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Investigation of the relationship of vocal, eye-tracking, and fMRI ROI time-series measures with preclinical mild traumatic brain injury*

Summary

In this work, we are examining correlations between vocal articulatory features, ocular smooth pursuit measures, and features from the fMRI BOLD response in regions of interest (ROI) time series in a high school athlete population susceptible to repeated head impact within a sports season. Initial results have indicated relationships between vocal features and brain ROIs that may show which components of the neural speech networks effected are effected by preclinical mild traumatic brain injury (mTBI). The data used for this study was collected by Purdue University on 32 high school athletes over the entirety of a sports season (Helfer, et al., 2014), and includes fMRI measurements made pre-season, in-season, and postseason. The athletes are 25 male football players and 7 female soccer players. The Immediate Post-Concussion Assessment and Cognitive Testing suite (ImPACT) was used as a means of assessing cognitive performance (Broglio, Ferrara, Macciocchi, Baumgartner, & Elliott, 2007). The test is made up of six sections, which measure verbal memory, visual memory, visual motor speed, reaction time, impulse control, and a total symptom composite. Using each test, a threshold is set for a change in cognitive performance. The threshold for each test is defined as a decline from baseline that exceeds one standard deviation, where the standard deviation is computed over the change from baseline across all subjects’ test scores. Speech features were extracted from audio recordings of the Grandfather Passage, which provides a standardized and phonetically balanced sample of speech. Oculomotor testing included two experimental conditions. In the smooth pursuit condition, a single target moving circularly, at constant speed. In the saccade condition, a target was jumped between one of three location along the horizontal midline of the screen. In both trial types, subjects visually tracked the targets during the trials, which lasted for one minute. The fMRI features are derived from the bold time-series data from resting state fMRI scans of the subjects. The pre-processing of the resting state fMRI and accompanying structural MRI data (for Atlas registration) was performed with the toolkit CONN (Whitfield-Gabrieli & Nieto-Castanon, 2012). Functional connectivity was generated using cortical and sub-cortical atlas registrations. This investigation will explores correlations between these three modalities and a cognitive performance assessment.
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Summary

In this work, we are examining correlations between vocal articulatory features, ocular smooth pursuit measures, and features from the fMRI BOLD response in regions of interest (ROI) time series in a high school athlete population susceptible to repeated head impact within a sports season. Initial results have indicated relationships between...

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Flexible glucose sensors and fuel cells for bioelectronic implants

Published in:
IEEE 60th Int. Midwest Symp. on Circuits and Systems, MWSCAS, 6-9 August 2017.

Summary

Microfabrication techniques were developed to create flexible 24 um thick glucose sensors on polyimide substrates. Measurements of the sensor performance, recorded as voltage potential, were carried out for a range of glucose concentrations (0 – 8 mM) in physiological saline (0.1 M NaCl, pH 7.4). The sensors show rapid response times (seconds to stable potential) and good sensitivity in the 0 – 4 mM range. Additionally, we demonstrate that the sensors can operate as fuel cells, generating peak power levels up to 0.94 uW/cm2. Such flexible devices, which can be rolled up to increase surface area within a fixed volume, may enable ultra-low-power bio-electronic implants for glucose sensing or glucose energy harvesting in the future.
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Summary

Microfabrication techniques were developed to create flexible 24 um thick glucose sensors on polyimide substrates. Measurements of the sensor performance, recorded as voltage potential, were carried out for a range of glucose concentrations (0 – 8 mM) in physiological saline (0.1 M NaCl, pH 7.4). The sensors show rapid response...

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FY11 Line-Supported Bio-Next Program - Multi-modal Early Detection Interactive Classifier (MEDIC) for mild traumatic brain injury (mTBI) triage

Summary

The Multi-modal Early Detection Interactive Classifier (MEDIC) is a triage system designed to enable rapid assessment of mild traumatic brain injury (mTBI) when access to expert diagnosis is limited as in a battlefield setting. MEDIC is based on supervised classification that requires three fundamental components to function correctly; these are data, features, and truth. The MEDIC system can act as a data collection device in addition to being an assessment tool. Therefore, it enables a solution to one of the fundamental challenges in understanding mTBI: the lack of useful data. The vision of MEDIC is to fuse results from stimulus tests in each of four modalitites - auditory, occular, vocal, and intracranial pressure - and provide them to a classifier. With appropriate data for training, the MEDIC classifier is expected to provide an immediate decision of whether the subject has a strong likelihood of having sustained an mTBI and therefore requires an expert diagnosis from a neurologist. The tests within each modalitity were designed to balance the capacity of objective assessment and the maturity of the underlying technology against the ability to distinguish injured from non-injured subjects according to published results. Selection of existing modalities and underlying features represents the best available, low cost, portable technology with a reasonable chance of success.
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Summary

The Multi-modal Early Detection Interactive Classifier (MEDIC) is a triage system designed to enable rapid assessment of mild traumatic brain injury (mTBI) when access to expert diagnosis is limited as in a battlefield setting. MEDIC is based on supervised classification that requires three fundamental components to function correctly; these are...

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