Project acronym 2STEPPARKIN
Project A novel two-step model for neurodegeneration in Parkinson’s disease
Researcher (PI) Emi Nagoshi
Host Institution (HI) UNIVERSITE DE GENEVE
Call Details Starting Grant (StG), LS5, ERC-2012-StG_20111109
Summary Parkinson’s disease (PD) is the second most common neurodegenerative disorder primarily caused by the progressive loss of dopaminergic (DA) neurons in the substantia nigra (SN). Despite the advances in gene discovery associated with PD, the knowledge of the PD pathogenesis is largely limited to the involvement of these genes in the generic cell death pathways, and why degeneration is specific to DA neurons and why the degeneration is progressive remain enigmatic. Broad goal of our work is therefore to elucidate the mechanisms underlying specific and progressive DA neuron degeneration in PD. Our new Drosophila model of PD ⎯Fer2 gene loss-of-function mutation⎯ is unusually well suited to address these questions. Fer2 mutants exhibit specific and progressive death of brain DA neurons as well as severe locomotor defects and short life span. Strikingly, the death of DA neuron is initiated in a small cluster of Fer2-expressing DA neurons and subsequently propagates to Fer2-negative DA neurons. We therefore propose a novel two-step model of the neurodegeneration in PD: primary cell death occurs in a specific subset of dopamindegic neurons that are genetically defined, and subsequently the failure of the neuronal connectivity triggers and propagates secondary cell death to remaining DA neurons. In this research, we will test this hypothesis and investigate the underlying molecular mechanisms. This will be the first study to examine circuit-dependency in DA neuron degeneration. Our approach will use a combination of non-biased genomic techniques and candidate-based screening, in addition to the powerful Drosophila genetic toolbox. Furthermore, to test this hypothesis beyond the Drosophila model, we will establish new mouse models of PD that exhibit progressive DA neuron degeneration. Outcome of this research will likely revolutionize the understanding of PD pathogenesis and open an avenue toward the discovery of effective therapy strategies against PD.
Summary
Parkinson’s disease (PD) is the second most common neurodegenerative disorder primarily caused by the progressive loss of dopaminergic (DA) neurons in the substantia nigra (SN). Despite the advances in gene discovery associated with PD, the knowledge of the PD pathogenesis is largely limited to the involvement of these genes in the generic cell death pathways, and why degeneration is specific to DA neurons and why the degeneration is progressive remain enigmatic. Broad goal of our work is therefore to elucidate the mechanisms underlying specific and progressive DA neuron degeneration in PD. Our new Drosophila model of PD ⎯Fer2 gene loss-of-function mutation⎯ is unusually well suited to address these questions. Fer2 mutants exhibit specific and progressive death of brain DA neurons as well as severe locomotor defects and short life span. Strikingly, the death of DA neuron is initiated in a small cluster of Fer2-expressing DA neurons and subsequently propagates to Fer2-negative DA neurons. We therefore propose a novel two-step model of the neurodegeneration in PD: primary cell death occurs in a specific subset of dopamindegic neurons that are genetically defined, and subsequently the failure of the neuronal connectivity triggers and propagates secondary cell death to remaining DA neurons. In this research, we will test this hypothesis and investigate the underlying molecular mechanisms. This will be the first study to examine circuit-dependency in DA neuron degeneration. Our approach will use a combination of non-biased genomic techniques and candidate-based screening, in addition to the powerful Drosophila genetic toolbox. Furthermore, to test this hypothesis beyond the Drosophila model, we will establish new mouse models of PD that exhibit progressive DA neuron degeneration. Outcome of this research will likely revolutionize the understanding of PD pathogenesis and open an avenue toward the discovery of effective therapy strategies against PD.
Max ERC Funding
1 518 960 €
Duration
Start date: 2013-06-01, End date: 2018-05-31
Project acronym 3D Reloaded
Project 3D Reloaded: Novel Algorithms for 3D Shape Inference and Analysis
Researcher (PI) Daniel Cremers
Host Institution (HI) TECHNISCHE UNIVERSITAET MUENCHEN
Call Details Consolidator Grant (CoG), PE6, ERC-2014-CoG
Summary Despite their amazing success, we believe that computer vision algorithms have only scratched the surface of what can be done in terms of modeling and understanding our world from images. We believe that novel image analysis techniques will be a major enabler and driving force behind next-generation technologies, enhancing everyday life and opening up radically new possibilities. And we believe that the key to achieving this is to develop algorithms for reconstructing and analyzing the 3D structure of our world.
In this project, we will focus on three lines of research:
A) We will develop algorithms for 3D reconstruction from standard color cameras and from RGB-D cameras. In particular, we will promote real-time-capable direct and dense methods. In contrast to the classical two-stage approach of sparse feature-point based motion estimation and subsequent dense reconstruction, these methods optimally exploit all color information to jointly estimate dense geometry and camera motion.
B) We will develop algorithms for 3D shape analysis, including rigid and non-rigid matching, decomposition and interpretation of 3D shapes. We will focus on algorithms which are optimal or near-optimal. One of the major computational challenges lies in generalizing existing 2D shape analysis techniques to shapes in 3D and 4D (temporal evolutions of 3D shape).
C) We will develop shape priors for 3D reconstruction. These can be learned from sample shapes or acquired during the reconstruction process. For example, when reconstructing a larger office algorithms may exploit the geometric self-similarity of the scene, storing a model of a chair and its multiple instances only once rather than multiple times.
Advancing the state of the art in geometric reconstruction and geometric analysis will have a profound impact well beyond computer vision. We strongly believe that we have the necessary competence to pursue this project. Preliminary results have been well received by the community.
Summary
Despite their amazing success, we believe that computer vision algorithms have only scratched the surface of what can be done in terms of modeling and understanding our world from images. We believe that novel image analysis techniques will be a major enabler and driving force behind next-generation technologies, enhancing everyday life and opening up radically new possibilities. And we believe that the key to achieving this is to develop algorithms for reconstructing and analyzing the 3D structure of our world.
In this project, we will focus on three lines of research:
A) We will develop algorithms for 3D reconstruction from standard color cameras and from RGB-D cameras. In particular, we will promote real-time-capable direct and dense methods. In contrast to the classical two-stage approach of sparse feature-point based motion estimation and subsequent dense reconstruction, these methods optimally exploit all color information to jointly estimate dense geometry and camera motion.
B) We will develop algorithms for 3D shape analysis, including rigid and non-rigid matching, decomposition and interpretation of 3D shapes. We will focus on algorithms which are optimal or near-optimal. One of the major computational challenges lies in generalizing existing 2D shape analysis techniques to shapes in 3D and 4D (temporal evolutions of 3D shape).
C) We will develop shape priors for 3D reconstruction. These can be learned from sample shapes or acquired during the reconstruction process. For example, when reconstructing a larger office algorithms may exploit the geometric self-similarity of the scene, storing a model of a chair and its multiple instances only once rather than multiple times.
Advancing the state of the art in geometric reconstruction and geometric analysis will have a profound impact well beyond computer vision. We strongly believe that we have the necessary competence to pursue this project. Preliminary results have been well received by the community.
Max ERC Funding
2 000 000 €
Duration
Start date: 2015-09-01, End date: 2020-08-31
Project acronym 3DPBio
Project Computational Models of Motion for Fabrication-aware Design of Bioinspired Systems
Researcher (PI) Stelian Coros
Host Institution (HI) EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
Call Details Consolidator Grant (CoG), PE6, ERC-2019-COG
Summary "Bridging the fields of Computer Animation and Computational Fabrication, this proposal will establish the foundations for algorithmic design of physical structures that can generate lifelike movements. Driven by embedded actuators, these types of structures will enable an abundance of possibilities for a wide array of real-world technologies: animatronic characters whose organic motions will enhance their ability to awe, entertain and educate; soft robotic creatures that are both skilled and safe to be around; patient-specific prosthetics and wearable devices that match the soft touch of the human body, etc. Recent advances in additive manufacturing (AM) technologies are particularly exciting in this context, as they allow us to create designs of unparalleled geometric complexity using a constantly expanding range of materials. And if past developments are an indication, within the next decade we will be able to fabricate physical structures that approach, at least at the macro scale, the functional sophistication of their biological counterparts. However, while this unprecedented capability enables fascinating opportunities, it also leads to an explosion in the dimensionality of the space that must be explored during the design process. As AM technologies keep evolving, the gap between ""what we can produce"" and ""what we can design"" is therefore rapidly growing.
To effectively leverage the extraordinary design possibilities enabled by AM, 3DPBio will develop the computational and mathematical foundations required to study a fundamental scientific question: how are physical deformations, mechanical movements and overall functional capabilities governed by geometric shape features, material compositions and the design of compliant actuation systems? By enabling computers to reason about this question, our work will establish new ways to algorithmically create digital designs that can be turned into mechanical lifeforms at the push of a button."
Summary
"Bridging the fields of Computer Animation and Computational Fabrication, this proposal will establish the foundations for algorithmic design of physical structures that can generate lifelike movements. Driven by embedded actuators, these types of structures will enable an abundance of possibilities for a wide array of real-world technologies: animatronic characters whose organic motions will enhance their ability to awe, entertain and educate; soft robotic creatures that are both skilled and safe to be around; patient-specific prosthetics and wearable devices that match the soft touch of the human body, etc. Recent advances in additive manufacturing (AM) technologies are particularly exciting in this context, as they allow us to create designs of unparalleled geometric complexity using a constantly expanding range of materials. And if past developments are an indication, within the next decade we will be able to fabricate physical structures that approach, at least at the macro scale, the functional sophistication of their biological counterparts. However, while this unprecedented capability enables fascinating opportunities, it also leads to an explosion in the dimensionality of the space that must be explored during the design process. As AM technologies keep evolving, the gap between ""what we can produce"" and ""what we can design"" is therefore rapidly growing.
To effectively leverage the extraordinary design possibilities enabled by AM, 3DPBio will develop the computational and mathematical foundations required to study a fundamental scientific question: how are physical deformations, mechanical movements and overall functional capabilities governed by geometric shape features, material compositions and the design of compliant actuation systems? By enabling computers to reason about this question, our work will establish new ways to algorithmically create digital designs that can be turned into mechanical lifeforms at the push of a button."
Max ERC Funding
2 000 000 €
Duration
Start date: 2020-02-01, End date: 2025-01-31
Project acronym 4DRepLy
Project Closing the 4D Real World Reconstruction Loop
Researcher (PI) Christian THEOBALT
Host Institution (HI) MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV
Call Details Consolidator Grant (CoG), PE6, ERC-2017-COG
Summary 4D reconstruction, the camera-based dense dynamic scene reconstruction, is a grand challenge in computer graphics and computer vision. Despite great progress, 4D capturing the complex, diverse real world outside a studio is still far from feasible. 4DRepLy builds a new generation of high-fidelity 4D reconstruction (4DRecon) methods. They will be the first to efficiently capture all types of deformable objects (humans and other types) in crowded real world scenes with a single color or depth camera. They capture space-time coherent deforming geometry, motion, high-frequency reflectance and illumination at unprecedented detail, and will be the first to handle difficult occlusions, topology changes and large groups of interacting objects. They automatically adapt to new scene types, yet deliver models with meaningful, interpretable parameters. This requires far reaching contributions: First, we develop groundbreaking new plasticity-enhanced model-based 4D reconstruction methods that automatically adapt to new scenes. Second, we develop radically new machine learning-based dense 4D reconstruction methods. Third, these model- and learning-based methods are combined in two revolutionary new classes of 4DRecon methods: 1) advanced fusion-based methods and 2) methods with deep architectural integration. Both, 1) and 2), are automatically designed in the 4D Real World Reconstruction Loop, a revolutionary new design paradigm in which 4DRecon methods refine and adapt themselves while continuously processing unlabeled real world input. This overcomes the previously unbreakable scalability barrier to real world scene diversity, complexity and generality. This paradigm shift opens up a new research direction in graphics and vision and has far reaching relevance across many scientific fields. It enables new applications of profound social pervasion and significant economic impact, e.g., for visual media and virtual/augmented reality, and for future autonomous and robotic systems.
Summary
4D reconstruction, the camera-based dense dynamic scene reconstruction, is a grand challenge in computer graphics and computer vision. Despite great progress, 4D capturing the complex, diverse real world outside a studio is still far from feasible. 4DRepLy builds a new generation of high-fidelity 4D reconstruction (4DRecon) methods. They will be the first to efficiently capture all types of deformable objects (humans and other types) in crowded real world scenes with a single color or depth camera. They capture space-time coherent deforming geometry, motion, high-frequency reflectance and illumination at unprecedented detail, and will be the first to handle difficult occlusions, topology changes and large groups of interacting objects. They automatically adapt to new scene types, yet deliver models with meaningful, interpretable parameters. This requires far reaching contributions: First, we develop groundbreaking new plasticity-enhanced model-based 4D reconstruction methods that automatically adapt to new scenes. Second, we develop radically new machine learning-based dense 4D reconstruction methods. Third, these model- and learning-based methods are combined in two revolutionary new classes of 4DRecon methods: 1) advanced fusion-based methods and 2) methods with deep architectural integration. Both, 1) and 2), are automatically designed in the 4D Real World Reconstruction Loop, a revolutionary new design paradigm in which 4DRecon methods refine and adapt themselves while continuously processing unlabeled real world input. This overcomes the previously unbreakable scalability barrier to real world scene diversity, complexity and generality. This paradigm shift opens up a new research direction in graphics and vision and has far reaching relevance across many scientific fields. It enables new applications of profound social pervasion and significant economic impact, e.g., for visual media and virtual/augmented reality, and for future autonomous and robotic systems.
Max ERC Funding
1 977 000 €
Duration
Start date: 2018-09-01, End date: 2023-08-31
Project acronym 5HT-OPTOGENETICS
Project Optogenetic Analysis of Serotonin Function in the Mammalian Brain
Researcher (PI) Zachary Mainen
Host Institution (HI) FUNDACAO D. ANNA SOMMER CHAMPALIMAUD E DR. CARLOS MONTEZ CHAMPALIMAUD
Call Details Advanced Grant (AdG), LS5, ERC-2009-AdG
Summary Serotonin (5-HT) is implicated in a wide spectrum of brain functions and disorders. However, its functions remain controversial and enigmatic. We suggest that past work on the 5-HT system have been significantly hampered by technical limitations in the selectivity and temporal resolution of the conventional pharmacological and electrophysiological methods that have been applied. We therefore propose to apply novel optogenetic methods that will allow us to overcome these limitations and thereby gain new insight into the biological functions of this important molecule. In preliminary studies, we have demonstrated that we can deliver exogenous proteins specifically to 5-HT neurons using viral vectors. Our objectives are to (1) record, (2) stimulate and (3) silence the activity of 5-HT neurons with high molecular selectivity and temporal precision by using genetically-encoded sensors, activators and inhibitors of neural function. These tools will allow us to monitor and control the 5-HT system in real-time in freely-behaving animals and thereby to establish causal links between information processing in 5-HT neurons and specific behaviors. In combination with quantitative behavioral assays, we will use this approach to define the role of 5-HT in sensory, motor and cognitive functions. The significance of the work is three-fold. First, we will establish a new arsenal of tools for probing the physiological and behavioral functions of 5-HT neurons. Second, we will make definitive tests of major hypotheses of 5-HT function. Third, we will have possible therapeutic applications. In this way, the proposed work has the potential for a major impact in research on the role of 5-HT in brain function and dysfunction.
Summary
Serotonin (5-HT) is implicated in a wide spectrum of brain functions and disorders. However, its functions remain controversial and enigmatic. We suggest that past work on the 5-HT system have been significantly hampered by technical limitations in the selectivity and temporal resolution of the conventional pharmacological and electrophysiological methods that have been applied. We therefore propose to apply novel optogenetic methods that will allow us to overcome these limitations and thereby gain new insight into the biological functions of this important molecule. In preliminary studies, we have demonstrated that we can deliver exogenous proteins specifically to 5-HT neurons using viral vectors. Our objectives are to (1) record, (2) stimulate and (3) silence the activity of 5-HT neurons with high molecular selectivity and temporal precision by using genetically-encoded sensors, activators and inhibitors of neural function. These tools will allow us to monitor and control the 5-HT system in real-time in freely-behaving animals and thereby to establish causal links between information processing in 5-HT neurons and specific behaviors. In combination with quantitative behavioral assays, we will use this approach to define the role of 5-HT in sensory, motor and cognitive functions. The significance of the work is three-fold. First, we will establish a new arsenal of tools for probing the physiological and behavioral functions of 5-HT neurons. Second, we will make definitive tests of major hypotheses of 5-HT function. Third, we will have possible therapeutic applications. In this way, the proposed work has the potential for a major impact in research on the role of 5-HT in brain function and dysfunction.
Max ERC Funding
2 318 636 €
Duration
Start date: 2010-07-01, End date: 2015-12-31
Project acronym 5HTCircuits
Project Modulation of cortical circuits and predictive neural coding by serotonin
Researcher (PI) Zachary Mainen
Host Institution (HI) FUNDACAO D. ANNA SOMMER CHAMPALIMAUD E DR. CARLOS MONTEZ CHAMPALIMAUD
Call Details Advanced Grant (AdG), LS5, ERC-2014-ADG
Summary Serotonin (5-HT) is a central neuromodulator and a major target of therapeutic psychoactive drugs, but relatively little is known about how it modulates information processing in neural circuits. The theory of predictive coding postulates that the brain combines raw bottom-up sensory information with top-down information from internal models to make perceptual inferences about the world. We hypothesize, based on preliminary data and prior literature, that a role of 5-HT in this process is to report prediction errors and promote the suppression and weakening of erroneous internal models. We propose that it does this by inhibiting top-down relative to bottom-up cortical information flow. To test this hypothesis, we propose a set of experiments in mice performing olfactory perceptual tasks. Our specific aims are: (1) We will test whether 5-HT neurons encode sensory prediction errors. (2) We will test their causal role in using predictive cues to guide perceptual decisions. (3) We will characterize how 5-HT influences the encoding of sensory information by neuronal populations in the olfactory cortex and identify the underlying circuitry. (4) Finally, we will map the effects of 5-HT across the whole brain and use this information to target further causal manipulations to specific 5-HT projections. We accomplish these aims using state-of-the-art optogenetic, electrophysiological and imaging techniques (including 9.4T small-animal functional magnetic resonance imaging) as well as psychophysical tasks amenable to quantitative analysis and computational theory. Together, these experiments will tackle multiple facets of an important general computational question, bringing to bear an array of cutting-edge technologies to address with unprecedented mechanistic detail how 5-HT impacts neural coding and perceptual decision-making.
Summary
Serotonin (5-HT) is a central neuromodulator and a major target of therapeutic psychoactive drugs, but relatively little is known about how it modulates information processing in neural circuits. The theory of predictive coding postulates that the brain combines raw bottom-up sensory information with top-down information from internal models to make perceptual inferences about the world. We hypothesize, based on preliminary data and prior literature, that a role of 5-HT in this process is to report prediction errors and promote the suppression and weakening of erroneous internal models. We propose that it does this by inhibiting top-down relative to bottom-up cortical information flow. To test this hypothesis, we propose a set of experiments in mice performing olfactory perceptual tasks. Our specific aims are: (1) We will test whether 5-HT neurons encode sensory prediction errors. (2) We will test their causal role in using predictive cues to guide perceptual decisions. (3) We will characterize how 5-HT influences the encoding of sensory information by neuronal populations in the olfactory cortex and identify the underlying circuitry. (4) Finally, we will map the effects of 5-HT across the whole brain and use this information to target further causal manipulations to specific 5-HT projections. We accomplish these aims using state-of-the-art optogenetic, electrophysiological and imaging techniques (including 9.4T small-animal functional magnetic resonance imaging) as well as psychophysical tasks amenable to quantitative analysis and computational theory. Together, these experiments will tackle multiple facets of an important general computational question, bringing to bear an array of cutting-edge technologies to address with unprecedented mechanistic detail how 5-HT impacts neural coding and perceptual decision-making.
Max ERC Funding
2 486 074 €
Duration
Start date: 2016-01-01, End date: 2020-12-31
Project acronym A-FRO
Project Actively Frozen - contextual modulation of freezing and its neuronal basis
Researcher (PI) Marta de Aragão Pacheco Moita
Host Institution (HI) FUNDACAO D. ANNA SOMMER CHAMPALIMAUD E DR. CARLOS MONTEZ CHAMPALIMAUD
Call Details Consolidator Grant (CoG), LS5, ERC-2018-COG
Summary When faced with a threat, an animal must decide whether to freeze, reducing its chances of being noticed, or to flee to the safety of a refuge. Animals from fish to primates choose between these two alternatives when confronted by an attacking predator, a choice that largely depends on the context in which the threat occurs. Recent work has made strides identifying the pre-motor circuits, and their inputs, which control freezing behavior in rodents, but how contextual information is integrated to guide this choice is still far from understood. We recently found that fruit flies in response to visual looming stimuli, simulating a large object on collision course, make rapid freeze/flee choices that depend on the social and spatial environment, and the fly’s internal state. Further, identification of looming detector neurons was recently reported and we identified the descending command neurons, DNp09, responsible for freezing in the fly. Knowing the sensory input and descending output for looming-evoked freezing, two environmental factors that modulate its expression, and using a genetically tractable system affording the use of large sample sizes, places us in an unique position to understand how a information about a threat is integrated with cues from the environment to guide the choice of whether to freeze (our goal). To assess how social information impinges on the circuit for freezing, we will examine the sensory inputs and neuromodulators that mediate this process, mapping their connections to DNp09 neurons (Aim 1). We ask whether learning is required for the spatial modulation of freezing, which cues flies are using to discriminate different places and which brain circuits mediate this process (Aim 2). Finally, we will study how activity of DNp09 neurons drives freezing (Aim 3). This project will provide a comprehensive understanding of the mechanism of freezing and its modulation by the environment, from single neurons to behaviour.
Summary
When faced with a threat, an animal must decide whether to freeze, reducing its chances of being noticed, or to flee to the safety of a refuge. Animals from fish to primates choose between these two alternatives when confronted by an attacking predator, a choice that largely depends on the context in which the threat occurs. Recent work has made strides identifying the pre-motor circuits, and their inputs, which control freezing behavior in rodents, but how contextual information is integrated to guide this choice is still far from understood. We recently found that fruit flies in response to visual looming stimuli, simulating a large object on collision course, make rapid freeze/flee choices that depend on the social and spatial environment, and the fly’s internal state. Further, identification of looming detector neurons was recently reported and we identified the descending command neurons, DNp09, responsible for freezing in the fly. Knowing the sensory input and descending output for looming-evoked freezing, two environmental factors that modulate its expression, and using a genetically tractable system affording the use of large sample sizes, places us in an unique position to understand how a information about a threat is integrated with cues from the environment to guide the choice of whether to freeze (our goal). To assess how social information impinges on the circuit for freezing, we will examine the sensory inputs and neuromodulators that mediate this process, mapping their connections to DNp09 neurons (Aim 1). We ask whether learning is required for the spatial modulation of freezing, which cues flies are using to discriminate different places and which brain circuits mediate this process (Aim 2). Finally, we will study how activity of DNp09 neurons drives freezing (Aim 3). This project will provide a comprehensive understanding of the mechanism of freezing and its modulation by the environment, from single neurons to behaviour.
Max ERC Funding
1 969 750 €
Duration
Start date: 2019-02-01, End date: 2024-01-31
Project acronym ABATSYNAPSE
Project Evolution of Alzheimer’s Disease: From dynamics of single synapses to memory loss
Researcher (PI) Inna Slutsky
Host Institution (HI) TEL AVIV UNIVERSITY
Call Details Starting Grant (StG), LS5, ERC-2011-StG_20101109
Summary A persistent challenge in unravelling mechanisms that regulate memory function is how to bridge the gap between inter-molecular dynamics of single proteins, activity of individual synapses and emerging properties of neuronal circuits. The prototype condition of disintegrating neuronal circuits is Alzheimer’s Disease (AD). Since the early time of Alois Alzheimer at the turn of the 20th century, scientists have been searching for a molecular entity that is in the roots of the cognitive deficits. Although diverse lines of evidence suggest that the amyloid-beta peptide (Abeta) plays a central role in synaptic dysfunctions of AD, several key questions remain unresolved. First, endogenous Abeta peptides are secreted by neurons throughout life, but their physiological functions are largely unknown. Second, experience-dependent physiological mechanisms that initiate the changes in Abeta composition in sporadic, the most frequent form of AD, are unidentified. And finally, molecular mechanisms that trigger Abeta-induced synaptic failure and memory decline remain elusive.
To target these questions, I propose to develop an integrative approach to correlate structure and function at the level of single synapses in hippocampal circuits. State-of-the-art techniques will enable the simultaneous real-time visualization of inter-molecular dynamics within signalling complexes and functional synaptic modifications. Utilizing FRET spectroscopy, high-resolution optical imaging, electrophysiology, molecular biology and biochemistry we will determine the casual relationship between ongoing neuronal activity, temporo-spatial dynamics and molecular composition of Abeta, structural rearrangements within the Abeta signalling complexes and plasticity of single synapses and whole networks. The proposed research will elucidate fundamental principles of neuronal circuits function and identify critical steps that initiate primary synaptic dysfunctions at the very early stages of sporadic AD.
Summary
A persistent challenge in unravelling mechanisms that regulate memory function is how to bridge the gap between inter-molecular dynamics of single proteins, activity of individual synapses and emerging properties of neuronal circuits. The prototype condition of disintegrating neuronal circuits is Alzheimer’s Disease (AD). Since the early time of Alois Alzheimer at the turn of the 20th century, scientists have been searching for a molecular entity that is in the roots of the cognitive deficits. Although diverse lines of evidence suggest that the amyloid-beta peptide (Abeta) plays a central role in synaptic dysfunctions of AD, several key questions remain unresolved. First, endogenous Abeta peptides are secreted by neurons throughout life, but their physiological functions are largely unknown. Second, experience-dependent physiological mechanisms that initiate the changes in Abeta composition in sporadic, the most frequent form of AD, are unidentified. And finally, molecular mechanisms that trigger Abeta-induced synaptic failure and memory decline remain elusive.
To target these questions, I propose to develop an integrative approach to correlate structure and function at the level of single synapses in hippocampal circuits. State-of-the-art techniques will enable the simultaneous real-time visualization of inter-molecular dynamics within signalling complexes and functional synaptic modifications. Utilizing FRET spectroscopy, high-resolution optical imaging, electrophysiology, molecular biology and biochemistry we will determine the casual relationship between ongoing neuronal activity, temporo-spatial dynamics and molecular composition of Abeta, structural rearrangements within the Abeta signalling complexes and plasticity of single synapses and whole networks. The proposed research will elucidate fundamental principles of neuronal circuits function and identify critical steps that initiate primary synaptic dysfunctions at the very early stages of sporadic AD.
Max ERC Funding
2 000 000 €
Duration
Start date: 2011-12-01, End date: 2017-09-30
Project acronym Acclimatize
Project Hypothalamic mechanisms of thermal homeostasis and adaptation
Researcher (PI) Jan SIEMENS
Host Institution (HI) UNIVERSITATSKLINIKUM HEIDELBERG
Call Details Consolidator Grant (CoG), LS5, ERC-2017-COG
Summary Mammalian organisms possess the remarkable ability to maintain internal body temperature (Tcore) within a narrow range close to 37°C despite wide environmental temperature variations. The brain’s neural “thermostat” is made up by central circuits in the hypothalamic preoptic area (POA), which orchestrate peripheral thermoregulatory responses to maintain Tcore. Thermogenesis requires metabolic fuel, suggesting intricate connections between the thermoregulatory centre and hypothalamic circuits controlling energy balance. How the POA detects and integrates temperature and metabolic information to achieve thermal balance is largely unknown. A major question is whether this circuitry could be harnessed therapeutically to treat obesity.
We have recently identified the first known molecular temperature sensor in thermoregulatory neurons of the POA, transient receptor potential melastatin 2 (TRPM2), a thermo-sensitive ion channel. I aim to use TRPM2 as a molecular marker to gain access to and probe the function of thermoregulatory neurons in vivo. I propose a multidisciplinary approach, combining local, in vivo POA temperature stimulation with optogenetic circuit-mapping to uncover the molecular and cellular logic of the hypothalamic thermoregulatory centre and to assess its medical potential to counteract metabolic syndrome.
Acclimation is a beneficial adaptive process that fortifies thermal responses upon environmental temperature challenges. Thermoregulatory neuron plasticity is thought to mediate acclimation. Conversely, maladaptive thermoregulatory changes affect obesity. The cell-type-specific neuronal plasticity mechanisms underlying these changes within the POA, however, are unknown.
Using ex-vivo slice electrophysiology and in vivo imaging, I propose to characterize acclimation- and obesity-induced plasticity of thermoregulatory neurons. Ultimately, I aim to manipulate thermoregulatory neuron plasticity to test its potential counter-balancing effect on obesity.
Summary
Mammalian organisms possess the remarkable ability to maintain internal body temperature (Tcore) within a narrow range close to 37°C despite wide environmental temperature variations. The brain’s neural “thermostat” is made up by central circuits in the hypothalamic preoptic area (POA), which orchestrate peripheral thermoregulatory responses to maintain Tcore. Thermogenesis requires metabolic fuel, suggesting intricate connections between the thermoregulatory centre and hypothalamic circuits controlling energy balance. How the POA detects and integrates temperature and metabolic information to achieve thermal balance is largely unknown. A major question is whether this circuitry could be harnessed therapeutically to treat obesity.
We have recently identified the first known molecular temperature sensor in thermoregulatory neurons of the POA, transient receptor potential melastatin 2 (TRPM2), a thermo-sensitive ion channel. I aim to use TRPM2 as a molecular marker to gain access to and probe the function of thermoregulatory neurons in vivo. I propose a multidisciplinary approach, combining local, in vivo POA temperature stimulation with optogenetic circuit-mapping to uncover the molecular and cellular logic of the hypothalamic thermoregulatory centre and to assess its medical potential to counteract metabolic syndrome.
Acclimation is a beneficial adaptive process that fortifies thermal responses upon environmental temperature challenges. Thermoregulatory neuron plasticity is thought to mediate acclimation. Conversely, maladaptive thermoregulatory changes affect obesity. The cell-type-specific neuronal plasticity mechanisms underlying these changes within the POA, however, are unknown.
Using ex-vivo slice electrophysiology and in vivo imaging, I propose to characterize acclimation- and obesity-induced plasticity of thermoregulatory neurons. Ultimately, I aim to manipulate thermoregulatory neuron plasticity to test its potential counter-balancing effect on obesity.
Max ERC Funding
1 902 500 €
Duration
Start date: 2018-09-01, End date: 2023-08-31
Project acronym ACCORD
Project Algorithms for Complex Collective Decisions on Structured Domains
Researcher (PI) Edith Elkind
Host Institution (HI) THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Call Details Starting Grant (StG), PE6, ERC-2014-STG
Summary Algorithms for Complex Collective Decisions on Structured Domains.
The aim of this proposal is to substantially advance the field of Computational Social Choice, by developing new tools and methodologies that can be used for making complex group decisions in rich and structured environments. We consider settings where each member of a decision-making body has preferences over a finite set of alternatives, and the goal is to synthesise a collective preference over these alternatives, which may take the form of a partial order over the set of alternatives with a predefined structure: examples include selecting a fixed-size set of alternatives, a ranking of the alternatives, a winner and up to two runner-ups, etc. We will formulate desiderata that apply to such preference aggregation procedures, design specific procedures that satisfy as many of these desiderata as possible, and develop efficient algorithms for computing them. As the latter step may be infeasible on general preference domains, we will focus on identifying the least restrictive domains that enable efficient computation, and use real-life preference data to verify whether the associated restrictions are likely to be satisfied in realistic preference aggregation scenarios. Also, we will determine whether our preference aggregation procedures are computationally resistant to malicious behavior. To lower the cognitive burden on the decision-makers, we will extend our procedures to accept partial rankings as inputs. Finally, to further contribute towards bridging the gap between theory and practice of collective decision making, we will provide open-source software implementations of our procedures, and reach out to the potential users to obtain feedback on their practical applicability.
Summary
Algorithms for Complex Collective Decisions on Structured Domains.
The aim of this proposal is to substantially advance the field of Computational Social Choice, by developing new tools and methodologies that can be used for making complex group decisions in rich and structured environments. We consider settings where each member of a decision-making body has preferences over a finite set of alternatives, and the goal is to synthesise a collective preference over these alternatives, which may take the form of a partial order over the set of alternatives with a predefined structure: examples include selecting a fixed-size set of alternatives, a ranking of the alternatives, a winner and up to two runner-ups, etc. We will formulate desiderata that apply to such preference aggregation procedures, design specific procedures that satisfy as many of these desiderata as possible, and develop efficient algorithms for computing them. As the latter step may be infeasible on general preference domains, we will focus on identifying the least restrictive domains that enable efficient computation, and use real-life preference data to verify whether the associated restrictions are likely to be satisfied in realistic preference aggregation scenarios. Also, we will determine whether our preference aggregation procedures are computationally resistant to malicious behavior. To lower the cognitive burden on the decision-makers, we will extend our procedures to accept partial rankings as inputs. Finally, to further contribute towards bridging the gap between theory and practice of collective decision making, we will provide open-source software implementations of our procedures, and reach out to the potential users to obtain feedback on their practical applicability.
Max ERC Funding
1 395 933 €
Duration
Start date: 2015-07-01, End date: 2020-06-30
