Project acronym AutoRecon
Project Molecular mechanisms of autophagosome formation during selective autophagy
Researcher (PI) Sascha Martens
Host Institution (HI) UNIVERSITAT WIEN
Call Details Consolidator Grant (CoG), LS3, ERC-2014-CoG
Summary I propose to study how eukaryotic cells generate autophagosomes, organelles bounded by a double membrane. These are formed during autophagy and mediate the degradation of cytoplasmic substances within the lysosomal compartment. Autophagy thereby protects the organism from pathological conditions such as neurodegeneration, cancer and infections. Many core factors required for autophagosome formation have been identified but the order in which they act and their mode of action is still unclear. We will use a combination of biochemical and cell biological approaches to elucidate the choreography and mechanism of these core factors. In particular, we will focus on selective autophagy and determine how the autophagic machinery generates an autophagosome that selectively contains the cargo.
To this end we will focus on the cytoplasm-to-vacuole-targeting pathway in S. cerevisiae that mediates the constitutive delivery of the prApe1 enzyme into the vacuole. We will use cargo mimetics or prApe1 complexes in combination with purified autophagy proteins and vesicles to reconstitute the process and so determine which factors are both necessary and sufficient for autophagosome formation, as well as elucidating their mechanism of action.
In parallel we will study selective autophagosome formation in human cells. This will reveal common principles and special adaptations. In particular, we will use cell lysates from genome-edited cells in combination with purified autophagy proteins to reconstitute selective autophagosome formation around ubiquitin-positive cargo material. The insights and hypotheses obtained from these reconstituted systems will be validated using cell biological approaches.
Taken together, our experiments will allow us to delineate the major steps of autophagosome formation during selective autophagy. Our results will yield detailed insights into how cells form and shape organelles in a de novo manner, which is major question in cell- and developmental biology.
Summary
I propose to study how eukaryotic cells generate autophagosomes, organelles bounded by a double membrane. These are formed during autophagy and mediate the degradation of cytoplasmic substances within the lysosomal compartment. Autophagy thereby protects the organism from pathological conditions such as neurodegeneration, cancer and infections. Many core factors required for autophagosome formation have been identified but the order in which they act and their mode of action is still unclear. We will use a combination of biochemical and cell biological approaches to elucidate the choreography and mechanism of these core factors. In particular, we will focus on selective autophagy and determine how the autophagic machinery generates an autophagosome that selectively contains the cargo.
To this end we will focus on the cytoplasm-to-vacuole-targeting pathway in S. cerevisiae that mediates the constitutive delivery of the prApe1 enzyme into the vacuole. We will use cargo mimetics or prApe1 complexes in combination with purified autophagy proteins and vesicles to reconstitute the process and so determine which factors are both necessary and sufficient for autophagosome formation, as well as elucidating their mechanism of action.
In parallel we will study selective autophagosome formation in human cells. This will reveal common principles and special adaptations. In particular, we will use cell lysates from genome-edited cells in combination with purified autophagy proteins to reconstitute selective autophagosome formation around ubiquitin-positive cargo material. The insights and hypotheses obtained from these reconstituted systems will be validated using cell biological approaches.
Taken together, our experiments will allow us to delineate the major steps of autophagosome formation during selective autophagy. Our results will yield detailed insights into how cells form and shape organelles in a de novo manner, which is major question in cell- and developmental biology.
Max ERC Funding
1 999 640 €
Duration
Start date: 2016-03-01, End date: 2021-02-28
Project acronym AuxinER
Project Mechanisms of Auxin-dependent Signaling in the Endoplasmic Reticulum
Researcher (PI) Jürgen Kleine-Vehn
Host Institution (HI) UNIVERSITAET FUER BODENKULTUR WIEN
Call Details Starting Grant (StG), LS3, ERC-2014-STG
Summary The phytohormone auxin has profound importance for plant development. The extracellular AUXIN BINDING PROTEIN1 (ABP1) and the nuclear AUXIN F-BOX PROTEINs (TIR1/AFBs) auxin receptors perceive fast, non-genomic and slow, genomic auxin responses, respectively. Despite the fact that ABP1 mainly localizes to the endoplasmic reticulum (ER), until now it has been proposed to be active only in the extracellular matrix (reviewed in Sauer and Kleine-Vehn, 2011). Just recently, ABP1 function was also linked to genomic responses, modulating TIR1/AFB-dependent processes (Tromas et al., 2013). Intriguingly, the genomic effect of ABP1 appears to be at least partially independent of the endogenous auxin indole 3-acetic acid (IAA) (Paque et al., 2014).
In this proposal my main research objective is to unravel the importance of the ER for genomic auxin responses. The PIN-LIKES (PILS) putative carriers for auxinic compounds also localize to the ER and determine the cellular sensitivity to auxin. PILS5 gain-of-function reduces canonical auxin signaling (Barbez et al., 2012) and phenocopies abp1 knock down lines (Barbez et al., 2012, Paque et al., 2014). Accordingly, a PILS-dependent substrate could be a negative regulator of ABP1 function in the ER. Based on our unpublished data, an IAA metabolite could play a role in ABP1-dependent processes in the ER, possibly providing feedback on the canonical nuclear IAA-signaling.
I hypothesize that the genomic auxin response may be an integration of auxin- and auxin-metabolite-dependent nuclear and ER localized signaling, respectively. This proposed project aims to characterize a novel auxin-signaling paradigm in plants. We will employ state of the art interdisciplinary (biochemical, biophysical, computational modeling, molecular, and genetic) methods to assess the projected research. The identification of the proposed auxin conjugate-dependent signal could have far reaching plant developmental and biotechnological importance.
Summary
The phytohormone auxin has profound importance for plant development. The extracellular AUXIN BINDING PROTEIN1 (ABP1) and the nuclear AUXIN F-BOX PROTEINs (TIR1/AFBs) auxin receptors perceive fast, non-genomic and slow, genomic auxin responses, respectively. Despite the fact that ABP1 mainly localizes to the endoplasmic reticulum (ER), until now it has been proposed to be active only in the extracellular matrix (reviewed in Sauer and Kleine-Vehn, 2011). Just recently, ABP1 function was also linked to genomic responses, modulating TIR1/AFB-dependent processes (Tromas et al., 2013). Intriguingly, the genomic effect of ABP1 appears to be at least partially independent of the endogenous auxin indole 3-acetic acid (IAA) (Paque et al., 2014).
In this proposal my main research objective is to unravel the importance of the ER for genomic auxin responses. The PIN-LIKES (PILS) putative carriers for auxinic compounds also localize to the ER and determine the cellular sensitivity to auxin. PILS5 gain-of-function reduces canonical auxin signaling (Barbez et al., 2012) and phenocopies abp1 knock down lines (Barbez et al., 2012, Paque et al., 2014). Accordingly, a PILS-dependent substrate could be a negative regulator of ABP1 function in the ER. Based on our unpublished data, an IAA metabolite could play a role in ABP1-dependent processes in the ER, possibly providing feedback on the canonical nuclear IAA-signaling.
I hypothesize that the genomic auxin response may be an integration of auxin- and auxin-metabolite-dependent nuclear and ER localized signaling, respectively. This proposed project aims to characterize a novel auxin-signaling paradigm in plants. We will employ state of the art interdisciplinary (biochemical, biophysical, computational modeling, molecular, and genetic) methods to assess the projected research. The identification of the proposed auxin conjugate-dependent signal could have far reaching plant developmental and biotechnological importance.
Max ERC Funding
1 441 125 €
Duration
Start date: 2015-06-01, End date: 2020-05-31
Project acronym Big Splash
Project Big Splash: Efficient Simulation of Natural Phenomena at Extremely Large Scales
Researcher (PI) Christopher John Wojtan
Host Institution (HI) Institute of Science and Technology Austria
Call Details Starting Grant (StG), PE6, ERC-2014-STG
Summary Computational simulations of natural phenomena are essential in science, engineering, product design, architecture, and computer graphics applications. However, despite progress in numerical algorithms and computational power, it is still unfeasible to compute detailed simulations at large scales. To make matters worse, important phenomena like turbulent splashing liquids and fracturing solids rely on delicate coupling between small-scale details and large-scale behavior. Brute-force computation of such phenomena is intractable, and current adaptive techniques are too fragile, too costly, or too crude to capture subtle instabilities at small scales. Increases in computational power and parallel algorithms will improve the situation, but progress will only be incremental until we address the problem at its source.
I propose two main approaches to this problem of efficiently simulating large-scale liquid and solid dynamics. My first avenue of research combines numerics and shape: I will investigate a careful de-coupling of dynamics from geometry, allowing essential shape details to be preserved and retrieved without wasting computation. I will also develop methods for merging small-scale analytical solutions with large-scale numerical algorithms. (These ideas show particular promise for phenomena like splashing liquids and fracturing solids, whose small-scale behaviors are poorly captured by standard finite element methods.) My second main research direction is the manipulation of large-scale simulation data: Given the redundant and parallel nature of physics computation, we will drastically speed up computation with novel dimension reduction and data compression approaches. We can also minimize unnecessary computation by re-using existing simulation data. The novel approaches resulting from this work will undoubtedly synergize to enable the simulation and understanding of complicated natural and biological processes that are presently unfeasible to compute.
Summary
Computational simulations of natural phenomena are essential in science, engineering, product design, architecture, and computer graphics applications. However, despite progress in numerical algorithms and computational power, it is still unfeasible to compute detailed simulations at large scales. To make matters worse, important phenomena like turbulent splashing liquids and fracturing solids rely on delicate coupling between small-scale details and large-scale behavior. Brute-force computation of such phenomena is intractable, and current adaptive techniques are too fragile, too costly, or too crude to capture subtle instabilities at small scales. Increases in computational power and parallel algorithms will improve the situation, but progress will only be incremental until we address the problem at its source.
I propose two main approaches to this problem of efficiently simulating large-scale liquid and solid dynamics. My first avenue of research combines numerics and shape: I will investigate a careful de-coupling of dynamics from geometry, allowing essential shape details to be preserved and retrieved without wasting computation. I will also develop methods for merging small-scale analytical solutions with large-scale numerical algorithms. (These ideas show particular promise for phenomena like splashing liquids and fracturing solids, whose small-scale behaviors are poorly captured by standard finite element methods.) My second main research direction is the manipulation of large-scale simulation data: Given the redundant and parallel nature of physics computation, we will drastically speed up computation with novel dimension reduction and data compression approaches. We can also minimize unnecessary computation by re-using existing simulation data. The novel approaches resulting from this work will undoubtedly synergize to enable the simulation and understanding of complicated natural and biological processes that are presently unfeasible to compute.
Max ERC Funding
1 500 000 €
Duration
Start date: 2015-03-01, End date: 2020-02-29
Project acronym CAFES
Project Causal Analysis of Feedback Systems
Researcher (PI) Joris Marten Mooij
Host Institution (HI) UNIVERSITEIT VAN AMSTERDAM
Call Details Starting Grant (StG), PE6, ERC-2014-STG
Summary Many questions in science, policy making and everyday life are of a causal nature: how would changing A influence B? Causal inference, a branch of statistics and machine learning, studies how cause-effect relationships can be discovered from data and how these can be used for making predictions in situations where a system has been perturbed by an external intervention. The ability to reliably make such causal predictions is of great value for practical applications in a variety of disciplines. Over the last two decades, remarkable progress has been made in the field. However, even though state-of-the-art causal inference algorithms work well on simulated data when all their assumptions are met, there is still a considerable gap between theory and practice. The goal of CAFES is to bridge that gap by developing theory and algorithms that will enable large-scale applications of causal inference in various challenging domains in science, industry and decision making.
The key challenge that will be addressed is how to deal with cyclic causal relationships ("feedback loops"). Feedback loops are very common in many domains (e.g., biology, economy and climatology), but have mostly been ignored so far in the field. Building on recently established connections between dynamical systems and causal models, CAFES will develop theory and algorithms for causal modeling, reasoning, discovery and prediction for cyclic causal systems. Extensions to stationary and non-stationary processes will be developed to advance the state-of-the-art in causal analysis of time-series data. In order to optimally use available resources, computationally efficient and statistically robust algorithms for causal inference from observational and interventional data in the context of confounders and feedback will be developed. The work will be done with a strong focus on applications in molecular biology, one of the most promising areas for automated causal inference from data.
Summary
Many questions in science, policy making and everyday life are of a causal nature: how would changing A influence B? Causal inference, a branch of statistics and machine learning, studies how cause-effect relationships can be discovered from data and how these can be used for making predictions in situations where a system has been perturbed by an external intervention. The ability to reliably make such causal predictions is of great value for practical applications in a variety of disciplines. Over the last two decades, remarkable progress has been made in the field. However, even though state-of-the-art causal inference algorithms work well on simulated data when all their assumptions are met, there is still a considerable gap between theory and practice. The goal of CAFES is to bridge that gap by developing theory and algorithms that will enable large-scale applications of causal inference in various challenging domains in science, industry and decision making.
The key challenge that will be addressed is how to deal with cyclic causal relationships ("feedback loops"). Feedback loops are very common in many domains (e.g., biology, economy and climatology), but have mostly been ignored so far in the field. Building on recently established connections between dynamical systems and causal models, CAFES will develop theory and algorithms for causal modeling, reasoning, discovery and prediction for cyclic causal systems. Extensions to stationary and non-stationary processes will be developed to advance the state-of-the-art in causal analysis of time-series data. In order to optimally use available resources, computationally efficient and statistically robust algorithms for causal inference from observational and interventional data in the context of confounders and feedback will be developed. The work will be done with a strong focus on applications in molecular biology, one of the most promising areas for automated causal inference from data.
Max ERC Funding
1 405 652 €
Duration
Start date: 2015-09-01, End date: 2020-08-31
Project acronym Con Espressione
Project Getting at the Heart of Things: Towards Expressivity-aware Computer Systems in Music
Researcher (PI) Gerhard Widmer
Host Institution (HI) UNIVERSITAT LINZ
Call Details Advanced Grant (AdG), PE6, ERC-2014-ADG
Summary What makes music so important, what can make a performance so special and stirring? It is the things the music expresses, the emotions it induces, the associations it evokes, the drama and characters it portrays. The sources of this expressivity are manifold: the music itself, its structure, orchestration, personal associations, social settings, but also – and very importantly – the act of performance, the interpretation and expressive intentions made explicit by the musicians through nuances in timing, dynamics etc.
Thanks to research in fields like Music Information Research (MIR), computers can do many useful things with music, from beat and rhythm detection to song identification and tracking. However, they are still far from grasping the essence of music: they cannot tell whether a performance expresses playfulness or ennui, solemnity or gaiety, determination or uncertainty; they cannot produce music with a desired expressive quality; they cannot interact with human musicians in a truly musical way, recognising and responding to the expressive intentions implied in their playing.
The project is about developing machines that are aware of certain dimensions of expressivity, specifically in the domain of (classical) music, where expressivity is both essential and – at least as far as it relates to the act of performance – can be traced back to well-defined and measurable parametric dimensions (such as timing, dynamics, articulation). We will develop systems that can recognise, characterise, search music by expressive aspects, generate, modify, and react to expressive qualities in music. To do so, we will (1) bring together the fields of AI, Machine Learning, MIR and Music Performance Research; (2) integrate theories from Musicology to build more well-founded models of music understanding; (3) support model learning and validation with massive musical corpora of a size and quality unprecedented in computational music research.
Summary
What makes music so important, what can make a performance so special and stirring? It is the things the music expresses, the emotions it induces, the associations it evokes, the drama and characters it portrays. The sources of this expressivity are manifold: the music itself, its structure, orchestration, personal associations, social settings, but also – and very importantly – the act of performance, the interpretation and expressive intentions made explicit by the musicians through nuances in timing, dynamics etc.
Thanks to research in fields like Music Information Research (MIR), computers can do many useful things with music, from beat and rhythm detection to song identification and tracking. However, they are still far from grasping the essence of music: they cannot tell whether a performance expresses playfulness or ennui, solemnity or gaiety, determination or uncertainty; they cannot produce music with a desired expressive quality; they cannot interact with human musicians in a truly musical way, recognising and responding to the expressive intentions implied in their playing.
The project is about developing machines that are aware of certain dimensions of expressivity, specifically in the domain of (classical) music, where expressivity is both essential and – at least as far as it relates to the act of performance – can be traced back to well-defined and measurable parametric dimensions (such as timing, dynamics, articulation). We will develop systems that can recognise, characterise, search music by expressive aspects, generate, modify, and react to expressive qualities in music. To do so, we will (1) bring together the fields of AI, Machine Learning, MIR and Music Performance Research; (2) integrate theories from Musicology to build more well-founded models of music understanding; (3) support model learning and validation with massive musical corpora of a size and quality unprecedented in computational music research.
Max ERC Funding
2 318 750 €
Duration
Start date: 2016-01-01, End date: 2021-12-31
Project acronym CoordinatedDopamine
Project Coordination of regional dopamine release in the striatum during habit formation and compulsive behaviour
Researcher (PI) Ingo Willuhn
Host Institution (HI) ACADEMISCH MEDISCH CENTRUM BIJ DE UNIVERSITEIT VAN AMSTERDAM
Call Details Starting Grant (StG), LS5, ERC-2014-STG
Summary The basal ganglia consist of a set of neuroanatomical structures that participate in the representation and execution of action sequences. Dopamine neurotransmission in the striatum, the main input nucleus of the basal ganglia, is a fundamental mechanism involved in learning and regulation of such actions. The striatum has multiple functional units, where the limbic striatum is thought to mediate motivational aspects of actions (e.g., goal-directedness) and the sensorimotor striatum their automation (e.g., habit formation). A long-standing question in the field is how limbic and sensorimotor domains communicate with each other, and specifically if they do so during the automation of action sequences. It has been suggested that such coordination is implemented by reciprocal loop connections between striatal projection neurons and the dopaminergic midbrain. Although very influential in theory the effectiveness of this limbic-sensorimotor “bridging” principle has yet to be verified. I hypothesize that during the automation of behaviour regional dopamine signalling is governed by a striatal hierarchy and that dysregulation of this coordination leads to compulsive execution of automatic actions characteristic of several psychiatric disorders. To test this hypothesis, we will conduct electrochemical measurements with real-time resolution simultaneously in limbic and sensorimotor striatum to assess the regional coordination of dopamine release in behaving animals. We developed novel chronically implantable electrodes to enable monitoring of dopamine dynamics throughout the development of habitual behaviour and its compulsive execution in transgenic rats - a species suitable for our complex behavioural assays. Novel rabies virus-mediated gene delivery for in vivo optogenetics in these rats will give us the unique opportunity to test whether specific loop pathways govern striatal dopamine transmission and are causally involved in habit formation and compulsive behaviour.
Summary
The basal ganglia consist of a set of neuroanatomical structures that participate in the representation and execution of action sequences. Dopamine neurotransmission in the striatum, the main input nucleus of the basal ganglia, is a fundamental mechanism involved in learning and regulation of such actions. The striatum has multiple functional units, where the limbic striatum is thought to mediate motivational aspects of actions (e.g., goal-directedness) and the sensorimotor striatum their automation (e.g., habit formation). A long-standing question in the field is how limbic and sensorimotor domains communicate with each other, and specifically if they do so during the automation of action sequences. It has been suggested that such coordination is implemented by reciprocal loop connections between striatal projection neurons and the dopaminergic midbrain. Although very influential in theory the effectiveness of this limbic-sensorimotor “bridging” principle has yet to be verified. I hypothesize that during the automation of behaviour regional dopamine signalling is governed by a striatal hierarchy and that dysregulation of this coordination leads to compulsive execution of automatic actions characteristic of several psychiatric disorders. To test this hypothesis, we will conduct electrochemical measurements with real-time resolution simultaneously in limbic and sensorimotor striatum to assess the regional coordination of dopamine release in behaving animals. We developed novel chronically implantable electrodes to enable monitoring of dopamine dynamics throughout the development of habitual behaviour and its compulsive execution in transgenic rats - a species suitable for our complex behavioural assays. Novel rabies virus-mediated gene delivery for in vivo optogenetics in these rats will give us the unique opportunity to test whether specific loop pathways govern striatal dopamine transmission and are causally involved in habit formation and compulsive behaviour.
Max ERC Funding
1 500 000 €
Duration
Start date: 2015-05-01, End date: 2020-04-30
Project acronym HOMOVIS
Project High-level Prior Models for Computer Vision
Researcher (PI) Thomas Pock
Host Institution (HI) TECHNISCHE UNIVERSITAET GRAZ
Call Details Starting Grant (StG), PE6, ERC-2014-STG
Summary Since more than 50 years, computer vision has been a very active research field but it is still far away from the abilities of the human visual system. This stunning performance of the human visual system can be mainly contributed to a highly efficient three-layer architecture: A low-level layer that sparsifies the visual information by detecting important image features such as image gradients, a mid-level layer that implements disocclusion and boundary completion processes and finally a high-level layer that is concerned with the recognition of objects.
Variational methods are certainly one of the most successful methods for low-level vision. However, it is very unlikely that these methods can be further improved without the integration of high-level prior models. Therefore, we propose a unified mathematical framework that allows for a natural integration of high-level priors into low-level variational models. In particular, we propose to represent images in a higher-dimensional space which is inspired by the architecture for the visual cortex. This space performs a decomposition of the image gradients into magnitude and direction and hence performs a lifting of the 2D image to a 3D space. This has several advantages: Firstly, the higher-dimensional embedding allows to implement mid-level tasks such as boundary completion and disocclusion processes in a very natural way. Secondly, the lifted space allows for an explicit access to the orientation and the magnitude of image gradients. In turn, distributions of gradient orientations – known to be highly effective for object detection – can be utilized as high-level priors. This inverts the bottom-up nature of object detectors and hence adds an efficient top-down process to low-level variational models.
The developed mathematical approaches will go significantly beyond traditional variational models for computer vision and hence will define a new state-of-the-art in the field.
Summary
Since more than 50 years, computer vision has been a very active research field but it is still far away from the abilities of the human visual system. This stunning performance of the human visual system can be mainly contributed to a highly efficient three-layer architecture: A low-level layer that sparsifies the visual information by detecting important image features such as image gradients, a mid-level layer that implements disocclusion and boundary completion processes and finally a high-level layer that is concerned with the recognition of objects.
Variational methods are certainly one of the most successful methods for low-level vision. However, it is very unlikely that these methods can be further improved without the integration of high-level prior models. Therefore, we propose a unified mathematical framework that allows for a natural integration of high-level priors into low-level variational models. In particular, we propose to represent images in a higher-dimensional space which is inspired by the architecture for the visual cortex. This space performs a decomposition of the image gradients into magnitude and direction and hence performs a lifting of the 2D image to a 3D space. This has several advantages: Firstly, the higher-dimensional embedding allows to implement mid-level tasks such as boundary completion and disocclusion processes in a very natural way. Secondly, the lifted space allows for an explicit access to the orientation and the magnitude of image gradients. In turn, distributions of gradient orientations – known to be highly effective for object detection – can be utilized as high-level priors. This inverts the bottom-up nature of object detectors and hence adds an efficient top-down process to low-level variational models.
The developed mathematical approaches will go significantly beyond traditional variational models for computer vision and hence will define a new state-of-the-art in the field.
Max ERC Funding
1 473 525 €
Duration
Start date: 2015-06-01, End date: 2020-05-31
Project acronym MidFrontalTheta2.0
Project MidFrontal Cortex Theta Oscillations: Causes and Consequences
Researcher (PI) Michael Steven Cohen
Host Institution (HI) STICHTING KATHOLIEKE UNIVERSITEIT
Call Details Starting Grant (StG), LS5, ERC-2014-STG
Summary While reading this text, pat your head and rub your stomach (if someone sees you, tell them it's OK; you're doing science). Right now you are engaging in an action that must be actively monitored and quickly adjusted to avoid making mistakes. Over the past five years, my collaborators and I discovered that there is a specific pattern of brain electrical activity that occurs during response conflict—competition between multiple conflicting actions when a mistake could be made. This brain activity is observed over the midfrontal cortex (MFC) and is characterized by oscillations at around 6 cycles per second (the theta band). MFC theta is a highly statistically robust marker of the neural networks involved in action monitoring and behavior adjustments, correlates with single-trial reaction time, and predicts how well people learn from mistakes. Despite these robust findings linking MFC theta to action monitoring, the significance of MFC theta for how neural microcircuits actually implement action monitoring and adjustments is unknown. In the ERC research we will use computer simulations and rodent models to understand how different types of neurons in different cortical layers might use action potentials and oscillations to implement action monitoring. The results will help us understand how the brain monitors behavior and avoids mistakes, and will also give insight into neural microcircuit organization as it relates to higher cognitive function. While developing these computer simulations and rodent models, we will also take our human research to the next level by asking: If action monitoring in the MFC is supported by theta oscillations, does this mean that our actions, and our ability to monitor and adjust them, occur with theta rhythmicity? To answer this question, we will develop new tasks combining data-gloves and EEG to test how the timing of human sequenced actions during keyboard typing (typists type in “theta”) corresponds to temporal dynamics of MFC theta.
Summary
While reading this text, pat your head and rub your stomach (if someone sees you, tell them it's OK; you're doing science). Right now you are engaging in an action that must be actively monitored and quickly adjusted to avoid making mistakes. Over the past five years, my collaborators and I discovered that there is a specific pattern of brain electrical activity that occurs during response conflict—competition between multiple conflicting actions when a mistake could be made. This brain activity is observed over the midfrontal cortex (MFC) and is characterized by oscillations at around 6 cycles per second (the theta band). MFC theta is a highly statistically robust marker of the neural networks involved in action monitoring and behavior adjustments, correlates with single-trial reaction time, and predicts how well people learn from mistakes. Despite these robust findings linking MFC theta to action monitoring, the significance of MFC theta for how neural microcircuits actually implement action monitoring and adjustments is unknown. In the ERC research we will use computer simulations and rodent models to understand how different types of neurons in different cortical layers might use action potentials and oscillations to implement action monitoring. The results will help us understand how the brain monitors behavior and avoids mistakes, and will also give insight into neural microcircuit organization as it relates to higher cognitive function. While developing these computer simulations and rodent models, we will also take our human research to the next level by asking: If action monitoring in the MFC is supported by theta oscillations, does this mean that our actions, and our ability to monitor and adjust them, occur with theta rhythmicity? To answer this question, we will develop new tasks combining data-gloves and EEG to test how the timing of human sequenced actions during keyboard typing (typists type in “theta”) corresponds to temporal dynamics of MFC theta.
Max ERC Funding
1 500 000 €
Duration
Start date: 2015-07-01, End date: 2020-06-30
Project acronym MULTICONNECT
Project Imaging Brain Circuits to Decode Brain Computations: Multimodal Multiscale Imaging of Cortical Microcircuits to Model Predictive Coding in Human Vision
Researcher (PI) Alard Franc Roebroeck
Host Institution (HI) UNIVERSITEIT MAASTRICHT
Call Details Starting Grant (StG), LS5, ERC-2014-STG
Summary The human brain is one of the largest and most complex biological networks known to exist. The architecture of its circuits, and therefore the computational basis of human cognition, remains largely unknown. The central aim of this proposal is to image human cortical connectivity at multiple spatial scales in order to understand human cortical computations.
Whereas canonical cortical microcircuits are an established theory of the repeating structure of the neocortex’s circuits, predictive coding provides a prominent proposal of what these circuits compute. This leads to the core hypothesis of this proposal: the variations in predictive coding computations performed by human cortical microcircuits in different visual areas are grounded in variations in their microcircuit connectivity. As a central case-study, this proposal investigates human visual apparent motion perception in V1/2/3 and V5/MT+.
The proposed research program is organized in two workpackages (WP I and II). WP I has the aim of imaging the multiscale connections of human neocortical microcircuits. The projects in WP I focus on structure and move from the mesoscale down to the microscale. WP II has the aim of modelling how microcircuits support predictive coding computations. The projects in WP II focus on function and move from the microscale back up to the mesoscale. Structural and functional assessment of microcircuitry in the human brain only recently became possible with the development of magnetic resonance imaging (MRI) at ultra-high field-strengths (UHF) of 7T and above. UHF diffusion MRI, combined with light microscopy, is used to image circuit structure in WP I. UHF functional MRI is used for computational modelling of computations in WP II.
Successful completion of the planned research will significantly advance our understanding of the computations in cortical microcircuits, deliver important new human connectomic reference data, and improve generative models of human cortical processing.
Summary
The human brain is one of the largest and most complex biological networks known to exist. The architecture of its circuits, and therefore the computational basis of human cognition, remains largely unknown. The central aim of this proposal is to image human cortical connectivity at multiple spatial scales in order to understand human cortical computations.
Whereas canonical cortical microcircuits are an established theory of the repeating structure of the neocortex’s circuits, predictive coding provides a prominent proposal of what these circuits compute. This leads to the core hypothesis of this proposal: the variations in predictive coding computations performed by human cortical microcircuits in different visual areas are grounded in variations in their microcircuit connectivity. As a central case-study, this proposal investigates human visual apparent motion perception in V1/2/3 and V5/MT+.
The proposed research program is organized in two workpackages (WP I and II). WP I has the aim of imaging the multiscale connections of human neocortical microcircuits. The projects in WP I focus on structure and move from the mesoscale down to the microscale. WP II has the aim of modelling how microcircuits support predictive coding computations. The projects in WP II focus on function and move from the microscale back up to the mesoscale. Structural and functional assessment of microcircuitry in the human brain only recently became possible with the development of magnetic resonance imaging (MRI) at ultra-high field-strengths (UHF) of 7T and above. UHF diffusion MRI, combined with light microscopy, is used to image circuit structure in WP I. UHF functional MRI is used for computational modelling of computations in WP II.
Successful completion of the planned research will significantly advance our understanding of the computations in cortical microcircuits, deliver important new human connectomic reference data, and improve generative models of human cortical processing.
Max ERC Funding
1 500 000 €
Duration
Start date: 2015-06-01, End date: 2020-05-31
Project acronym Organoid
Project Dissecting microbiome and immune interactions in human intestinal (cancer) organoids
Researcher (PI) Johannes Carolus Clevers
Host Institution (HI) KONINKLIJKE NEDERLANDSE AKADEMIE VAN WETENSCHAPPEN - KNAW
Call Details Advanced Grant (AdG), LS3, ERC-2014-ADG
Summary We pioneered the essential role of Wnt signals in adult stem cells, i.e. in intestinal crypts. We also found that loss of the APC gene activates the Wnt pathway and causes colorectal cancer (CRC). We then identified a Wnt target gene, Lgr5, which allowed us to define the crypt stem cells. In a previous ERC grant based on these findings, we identified novel Lgr5 stem cells in multiple organs, and defined in vitro culture conditions to grow epithelial organoids from single Lgr5 stem cells. Crucial in this was our identification of the Wnt agonistic R-spondins as the Lgr5 ligands. Cultured 'mini-guts' display all characteristics of normal gut, can be expanded for years, transplanted, and remain genetically stable.
Here, I propose a reductionist, ‘mini-gut’-based approach to two exciting research fields that currently mostly focus at the organismal/patient level: Microbiome research leans on deep-sequencing of complex microbial communities in health and disease; and immune checkpoint research in cancer rests largely on clinical trials of checkpoint-blocking antibodies. While many insights exist into the gut microbiome and -immune system, the epithelium is often treated as a neutral player. ‘Mini-gut’ technology allows us to dissect interactions of the gut microbiome with healthy and diseased epithelium, and of Tumor-Infiltrating Lymphocytes (TILs) with CRC 'mini-guts' (tumoroids).
To this end, we will describe/study
1) All immune receptors, -regulators and -effectors in the individual epithelial cell types.
2) 'Mini-guts' recombined with individual bacterial species,
3) CRC tumoroids recombined with their cultured TILs and subjected to immune checkpoint manipulation.
Using advanced molecular and imaging technologies, we will chart the molecular mechanisms that underlie the interactions from the ‘epithelial perspective’. Ultimately, this program will provide molecular detail to the effects of the microbiome and immune system on our gut, in health and disease.
Summary
We pioneered the essential role of Wnt signals in adult stem cells, i.e. in intestinal crypts. We also found that loss of the APC gene activates the Wnt pathway and causes colorectal cancer (CRC). We then identified a Wnt target gene, Lgr5, which allowed us to define the crypt stem cells. In a previous ERC grant based on these findings, we identified novel Lgr5 stem cells in multiple organs, and defined in vitro culture conditions to grow epithelial organoids from single Lgr5 stem cells. Crucial in this was our identification of the Wnt agonistic R-spondins as the Lgr5 ligands. Cultured 'mini-guts' display all characteristics of normal gut, can be expanded for years, transplanted, and remain genetically stable.
Here, I propose a reductionist, ‘mini-gut’-based approach to two exciting research fields that currently mostly focus at the organismal/patient level: Microbiome research leans on deep-sequencing of complex microbial communities in health and disease; and immune checkpoint research in cancer rests largely on clinical trials of checkpoint-blocking antibodies. While many insights exist into the gut microbiome and -immune system, the epithelium is often treated as a neutral player. ‘Mini-gut’ technology allows us to dissect interactions of the gut microbiome with healthy and diseased epithelium, and of Tumor-Infiltrating Lymphocytes (TILs) with CRC 'mini-guts' (tumoroids).
To this end, we will describe/study
1) All immune receptors, -regulators and -effectors in the individual epithelial cell types.
2) 'Mini-guts' recombined with individual bacterial species,
3) CRC tumoroids recombined with their cultured TILs and subjected to immune checkpoint manipulation.
Using advanced molecular and imaging technologies, we will chart the molecular mechanisms that underlie the interactions from the ‘epithelial perspective’. Ultimately, this program will provide molecular detail to the effects of the microbiome and immune system on our gut, in health and disease.
Max ERC Funding
3 062 438 €
Duration
Start date: 2015-11-01, End date: 2020-10-31
