Caleb Kemere - US grants

This Integrative Graduate Education and Research Traineeship (IGERT) award provides Ph.D. students at Rice University with innovative training in neuroengineering, spanning the disciplines of neuroscience, electrical engineering, mechanical engineering, and bioengineering. In collaboration with Baylor College of Medicine and seven other universities and participating organizations, Rice University trainees are developing the tools to understand, interface with, model, and manipulate the nervous system.

Intellectual Merit: Due to improved technologies that enable neuroscientists to interact with brain cells, and due to the increasing types of neuroscientific data collected through electrical and optical methods, the neuroengineers who create and work with these complex data sets require highly specialized training. This program trains students in three specific areas: (1) cellular systems neuroengineering, which studies the nervous system?s signaling processes at the molecular and cellular levels; (2) engineering multi-neuron circuits, which involves collecting and analyzing data from groups of brain cells and devising methods to induce them to produce new functional responses; and (3) translational neuroengineering, which develops systematic approaches to improve clinical devices such as prosthetics and deep brain stimulators. Trainees in this program are learning to be technologically innovative; to be aware of social, cultural, and ethical aspects of neuroengineering; to communicate their work effectively to a wide variety of audiences; and to understand the pathways to commercialize their discoveries.

Broader Impacts: As this program trains neuroengineers to develop advanced solutions to functional and structural problems in the brain, a new problem-based learning curriculum will result and will be shared with the public through open education resources. By cultivating relationships with biomedical device companies, international researchers, and collaborators within two minority-serving institutions in Texas, students in this program are expanding the applications of their training and increasing participation in their research.

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 leadership skills needed for the career demands of the future. The program is intended to establish new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries, and to engage students in understanding the processes by which research is translated into innovations that benefit society.

Proposal Number: 1351692
Institution: William Marsh Rice University
PI: Kemere, Caleb T.
Title: CAREER: Dynamic Deep Brain Stimulation: From Theory to Technologies

Deep brain stimulation (DBS) is a remarkable treatment for later stages of Parkinson's disease and an emerging revolutionary therapy for other neurological and psychiatric disorders. Currently DBS is employed in a static fashion, in the sense that stimulation intensity and frequency are manually adjusted. However, the neural circuits of the brain are highly dynamic, so major advances to DBS therapy must be "dynamic" systems as well, with recording and processing neural signals in real-time and modulating the applied stimulation based on the dynamic state of the brain. The overall goal of this project is to lay the scientific and technical foundations for dynamic DBS systems that will radically increase the efficacy of DBS in treating neurological and psychiatric disorders. The proposed studies are therefore significant in terms of advancing both basic science, by enabling new and faster experiments, and clinically, by accelerating the development and implementation of DBS therapies. As part of education and outreach activities, high school, college and graduate students will be engaged in neuroengineering education and research by (i) using an engaging module to teach high school students basics of neurobiology theory and experiment; (ii) challenging college juniors to build their own rat DBS with power and size constraints; and (iii) teaching graduate students neural signal processing using recorded neural activity.

The overall goal of this project is to advance the scientific and technical foundations for dynamic DBS systems that will radically increase the efficacy of DBS in existing indications and enable rapid development for other neurological and psychiatric disorders. As an initial target for innovation, this proposal focuses on DBS of the subthalamic nucleus (STN) for Parkinson's disease. The following specific aims are proposed. 1 Use new materials to develop joint stimulation and recording electrodes. Record the activity of cortico-striatal-thalamic neurons in healthy, diseased, and DBS-stimulated states during intentional movements. 2 Develop a latent variable model of the dynamics of neural activity during intentional movement. Build a rodent-scale dynamic DBS device that implements model-based, closed-loop dynamic stimulation. 3 Evaluate the effects of long-term dynamic DBS across large cohorts of animals using an automated, high-throughput behavioral monitoring system. The proposed studies are expected to lay the foundation for novel dynamic DBS therapies; generate new conceptualizations of the neural basis of motor symptoms and STN DBS therapy in Parkinson's disease; transition computational models of DBS from the current static approaches to ones that incorporate the time-varying nature of movement-related neural activity; and help discover the neuroprotective and non-motor effects of static and dynamic DBS.

Memories of facts and experiences take time to be stored robustly in the brain. Understanding of the underlying mechanisms would not only yield insight into a fundamental ability found in all animals, but also allow for the optimization of learning and memory in healthy individuals as well as interventions in humans with compromised memory. Many current hypotheses of memory posit the importance of "replay" within ensembles of neurons in the brain. The idea is that in periods of quiescence or sleep, neurons in the hippocampus, a key brain structure for memory, re-excite the patterns of activity that occurred during the original learning/experience. This neural replay of the original activity patterns enables distributed cortical regions to form robust, lasting interconnections and thereby support memory. In rats learning to navigate mazes, the signals of dozens of individual hippocampal neurons can be accessed simultaneously. In this project, neural recording, computational, and statistical tools are developed and tested to observe neuronal activity in the hippocampus of rats over long periods of time-- periods that include repeated active learning and quiescence/sleep episodes-- and address fundamental questions about replay and how individual neurons participate in replay/learning over time. The algorithms developed in this project are shared widely through on-line media. The data collected contribute to instructional material used in interdisciplinary graduate training programs and courses focused on neural signal processing.

In this project, new neural recording technology and novel and computational analysis techniques are used to acquire and interpret week-long continuous recordings of hippocampal activity while an animal learns a novel task. The result is the first ever qualitative and quantitative assessment of replay over a period of this length. The resulting data have the potential to yield great insight into how changes in patterns of neural activity at the scales of milliseconds (replay) interact with changes at the scales of hours (circadian) and days (learning). The algorithms developed, which are shared through a public on-line site, may be foundational to a new experimental paradigm where neurons are recorded continuously over time. A particularly valuable outcome of the project are novel algorithms for automatically isolating the signals of individual neurons from each other, and tracking slow changes to their signatures over time. Additionally, a new latent-variable model technique for quantifying how individual replay events compare with the patterns exhibited during learning enables the statistics of replay variability to be assessed. The impacts of this work have the potential to be broad, from techniques that revolutionize systems neuroscience to data that underpins new models of learning and memory.

To better understand how the brain functions, scientists are in search of new methods to activate specific neurons in laboratory animals without restricting their behaviors. This research effort will create technologies that rely on magnetic fields to stimulate specific, genetically modified neurons. A major advantage of this "magnetogenetic" technology is fact that magnetic fields easily penetrate bone, skin and tissue making it easier for scientists to probe the role of specific cells deep within the body without using any implanted devices that could otherwise interfere with normal animal behavior or cause damage to the target tissue. Better understanding of how the brain works in laboratory animals will help reveal fundamental principles of computation in the brain that may apply across animal species to include humans. This deeper understanding of the brain is key for developing better diagnosis and treatments for neural disorders and to improve artificial systems like neural networks designed to operate like the human brain.

This work will focus on understanding how biogenic magnetic nanoparticles tethered to temperature sensitive ion channels render specific cells sensitive to magnetic fields. The major goals of this effort will be the development of a comprehensive theory for the mechanism of action for these magnetogenetic channels, and the creation of a set of transgenic fly lines that display robust behavioral responses to magnetic fields. These magnetogenetic fly lines will be compatible with Gal4/UAS system such that magnetogenetic channels can be easily targeted to specific cells. The outcome of this work will be both a fundamental understanding of magnetogenetic mechanisms and a set of transgenic fly strains that will empower researchers to probe neural circuits in this common model organism.

Project Summary: Large-scale measurement of individual neuronal activity in intact animals will accelerate the understanding of the brain and treatment of neurological disorders. Fortunately, the last decade has witnessed dramatic improvements in optical methods to record neural activity based on new genetically encoded calcium- or voltage-dependent fluorescence proteins. These optical techniques have the potential to record activity from many thousands of neurons with single-cell resolution because cell activity can be imaged from the surface of the brain without implants that damage neural tissue. Unfortunately, the current tools for fluorescence microscopy in freely moving animals are incapable of recording from more than a few hundred cells at a time due to the large microscope size and small field of view (FOV). To achieve simultaneous imaging of thousands of individual neurons in mammals, fluorescence microscopes must be miniaturized and arrayed so that animals can freely interact with their environment while images of neural activity are constantly recorded over large areas of brain. The goal of our work is to create a new class of flat microscopes (each with a large FOV) that can be arrayed and placed on the brain of a free-moving animal. These microscope arrays will thereby provide continuous imaging over large areas of the brain with cellular resolution in freely moving animals. We also envision that these FlatScopes could be implanted into the brain to measure neural activity from regions that are too deep to image from the surface. To create these FlatScopes we will exploit emerging technologies from the field computational imaging, which make it now possible to replace the expensive, heavy and thick lenses in microscopes with a compact, lightweight, and inexpensive diffractive mask placed near the sensor. Images can then be reconstructed using algorithms that recover the fluorescence images from the multiplexed sensor measurements. Our team of PIs with expertise in computational imaging, nanofabricated neural interfaces, and in vivo neural data acquisition will work to translate the ideas of lens-free imaging to implantable microfabricated FlatScopes that can image neural activity in freely moving animals. Our goal with R21 funding is to design, fabricate, and characterize individual implantable FlatScopes, both in vitro and in vivo, laying the groundwork for a scalable imaging technology to measure calcium- or voltage- sensitive fluorescence in thousands of neurons with single cell resolution.

In unit recordings from large populations of neurons, fast compressed sequential firing of neurons during rest and early sleep have been found to replay patterns first observed in active awake experience. These remarkable patterns have sparked widespread interest in the scientific community and beyond. Sequence replay is now considered to play a critical role in the long-term stabilization and storage of mnemonically important information. However, despite the general acknowledgement of the importance of the sequential structure, very little is known about the null background against which replay is compared. Specifically, are apparently 'non-replaying' spike patterns, as seen in late sleep, just simply noise? Because replay is typically assessed by comparison against a fixed known template, most methods can only determine whether the resemblance to the template is more than what might be expected from random spike trains. But these methods cannot appraise whether other patterns remain in the nonsignificant events. Recently, the Diba and Kemere labs successfully collaborated to address precisely this issue. We developed methods based on hidden Markov models (HMMs) to uncover temporal structure in spike trains of neurons in an unsupervised template-free manner. In this proposal, we aim to further improve these methods and to evaluate the hidden structure of spike trains in hippocampal neuronal populations during sleep. In our second specific aim, we will use HMMs to determine both co-active ensemble (contextual) and temporal patterns (sequential) structure in hippocampal spike trains in both pre- and post-task sleep. In the third specific aim, we will probe the essence of sleep replay further, by exposing animals to multiple novel and familiar maze environments prior to long durations of sleep. In the fourth specific aim, we will perform closed-loop disruption of neuronal population patterns to examine the causal interplay and reverberation of these patterns from early to late sleep. In summary, our proposal is designed to provide strongest characterization to date of the structure of noise in replay events. RELEVANCE (See instructions): This study will provide an opening to evaluate the role of sleep in reorganizing information in the brain and help to identify critical time windows and neuronal activities during sleep which are particularly important for information storage and stabilization. Our assumptions and deductions about the nature and purpose of sleep implicitly inform all manner of public policy, from the durations of shifts for hospital and relief workers, to morning start times of public schools. Understanding the function and mechanisms of sleep H