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\@writefile{toc}{\contentsline {section}{\numberline {1}Gray Matter Dilation}{1}}
\@writefile{toc}{\contentsline {section}{\numberline {2}Calculation of Connected Surface Area (CSA) Feature}{1}}
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\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Illustration of gray matter dilation. \relax }}{2}}
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\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Illustration of calculating the CSA for a given ROI. \relax }}{3}}
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\@writefile{loa}{\contentsline {algocf}{\numberline {2}{\ignorespaces Extraction of CSA feature \relax }}{3}}
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\@writefile{toc}{\contentsline {section}{\numberline {3}Different Distance Metrics for Comparing Weighted Networks. }{4}}
\@writefile{toc}{\contentsline {section}{\numberline {4}More Results for the Compression Procedure }{4}}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.1}Simulation study for representation efficiency of PTCS}{4}}
\citation{anuj2011}
\citation{Huang2016}
\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Comparison of reproducibility (dICC score) of different distances. The highest dICC score calculated under each distance is bold. \relax }}{5}}
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\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces The $200$ simulated streamlines. \relax }}{6}}
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\@writefile{toc}{\contentsline {subsection}{\numberline {4.2}Robustness of the PTCS}{6}}
\citation{satterthwaite2016philadelphia}
\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces Comparison of the representation efficiency at different scenarios of separating shape confounding parameters. \relax }}{7}}
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\citation{chang2015human}
\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces Evaluation of the proposed compression method with one PNC subject. The basis functions were learned using the HCP subjects. Here we directly applied the learned basis to represent the streamlines extracted from one PNC subject. Similar compression power is observed comparing with the HCP result.\relax }}{9}}
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\@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces Compression ratios (in percentage) of tractography generated from data in three different sites. Standard deviation is shown in the subscript parenthesis. \relax }}{9}}
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\@writefile{lot}{\contentsline {table}{\numberline {3}{\ignorespaces Mean diffusion integrity changes ($\%$) after compression in each connection \relax }}{10}}
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\@writefile{toc}{\contentsline {subsection}{\numberline {4.3} Impact of Diffusion Measures Along Bundles After Compression}{10}}
\@writefile{toc}{\contentsline {section}{\numberline {5} Heritability of Weighted Structural Network}{10}}
\@writefile{lot}{\contentsline {table}{\numberline {4}{\ignorespaces Selected connections with heritability larger than 0.8. The p-values are adjusted by Bonferroni correction. $h^2$ is referred as the heritability score. This analysis used mean FA weight matrix (under the Desikan-Killiany parcellation) as the phenotype of interest. \relax }}{11}}
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\bibdata{paper,Brain_2015new_DVW}
\bibcite{chang2015human}{{1}{2015}{{Chang et~al.}}{{Chang, Sundman, Petit, Guhaniyogi, Chu, Petty, Song and Chen}}}
\bibcite{Huang2016}{{2}{2016}{{Huang et~al.}}{{Huang, Gallivan, Srivastava and Absil}}}
\bibcite{satterthwaite2016philadelphia}{{3}{2016}{{Satterthwaite et~al.}}{{Satterthwaite, Connolly, Ruparel, Calkins, Jackson, Elliott, Roalf, Hopson, Prabhakaran, Behr et~al.}}}
\bibcite{anuj2011}{{4}{2011}{{Srivastava et~al.}}{{Srivastava, Klassen, Joshi and Jermyn}}}
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