Decoding visual information from high-density diffuse optical tomography neuroimaging data

BACKGROUND: Neural decoding could be useful in many ways, from serving as a neuroscience research tool to providing a means of augmented communication for patients with neurological conditions. However, applications of decoding are currently constrained by the limitations of traditional neuroimaging modalities. Electrocorticography requires invasive neurosurgery, magnetic resonance imaging (MRI) is too cumbersome for uses like daily communication, and alternatives like functional near-infrared spectroscopy (fNIRS) offer poor image quality. High-density diffuse optical tomography (HD-DOT) is an emerging modality that uses denser optode arrays than fNIRS to combine logistical advantages of optical neuroimaging with enhanced image quality. Despite the resulting promise of HD-DOT for facilitating field applications of neuroimaging, decoding of brain activity as measured by HD-DOT has yet to be evaluated. OBJECTIVE: To assess the feasibility and performance of decoding with HD-DOT in visual cortex. METHODS AND RESULTS: To establish the feasibility of decoding at the single-trial level with HD-DOT, a template matching strategy was used to decode visual stimulus position. A receiver operating characteristic (ROC) analysis was used to quantify the sensitivity, specificity, and reproducibility of binary visual decoding. Mean areas under the curve (AUCs) greater than 0.97 across 10 imaging sessions in a highly sampled participant were observed. ROC analyses of decoding across 5 participants established both reproducibility in multiple individuals and the feasibility of inter-individual decoding (mean AUCs \u3e 0.7), although decoding performance varied between individuals. Phase-encoded checkerboard stimuli were used to assess more complex, non-binary decoding with HD-DOT. Across 3 highly sampled participants, the phase of a 60° wide checkerboard wedge rotating 10° per second through 360° was decoded with a within-participant error of 25.8±24.7°. Decoding between participants was also feasible based on permutation-based significance testing. CONCLUSIONS: Visual stimulus information can be decoded accurately, reproducibly, and across a range of detail (for both binary and non-binary outcomes) at the single-trial level (without needing to block-average test data) using HD-DOT data. These results lay the foundation for future studies of more complex decoding with HD-DOT and applications in clinical populations

Development of Software for Characterization of Local Atomic Structures in Amorphous Metals Via Weighted Voronoi Diagrams

Mentor: Kenneth F. Kelton From the Washington University Undergraduate Research Digest: WUURD, Volume 8, Issue 1, Fall 2012. Published by the Office of Undergraduate Research, Joy Zalis Kiefer Director of Undergraduate Research and Assistant Dean in the College of Arts & Sciences

Structural Characterization of Amorphous Cu46Zr54 using Molecular Dynamics Simulations, Reverse-Monte Carlo Simulations, and Weighted Voronoi Diagrams

Mentor: Kenneth Kelton From the Washington University Undergraduate Research Digest: WUURD, Volume 9, Issue 1, Fall 2013. Published by the Office of Undergraduate Research. Joy Zalis Kiefer Director of Undergraduate Research and Assistant Dean in the College of Arts & Sciences

Structural Characterization of Amorphous Cu46Zr54 Using Molecular Dynamics Simulations, Reverse-Monte Carlo Simulations, and Weighted Voronoi Diagrams

From the Washington University Senior Honors Thesis Abstracts (WUSHTA), Volume 6, Spring 2014. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Sciences; Jane Green and Stacy Ross, Editors; Kristin G. Sobotka, Undergraduate Research Coordinator. Mentor: Kenneth F. Kelto

Dissociative conceptual and quantitative problem solving outcomes across interactive engagement and traditional format introductory physics

The existing literature indicates that interactive-engagement (IE) based general physics classes improve conceptual learning relative to more traditional lecture-oriented classrooms. Very little research, however, has examined quantitative problem-solving outcomes from IE based relative to traditional lecture-based physics classes. The present study included both pre- and post-course conceptual-learning assessments and a new quantitative physics problem-solving assessment that included three representative conservation of energy problems from a first-semester calculus-based college physics course. Scores for problem translation, plan coherence, solution execution, and evaluation of solution plausibility were extracted for each problem. Over 450 students in three IE-based sections and two traditional lecture sections taught at the same university during the same semester participated. As expected, the IE-based course produced more robust gains on a Force Concept Inventory than did the lecture course. By contrast, when the full sample was considered, gains in quantitative problem solving were significantly greater for lecture than IE-based physics; when students were matched on pre-test scores, there was still no advantage for IE-based physics on gains in quantitative problem solving. Further, the association between performance on the concept inventory and quantitative problem solving was minimal. These results highlight that improved conceptual understanding does not necessarily support improved quantitative physics problem solving, and that the instructional method appears to have less bearing on gains in quantitative problem solving than does the kinds of problems emphasized in the courses and homework and the overlap of these problems to those on the assessment