Matthew Johnson, Ph.D. - US grants

DESCRIPTION (provided by applicant): Both healthy and pathological aging entail some degree of cognitive and neural decline. Executive functions appear to be particularly affected by aging (Hasher &Zacks, 1988). Executive function has traditionally been associated most strongly with the prefrontal cortex (PFC), but recent research has shown that interactions between PFC and sensory association cortex also play an important role in executive processes. The broad objective of this proposal is to shed further light on how top-down executive processes affect activity in category-selective extrastriate (CSE) cortex, developing paradigms first in healthy young adults and then using them to study these processes'decline in older adults. Specific Aim #1: Examine mechanisms by which top-down processes suppress as well as enhance percepts and representations in young adults. Two functional magnetic resonance imaging (fMRI) studies will examine how perceptual and reflective attention processes act to suppress activity in CSE cortex related to non-attended stimuli. This aim has particular relevance to the mission of the NIA as recent studies have shown specific deficits in top-down suppression in older adults (Gazzaley et al., 2005), but relatively little is known about the specific mechanisms underlying such suppression, especially for reflective attention. Specific Aim #2: Analyze the information content of activity in CSE cortex caused by reflective processing. It is thought that activity in CSE cortex induced by reflective processes (e.g., working memory, mental imagery) represents information about the specific item in mind, but there is relatively little evidence for this in the literature. This fMRI experiment is aimed at decoding item information in CSE cortex during mental imagery. Future studies will then examine how information representation differs in young and older adults. This research is designed to further our knowledge of how the human brain performs high-level cognitive operations, collectively called "executive functions," which are known to be particularly susceptible to the cognitive decline that occurs in both healthy and pathological aging. The research plan includes three brain imaging experiments aimed at determining how several simple executive operations affect activity in areas of the brain involved in processing visual information. The ultimate goal is to compare the brain activity of younger adults to older adults while performing different types of executive functions in order to study how these processes deteriorate as people age.

This Integrative Graduate Education and Research Traineeship (IGERT) award supports the development of a multi-disciplinary, integrative graduate education and training program in NeuroEngineering (NE) at the University of Minnesota at Twin Cities. Intellectual Merit: The purpose of this program is to train doctoral students to develop the skills to revolutionize technologies for interfacing with the brain and advance the fundamental understanding of neuroscience processes that arise when interfacing with and modulating the brain.

Broader impacts include the development of a multi-disciplinary training program that blurs the boundary between neuroscientists and engineers, thus enabling a new generation of scientists to competently and confidently take on the grand challenges in the interdisciplinary field of NeuroEngineering. The NE program includes major research themes in decoding brain signals, modulating brain dynamics, and bi-directional brain interfacing. The program is a "degree-plus" model in which pre-doctoral students are admitted to one of the participating graduate programs (Biomedical Engineering, Electrical Engineering, Mechanical Engineering, and Neuroscience), and are trained through a series of hands-on, modular neuroengineering courses. All NE Fellows will immerse themselves in a lab outside their major in the summer of their first year, engage in multiple lab rotations, and participate in at least one clinical lab rotation, summer internship at a neurotechnology company, or summer international research experience. NE Fellows will have co-advisors beginning in their first year, one from the engineering sciences and one from the basic or clinical neurosciences. The training program incorporates several outreach efforts to recruit women and underrepresented minorities, provide outreach to K-12 and industry, and train NE Fellows to be effective communicators.

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.

DESCRIPTION (provided by applicant): Essential tremor (ET) is the most common movement disorder in the United States, affecting 4% of all adults over the age of 40. For individuals whose motor symptoms are refractory to medication and significantly impair their daily living, deep brain stimulation (DBS) is considered to be the only therapeutic option. Despite recent advances in DBS technology, a significant portion of ET patients with DBS implants will receive inadequate tremor control because of poorly placed DBS leads, while others will lose efficacy of the therapy after 1-2 years due in part to inflexible neurostimulator programming options. There is a strong and growing clinical need for implantable DBS lead designs that can enable clinicians to better sculpt electric fields within the brain, especially in cases where stimulation through a poorly placed DBS lead results in low-threshold side-effects. Our recent studies with a radially-segmented DBS lead have shown promising results, but knowing how to program the stimulation settings on such a lead remains a critical challenge towards making these leads practical in a clinical setting. Our proposed study will integrate high-field magnetic resonance imaging, computational modeling, and electrophysiology to develop an experimentally-validated computational programming algorithm that facilitates clinical determination of subject-specific neurostimulator settings through high-dimensional DBS electrode arrays. Specifically, we will: 1) develop a computational algorithm that can simplify the programming process of thalamic deep brain stimulation leads with radially-segmented electrode arrays; 2) quantify the degree to which the computational algorithms can accurately predict current steering through poorly targeted DBS arrays in the thalamus in non-human primates; and 3) compare the layer-specific neuronal dynamics induced in primary motor cortex (M1) during stimulation of the cerebellothalamic versus thalamocortical pathway in non-human primates. PUBLIC HEALTH RELEVANCE: Deep brain stimulation (DBS) is a proven therapy for patients with medication-refractory essential tremor, but a significant portion of patients with these implants do not receive adequate tremor control because of poorly placed DBS leads or inflexible DBS programming options. There is a strong and growing clinical need for implantable DBS lead designs that can enable clinicians to better sculpt electric fields in the brain. Our research study will experimentally evaluate a computational modeling approach to program a novel DBS lead with radially-segmented electrodes for improved targeting of stimulation within thalamus so as to improve the functional outcome for all patients requiring DBS to manage their essential tremor.

PROJECT SUMMARY AND ABSTRACT The basal ganglia have a rich, functional topography composed of motor subcircuits and oscillatory networks that are thought to be critically important to the pathophysiology of Parkinson's disease (PD) and the successful application of deep brain stimulation therapy (DBS) in managing each cardinal motor sign of PD. There is a strong clinical need to better understand these processes and in turn harness them to deliver therapy that is tailored to an individual patient and a patient's own symptomatology. In this project, we seek to develop a novel spatiotemporally optimized DBS therapy and evaluate its efficacy in a non-human primate model of PD. The optimization approach leverages the unique capabilities of (1) high-field MR imaging (7T and 10.5T), (2) subject-specific computational models of DBS, (3) a high-density DBS lead with electrodes arranged along and around the lead shank, and (4) a real-time signal processing interface that can readily adapt stimulation parameters on the DBS array based on analysis of ongoing oscillatory activity at and downstream of the site of stimulation. High-density DBS arrays spanning the subthalamic nucleus (STN) and thalamic fasciculus (Array A) and the external and internal globus pallidus (GP) (Array B) will be implanted in each subject. Aim 1 will characterize the magnitude and time course of therapeutic effects on each parkinsonian motor sign when targeting electrical stimulation within and around the STN and GP. Aim 2 will investigate how targeted stimulation differentially affects oscillatory activity at and downstream of the site of stimulation and relates to improvement in each parkinsonian motor sign. Aim 3 will develop and apply a novel set of optimization algorithms, including chaotic desynchronization and real-time closed-loop phasic stimulation, to test the hypothesis that optimizing suppression of exaggerated phase amplitude coupling in the STN and GP will further increase the overall magnitude of DBS therapy. Together, this project will enhance our understanding of the pathophysiology of PD and provide critical data towards translating a patient-optimized DBS therapy that integrates high-density DBS leads with novel closed-loop stimulation.

Abstract: The overall goal of project 3 of the University of Minnesota (UMN) Udall Center is to investigate why deep brain stimulation (DBS) therapy for Parkinson's disease (PD) works better in some individuals than in others and to develop methods to decrease the variability of DBS therapy for individual motor signs of PD. To deliver DBS therapy at a level consistent with the best responders, it is critical to investigate the (1) emergence of DBS- induced side effects that impede the delivery of more effective stimulation parameters, (2) logistical challenges in optimizing stimulation settings for each parkinsonian motor sign on an individual basis, and (3) multi-scale neurophysiological differences across the basal ganglia, thalamus, and brainstem that underlie the individual variability to DBS therapy. This project will leverage the well-characterized non-human primate model of PD (systemic MPTP) implanted with two scaled-down versions of the human DBS lead (subthalamic nucleus, STN and globus pallidus, GP). The approach involves a novel combination of high-field imaging (7T/10.5T, Imaging Core), computational neuron modeling of DBS, development of optimization algorithms based on quantitative behavioral assessments, multi-parameter regression analysis techniques (Biostatistics Core), and multi-scale electrophysiological analysis of DBS therapy that spans single-cell, ensemble, and whole-brain levels. Aim 1 will investigate the ability for narrow DBS pulse widths to extend the therapeutic parameter space window between alleviating parkinsonian motor signs and evoking motor side-effects. This aim will further enhance our understanding of the functional relationships between DBS parameter settings and their resultant therapeutic effect sizes and wash-in/wash-out time constants on a subject-specific, pathway-specific basis. Aim 2 will develop a novel real-time, behavior-based optimization algorithm for automatic and efficient selection of DBS parameters that minimize the expression of individual parkinsonian motor signs including rigidity, bradykinesia, akinesia, and gait/posture. Aim 3 will identify the subject-specific electrophysiological features that most closely correlate with the temporal and steady-state behavioral responses to DBS found in the first two aims. The simultaneous recordings will include local field potentials in the STN and GP as well as unit-spike recordings in three nuclei innervated by pallidofugal projection neurons (i.e. motor thalamus, centromedian-parafascicular complex of thalamus, and pedunculopontine nucleus). At the conclusion of the experiments, whole-brain transcription factor analysis for two metabolic markers (c-fos and egr-1) will be conducted through histological techniques to provide single-cell resolution for the neural pathways modulated by behaviorally-optimized DBS therapy. Together, these aims will provide critical new insight into the pathophysiological basis for the expression of each parkinsonian motor sign and which specific targeted pathways and electrophysiological features are most relevant to delivering the most effective and efficient level DBS therapy for each individual.

PROJECT SUMMARY AND ABSTRACT Existing neurotechnologies continue to make significant clinical impact, but challenges remain for scientists and engineers in moving new devices from the bench to the bedside. This grant will support the development of a comprehensive educational training program on the best practices for successfully navigating the translational and commercialization pathway for neural medical devices. The educational program will include (1) a series of publicly available video lectures that will be assembled and curated into a certificate program by a diverse group of program faculty with significant experience in moving neurotechnologies from the bench to the bedside. These short-course video lectures will cover the following topics: preclinical model systems, safety and efficacy studies, good laboratory practices, device testing, quality system processes, regulatory agency interactions, steps in developing an investigational device exemption (IDE) application, reimbursement agency interactions, clinical trial design with an emphasis on quantitative outcome measures of target engagement, bioethical considerations that are specific to neural medical devices, techniques for securing strong intellectual property claims, funding opportunities available for technology development and clinical trials, and advice on moving neurotechnology into successful commercial ventures. In addition, the educational program will provide (2) an annual three-day workshop in which participants will work with program faculty to think through and develop submissions to an institutional review board (IRB), the FDA, and/or funding groups for their own devices or devices inspired by relevant case studies. The workshop will be open to academic researchers, clinician scientists, and small-business entrepreneurs who are interested in gaining expertise that will help them translate their own neural medical devices. Participants of the workshop will be encouraged to continue interacting with program faculty through regular online follow-ups. The hands-on workshop will be offered for two years during the grant funding period with the goal of extending the workshop in future years through collaborations with our ongoing relationships with neural interfaces conferences.

The goal of this project, led by a team at Educational Testing Services, is to develop automated tools by which assessments aligned with the Next Generation Science Standards (NGSS) can be scored to reveal student reasoning patterns, some of which would reflect particular weaknesses in student reasoning. Reasoning patterns refer to various ways of student thinking when making sense of a natural phenomenon or trying to solve a problem. The investigators will conduct a proof of concept study to develop automated diagnostics that can identify middle-school students? reasoning patterns based on their written responses to assessments of their understanding of concepts in ecology. With the states increasingly moving towards implementing assessments aligned to the NGSS, feedback based on individual students? reasoning patterns would allow teachers the ability to develop more individualized feedback and would also support the design of automated instruction based on evidence of what students know and how they learn, rather than instruction based simply on whether they had answered correctly or not. The project is funded by the EHR Core Research (ECR) program, which supports work that advances the fundamental research literature on STEM learning.

The investigators will conduct a proof of concept study to identity student reasoning patterns for making sense of ecosystems by investigating student responses to constructed test items collected from an NGSS-aligned assessment database. In the first stage of the study, the investigators will use a 3-dimensional (content knowledge, procedural knowledge, and epistemic knowledge) approach to assessment that aligns with the NGSS. They will leverage cutting-edge natural language processing (NLP) techniques to identify student reasoning patterns, attempting to label text as data description and system relationship description and attempting to identify superficial integration as opposed to integrated reasoning. The investigators will engage in an iterative process to compare the classification produced by the automated tools with that produced by human scorers. With this classification developed and validated, the investigators will have demonstrated the feasibility of later attempting to develop an NLP-based automated system that could provide immediate feedback to students, identifying weaknesses in reasoning rather than only whether an answer was correct, and to teachers, allowing them to tailor instruction to individual students.

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.