Functional near-infrared spectroscopy (fNIRS) research to date has tended to publish group-averaged rather than individual infant data due to normative basic research goals. Acquisition of individual infant time courses holds interest, however, both for cognitive science and particularly for clinical applications. Infants are more difficult to study than adults as they cannot be instructed to remain still. In addressing this, upright infants pose several associated complications for the researcher. We identified and optimized the factors that affect the quality of fNIRS data from individual 6- to 9-month-old infants exposed to a visual stimulation paradigm. The fNIRS headpiece was reconfigured to reduce inertia, increase comfort, and improve conformity to the head, while preserving fiber density to avoid missing the visual cortex activation. The visual-stimulation protocol was modified to keep the attention of infants throughout the measurement, thus helping to reduce motion artifacts. Adequate optical contact was verified by checking power levels before each measurement. By revising our experimental process and our data rejection criteria to prioritize good optical contact, we report for the first time usable hemodynamic data from 83% of infants and that two-thirds of infants produced a statistically significant fNIRS response.
The goal of this project is to estimate non-nuclear organelle size distributions in single cells by measuring angular scattering patterns and fitting them with Mie theory. Simulations have indicated that the large relative size distribution of organelles (mean:width≈2) leads to unstable Mie fits unless scattering is collected at polar angles less than 20 degrees. Our optical system has therefore been modified to collect angles down to 10 degrees. Initial validations will be performed on polystyrene bead populations whose size distributions resemble those of cell organelles. Unlike with the narrow bead distributions that are often used for calibration, we expect to see an order-of-magnitude improvement in the stability of the size estimates as the minimum angle decreases from 20 to 10 degrees. Scattering patterns will then be acquired and analyzed from single cells (EMT6 mouse cancer cells), both fixed and live, at multiple time points. Fixed cells, with no changes in organelle sizes over time, will be measured to determine the fluctuation level in estimated size distribution due to measurement imperfections alone. Subsequent measurements on live cells will determine whether there is a higher level of fluctuation that could be attributed to dynamic changes in organelle size. Studies on unperturbed cells are precursors to ones in which the effects of exogenous agents are monitored over time.
The literature contains several reports of Mie-like fits to angular-domain elastic scattering measurements from multiple cells or isolated mitochondria. In these studies, the sampling volume typically contains hundreds or thousands of mitochondria, allowing for the size distribution of mitochondria to be modeled as a smooth function, (e.g. Gaussian or log-normal) with a small number of free parameters. In the case of a single-cell volume containing significantly fewer mitochondria, the true size distribution will no longer be as smooth. Increasing the number of free parameters can lead to unstable fits, however, as the forward-directed angular scattering pattern from such a population illuminated with 785 nm light is a monotonically decaying radial function with few distinct features. Using simulations, we have investigated the limitations of modeling single-cell mitochondrial scattering using smooth population distributions of Mie scatterers. In different instances, the fidelity of the estimated size information can be limited by the number of organelles, the angular detection range, or the non-ideality of the data (both speckle and shot noise). We will describe the conditions under which each of these effects dominates. We will also discuss whether mean and standard deviation are the best sizes to report from such Mie modeling, or if there are other size parameters that have greater fidelity to the true, non-smooth size distributions.