Michal Zochowski - US grants

DESCRIPTION (provided by applicant): Monitoring activity at multiple independent sites is often fundamental in understanding distributed processes underlying functioning of biological systems. The necessity to monitor the spatio-temporal activity patterns that are essential for information processing in neural systems are a primary example. The optical imaging is particularly fitted for this purpose. The goal of this proposal it to develop a system which would utilize optical imaging using voltage sensitive dyes and high frame-rate CCD camera in conjunction with multisite electrical stimulation to understand underlying mechanisms of neural communication and formation of spatio-temporal activity patterns in cortical cultures. Since the cultures form two-dimensional randomly connected networks, optical imaging will allow monitoring activity of every neuron in the field of view of the microscope, yielding information about neural communication with unprecedented detail. Application of optical imaging will allow observation of cell-cell interactions and formation of spatially distributed temporal patterns based on relative firing patterns of the interconnected neurons, whereas use of planar multielectrode arrays will enable stimulation of the network with stimuli having different spatio-temporal properties. Additionally we will develop analytical and numerical measures to monitor dynamical changes in cell interactions due to synaptic modifications. We will use them to test theoretically obtained prediction that relative excitation levels of interacting neurons can control relative spike timing and thus influence directionality of information flow in the network. In all, the developed system will allow us to monitor changes in patterns of activity of different neural types due to electrical and/or pharmacological manipulation, allowing for better future understanding of cell-cell interactions as well as providing a possible test bed for drug screening.

[unreadable] DESCRIPTION (provided by investigator): It is evident that both the microscopic and macroscopic organization of network structures underlies their function. Changes in the connectivity of neuronal networks are thought to be essential for cognitive phenomena such as memory formation, and may also underlie brain pathologies such as epileptic seizures. It is therefore imperative to develop tools to investigate functional topologies of local networks formed in the healthy and pathological brain. Lately, much interest has been focused on the development of tools that allow for the detection of community structure in networks. However, these tools generally search for specific clustering in the network connectivity patterns to parse the network into communities. The temporal dynamics of the network nodes is usually not considered. Additionally, the elucidation of functional connectivity in neuronal networks presents a very specific challenge, since it is impossible to experimentally establish the anatomical connectivity of the network. Because of those problems, we propose to: 1) Develop a set of analytical tools tailored for the measurement of functional network topologies and/or detection of community structures based on neural activity patterns; and 2) Use the developed tools to investigate the formation of functional communities in rat hippocampal dissociated cell cultures. Here, we plan to use multielectrode recordings of neural activity and calcium imaging to monitor the changes in functional community structure for different levels of network excitation and to examine the changes in functional community structure due to focal pharmacological stimulation. 3) Finally, we will apply the developed toolbox to the in vivo recordings. Here, we will analyze the recordings from chronically implanted tetrodes in freely behaving mice and investigate changes in dynamical clustering during different cognitive tasks such as the exploration of novel and familiar environments, or memory formation. As a result, the proposed research will provide a set of tools needed to identify the dynamical and network correlates of cognitive function. The development of these tools has very great potential impact within the biological sciences. For example, in systems neuroscience they would allow for a better understanding of brain functions such as memory formation, memory reactivation, and recall, and clinically they would assist in identifying topological/functional network pathologies leading to epilepsy. PUBLIC HEALTH RELEVANCE: In this proposal we aim to develop tools tailored at the detection of functional community structure of a network from temporal activity of a subset of its elements. The new tools will be tested on simulation, in vitro and in vivo data. The development of such tools will widely impact the neuroscience field and also other biosciences. In terms of neuroscience research, it will allow for a better understanding of neuronal mechanisms of such functions such as memory formation, memory reactivation or recall. Also, it may provide valuable tools for identifying topological/functional network pathologies leading to such diseases as epilepsy. [unreadable] [unreadable]

This award supports a new REU site in the Biophysics Department at the University of Michigan. The site will support seven students per year for ten weeks of interdisciplinary research experience at the intersection of chemistry, physics, mathematics and the life sciences. The Biophysics Program at UM is one of very few standalone biophysics programs in the US, created to foster multidisciplinary research and provide interdisciplinary undergraduate and graduate education. Since Biophysics is intellectually extremely diverse, the aim is not only to provide research experience in a subfield of the discipline, but also provide an overview of state-of the-art research in the field, and the experimental methods and techniques that are commonly used. Therefore, the research experience is constructed to have two components: 1) the students will be assigned to the labs that match their interest and are run by experienced researchers to participate innovative scientific exploration, 2) the REU students will participate in hands-on exploration of biophysical methods and techniques (HEBMAT) that will introduce them to various modern experimental and computational research techniques. Upon completion of the program the students will have gained exciting research experience and will have acquired a biophysical toolkit making them more successful and competitive in the scientific and academic environment. This core experience will be augmented by weekly research seminars and workshops, plus opportunities for social interactions, which will often be coordinated with other summer undergraduate research programs to broaden the students' integration into our scientific and professional communities. To match the increasing intellectual diversity of biophysics, the site strongly emphasizes the importance of demographic diversity in the program as a seed for making the field as a whole more diverse in the future. This award is co-funded by the Division of Physics, the Biology Directorate, and the Chemistry Division at the National Science Foundation

The goal of this work is to elucidate evolving multimodal interactions between structural, dynamical, and functional network properties based on the interdependencies observed between neuronal and astrocytic networks acting on diverse spatial and temporal scales. Specifically we will investigate how dynamical and structural network characteristics interact on these different time scales to form evolving, functional neural ensembles. To achieve this goal we plan to combine computational modeling with experimental approaches that include calcium optical imaging, multi-electrode recordings, and structural labeling studies in primary neuronal cultures. This will allow for monitoring of multi-scale, simultaneous dynamical and structural changes in networks under different conditions. In particular we want to address the following questions: What properties of spatio-temporal patterning are mediated through fast and/or slow network interactions? How does network connectivity influence multimodal network activity? Whether and how do local network changes modify local patterns of fast and slow dynamics? Finally, we want to understand how the functions of these dynamical modes evolve during network development.

Networks of interacting elements are ubiquitous in nature and man-made systems. The interactions in these networks happen on different spatial and temporal scales. Neural networks are a prime example of such a complex system. While interconnected neurons establish fast modes of communication, the slower astrocytes can modulate overall activity in the network that in turn will affect its connectivity structure and function. It becomes then pertinent to understand the interactions between these two modes of transmission and what their differential roles in network function are. This interdisciplinary project connects neurobiology with dynamical systems approaches to tackle these problems and can potentially have important impact on diverse fields. Identifying dynamical mechanisms underlying these interactions will allow for a better understanding of the network processes that underlie both cognitive tasks and brain pathologies: for example, on one hand structural network reconfiguration is crucial for memory formation but at the same time may drive formation of epileptic seizures. At the same time, the use of these dynamical modes for controlled network development may also lead to formulation of new strategies for network repair after injury. Finally, the insight obtained from this work could be generalized and applied to man-made networks that employ multi-scale temporal dynamics and could undergo functional reorganization online.

This project will also provide opportunity for graduate and undergraduate students to experience interdisciplinary research connecting biology, physics and dynamical systems theory.

The discovery that new neurons are born in adult brains and integrate into functional networks raised questions about the dynamics of this process. Namely, what are the activity dependent queues guiding the integration of the new cells into existing networks and how do these queues depend on the intrinsic properties of the augmented networks? The focus of this project is to develop an integrated computational and experimental framework, which will allow for investigation of dynamical mechanisms underlying migration and incorporation of newly born neurons into existing networks. We specifically want to understand whether, and how, network augmentation depends on the ongoing activity of the original network, and to discern the collective changes in the network activity patterns specifically due to network augmentation. To do so the PI will develop a computational approach that will allow him to elucidate links between cellular mechanisms of network augmentation and their network-wide outcomes. In addition the PI will use an in vitro experimental system based on dissociated cell cultures to monitor patterns of network augmentation and changes in spatio-temporal activity, after GFP labeled neuroblasts are added. The neural activity using multi-electrode arrays and calcium imaging will be recorded, and then labeling studies to elucidate structural patterns of neural augmentation will be performed. The proposed project will provide a better understanding of the interaction of cellular and network mechanisms underlying function-dependent network augmentation. This is critical for identifying dynamical mechanisms of self-reorganization in these types systems.

This project will allow three collaborating laboratories to serve as a training resource for undergraduate and graduate students. The students will have the opportunity to gain experience in truly interdisciplinary research combining theoretical physics, modeling and neurobiology. They will further gain experience in the scientific process through participation in interdisciplinary meetings to present their results. The knowledge gained through this research will be disseminated to members of the scientific community through publications, seminars, and workshops. Additionally, the obtained results will be incorporated in the "Biocomplexity" course that the PI has developed for the advanced undergraduates.

DESCRIPTION (provided by applicant): It has become clear that spatio-temporal patterning of neuronal activity reflects a complex interaction between dynamical properties of neurons and those of the networks they form. The constant reorganization of these properties underlies most cognitive processes in the brain, and their dysregulation causes brain pathologies. The development of multisite optical imaging and electrophysiological recording techniques has enabled the identification and monitoring of dynamic network organization (so called functional network structure) on different time and spatial scales. To fully understand functional dynamics among neurons in in vitro and in vivo situations, it is imperative to develop analytical and computational tools to detect and characterize these distributed functional network structures from experimental recordings. Within the theoretical community, much interest has been focused on developing tools that allow detection of community structure in networks. These tools are generally optimized to analyze the physical (i.e., anatomical) space of network connections, and to parse the network connectivity structure into communities. Elucidation of functional connectivity in neuronal networks presents a very specific challenge, since anatomical connectivity is only one of the factors that mediate formation of functional interactions. The primary challenge is to construct tools that efficiently detect the formation of dynamic functional communities based on the spatio- temporal patterning of neural electrical activity, and that reliably quantify the properties of the detected communities. We have recently developed a functional community detection method that answers this challenge. We propose to couple it with optimal metrics that permit robust detection of dynamically changing network communities and test it thoroughly in computational, in vitro and in vivo settings. Specifically, w propose to: develop and test, through computer simulations, a set of linear and non-linear metrics tailored for the measurement of dynamic changes in functional network communities (AIM 1); use these tools to investigate formation of functional communities in dissociated, mouse hippocampal cell cultures (AIM 2); and apply the tools to in vivo hippocampal multisite recordings obtained from freely behaving mice undergoing a cognitive task (AIM 3). Tools to detect dynamic formation of functional network structures from temporal activity patterns in a subset of network elements will have a very significant impact on neuroscience, as well as in other biosciences where such information is available. Within neuroscience, such tools will provide a better understanding of brain function during different cognitive tasks. They will also provide a valuable diagnostic method for identifying functional network pathologies.

While the exact physiological function of sleep remains unknown, there is mounting evidence that it plays an important role in the consolidation of long-term memories. In particular, it appears that sleep promotes the consolidation of declarative memories that require a functionally intact hippocampus, including memories of place. In rodents, place-dependent fear memory is promoted by sleep and disrupted by sleep deprivation. Sleep deprivation also disrupts a number biochemical and neurophysiological processes that are thought to be involved in memory consolidation. These studies have led many to suggest that sleep promotes long-term memory consolidation by modulating neural network dynamics and synaptic plasticity within the hippocampus. In experimental studies, the co-PIs have recently identified changes in hippocampal neural network dynamics during sleep that are induced by place-dependent fear learning. In computational modeling studies, the co-PIs have shown that acetylcholine, a modulatory chemical whose levels vary across sleep states, can change neural network dynamics in a similar way. The proposed projects take a multidisciplinary, multi-scale approach to bridge the gap between experimental and computational results, to identify how effects of acetylcholine on neurons lead to changes in neural network dynamics to promote learning, ultimately leading to learning and memory behavior. While the focus is on fear learning and memory consolidation, the fundamental knowledge of learning-related and sleep-related brain network dynamics gained by the proposed experiments and computations will provide valuable insights into mechanisms for all types of learning.

At present, it is unclear how sleep-related changes in hippocampal network dynamics might promote contextual fear memory consolidation. The team's recent experimental studies have shown that contextual fear conditioning produces long-lasting, sleep-dependent increases in the stability of hippocampal network functional connectivity patterns. These results, coupled with the team's recent computational studies describing a role for acetylcholine in network-wide activity and synaptic plasticity patterning, have led to the hypothesis that sleep promotes memory consolidation, at least in part, by dynamically shifting patterns in hippocampal neural network activity during naturally-occurring rapid eye movement and slow wave sleep brain states. Sleep-dependent acetylcholine has a primary role in driving these shifts in network activity through its effects on cellular excitability properties. The proposed projects use behavioral, physiological, and computational approaches to tackle the missing links that will show the hypothesized network mechanisms occur in brain hippocampal networks and participate in fear learning and memory. Hippocampal acetylcholine levels will be manipulated across wake and sleep states while recording multi-unit activity in hippocampus to quantify changes in spike timing dynamics in the context of fear and subsequent sleep or sleep deprivation. The team has developed a suite of quantitative measures to identify learning-related changes in network dynamics. In addition, acetylcholine-induced changes in cellular membrane properties that affect network dynamics will be measured in hippocampal pyramidal cell and inhibitory interneuron populations. The results will be used to inform details of biophysical neural network models to identify specific dynamical mechanisms by which acetylcholine-mediated changes in network activity dynamics promote network stability, structural changes and synaptic reorganization associated with learning and consolidation.