Project acronym BENDER
Project BiogENesis and Degradation of Endoplasmic Reticulum proteins
Researcher (PI) Friedrich Förster
Host Institution (HI) UNIVERSITEIT UTRECHT
Call Details Consolidator Grant (CoG), LS1, ERC-2016-COG
Summary The Endoplasmic Reticulum (ER) membrane in all eukaryotic cells has an intricate protein network that facilitates protein biogene-sis and homeostasis. The molecular complexity and sophisticated regulation of this machinery favours study-ing it in its native microenvironment by novel approaches. Cryo-electron tomography (CET) allows 3D im-aging of membrane-associated complexes in their native surrounding. Computational analysis of many sub-tomograms depicting the same type of macromolecule, a technology I pioneered, provides subnanometer resolution insights into different conformations of native complexes.
I propose to leverage CET of cellular and cell-free systems to reveal the molecular details of ER protein bio-genesis and homeostasis. In detail, I will study: (a) The structure of the ER translocon, the dynamic gateway for import of nascent proteins into the ER and their maturation. The largest component is the oligosaccharyl transferase complex. (b) Cotranslational ER import, N-glycosylation, chaperone-mediated stabilization and folding as well as oligomerization of established model substrate such a major histocompatibility complex (MHC) class I and II complexes. (c) The degradation of misfolded ER-residing proteins by the cytosolic 26S proteasome using cytomegalovirus-induced depletion of MHC class I as a model system. (d) The structural changes of the ER-bound translation machinery upon ER stress through IRE1-mediated degradation of mRNA that is specific for ER-targeted proteins. (e) The improved ‘in silico purification’ of different states of native macromolecules by maximum likelihood subtomogram classification and its application to a-d.
This project will be the blueprint for a new approach to structural biology of membrane-associated processes. It will contribute to our mechanistic understanding of viral immune evasion and glycosylation disorders as well as numerous diseases involving chronic ER stress including diabetes and neurodegenerative diseases.
Summary
The Endoplasmic Reticulum (ER) membrane in all eukaryotic cells has an intricate protein network that facilitates protein biogene-sis and homeostasis. The molecular complexity and sophisticated regulation of this machinery favours study-ing it in its native microenvironment by novel approaches. Cryo-electron tomography (CET) allows 3D im-aging of membrane-associated complexes in their native surrounding. Computational analysis of many sub-tomograms depicting the same type of macromolecule, a technology I pioneered, provides subnanometer resolution insights into different conformations of native complexes.
I propose to leverage CET of cellular and cell-free systems to reveal the molecular details of ER protein bio-genesis and homeostasis. In detail, I will study: (a) The structure of the ER translocon, the dynamic gateway for import of nascent proteins into the ER and their maturation. The largest component is the oligosaccharyl transferase complex. (b) Cotranslational ER import, N-glycosylation, chaperone-mediated stabilization and folding as well as oligomerization of established model substrate such a major histocompatibility complex (MHC) class I and II complexes. (c) The degradation of misfolded ER-residing proteins by the cytosolic 26S proteasome using cytomegalovirus-induced depletion of MHC class I as a model system. (d) The structural changes of the ER-bound translation machinery upon ER stress through IRE1-mediated degradation of mRNA that is specific for ER-targeted proteins. (e) The improved ‘in silico purification’ of different states of native macromolecules by maximum likelihood subtomogram classification and its application to a-d.
This project will be the blueprint for a new approach to structural biology of membrane-associated processes. It will contribute to our mechanistic understanding of viral immune evasion and glycosylation disorders as well as numerous diseases involving chronic ER stress including diabetes and neurodegenerative diseases.
Max ERC Funding
2 496 611 €
Duration
Start date: 2017-04-01, End date: 2022-03-31
Project acronym BiT
Project How the Human Brain Masters Time
Researcher (PI) Domenica Bueti
Host Institution (HI) SCUOLA INTERNAZIONALE SUPERIORE DI STUDI AVANZATI DI TRIESTE
Call Details Consolidator Grant (CoG), SH4, ERC-2015-CoG
Summary If you suddenly hear your song on the radio and spontaneously decide to burst into dance in your living room, you need to precisely time your movements if you do not want to find yourself on your bookshelf. Most of what we do or perceive depends on how accurately we represent the temporal properties of the environment however we cannot see or touch time. As such, time in the millisecond range is both a fundamental and elusive dimension of everyday experiences. Despite the obvious importance of time to information processing and to behavior in general, little is known yet about how the human brain process time. Existing approaches to the study of the neural mechanisms of time mainly focus on the identification of brain regions involved in temporal computations (‘where’ time is processed in the brain), whereas most computational models vary in their biological plausibility and do not always make clear testable predictions. BiT is a groundbreaking research program designed to challenge current models of time perception and to offer a new perspective in the study of the neural basis of time. The groundbreaking nature of BiT derives from the novelty of the questions asked (‘when’ and ‘how’ time is processed in the brain) and from addressing them using complementary but distinct research approaches (from human neuroimaging to brain stimulation techniques, from the investigation of the whole brain to the focus on specific brain regions). By testing a new biologically plausible hypothesis of temporal representation (via duration tuning and ‘chronotopy’) and by scrutinizing the functional properties and, for the first time, the temporal hierarchies of ‘putative’ time regions, BiT will offer a multifaceted knowledge of how the human brain represents time. This new knowledge will challenge our understanding of brain organization and function that typically lacks of a time angle and will impact our understanding of how the brain uses time information for perception and action
Summary
If you suddenly hear your song on the radio and spontaneously decide to burst into dance in your living room, you need to precisely time your movements if you do not want to find yourself on your bookshelf. Most of what we do or perceive depends on how accurately we represent the temporal properties of the environment however we cannot see or touch time. As such, time in the millisecond range is both a fundamental and elusive dimension of everyday experiences. Despite the obvious importance of time to information processing and to behavior in general, little is known yet about how the human brain process time. Existing approaches to the study of the neural mechanisms of time mainly focus on the identification of brain regions involved in temporal computations (‘where’ time is processed in the brain), whereas most computational models vary in their biological plausibility and do not always make clear testable predictions. BiT is a groundbreaking research program designed to challenge current models of time perception and to offer a new perspective in the study of the neural basis of time. The groundbreaking nature of BiT derives from the novelty of the questions asked (‘when’ and ‘how’ time is processed in the brain) and from addressing them using complementary but distinct research approaches (from human neuroimaging to brain stimulation techniques, from the investigation of the whole brain to the focus on specific brain regions). By testing a new biologically plausible hypothesis of temporal representation (via duration tuning and ‘chronotopy’) and by scrutinizing the functional properties and, for the first time, the temporal hierarchies of ‘putative’ time regions, BiT will offer a multifaceted knowledge of how the human brain represents time. This new knowledge will challenge our understanding of brain organization and function that typically lacks of a time angle and will impact our understanding of how the brain uses time information for perception and action
Max ERC Funding
1 670 830 €
Duration
Start date: 2016-10-01, End date: 2021-09-30
Project acronym ChronHib
Project Chronologicon Hibernicum – A Probabilistic Chronological Framework for Dating Early Irish Language Developments and Literature
Researcher (PI) David Stifter
Host Institution (HI) NATIONAL UNIVERSITY OF IRELAND MAYNOOTH
Call Details Consolidator Grant (CoG), SH4, ERC-2014-CoG
Summary Early Medieval Irish literature (7th–10th centuries) is vast in extent and rich in genres, but owing to its mostly anonymous transmission, for most texts the precise time and circumstances of composition are unknown. Unless where texts contain historical references, the only clues for a rough chronological positioning of the texts are to be found in their linguistic peculiarities. Phonology, morphology, syntax and the lexicon of the Irish language changed considerably from Early Old Irish (7th c.) into Middle Irish (c. 10th–12th centuries). However, only the relative sequence of changes is well understood; for most sound changes very few narrow dates have been proposed so far.
It is the aim of Chronologicon Hibernicum to find a common solution for both problems: through the linguistic profiling of externally dated texts (esp. annalistic writing and sources with a clear historical anchorage) and through serialising the emerging linguistic and chronological data, progress will be made in assigning dates to the linguistic changes. Groundbreakingly, this will be done by using statistical methods for the seriation of the data, and for estimating dates using Bayesian inference.
The resultant information will then be used to find new dates for hitherto undated texts. On this basis, a much tighter chronological framework for the developments of the Early Medieval Irish language will be created. In a further step it will be possible to arrive at a better chronological description of medieval Irish literature as a whole, which will have repercussions on the study of the history and cultural and intellectual environment of medieval Ireland and on its connections with the wider world.
The data collected and analysed in this project will form the database Chronologicon Hibernicum which will serve as the authoritative guideline and reference point for the linguistic dating of Irish texts. In the future, the methodology will be transferable to other languages.
Summary
Early Medieval Irish literature (7th–10th centuries) is vast in extent and rich in genres, but owing to its mostly anonymous transmission, for most texts the precise time and circumstances of composition are unknown. Unless where texts contain historical references, the only clues for a rough chronological positioning of the texts are to be found in their linguistic peculiarities. Phonology, morphology, syntax and the lexicon of the Irish language changed considerably from Early Old Irish (7th c.) into Middle Irish (c. 10th–12th centuries). However, only the relative sequence of changes is well understood; for most sound changes very few narrow dates have been proposed so far.
It is the aim of Chronologicon Hibernicum to find a common solution for both problems: through the linguistic profiling of externally dated texts (esp. annalistic writing and sources with a clear historical anchorage) and through serialising the emerging linguistic and chronological data, progress will be made in assigning dates to the linguistic changes. Groundbreakingly, this will be done by using statistical methods for the seriation of the data, and for estimating dates using Bayesian inference.
The resultant information will then be used to find new dates for hitherto undated texts. On this basis, a much tighter chronological framework for the developments of the Early Medieval Irish language will be created. In a further step it will be possible to arrive at a better chronological description of medieval Irish literature as a whole, which will have repercussions on the study of the history and cultural and intellectual environment of medieval Ireland and on its connections with the wider world.
The data collected and analysed in this project will form the database Chronologicon Hibernicum which will serve as the authoritative guideline and reference point for the linguistic dating of Irish texts. In the future, the methodology will be transferable to other languages.
Max ERC Funding
1 804 230 €
Duration
Start date: 2015-09-01, End date: 2020-08-31
Project acronym CoAct
Project Communication in Action: Towards a model of Contextualized Action and Language Processing
Researcher (PI) Judith HOLLER
Host Institution (HI) STICHTING KATHOLIEKE UNIVERSITEIT
Call Details Consolidator Grant (CoG), SH4, ERC-2017-COG
Summary Language is fundamental to human sociality. While the last century has given us many fundamental insights into how we use and understand it, core issues that we face when doing so within its natural environment—face-to-face conversation—remain untackled. When we speak we also send signals with our head, eyes, face, hands, torso, etc. How do we orchestrate and integrate all this information into meaningful messages? CoAct will lead to a new model with in situ language processing at its core, the Contextualized Action and Language (CoALa) processing model. The defining characteristic of in situ language is its multimodal nature. Moreover, the essence of language use is social action; that is, we use language to do things—we question, offer, decline etc. These social actions are embedded in conversational structure where one speaking turn follows another at a remarkable speed, with millisecond gaps between them. Conversation thus confronts us with a significant psycholinguistic challenge. While one could expect that the many co-speech bodily signals exacerbate this challenge, CoAct proposes that they actually play a key role in dealing with it. It tests this in three subprojects that combine methods from a variety of disciplines but focus on the social actions performed by questions and responses as a uniting theme: (1) ProdAct uses conversational corpora to investigate the multimodal architecture of social actions with the assumption that they differ in their ‘visual signatures’, (2) CompAct tests whether these bodily signatures contribute to social action comprehension, and if they do so early and rapidly, (3) IntAct investigates whether bodily signals play a facilitating role also when faced with the complex task of comprehending while planning a next social action. Thus, CoAct aims to advance current psycholinguistic theory by developing a new model of language processing based on an integrative framework uniting aspects from psychology , philosophy and sociology.
Summary
Language is fundamental to human sociality. While the last century has given us many fundamental insights into how we use and understand it, core issues that we face when doing so within its natural environment—face-to-face conversation—remain untackled. When we speak we also send signals with our head, eyes, face, hands, torso, etc. How do we orchestrate and integrate all this information into meaningful messages? CoAct will lead to a new model with in situ language processing at its core, the Contextualized Action and Language (CoALa) processing model. The defining characteristic of in situ language is its multimodal nature. Moreover, the essence of language use is social action; that is, we use language to do things—we question, offer, decline etc. These social actions are embedded in conversational structure where one speaking turn follows another at a remarkable speed, with millisecond gaps between them. Conversation thus confronts us with a significant psycholinguistic challenge. While one could expect that the many co-speech bodily signals exacerbate this challenge, CoAct proposes that they actually play a key role in dealing with it. It tests this in three subprojects that combine methods from a variety of disciplines but focus on the social actions performed by questions and responses as a uniting theme: (1) ProdAct uses conversational corpora to investigate the multimodal architecture of social actions with the assumption that they differ in their ‘visual signatures’, (2) CompAct tests whether these bodily signatures contribute to social action comprehension, and if they do so early and rapidly, (3) IntAct investigates whether bodily signals play a facilitating role also when faced with the complex task of comprehending while planning a next social action. Thus, CoAct aims to advance current psycholinguistic theory by developing a new model of language processing based on an integrative framework uniting aspects from psychology , philosophy and sociology.
Max ERC Funding
2 000 000 €
Duration
Start date: 2018-09-01, End date: 2023-08-31
Project acronym DARE2APPROACH
Project Dare to Approach: A Neurocognitive Approach to Alleviating Persistent Avoidance in Anxiety Disorders
Researcher (PI) karin ROELOFS
Host Institution (HI) STICHTING KATHOLIEKE UNIVERSITEIT
Call Details Consolidator Grant (CoG), SH4, ERC-2017-COG
Summary How did three soldiers override their initial freezing response to overpower an armed terrorist in the Thalys-train to Paris in 2015? This question is relevant for anyone aiming to optimize approach-avoidance (AA) decisions during threat. It is particularly relevant for patients with anxiety disorders whose persistent avoidance is key to the maintenance of their anxiety.
Computational psychiatry has made great progress in formalizing how we make (mal)adaptive decisions. Current models, however, largely ignore the transient psychophysiological state of the decision maker. Parasympathetic state and flexibility in switching between para- and sympathetic states are directly related to freezing, and are known to bias AA-decisions toward avoidance. The central aim of this research program is to forge a mechanistic understanding of how we compute AA-decisions on the basis of those psychophysiological states, and to identify alterations in anxiety patients in order to guide new personalized neurocognitive interventions into their persistent avoidance.
I will develop a neurocomputational model of AA-decisions that accounts for transient psychophysiological states, in order to define which decision parameters are altered in active and passive avoidance in anxiety. I will test causal premises of the model using state-of-the-art techniques, including pharmacological and electrophysiological interventions. Based on these insights I will for the first time apply personalized brain stimulation to anxiety patients.
Clinically, this project should open the way to effective intervention with fearful avoidance in anxiety disorders that rank among the most common, costly and persistent mental disorders. Theoretically, conceptualizing transient psychophysiological states as causal factor in AA-decision models is essential to understanding passive and active avoidance. Optimizing AA-decisions also holds broad societal relevance given currently increased fearful avoidance of outgroups.
Summary
How did three soldiers override their initial freezing response to overpower an armed terrorist in the Thalys-train to Paris in 2015? This question is relevant for anyone aiming to optimize approach-avoidance (AA) decisions during threat. It is particularly relevant for patients with anxiety disorders whose persistent avoidance is key to the maintenance of their anxiety.
Computational psychiatry has made great progress in formalizing how we make (mal)adaptive decisions. Current models, however, largely ignore the transient psychophysiological state of the decision maker. Parasympathetic state and flexibility in switching between para- and sympathetic states are directly related to freezing, and are known to bias AA-decisions toward avoidance. The central aim of this research program is to forge a mechanistic understanding of how we compute AA-decisions on the basis of those psychophysiological states, and to identify alterations in anxiety patients in order to guide new personalized neurocognitive interventions into their persistent avoidance.
I will develop a neurocomputational model of AA-decisions that accounts for transient psychophysiological states, in order to define which decision parameters are altered in active and passive avoidance in anxiety. I will test causal premises of the model using state-of-the-art techniques, including pharmacological and electrophysiological interventions. Based on these insights I will for the first time apply personalized brain stimulation to anxiety patients.
Clinically, this project should open the way to effective intervention with fearful avoidance in anxiety disorders that rank among the most common, costly and persistent mental disorders. Theoretically, conceptualizing transient psychophysiological states as causal factor in AA-decision models is essential to understanding passive and active avoidance. Optimizing AA-decisions also holds broad societal relevance given currently increased fearful avoidance of outgroups.
Max ERC Funding
2 000 000 €
Duration
Start date: 2019-01-01, End date: 2023-12-31
Project acronym DNAMEREP
Project The role of essential DNA metabolism genes in vertebrate chromosome replication
Researcher (PI) Vincenzo Costanzo
Host Institution (HI) IFOM FONDAZIONE ISTITUTO FIRC DI ONCOLOGIA MOLECOLARE
Call Details Consolidator Grant (CoG), LS1, ERC-2013-CoG
Summary "Faithful chromosomal DNA replication is essential to maintain genome stability. A number of DNA metabolism genes are involved at different levels in DNA replication. These factors are thought to facilitate the establishment of replication origins, assist the replication of chromatin regions with repetitive DNA, coordinate the repair of DNA molecules resulting from aberrant DNA replication events or protect replication forks in the presence of DNA lesions that impair their progression. Some DNA metabolism genes are present mainly in higher eukaryotes, suggesting the existence of more complex repair and replication mechanisms in organisms with complex genomes. The impact on cell survival of many DNA metabolism genes has so far precluded in depth molecular analysis. The use of cell free extracts able to recapitulate cell cycle events might help overcoming survival issues and facilitate these studies. The Xenopus laevis egg cell free extract represents an ideal system to study replication-associated functions of essential genes in vertebrate organisms. We will take advantage of this system together with innovative imaging and proteomic based experimental approaches that we are currently developing to characterize the molecular function of some essential DNA metabolism genes. In particular, we will characterize DNA metabolism genes involved in the assembly and distribution of replication origins in vertebrate cells, elucidate molecular mechanisms underlying the role of essential homologous recombination and fork protection proteins in chromosomal DNA replication, and finally identify and characterize factors required for faithful replication of specific vertebrate genomic regions.
The results of these studies will provide groundbreaking information on several aspects of vertebrate genome metabolism and will allow long-awaited understanding of the function of a number of vertebrate essential DNA metabolism genes involved in the duplication of large and complex genomes."
Summary
"Faithful chromosomal DNA replication is essential to maintain genome stability. A number of DNA metabolism genes are involved at different levels in DNA replication. These factors are thought to facilitate the establishment of replication origins, assist the replication of chromatin regions with repetitive DNA, coordinate the repair of DNA molecules resulting from aberrant DNA replication events or protect replication forks in the presence of DNA lesions that impair their progression. Some DNA metabolism genes are present mainly in higher eukaryotes, suggesting the existence of more complex repair and replication mechanisms in organisms with complex genomes. The impact on cell survival of many DNA metabolism genes has so far precluded in depth molecular analysis. The use of cell free extracts able to recapitulate cell cycle events might help overcoming survival issues and facilitate these studies. The Xenopus laevis egg cell free extract represents an ideal system to study replication-associated functions of essential genes in vertebrate organisms. We will take advantage of this system together with innovative imaging and proteomic based experimental approaches that we are currently developing to characterize the molecular function of some essential DNA metabolism genes. In particular, we will characterize DNA metabolism genes involved in the assembly and distribution of replication origins in vertebrate cells, elucidate molecular mechanisms underlying the role of essential homologous recombination and fork protection proteins in chromosomal DNA replication, and finally identify and characterize factors required for faithful replication of specific vertebrate genomic regions.
The results of these studies will provide groundbreaking information on several aspects of vertebrate genome metabolism and will allow long-awaited understanding of the function of a number of vertebrate essential DNA metabolism genes involved in the duplication of large and complex genomes."
Max ERC Funding
1 999 800 €
Duration
Start date: 2014-06-01, End date: 2019-05-31
Project acronym DREAM
Project Distributed dynamic REpresentations for diAlogue Management
Researcher (PI) Raquel FERNANDEZ Rovira
Host Institution (HI) UNIVERSITEIT VAN AMSTERDAM
Call Details Consolidator Grant (CoG), SH4, ERC-2018-COG
Summary Our ability to communicate using language in conversation is considered the hallmark of human intelligence. Yet, while holding a dialogue is effortless for most of us, modelling this basic human skill by computational means has proven extremely difficult. In DREAM, I address this challenge by establishing a new computational model of a dialogue agent that can learn to take part in conversation directly from data about language use. DREAM stands at the crossroads of the symbolic and the sub-symbolic traditions regarding the nature of human cognitive processing and, by extension, its computational modelling. My model is grounded in linguistic theories of dialogue, rooted in the symbolic tradition, but exploits recent advances in computational learning that allow the agent to learn the representations that it manipulates, which are distributed and sub-symbolic, directly from experience. This is an original approach that constitutes a paradigm shift in dialogue modelling --- from predefined symbolic representations to automatic representation learning --- that will break new scientific ground in Computational Linguistics, Linguistics, and Artificial Intelligence. The DREAM agent will be implemented as an artificial neural network system and trained with task-oriented conversations where the participants have a well-defined end goal. The agent will be able to integrate linguistic and perceptual information and will be endowed with the capability to dynamically track both speaker commitments and partner-specific conventions, leading to more human-like and effective communication. Besides providing a breakthrough in our capacity to build sophisticated conversational agents, DREAM will have substantial impact on our scientific understanding of human language use, thanks to its emphasis on theory-driven hypotheses and model analysis.
Summary
Our ability to communicate using language in conversation is considered the hallmark of human intelligence. Yet, while holding a dialogue is effortless for most of us, modelling this basic human skill by computational means has proven extremely difficult. In DREAM, I address this challenge by establishing a new computational model of a dialogue agent that can learn to take part in conversation directly from data about language use. DREAM stands at the crossroads of the symbolic and the sub-symbolic traditions regarding the nature of human cognitive processing and, by extension, its computational modelling. My model is grounded in linguistic theories of dialogue, rooted in the symbolic tradition, but exploits recent advances in computational learning that allow the agent to learn the representations that it manipulates, which are distributed and sub-symbolic, directly from experience. This is an original approach that constitutes a paradigm shift in dialogue modelling --- from predefined symbolic representations to automatic representation learning --- that will break new scientific ground in Computational Linguistics, Linguistics, and Artificial Intelligence. The DREAM agent will be implemented as an artificial neural network system and trained with task-oriented conversations where the participants have a well-defined end goal. The agent will be able to integrate linguistic and perceptual information and will be endowed with the capability to dynamically track both speaker commitments and partner-specific conventions, leading to more human-like and effective communication. Besides providing a breakthrough in our capacity to build sophisticated conversational agents, DREAM will have substantial impact on our scientific understanding of human language use, thanks to its emphasis on theory-driven hypotheses and model analysis.
Max ERC Funding
2 000 000 €
Duration
Start date: 2019-09-01, End date: 2024-08-31
Project acronym EXPECT HEAL-TH
Project Empowering expectations for health and disease: training the immune and endocrine system
Researcher (PI) Andrea Evers
Host Institution (HI) UNIVERSITEIT LEIDEN
Call Details Consolidator Grant (CoG), SH4, ERC-2013-CoG
Summary Expectations about health and disease induce immune and endocrine responses and directly affect health and treatment outcomes. However, there is an urge to understand the mechanical underpinnings how expectations affect immune and endocrine responses and how this knowledge can be used for therapeutic interventions.
My research group studies the main expectancy learning mechanisms for itch and pain as a generic expectancy model across symptoms and conditions. We recently showed that dual expectancy learning processes (i.e. conditioning and suggestions) are most powerful for itch symptoms, corresponding with findings for other symptoms and conditions. Based on these studies, I propose a groundbreaking dual expectancy learning approach, testing whether combined expectancy learning processes (i.e. both conditioning and suggestions, offered with personalized cues and exposure to relevant stressors) affect most profoundly the immune and endocrine system, in turn affecting health and disease outcomes.
The major aim is to unravel the central mechanisms of how peoples’ expectations affect immune and endocrine responses and related health outcomes, through the use of pioneering multidisciplinary methods in healthy and clinical populations. First, we systematically train immune and endocrine responses and relate them to psychological, neurobiological and genetic mechanisms. Second, we test these manipulations for physical health challenges (e.g. inflammatory or allergic histamine reactions) in healthy subjects and patients. Third, we study the long-term effects in chronic inflammatory itch and pain conditions (e.g. replacing anti-inflammatory pharmacotherapies, reducing side effects).
This interdisciplinary, cross-boundary project progresses key theoretical knowledge of the central expectation mechanisms for immune and endocrine responses. Findings open new horizons for health prevention and therapeutic interventions for various inflammatory conditions and physical symptoms.
Summary
Expectations about health and disease induce immune and endocrine responses and directly affect health and treatment outcomes. However, there is an urge to understand the mechanical underpinnings how expectations affect immune and endocrine responses and how this knowledge can be used for therapeutic interventions.
My research group studies the main expectancy learning mechanisms for itch and pain as a generic expectancy model across symptoms and conditions. We recently showed that dual expectancy learning processes (i.e. conditioning and suggestions) are most powerful for itch symptoms, corresponding with findings for other symptoms and conditions. Based on these studies, I propose a groundbreaking dual expectancy learning approach, testing whether combined expectancy learning processes (i.e. both conditioning and suggestions, offered with personalized cues and exposure to relevant stressors) affect most profoundly the immune and endocrine system, in turn affecting health and disease outcomes.
The major aim is to unravel the central mechanisms of how peoples’ expectations affect immune and endocrine responses and related health outcomes, through the use of pioneering multidisciplinary methods in healthy and clinical populations. First, we systematically train immune and endocrine responses and relate them to psychological, neurobiological and genetic mechanisms. Second, we test these manipulations for physical health challenges (e.g. inflammatory or allergic histamine reactions) in healthy subjects and patients. Third, we study the long-term effects in chronic inflammatory itch and pain conditions (e.g. replacing anti-inflammatory pharmacotherapies, reducing side effects).
This interdisciplinary, cross-boundary project progresses key theoretical knowledge of the central expectation mechanisms for immune and endocrine responses. Findings open new horizons for health prevention and therapeutic interventions for various inflammatory conditions and physical symptoms.
Max ERC Funding
1 981 009 €
Duration
Start date: 2014-09-01, End date: 2019-08-31
Project acronym GEMH
Project Video games for the prevention of depression and anxiety: A 21st century approach to emotional and mental health in adolescents
Researcher (PI) Isabela Granic
Host Institution (HI) STICHTING KATHOLIEKE UNIVERSITEIT
Call Details Consolidator Grant (CoG), SH4, ERC-2015-CoG
Summary Depression and anxiety are the most frequently diagnosed mental health problems, leading to devastating long-term outcomes that affect a huge proportion of adolescents across the globe. Effective prevention programs are urgently needed; however, even our most advanced programs often lead to disappointing outcomes. Video games promise a groundbreaking, 21st century innovation: games provide learning environments that keep youth motivated and engaged to practice emotional resiliency skills that help prevent anxiety and depression. The education and medical fields have begun to harness the immense potential of games to teach new forms of thought and behaviour. Yet validated games for mental health are virtually nonexistent. The proposed program of research will: (a) Develop genre-breaking games which integrate biofeedback and evidence-based game mechanics that target anxiety and depression; (b) Test whether these games are more effective at reducing anxiety and depressive symptoms than the best conventional evidence-based prevention programs; and (c) Apply a novel, game-based methodology, to pinpoint the precise game mechanics responsible for training emotional resilience. With the multidisciplinary network of scientists and game designers that I lead – and a wide array of research designs, multi-method assessments and analytic techniques – this program of research promises to be the first of its kind. Results will establish the precise game mechanics that can successfully change causal factors that maintain anxiety and depression and provide a validated toolbox for efficient development of future applied games for a range of mental health concerns. These game-based engines for behavioural and emotional change will be mobilized through massive distribution channels (schools, social media), potentially making an unprecedented impact on the next generation’s prevalence rates of anxiety and depression.
Summary
Depression and anxiety are the most frequently diagnosed mental health problems, leading to devastating long-term outcomes that affect a huge proportion of adolescents across the globe. Effective prevention programs are urgently needed; however, even our most advanced programs often lead to disappointing outcomes. Video games promise a groundbreaking, 21st century innovation: games provide learning environments that keep youth motivated and engaged to practice emotional resiliency skills that help prevent anxiety and depression. The education and medical fields have begun to harness the immense potential of games to teach new forms of thought and behaviour. Yet validated games for mental health are virtually nonexistent. The proposed program of research will: (a) Develop genre-breaking games which integrate biofeedback and evidence-based game mechanics that target anxiety and depression; (b) Test whether these games are more effective at reducing anxiety and depressive symptoms than the best conventional evidence-based prevention programs; and (c) Apply a novel, game-based methodology, to pinpoint the precise game mechanics responsible for training emotional resilience. With the multidisciplinary network of scientists and game designers that I lead – and a wide array of research designs, multi-method assessments and analytic techniques – this program of research promises to be the first of its kind. Results will establish the precise game mechanics that can successfully change causal factors that maintain anxiety and depression and provide a validated toolbox for efficient development of future applied games for a range of mental health concerns. These game-based engines for behavioural and emotional change will be mobilized through massive distribution channels (schools, social media), potentially making an unprecedented impact on the next generation’s prevalence rates of anxiety and depression.
Max ERC Funding
1 999 510 €
Duration
Start date: 2016-08-01, End date: 2021-07-31
Project acronym IMPROVE
Project Innovative Methods for Psychology: Reproducible, Open, Valid, and Efficient
Researcher (PI) Jelte WICHERTS
Host Institution (HI) STICHTING KATHOLIEKE UNIVERSITEIT BRABANT
Call Details Consolidator Grant (CoG), SH4, ERC-2016-COG
Summary With numerous failures to replicate, common misreporting of results, widespread failure to publish non-significant results or to share data, and considerable potential bias due the flexibility of analyses of data and researcher’s tendency to exploit that flexibility, psychological science is said to experience a crisis of confidence. These issues lead to dissemination of false positive results and inflate effect size estimates in meta-analyses. This leads to poor theory building, an inefficient scientific system, a waste of resources, lower trust in psychological science, and psychology’s outcomes being less useful for society. After having contributed to the literature highlighting these problems the goal of my ERC project is to improve psychological science by offering novel solutions to five vexing challenges: (1) I want to counter misreporting of results by using our new tool statcheck in several studies on reviewers’ tendency to demand perfection and by applying it to actual peer review. (2) I want to counter the biasing effects of common explorations of data (p-hacking) by professing and studying pre-registration and by developing promising new approaches called blind analysis and cross-validation using differential privacy that simultaneously allows for exploration and confirmation with the same data. (3) I want to counter the common problem of selective outcome reporting in psychological experiments by developing powerful latent variable methods that render it fruitless to not report all outcome variables in a study. (4) I want to counter the problem of publication bias by studying and correcting misinterpretations of non-significance. (5) I want to develop and refine meta-analytic methods that allow for the correction of biases that currently inflate estimates of effects and obscure moderation. The innovative tools I develop have the potential to improve the way psychologists (and other scientists) analyse data, disseminate findings, and draw inferences.
Summary
With numerous failures to replicate, common misreporting of results, widespread failure to publish non-significant results or to share data, and considerable potential bias due the flexibility of analyses of data and researcher’s tendency to exploit that flexibility, psychological science is said to experience a crisis of confidence. These issues lead to dissemination of false positive results and inflate effect size estimates in meta-analyses. This leads to poor theory building, an inefficient scientific system, a waste of resources, lower trust in psychological science, and psychology’s outcomes being less useful for society. After having contributed to the literature highlighting these problems the goal of my ERC project is to improve psychological science by offering novel solutions to five vexing challenges: (1) I want to counter misreporting of results by using our new tool statcheck in several studies on reviewers’ tendency to demand perfection and by applying it to actual peer review. (2) I want to counter the biasing effects of common explorations of data (p-hacking) by professing and studying pre-registration and by developing promising new approaches called blind analysis and cross-validation using differential privacy that simultaneously allows for exploration and confirmation with the same data. (3) I want to counter the common problem of selective outcome reporting in psychological experiments by developing powerful latent variable methods that render it fruitless to not report all outcome variables in a study. (4) I want to counter the problem of publication bias by studying and correcting misinterpretations of non-significance. (5) I want to develop and refine meta-analytic methods that allow for the correction of biases that currently inflate estimates of effects and obscure moderation. The innovative tools I develop have the potential to improve the way psychologists (and other scientists) analyse data, disseminate findings, and draw inferences.
Max ERC Funding
1 999 748 €
Duration
Start date: 2017-06-01, End date: 2022-05-31
