Publications (2)5.53 Total impact
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Article: Strength of default mode resting-state connectivity relates to white matter integrity in children.
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ABSTRACT: A default mode network of brain regions is known to demonstrate coordinated activity during the resting state. While the default mode network is well characterized in adults, few investigations have focused upon its development. We scanned 9-13-year-old children with diffusion tensor imaging and resting-state functional magnetic resonance imaging. We identified resting-state networks using Independent Component Analysis and tested whether the functional connectivity between the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) depends upon the maturation of the underlying cingulum white matter tract. To determine the generalizability of this relationship, we also tested whether functional connectivity depends on white matter maturity between bilateral lateral prefrontal cortex (lateral PFC) within the executive control network. We found a positive relationship between mPFC-PCC connectivity and fractional anisotropy of the cingulum bundle; this positive relationship was moderated by the age of the subjects such that it was stronger in older children. By contrast, no such structure-function relationship emerged between right and left lateral PFC. However, functional and structural connectivity of this tract related positively with cognitive speed, fluency, and set-switching neuropsychological measures.Developmental Science 07/2011; 14(4):738-51. · 3.89 Impact Factor -
Article: Document classification for mining host pathogen protein-protein interactions.
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ABSTRACT: Scientific findings regarding human pathogens and their host responses are buried in the growing volume of biomedical literature and there is an urgent need to mine information pertaining to pathogenesis-related proteins especially host pathogen protein-protein interactions (HP-PPIs) from literature. In this paper, we report our exploration of developing an automated system to identify MEDLINE abstracts referring to HP-PPIs. An annotated corpus consisting of 1360 MEDLINE abstracts was generated. With this corpus, we developed and evaluated document classification systems using support vector machines (SVMs). We also investigated the effects of three feature selection methods:information gain (IG), chi(2) test, and specific mutual information (SI). The performance was measured using normalized discounted cumulative gain (NDCG) and positive predictive value (PPV) and all measures were obtained through 10-fold cross validation. NDCG measures for classification systems using all features or a subset of features selected using IG and chi(2) test range from 0.83 to 0.89 while classification systems built based on features selected using SI had relatively lower NDCG measures. The classification system achieved a PPV of 50.7% for the top 10% ranked documents comparing to a baseline PPV of 10.0%. Our results indicate that document classification systems can be constructed to efficiently retrieve HP-PPI related documents. Feature selection was effective in reducing the dimensionality of features to build a compact system.Artificial intelligence in medicine 07/2010; 49(3):155-60. · 1.65 Impact Factor
