2016 — 2020 |
Saalmann, Yuri B |
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. |
Prefrontal Cortico-Thalamic Dynamics in Cognitive Control @ University of Wisconsin-Madison
The ability to flexibly adapt behavior based on current goals and context is called cognitive control, which is essential for responding appropriately to the diverse situations we face in life. This becomes strikingly clear when cognitive control is impaired, as in schizophrenia, obsessive-compulsive disorder and attention-deficit hyperactivity disorder. An important implementation of cognitive control is the application of rules, which map cues to actions according to context. When we carry out actions, we often start by applying abstract rules (e.g. morning means coffee), which aid in the selection of more concrete rules (e.g. coffee means grind beans) closer to action specification. Prefrontal cortex (PFC), especially areas 46 and 9/46, is vital for processing rules, and PFC neurons have been shown to represent concrete and abstract rules. This raises two fundamental questions. First, how are neurons that represent abstract and concrete rules organized in PFC? Although there are two prominent proposals for the functional organization of PFC, one suggesting an anterior- posterior gradient based on rule abstraction and the other suggesting individual neurons contribute to the processing of multiple rules (instead of being topographically organized), there is a lack of electrophysiology studies testing these proposals. The second question is how are distinct ensembles of PFC neurons flexibly and selectively activated based on behaviorally relevant rules? Neural synchrony may be a suitable selection mechanism, dynamically routing information between synchronized cells. Evidence suggests that the higher- order thalamus, which forms indirect pathways between cortical areas, can regulate cortical oscillations and synchrony. The mediodorsal thalamic nucleus (MD) is extensively connected with PFC and is thus well positioned to influence PFC activity. Functional MRI and lesion studies suggest that MD plays an important role in rule processing and cognitive control in general. However, there have been very few electrophysiology studies probing the role of MD in cognitive control, and none have probed how MD and PFC interact in primates. The goal of the proposed research is to characterize how MD contributes to rule processing (SA#1), how PFC is functionally organized (SA#2), and how MD and PFC interact during rule-guided behavior (SA#3). The central hypothesis is that MD regulates information transmission between PFC neurons. A key mechanism may involve MD synchronizing PFC neurons that represent task-relevant rules. To test this hypothesis, we simultaneously record neural activity in MD and areas 46 and 9/46 of monkeys performing a rule-based task. We also stimulate MD to test whether MD has a causal influence on oscillatory activity, neural synchrony and information transmission across PFC. To translate this work to humans, we acquire intracranial recordings in epilepsy patients performing the same rule-based task. The proposed research will advance our understanding of the large-scale network dynamics that mediate cognitive control. Defining the basic mechanisms of cognitive control is a first necessary step in developing effective treatment strategies for cognitive control deficits.
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2020 — 2025 |
Saalmann, Yuri |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Proposal: Gcr: in Search For the Interactions That Create Consciousness @ University of Wisconsin-Madison
This project focuses on the physical footprints of consciousness. A convergent team of engineers, neurosurgeons and neuroscientists will focus on the fundamental problem of understanding what causes the emergence of consciousness. Using techniques from systems neuroscience, neurosurgery, signal processing and machine learning, and data collected with humans and non-human primates, the research aims to develop models of the brain circuit interactions supporting consciousness. Improved understanding of the mechanism of consciousness will facilitate the advancement of new therapeutic approaches for disorders of consciousness and cognitive problems (among other broader impacts). The project includes interdisciplinary research training, will integrate research findings into teaching, and includes outreach to high-school and middle-school students.
The theory that the thalamus supports the flexible formation/activation of cortical neural ensembles required for the content of consciousness will be refined, and methods that will restore consciousness in non-human primates will be developed. These advances will be enabled by building novel models, advancing machine learning methods, and researching their theoretical and practical underpinnings. The new methods will be scalable and it is anticipated that they will able to identify causal interactions between brain regions, i.e., whether activity in one area causes activity in another.
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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0.915 |
2020 — 2021 |
Saalmann, Yuri B Sanders, Robert D |
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. |
Receptors, Microcircuits and Hierarchical Connectivity in Predictive Coding and Sensory Awareness @ University of Wisconsin-Madison
SUMMARY The standard view of how we make sense of the world around us focuses on reconstructing our environment from the information received by our sensory organs. In this view, low-level brain areas (e.g., primary sensory cortex) represent basic features of objects, which are elaborated on in successive processing stages, until representations become increasingly complex in high-level areas (e.g., frontal cortex). An alternative view is predictive coding (PC), in which we model our environment to generate sensory predictions. In PC, high-level brain areas generate predictions of sensory activity and transmit them to low-level areas. A prediction that does not match the sensory information gives rise to a prediction error. This error signal is sent from low- to high-level brain areas to update the model of our environment, thereby improving future predictions to minimize errors. Modeling studies show PC is a fast and efficient way to process sensory information, and PC provides innovative hypotheses for understanding sleep and anesthesia, particularly when disconnected consciousness occurs (consciousness without awareness of the environment), like dreaming. PC also holds great promise for conceptualizing and treating brain disorders, including schizophrenia and depression. But key central features of PC have not been empirically tested and little is known about the underlying neural mechanisms. The goal of the proposed project is to characterize the neural dynamics, circuits and receptors enabling PC. There are two principle hypotheses. First, predictions depend on N-methyl-D-aspartate receptors (NMDAR) because NMDAR influence the activity of high-level brain areas where predictions are generated, and NMDAR are enriched on neurons in lower-level areas receiving predictions. Second, in disconnected consciousness, a breakdown of information transmission from low-level to high-level brain areas, as well as a breakdown of computations within each area, explains why models of our environment are not updated by external sensory information. These breakdowns prevent the comparison of predictions and sensory information, as well as the transmission of prediction errors to high-level brain areas. To test these hypotheses, we use a cross-species experimental design connecting cellular, circuit and systems levels to behavior. We will perform electroencephalography, machine learning and computational modeling to define the neural basis of PC in humans performing prediction tasks. Then we will manipulate PC using different anesthetic agents with diverse mechanisms, establishing causal relationships between receptors, large-scale brain networks and PC. In parallel, we will simultaneously record activity from sensory and high-level brain areas of non-human primates (NHPs) using the same PC tasks and pharmacological interventions to measure cellular and circuit level contributions to PC. Investigating PC will illuminate the fundamental mechanisms of perception, providing critical insights to guide therapeutic development for multiple health conditions.
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