Midlatitude daytime D region ionosphere variations measured from radio atmospherics

We measured the midlatitude daytime ionospheric D region electron density profile height variations in July and August 2005 near Duke University by using radio atmospherics (or sferics for short), which are the high-power, broadband very low frequency (VLF) signals launched by lightning discharges. As expected, the measured daytime D region electron density profile heights showed temporal variations quantitatively correlated with solar zenith angle changes. In the midlatitude geographical regions near Duke University, the observed quiet time heights decreased from ∼80 km near sunrise to ∼71 km near noon when the solar zenith angle was minimum. The measured height quantitative dependence on the solar zenith angle was slightly different from the low-latitude measurement given in a previous work. We also observed unexpected spatial variations not linked to the solar zenith angle on some days, with 15% of days exhibiting regional differences larger than 0.5 km. In these 2 months, 14 days had sudden height drops caused by solar flare X-rays, with a minimum height of 63.4 km observed. The induced height change during a solar flare event was approximately proportional to the logarithm of the X-ray flux. In the long waveband (wavelength, 1-8 Å), an increase in flux by a factor of 10 resulted in 6.3 km decrease of the height at the flux peak time, nearly a perfect agreement with the previous measurement. During the rising and decaying phases of the solar flare, the height changes correlated more consistently with the short, rather than the long, wavelength X-ray flux changes. © 2010 by the American Geophysical Union.

Assessment of the Spatial and Temporal Distribution of Functional Connectivity in Resting-State BOLD fMRI

Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate the spatial and temporal functional network information found within resting-state data, and aims to investigate the feasibility of extracting functional connectivity networks using different methods as well as the dynamic variability within some of the methods. Furthermore, this work looks into producing valid networks using a sparsely-sampled sub-set of the original data.

In this work we utilize four main methods: independent component analysis (ICA), principal component analysis (PCA), correlation, and a point-processing technique. Each method comes with unique assumptions, as well as strengths and limitations into exploring how the resting state components interact in space and time.

Correlation is perhaps the simplest technique. Using this technique, resting-state patterns can be identified based on how similar the time profile is to a seed region’s time profile. However, this method requires a seed region and can only identify one resting state network at a time. This simple correlation technique is able to reproduce the resting state network using subject data from one subject’s scan session as well as with 16 subjects.

Independent component analysis, the second technique, has established software programs that can be used to implement this technique. ICA can extract multiple components from a data set in a single analysis. The disadvantage is that the resting state networks it produces are all independent of each other, making the assumption that the spatial pattern of functional connectivity is the same across all the time points. ICA is successfully able to reproduce resting state connectivity patterns for both one subject and a 16 subject concatenated data set.

Using principal component analysis, the dimensionality of the data is compressed to find the directions in which the variance of the data is most significant. This method utilizes the same basic matrix math as ICA with a few important differences that will be outlined later in this text. Using this method, sometimes different functional connectivity patterns are identifiable but with a large amount of noise and variability.

To begin to investigate the dynamics of the functional connectivity, the correlation technique is used to compare the first and second halves of a scan session. Minor differences are discernable between the correlation results of the scan session halves. Further, a sliding window technique is implemented to study the correlation coefficients through different sizes of correlation windows throughout time. From this technique it is apparent that the correlation level with the seed region is not static throughout the scan length.

The last method introduced, a point processing method, is one of the more novel techniques because it does not require analysis of the continuous time points. Here, network information is extracted based on brief occurrences of high or low amplitude signals within a seed region. Because point processing utilizes less time points from the data, the statistical power of the results is lower. There are also larger variations in DMN patterns between subjects. In addition to boosted computational efficiency, the benefit of using a point-process method is that the patterns produced for different seed regions do not have to be independent of one another.

This work compares four unique methods of identifying functional connectivity patterns. ICA is a technique that is currently used by many scientists studying functional connectivity patterns. The PCA technique is not optimal for the level of noise and the distribution of the data sets. The correlation technique is simple and obtains good results, however a seed region is needed and the method assumes that the DMN regions is correlated throughout the entire scan. Looking at the more dynamic aspects of correlation changing patterns of correlation were evident. The last point-processing method produces a promising results of identifying functional connectivity networks using only low and high amplitude BOLD signals.