2002 — 2003 |
Sporns, Olaf |
R21Activity Code Description: To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
Neuro-Robotic Models of Learning and Addiction @ Indiana University Bloomington
DESCRIPTION: (provided by applicant) The overall aim of this research program is to provide a realistic computational model of how neuromodulatory systems act upon cortical and subcortical networks of the human brain, and of how their actions influence normal and addictive modes of behavior in the real world. An integrated systems-level computational approach will be pursued in order to (a) discover and implement anatomical and physiological principles underlying neuromodulatory functions, (b) study their integration with other brain areas and processes, especially those underlying learning and memory, and (c) study the interactions between (internal) neural events and (external) behaviors. In stages of increasing complexity, a detailed neuronal network model of sensory and motor cortical areas, subcortical circuits and neuromodulatory nuclei will be designed and implemented in an autonomous robot. The model will incorporate realistic anatomical and physiological properties and be capable of plastic changes in connectivity depending upon actual sensory experience and behavior. In a first stage, we plan to implement a neuromodulatory system with properties similar to a midbrain dopamine system, producing neural responses that are related to reward and reward predicting stimuli. In addition to this reward system we will implement a separate system responsive to aversive stimuli and investigate possible modes of functional interaction between them. In order to investigate the hypothesized connection between the development of addictive behavior and processes related to memory and learning we will expand the model to include additional cortical and subcortical networks. Our modeling studies will allow us to provide an analysis of the causal roles played by different components of the neural architecture, of pharmacological and physiological properties, of learning and memory and of actual behavior in the switch from normal and controlled modes of behavior to addiction. A comprehensive and detailed embodied (robot) model has the potential of serving as a unique explanatory and predictive tool aiding in future empirical research on drug abuse and addiction.
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2009 — 2015 |
Beer, Randall [⬀] Smith, Linda (co-PI) [⬀] Goldstone, Robert (co-PI) [⬀] Sporns, Olaf |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Igert: the Dynamics of Brain-Body-Environment Systems in Behavior and Cognition
This Integrative Graduate Education and Research Traineeship (IGERT) award supports a training program on the dynamics of brain-body-environment interaction in behavior and cognition at Indiana University. The purpose of the training program is to create a new kind of scientist with expertise in both the experimental and theoretical tools necessary to analyze intelligence as an emergent property of a complex dynamic system. The training program includes new courses, a professional development seminar, a colloquium series that provides opportunities for extended interactions between students and top researchers, research internships, and opportunities for international collaboration. The program also includes a detailed assessment plan and a summer program for undergraduates from underrepresented groups run in partnership with Indiana University Northwest, approximately 80% of whose students come from minority, first-generation college, female or low socioeconomic categories. Broader impacts of this program include recruiting new students from underrepresented groups into cognitive science and providing graduate students with the opportunity to participate in cutting-edge multidisciplinary research. All materials produced by this program will be made freely available on the web. More generally, this program will foster new kinds of discourse between the various disciplines that make up cognitive science. Finally, by placing cognition within its proper embodied and situated context, the proposed training program may impact how society fosters and measures cognitive ability. IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries.
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2015 — 2017 |
Sporns, Olaf |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Crcns: Linking Connectomic and Large-Scale Dynamics of the Human Brain @ Indiana University Bloomington
DESCRIPTION (provided by applicant): Complex spatiotemporal patterns of neural activity unfolding within an intricate structural network of regions and inter-regional pathways are thought to underlie all of human behavior and cognition. Understanding how structural networks shape and constrain functional brain networks therefore represents a key challenge to computational cognitive neuroscience. The proposed project aims to a) characterize the repertoire of structural and dynamic functional networks of the human brain as measured with noninvasive neuroimaging techniques; b) create a catalogue of computational models that are based on the anatomy of structural networks and simple biophysical local models of neuronal populations, and that can generate realistic large-scale neural dynamics; and c) apply systematic criteria of model comparison and inference to gain insight into which model components and parameters are critical for generating biologically plausible patterns of brain dynamics that closely match empirical data. Identifying these models would offer potential insights into biological network structures and mechanisms that underpin stationary features of functional brain connectivity, as well as their dynamic reconfigurations. An additional goal of the project is to create such models based on network data acquired from individual subjects, thus paving the way for using modeling tools to compare and characterize individual differences in key features of brain dynamics. In the pursuit of these central aims, new knowledge will be created. The project aims to add to our understanding of the factors and constraints that shape the relation of structural connectivity and local biophysical properties of circuits with the emerging large-scale dynamics of the human brain. The core of the proposal is to deploy sophisticated computational modeling methods in order to build realistic and neurobiologically grounded models of human brain dynamics. In taking this empirically-based computational approach the project will help to advance the rapidly growing fields of brain connectivity and dynamics by creating new bridges between data relating to brain structure and function. It will also add an important dimension to the ongoing quest, pursued in a number of national and international initiatives, to create comprehensive and neurobiologically realistic computational models of the structure and function of the human brain. This U.S./German collaboration will contribute to trans-Atlantic cooperation in an important research area. The multi-disciplinary character of the project (combining brain imaging and EEG recording, dynamic brain modeling, network science and graph theory) will provide a rich educational environment for graduate and post-graduate trainees, allowing them to acquire broad skills at an early point in their scientific careers. Trainees will be exposed to laboratory practices and the scientific landscape in both Europe and the United States. An additional area of broader impact relates to data/tool sharing. While computational methods are becoming more widely used in modern neuroscience, the configuration of software tools, and implementation of neurobiologically realistic simulations still requires significant knowledge and training. Through their joint involvement in previous projects, the PI and Co-PIs have a proven track record of publicly sharing computational tools and resources, organizing educational activities to broaden access to sophisticated computational platforms, and dedication to graduate and post-graduate training in computational neuroscience. All computational tools, methods and results coming from the proposed project will be freely and openly shared with the larger neuroscience community. A third and longer term area of broader impact is to deploy the computational modeling approach underlying this proposal for targeted clinical applications. Personalized brain modeling may ultimately help to monitor dynamic signatures of brain health in individual patients. Further clinical applications of the dynamic network modeling approaches developed here could include novel therapeutic strategies in the case of brain injury or pathology.
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2016 — 2019 |
Wang, Lei Saykin, Andrew (co-PI) [⬀] Sporns, Olaf Pestilli, Franco [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Bd Spokes: Spoke: Midwest: Collaborative: Advanced Computational Neuroscience Network (Acnn)
Novel neuroscience tools and techniques are necessary to enable insight into the building blocks of neural circuits, the interactions between these circuits that underpin the functions of the human brain, and modulation of these circuits that affect our behavior. To leverage rapid technological development in sensing, imaging, and data analysis new ground breaking advances in neuroscience are necessary to facilitate knowledge discovery using data science methods. To address this societal grand challenge, the project will foster new interdisciplinary collaborations across computing, biological, mathematical, and behavioral science disciplines together with partnerships in academia, industry, and government at multiple levels. The Big Data Neuroscience Spoke titled Midwest: Advanced Computational Neuroscience Network (ACNN) is strongly aligned with the national priority area of neuroscience and brings together a diverse set of committed regional partners to enable the Midwest region to realize the promise of Big Data for neuroscience. The ACNN Spoke will build broad consensus on the core requirements, infrastructure, and components needed to develop a new generation of sustainable interdisciplinary Neuroscience Big Data research. ACNN will leverage the strengths and resources in the Midwest region to increase innovation and collaboration for the understanding of the structure, physiology, and function of the human brain through partnerships and services in education, tools, and best practices.
The ACNN will design, pilot and support powerful neuroscientific computational resources for high-throughput, collaborative, and service-oriented data aggregation, processing and open-reproducible science. The ACNN Spoke framework will address three specific problems related to neuroscience Big Data: (1) data capture, organization, and management involving multiple centers and research groups, (2) quality assurance, preprocessing and analysis that incorporates contextual metadata, and (3) data communication to software and hardware computational resources that can scale with the volume, velocity, and variety of neuroscience datasets. The ACNN will build a sustainable ecosystem of neuroscience community partners in both academia and industry using existing technologies for collaboration and virtual meeting together with face-to-face group meetings. The planned activities of the ACNN Spoke will also allow the Midwest Big Data Hub to disseminate additional Big Data technologies resources to the neuroscience community, including access to supercomputing facilities, best practices, and platforms.
This award received co-funding from CISE Divisions of Advanced Cyberinfrastructure (ACI) and Information and Intelligent Systems (IIS).
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2017 — 2022 |
Rocha, Luis Razo, Armando (co-PI) [⬀] Pescosolido, Bernice (co-PI) [⬀] Borner, Katy Sporns, Olaf |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Nrt: Interdisciplinary Training in Complex Networks and Systems
Understanding complex networked systems is key to solving some of the most vexing problems confronting humankind, from discovering how dynamic brain connections give rise to thoughts and behaviors, to detecting and preventing the spread of misinformation or unhealthy behaviors across a population. Graduate training, however, typically occurs in one of two dimensions: experimental and observational methods in a specific area such as biology and sociology, or in general methodologies such as machine learning and data science. With more and more students seeking to gain sufficient expertise in mathematical and computational methods on top of domain-specific laboratory and social analysis methodologies, a greater demand for more efficient training is emerging. This National Science Foundation Research Traineeship (NRT) award to Indiana University will address this growing need with an integrated dual PhD program that trains students to be "bidisciplinary" in Complex Networks and Systems (CNS) and another discipline of their choosing from the natural and social sciences. It will seamlessly integrate traditional education with interdisciplinary hands-on research in a culture of academic and human diversity. This program will provide unique interdisciplinary training for thirty-four (34) PhD students, including twenty-two (22) funded trainees. The program will provide additional training experience to 40 summer affiliate students and a population of more than 300 participants across the participating PhD programs.
The training program capitalizes on the new Indiana University Network Science Institute (IUNI). The Institute's 165+ faculty members will serve in interdisciplinary PhD program committees to be co-chaired by research mentors from both CNS and the target empirical domain. Project-driven, team-based research at IUNI will seamlessly integrate academic education with interdisciplinary hands-on scientific and industrial research. Trainees will learn to connect the general-purpose, computational expertise of CNS to the deep, domain-specific research methodologies of the natural, behavioral, and social sciences thus bridging the gap between distinct training cultures. They will be a new breed of STEM scientists that escapes the silos of disciplinary training to address the complex problems of the 21st century. Specifically, the four goals of training activity are: 1) provide dual research proficiency; 2) develop collaborative skills via early integration into problem-driven, interdisciplinary research; 3) produce a diverse workforce by recruiting student cohorts from a broad set of disciplines and varied backgrounds to be trained within a team culture; 4) establish a sustainable interdisciplinary training model by enlarging the institutional channels created between informatics and natural and social sciences to other Indiana University departments and institutions. A science-of-science study conducted throughout the NRT project will evaluate the efficacy of interdisciplinary training of the students in this program. This project will develop a flexible dual PhD program and best-practices to allow additional departments at Indiana University to join the program in the future, as well as other institutions to develop similar programs.
The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.
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2019 — 2021 |
Sporns, Olaf |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Crcns: Macaque Cortex Neuronal Network Dynamics Related Cognition and Behavior @ Indiana University Bloomington
Complex cognition and behavior is generated by network interactions of neurons in the brain. Network structure shapes and constrains information encoding, and networks must be dynamically coordinated in order to allow for flexible information processing. Understanding the neuronal information encoding, the neuronal network structure, the dynamic coordination mechanisms for flexible information processing, and especially their interplay, is a key challenge for computational neuroscience. Importantly, thls challenge must be addressed at the level of single neuron actlvity, since coarser brain signals and recording techniques lose important detail due to averaging over highly-structured, fast and heterogeneous neuronal responses. The proposed project aims to a) characterize the interplay of behaviorally relevant intra- and inter-area neuronal information encoding, network communication structure, and oscillatory communication during a cognitively simple visuomotor decision task utilizing existing simultaneous macaque monkey recordings of many neurons from two cortical areas; b) extend and generalize the identified encoding, communication and oscillatory synchronization structure to larger neuronal networks spanning four cortical areas during a cognitively more complex visuomotor decision task including systematic variation of the essential higher cognitive factors of reward and effort. To this end two monkeys will be trained on a corresponding decision-making task and implanted with state-of-the art electrode arrays for large-scale simultaneous recordings; c) analyze the cause and effect of the dynamic interaction of information encoding, communication and oscillatory communication during cognitively simple and complex visuomotor decisions uslng all available datasets. Identifying the dynamic interaction of network properties at the neuronal level will offer significant insight into the neuronal mechanisms underlying cognition and behavlor. RELEVANCE (See instructions): Network interactions of dlstributed sets of neurons in the cerebral cortex are important for adaptlve cognition and behavior. Using a combination of empirical and computational approaches the project will determine how neuronal information is encoded and dynamically coordinated in neuronal networks spanning multiple brain areas. The resulting knowledge will provide new insights into the neuronal basis of brain function, including disturbances of brain networks in a range of clinical disorders.
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2019 — 2021 |
Sporns, Olaf |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Crcns: the Evolution of the Mammalian Connectome @ Indiana University Bloomington
Nervous systems are complex networks, composed of neurons in various brain regions that are interconnected by synaptic connections and inter-regional pathways. While much effort has been expended to map the connectome of humans and a few model organisms, a more systematic comparative acquisition and analysis of connectomes across many species is lacking so far. Hence, the evolutionary processes that have shaped connectome architecture are largely unknown. Here, we address this gap in knowledge by generating connectivity data from a large set of mammalian species in order to allow comprehensive and comparative network analysis as well as to relate network features to evolutionary anatomic and behavioral adaptations. Lead by experts in magnetic resonance imaging, network theory and behavior, this interdisciplinary team will a) create a unique unprecedented data base of mammalian connectomes (covering 5% of all mammalian species), acquired using cutting-edge diffusion imaging and tractography; b) configure and deploy network analysis techniques to mine these data and discover patterns that trace the evolution of the connectome across species; and c) formulate and test specific hypotheses that illuminate evolutionary patterns in the relation between brain connectivity and behavior. The project will thus create a first-of-its-kind opportunity to study the evolution of the connectome across the mammalian class. RELEVANCE (See instructions): Complex brain networks enable and support human cognition and behavior. This project will reveal principles of network organization across a broad range of species in the mammalian class, including those of commonly used model organisms such as non-human primates and rodents. The resulting knowledge will illuminate the evolutionary origins of brain networks and thus provide new insights into their neurobiological function and dysfunction in humans.
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2020 — 2023 |
Sporns, Olaf Ahn, Yong-Yeol Betzel, Richard [⬀] Mejia, Amanda |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Ncs-Fo: Edge-Centric Maps of Functional Brain Network Organization and Dynamics
The human brain is made up of functionally and structurally connected neural elements that form a brain-wide complex network. A principal goal of network neuroscience is to understand how the organization of this network helps support cognition, evolves over the course of the human lifespan, and becomes compromised in disease and neuropsychiatric disorders. However, virtually all progress made towards addressing these questions has relied upon one particular network model for mathematically representing patterns of brain connectivity, at the expense of other models that could provide complementary or unique insight. This project aims to extend and validate an alternative edge-centric framework for representing and analyzing patterns of brain connectivity. The project will deliver new insights into the relationship of brain network organization with cognitive/behavioral phenotypes and shed light on brain network dynamics at ultra-fast timescales are paralleled by changes in subjects' cognitive states. This research will support cross-disciplinary collaboration among the brain sciences, informatics, and statistics, and will support a diverse set of trainees at all levels, from high school to postdoctoral.
This principal innovation of the edge-centric framework is a spatiotemporal decomposition of functional connections into their framewise contributions. This decomposition yields a time series of co-fluctuations for every pair of brain regions (edges in the network). The first aim investigates the novel construct of edge functional connectivity -- the correlation pattern estimated among all pairs of co-fluctuation time series. Edge connectivity will be generated for a large cohort of subjects (N > 1000) using imaging data acquired both at rest and while subjects were performing cognitively demanding tasks. Multivariate statistical methods will be used to discover robust associations between edge connectivity and subjects' behavioral, demographic, and clinical measures. The second aim analyzes co-fluctuation time series directly, taking advantage of the ultra-fast timescale at which they are estimated to investigate potential drivers of brain network reconfiguration during naturalistic viewing (movie-watching). This project advances the edge-centric framework as a viable tool for general neuroscientific discovery and will open the door for future studies to investigate brain-behavior relationships and network dynamics in applied contexts and not restricted to large-scale imaging data.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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