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.

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.

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.

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.