Motion artifacts are often a significant component of the measured signal in functional near-infrared spectroscopy (fNIRS) experiments. A variety of methods have been proposed to address this issue, including principal component analyses (PCA), Kalman filtering, correlation-based signal improvement (CBSI), wavelet filtering, spline interpolation, and autoregressive algorithms. The efficacy of these techniques has been compared using simulated data; however, our understanding of how these techniques fare when dealing with task-based cognitive data is limited. Recently, Brigadoi et al. (2014) quantitatively compared 6 motion correction techniques in a sample of adult data measured during a simple cognitive task. Wavelet filtering showed the most promise as an optimal technique for motion correction. Because fNIRS is often used with infants and young children, it is critical to evaluate the effectiveness of motion correction techniques directly with data from these age groups. Here we examined which techniques are most effective with data from young children. The efficacy of each technique was compared quantitatively using objective metrics related to the physiological properties of the hemodynamic response using two different sets of parameters to ensure maximum retention of included trials. Results showed that targeted PCA (tPCA) and CBSI retained a higher number of trials. These techniques also performed well in direct head-to-head comparisons with the other approaches using both quantitative metrics and a qualitative assessment. The CBSI technique corrected many of the artifacts present in our data; however, this technique was highly influenced by the parameters used to detect motion. The tPCA technique, by contrast, was robust across changes in parameters while also performing well across all comparison metrics. We conclude, therefore, that tPCA is an effective technique for the correction of motion artifacts in fNIRS data from young children.

Thesis: M.S., Massachusetts Institute of Technology, Department of Physics, 1976 / Includes bibliographical references. / M.S. / M.S. Massachusetts Institute of Technology, Department of Physics

This dissertation reports on infrared optical spectroscopic studies of a novel class of materials named Dirac materials, which cover a broad range of materials including Topological Insulators (TI) and Dirac/Nodal-line semimetals. These materials share a similar low-energy Hamiltonian that can be described by massless/massive Dirac fermions. Adding out-of-plane magnetic field generates additional features in the optical spectra that allow us to distinguish Dirac fermions with usual fermions with parabolic bands. I will first demonstrate identifications of surface states (SS) of TI using Faraday rotation spectroscopy, where both the top and bottom SS can be identified and found to host carriers of opposite sign. Secondly, I will generalize the power-law behavior for two-dimensional (2D) and three-dimensional (3D) Dirac semimetals to dispersive nodal-line semimetals. This leads to the discoveries of Dirac nodal-lines in topological semimetal NbAs2. Finally, the optical signatures of electronic correlations are discussed and the unexplored overlapping area between strongly correlated systems and Dirac semimetals are studied. The prominent correlation effects in nodal-line semimetal ZrSiSe uncovered by a combination of optical and magneto-optical spectroscopies will be discussed.

The functional and pathogenic roles of biomolecules are often coupled to the self-association of their basic units into oligomers and aggregates whose structural details are difficult to distinguish because of their insoluble and heterogenous nature. This work focuses on DNA G-quadruplex motifs and amyloid peptides whose oligomers and aggregates are associated with numerous biological roles and human diseases. Infrared (IR) spectroscopy is a powerful tool which probes vibrational transitions whose signatures report on their arrangement within molecules. Advances in two-dimensional infrared (2D IR) spectroscopy have allowed structural characterization in increasingly complex biomolecules that are not amenable to traditional high-resolution techniques. However, careful consideration of the physical phenomena that lead to IR spectra are necessary to make accurate assignments. In the first portion of this work, using FTIR and 2D IR, we determine spectral markers that can differentiate size, metal ion coordination, and topology in DNA G-quadruplex motifs. IR studies aided by isotope labeling define the physical origin of these markers and allow for the construction of a structural landscape in parallel DNA G-quadruplex motifs. It is also shown that 2D IR and isotope editing probes site-specific structural changes in G-quadruplex motifs that can differentiate ion identity and location based on spectral shifts. In the latter portion of this work, we use a combination of spectroscopy and imaging techniques to show that a peptide derived from the human pro-apoptotic protein BAX forms amyloid aggregates whose structure is dependent on the presence of model membranes. Combined, the work in this thesis allows for the formulation of multiple hypotheses based on IR structural assignments regarding disease states and functional mechanisms of these systems.

The use of near infrared (NIR) spectroscopy for predicting levels of degradation in laboratory soil block tests was investigated. Calibrations were developed for mass loss, compression strength, and exposure period using data measured from the prior methods, and untreated and mathematically treated (multiplicative scatter correction and first and second derivative) NIR spectra from various spans of wavelengths by partial least squares regression. Strong correlations were obtained from each study conducted, while calibrations developed from NIR spectra from the cross-sectional face of southern yellow pine presented the strongest predictions. Of them, calibrations for mass loss resulted in the strongest predictions. Calibrations constructed from spectra obtained from the radial face of southern yellow pine also produced strong predictions, where the strongest model was for exposure period. While, calibrations developed for cottonwood presented the weakest statistics, the strongest calibration found was for exposure period.

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