, then collapsing during the headache. Our data provide strong evidence for the predominant role of the hypothalamus as a zeitgeber for generating migraine attacks. Our findings suggest that migraine attacks are the result of the hypothalamus losing control over the limbic system.

neuroscience↗

bioRxiv2020

Chronic Pain Patients Exhibit Individually Unique Cortical Signatures of Pain

We investigated how the trajectory of pain patients ongoing and fluctuating pain is encoded in the brain. In repeated fMRI sessions, 20 chronic back pain patients and 20 chronic migraineurs were asked to continuously rate the intensity of their endogenous pain. Linear mixed effects models were used to disentangle cortical processes related to pain intensity and to pain intensity changes. We found that the intensity of pain in chronic back pain patients is encoded in the anterior insula, the frontal operculum, and the pons; the change of pain of chronic back pain and chronic migraine patients is mainly encoded in the anterior insula. At the individual level, we identified a more complex picture where each patient exhibited their own signature of endogenous pain encoding. The diversity of the individual cortical signatures of chronic pain encoding results adds to the understanding of chronic pain as a complex and multifaceted disease.

neuroscience↗

bioRxiv2020

Inference and search on graph-structured spaces

How do people learn functions on structured spaces? And how do they use this knowledge to guide their search for rewards in situations where the number of options is large? We study human behavior on structures with graph-correlated values and propose a Bayesian model of function learning to describe and predict their behavior. Across two experiments, one assessing function learning and one assessing the search for rewards, we find that our model captures human predictions and sampling behavior better than several alternatives, generates human-like learning curves, and also captures participants confidence judgements. Our results extend past models of human function learning and reward learning to more complex, graph-structured domains.

animal behavior and cognition↗

bioRxiv2020

T cells limit accumulation of aggregate pathology following intrastriatal injection of α-synuclein fibrils

Background: -Synuclein (-syn) is the predominant protein in Lewy-body inclusions, which are pathological hallmarks of -synucleinopathies, such as Parkinsons disease (PD) and multiple system atrophy (MSA). Other hallmarks include activation of microglia, elevation of pro-inflammatory cytokines, as well as the activation of T and B cells. These immune changes point towards a dysregulation of both the innate and the adaptive immune system. T cells have been shown to recognize epitopes derived from -syn and altered populations of T cells have been found in PD and MSA patients, providing evidence that these cells can be key to the pathogenesis of the disease. Objective: To study the role of the adaptive immune system with respect to -syn pathology. Methods: We injected human -syn preformed fibrils (PFFs) into the striatum of immunocompromised mice (NSG) and assessed accumulation of phosphorylated -syn pathology, proteinase K-resistant -syn pathology and microgliosis in the striatum, substantia nigra and frontal cortex. We also assessed the impact of adoptive transfer of naive T and B cells into PFF-injected immunocompromised mice. Results: Compared to wildtype mice, NSG mice had an 8-fold increase in phosphorylated -syn pathology in the substantia nigra. Reconstituting the T cell population decreased the accumulation of phosphorylated -syn pathology and resulted in persistent microgliosis in the striatum when compared to non-transplanted mice. Conclusion: Our work provides evidence that T cells play a role in the pathogenesis of experimental -synucleinopathy.

neuroscience↗

bioRxiv2020

Similarities and differences in spatial and non-spatial cognitive maps

Learning and generalization in spatial domains is often thought to rely on a "cognitive map", representing relationships between spatial locations. Recent research suggests that this same neural machinery is also recruited for reasoning about more abstract, conceptual forms of knowledge. Yet, to what extent do spatial and conceptual reasoning share common computational principles, and what are the implications for behavior? Using a within-subject design we studied how participants used spatial or conceptual distances to generalize and search for correlated rewards in successive multi-armed bandit tasks. Participant behavior indicated sensitivity to both spatial and conceptual distance, and was best captured using a Bayesian model of generalization that formalized distance-dependent generalization and uncertainty-guided exploration as a Gaussian Process regression with a radial basis function kernel. The same Gaussian Process model best captured human search decisions and judgments in both domains, and could simulate realistic learning curves, where we found equivalent levels of generalization in spatial and conceptual tasks. At the same time, we also find characteristic differences between domains. Relative to the spatial domain, participants showed reduced levels of uncertainty-directed exploration and increased levels of random exploration in the conceptual domain. Participants also displayed a one-directional transfer effect, where experience in the spatial task boosted performance in the conceptual task, but not vice versa. While confidence judgments indicated that participants were sensitive to the uncertainty of their knowledge in both tasks, they did not or could not leverage their estimates of uncertainty to guide exploration in the conceptual task. These results support the notion that value-guided learning and generalization recruit cognitive-map dependent computational mechanisms in spatial and conceptual domains. Yet both behavioral and model-based analyses suggest domain specific differences in how these representations map onto actions. Author summaryThere is a resurgence of interest in "cognitive maps" based on recent evidence that the hippocampal-entorhinal system encodes both spatial and non-spatial relational information, with far-reaching implications for human behavior. Yet little is known about the commonalities and differences in the computational principles underlying human learning and decision making in spatial and non-spatial domains. We use a within-subject design to examine how humans search for either spatially or conceptually correlated rewards. Using a Bayesian learning model, we find evidence for the same computational mechanisms of generalization across domains. While participants were sensitive to expected rewards and uncertainty in both tasks, how they leveraged this knowledge to guide exploration was different: participants displayed less uncertainty-directed and more random exploration in the conceptual domain. Moreover, experience with the spatial task improved conceptual performance, but not vice versa. These results provide important insights about the degree of overlap between spatial and conceptual cognition.

animal behavior and cognition↗

bioRxiv2019

Alpha-synuclein antisense oligonucleotides as a disease-modifying therapy for Parkinson’s disease

Parkinsons disease (PD) is a prevalent neurodegenerative disease with no approved disease-modifying therapies. Multiplications, mutations, and single nucleotide polymorphisms in the SNCA gene, encoding alpha-synuclein protein (aSyn), either cause or increase risk for PD. Intracellular accumulations of aSyn are pathological hallmarks of PD. Taken together, reduction of aSyn production may provide a disease-modifying therapy for PD. We show that antisense oligonucleotides (ASOs) reduce production of aSyn in rodent pre-formed fibril (PFF) models of PD. Reduced aSyn production leads to prevention and removal of established aSyn pathology and prevents dopaminergic cell dysfunction. In addition, we address the translational potential of the approach through characterization of human SNCA targeting ASOs that efficiently suppress the human SNCA transcript in vivo. We demonstrate broad activity and distribution of the human SNCA ASOs throughout the non-human primate brain and a corresponding decrease in aSyn cerebral spinal fluid (CSF) levels. Taken together, these data suggest that by inhibiting production of aSyn it may be possible to reverse established pathology and thus supports the development of SNCA ASOs as a potentially disease modifying therapy for PD and related synucleinopathies.\n\nSummaryAntisense oligonucleotides designed against SNCA, which are progressing to the clinic, have the potential to be a disease modifying therapeutic for Parkinsons disease patients.

neuroscience↗

bioRxiv2019

Multi-Task Reinforcement Learning in Humans

The ability to transfer knowledge across tasks and generalize to novel ones is an important hallmark of human intelligence. Yet not much is known about human multi-task reinforcement learning. We study participants behavior in a novel two-step decision making task with multiple features and changing reward functions. We compare their behavior to two state-of-the-art algorithms for multi-task reinforcement learning, one that maps previous policies and encountered features to new reward functions and one that approximates value functions across tasks, as well as to standard model-based and model-free algorithms. Across three exploratory experiments and a large preregistered experiment, our results provide strong evidence for a strategy that maps previously learned policies to novel scenarios. These results enrich our understanding of human reinforcement learning in complex environments with changing task demands.

neuroscience↗

bioRxiv2019

Ultra-high field imaging reveals increased whole brain connectivity underpins cognitive strategies that attenuate pain

We investigate how the attenuation of pain with cognitive interventions affects the strength of cortical connections by pursuing a whole brain approach. While receiving tonic cold pain, 20 healthy participants were asked to utilise three different pain attenuation strategies. During a 7T fMRI recording, participants were asked to rate their pain after each single trial. We related the trial-by-trial variability of the attenuation performance to the trial-by-trial functional connectivity of the cortical data. Across all conditions, we found that a higher performance of pain attenuation was predominantly associated with higher functional connectivity. Of note, we observed an association between low pain and high connectivity for regions that belong to the core areas of pain processing, i.e. the insular and cingulate cortices. For one of the cognitive strategies (safe place), the performance of pain attenuation was explained by diffusion tensor imaging metrics of increased white matter integrity. Impact StatementIn a single trial analysis, the more effective attempts to attenuate pain in three different conditions are associated with general higher functional connectivity across the entire brain.

neuroscience↗

bioRxiv2019

A theory of learning to infer

Bayesian theories of cognition assume that people can integrate probabilities rationally. However, several empirical findings contradict this proposition: human probabilistic inferences are prone to systematic deviations from optimality. Puzzlingly, these deviations sometimes go in opposite directions. Whereas some studies suggest that people under-react to prior probabilities (base rate neglect), other studies find that people under-react to the likelihood of the data (conservatism). We argue that these deviations arise because the human brain does not rely solely on a general-purpose mechanism for approximating Bayesian inference that is invariant across queries. Instead, the brain is equipped with a recognition model that maps queries to probability distributions. The parameters of this recognition model are optimized to get the output as close as possible, on average, to the true posterior. Because of our limited computational resources, the recognition model will allocate its resources so as to be more accurate for high probability queries than for low probability queries. By adapting to the query distribution, the recognition model \"learns to infer.\" We show that this theory can explain why and when people under-react to the data or the prior, and a new experiment demonstrates that these two forms of under-reaction can be systematically controlled by manipulating the query distribution. The theory also explains a range of related phenomena: memory effects, belief bias, and the structure of response variability in probabilistic reasoning. We also discuss how the theory can be integrated with prior sampling-based accounts of approximate inference.

animal behavior and cognition↗

bioRxiv2019

Exploring the space of human exploration

What drives peoples exploration in complex scenarios where they have to actively acquire information by making queries? How do people adapt their selection of queries to their environment? We explore these questions using Entropy Mastermind, a novel variant of the Mastermind code-breaking game, in which participants have to guess a secret code by making useful queries. Participants solved games more efficiently and more quickly if the entropy of the game environment was low; moreover, people adapted their initial queries to the scenario they were in. We also investigated whether it would be possible to predict participants queries within the generalized Sharma-Mittal information-theoretic framework. Although predicting individual queries is difficult, the modeling framework offered important insight on human behavior. Entropy Mastermind offers rich possibilities for modeling and behavioral research.

animal behavior and cognition↗

bioRxiv2019

Generalization as diffusion: human function learning on graphs

From social networks to public transportation, graph structures are a ubiquitous feature of life. Yet little is known about how humans learn functions on graphs, where relationships are defined by the connectivity structure. We adapt a Bayesian framework for function learning to graph structures, and propose that people perform generalization by diffusing observed function values across the graph. We test the predictions of this model by asking participants to make predictions about passenger volume in a virtual subway network. The model captures both generalization and confidence judgments, and is a quantitatively superior account relative to several heuristic models. Our work suggests that people exploit graph structure to make generalizations about functions in complex discrete spaces.

animal behavior and cognition↗

bioRxiv2018

Exploration in the wild

Making good decisions requires people to appropriately explore their available options and generalize what they have learned. While computational models have successfully explained exploratory behavior in constrained laboratory tasks, it is unclear to what extent these models generalize to complex real world choice problems. We investigate the factors guiding exploratory behavior in a data set consisting of 195,333 customers placing 1,613,967 orders from a large online food delivery service. We find important hallmarks of adaptive exploration and generalization, which we analyze using computational models. We find evidence for several theoretical predictions: (1) customers engage in uncertainty-directed exploration, (2) they adjust their level of exploration to the average restaurant quality in a city, and (3) they use feature-based generalization to guide exploration towards promising restaurants. Our results provide new evidence that people use sophisticated strategies to explore complex, real-world environments.

animal behavior and cognition↗

bioRxiv2018

Communicating compositional patterns

How do people perceive and communicate structure? We investigate this question by letting participants play a communication game, where one player describes a pattern, and another player redraws it based on the description alone. We use this paradigm to compare two models of pattern description, one compositional (complex structures built out of simpler ones) and one non-compositional. We find that compositional patterns are communicated more effectively than non-compositional patterns, that a compositional model of pattern description predicts which patterns are harder to describe, and that this model can be used to evaluate participants drawings, producing human-like quality ratings. Our results suggest that natural language can tap into a compositionally structured pattern description language.

animal behavior and cognition↗

bioRxiv2018

Finding structure in multi-armed bandits

How do humans search for rewards? This question is commonly studied using multi-armed bandit tasks, which require participants to trade off exploration and exploitation. Standard multi-armed bandits assume that each option has an independent reward distribution. However, learning about options independently is unrealistic, since in the real world options often share an underlying structure. We study a class of structured bandit tasks, which we use to probe how generalization guides exploration. In a structured multi-armed bandit, options have a correlation structure dictated by a latent function. We focus on bandits in which rewards are linear functions of an options spatial position. Across 5 experiments, we find evidence that participants utilize functional structure to guide their exploration, and also exhibit a learning-to-learn effect across rounds, becoming progressively faster at identifying the latent function. Our experiments rule out several heuristic explanations and show that the same findings obtain with non-linear functions. Comparing several models of learning and decision making, we find that the best model of human behavior in our tasks combines three computational mechanisms: (1) function learning, (2) clustering of reward distributions across rounds, and (3) uncertainty-guided exploration. Our results suggest that human reinforcement learning can utilize latent structure in sophisticated ways to improve efficiency.

animal behavior and cognition↗

bioRxiv2018

Searching for rewards like a child means less generalization and more directed exploration

How do children and adults differ in their search for rewards? We consider three different hypotheses that attribute developmental differences to either childrens increased random sampling, more directed exploration towards uncertain options, or narrower generalization. Using a search task in which noisy rewards are spatially correlated on a grid, we compare 55 younger children (age 7-8), 55 older children (age 9-11), and 50 adults (age 19-55) in their ability to successfully generalize about unobserved outcomes and balance the exploration-exploitation dilemma. Our results show that children explore more eagerly than adults, but obtain lower rewards. Building a predictive model of search to disentangle the unique contributions of the three hypotheses of developmental differences, we find robust and recoverable parameter estimates indicating that children generalize less and rely on directed exploration more than adults. We do not, however, find reliable differences in terms of random sampling.

animal behavior and cognition↗

bioRxiv2018

Learning to act by integrating mental simulations and physical experiments

People can learn about the effects of their actions either by performing physical experiments or by running mental simulations. Physical experiments are reliable but risky; mental simulations are unreliable but safe. We investigate how people negotiate the balance between these strategies. Participants attempted to shoot a ball at a target, and could pay to take practice shots (physical experiments). They could also simply think (run mental simulations), but were incentivized to act quickly by paying for time. We demonstrate that the amount of thinking time and physical experiments is sensitive to trial characteristics in a way that is consistent with a model that integrates information across simulation and experimentation and decides online when to perform each.

animal behavior and cognition↗