Daisy Wang

© 2015 Wiley Periodicals, Inc. One of the major findings from multimodal neuroimaging studies in the past decade is that the human brain is anatomically and functionally organized into large-scale networks. In resting state fMRI, spatial patterns emerge when temporal correlations between various brain regions are tallied, evidencing networks of ongoing intercortical cooperation. However, the dynamic structure governing the brain's spontaneous activity is far less understood due to the short and noisy nature of the rs-fMRI signal. Here, we develop a wavelet-based regularity analysis based on noise estimation capabilities of the wavelet transform to measure recurrent temporal pattern stability within the rs-fMRI signal across multiple temporal scales. The method consists of performing a stationary wavelet transform to preserve signal structure, followed by construction of "lagged" subsequences to adjust for correlated features, and finally the calculation of sample entropy across wavelet scales based on an "objective" estimate of noise level at each scale. We found that the brain's default mode network areas manifest a higher level of irregularity in rs-fMRI time series than rest of the brain. In 25 aged subjects with mild cognitive impairment and 25 matched healthy controls, wavelet-based regularity analysis showed improved sensitivity in detecting changes in the regularity of rs-fMRI signals between the two groups within the DMN and executive control networks, compared with standard multiscale entropy analysis. Wavelet-based regularity analysis based on noise estimation capabilities of the wavelet transform is a promising technique to characterize the dynamic structure of rs-fMRI as well as other biological signals.

Purpose To investigate the reliability and accuracy of two pseudo-continuous arterial spin labeling sequences, using two-dimensional gradient-echo echo planar imaging and 3D gradient and spin echo as the readout, respectively. Materials and Methods Each sequence was performed twice 4 weeks apart on six normal control subjects, six elderly subjects with mild cognitive impairment, and one participant with Alzheimer's disease. Eight of these subjects also underwent H 215O positron emission tomography scans on the same day or proximal to their second MRI scan. The longitudinal repeatability of EPI and GRASE pCASL were evaluated with the intraclass correlation coefficient and within-subject coefficient of variation. Results The ICCs of global perfusion measurements were 0.697 and 0.413 for GRASE and EPI based pCASL respectively. GRASE pCASL also demonstrated a higher longitudinal repeatability for regional perfusion measurements across 24 regions-of-interests compared with EPI pCASL. When compared with PET, EPI pCASL showed a higher degree of spatial correlation with PET than GRASE pCASL, although the difference was not statistically significant. Conclusion The 3D GRASE pCASL offers better repeatability than 2D EPI pCASL, thereby may provide a reliable imaging marker for the evaluation of disease progression and treatment effects in MCI and early AD subjects. © 2013 Wiley Periodicals, Inc.

A large number of cross-references to various bodies of text are used in legal texts, each serving a different purpose. It is often necessary for authorities and companies to look into certain types of these citations. Yet, there is a lack of automatic tools to aid in this process. Recently, citation graphs have been used to improve the intelligibility of complex rule frameworks. We propose an algorithm that builds the citation graph from a document and automatically labels each edge according to its purpose. Our method uses the citing text only and thus works only on citations who’s purpose can be uniquely identified by their surrounding text. This framework is then applied to the US code. This paper includes defining and evaluating a standard gold set of labels that cover a vast majority of citation types which appear in the “US Code” but are still short enough for practical use. We also proposed a novel linear-chain conditional random field model that extracts the features required for labeling the citations from the surrounding text. We then analyzed the effectiveness of different clustering methods such as K-means and support vector machine to automatically label each citation with the corresponding label. Besides this, we talk about the practical difficulties of this task and give a comparison of human accuracy compared to our end-to-end algorithm.