Changepoint analysis of putamen and thalamus subregions in premanifest huntington's disease

Tan, Xiaoqing; Ross, Christopher A.; Miller, M.; Tang, Xiaoying

doi:10.1109/isbi.2018.8363632

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Although use of these novel data streams create new challenges due to limitations such as high noise level, high volume, and selection bias, many recent efforts have explored social media data as a complementary source to traditional health care data and applied topic models to understand public concerns toward COVID-19 (Doogan et al, 2020;Stokes et al, 2020;Boon-Itt and Skunkan, 2020;Xue et al, 2020;Jang et al, 2020;Du et al, 2017;Liu et al, 2019;Tan et al, 2022a,d), as well as related socioeconomic issues (Su et al, 2021;Sha et al, 2020;Liu et al, 2020;Tan et al, 2018). Here, we extract information from Twitter, a particularly popular social media platform, and focus on studying its spatiotemporal behaviors that are believed to be affected by COVID-19.…”

Section: Application To Twitter Datamentioning

confidence: 99%

Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes

Wang¹

2023

Preprint

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Columbia in Canada. My research career started with Jim and Nhu, who are both brilliant researchers and caring mentors. Jim has always been a role model to me, and without him, I would not have gone this far in this journey. To all my friends that I made and all the people that I met throughout the Ph.D. studies: This journey would not have been so rewarding without you! Shout out to everyone in our research labs, especially Byoung and Zeyu from the Hero Group that I was fortunate to collaborate with; Leo who organized those fun board games; and Chengcheng, Cheng, Yangyi, and Ziping in my cohort. It was a great fun to spend time with you, and I have learned a lot from our interactions. Last but not least, I would like to thank my parents, Xiaoqing Tan the duck, Kitty & Bunny the cats, and Larry the chinchilla for their unwavering support and unconditioned love.iii

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Section: Application To Twitter Datamentioning

confidence: 99%

Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes

Wang¹

2023

Preprint

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“…There is a large range of practical problems for which Kronecker covariance decomposition models and our software have application. Some applications where such models have been applied include: longitudinal gene expression [36], EEG signal analysis [13,36,37], spatial imaging [38], radar imaging [39,40], radar target tracking [41], and analysis of healthcare data [42]. It is hoped that the computationally efficient and accessible Julia notebooks provided with this paper will help further expand the impact on applications of these powerful techniques.…”

Section: Ongoing Research and Further Developmentmentioning

confidence: 99%

TensorGraphicalModels: A Julia toolbox for multiway covariance models and ensemble Kalman filter

2022

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“…The central hypothesis of this study is that a more comprehensive shape analysis on specific cortical features may uncover more Huntington's disease‐caused cortical alterations than previously known. Other shape analyses often assess general atrophy, analyzing whole‐brain segmentation (Wu et al, 2018 ), the subcortical structures (Tan et al, 2018 ; Tang et al, 2019 ), or the white matter tracts connecting subcortical structures to cortical areas corresponding to certain functions (Hong et al, 2018 ). In contrast, we focus only on the cortical surface, and we estimate cortical changes using three complementary cortical shape features—cortical thickness, sulcal depth, and local gyrification index—to discover significant differences in affected cortical regions.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive shape analysis of the cortex in Huntington's disease

Stoebner

Hett

Lyu

et al. 2022

Human Brain Mapping

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The striatum has traditionally been the focus of Huntington's disease research due to the primary insult to this region and its central role in motor symptoms. Beyond the striatum, evidence of cortical alterations caused by Huntington's disease has surfaced. However, findings are not coherent between studies which have used cortical thickness for Huntington's disease since it is the well-established cortical metric of interest in other diseases. In this study, we propose a more comprehensive approach to cortical morphology in Huntington's disease using cortical thickness, sulcal depth, and local gyrification index. Our results show consistency with prior findings in cortical thickness, including its limitations. Our comparison between cortical thickness and local gyrification index underscores the complementary nature of these two measures-cortical thickness detects changes in the sensorimotor and posterior areas while local gyrification index identifies insular differences. Since local gyrification index and cortical thickness measures detect changes in different regions, the two used in tandem could provide a clinically relevant measure of disease progression.Our findings suggest that differences in insular regions may correspond to earlier neurodegeneration and may provide a complementary cortical measure for detection of subtle early cortical changes due to Huntington's disease.

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Changepoint analysis of putamen and thalamus subregions in premanifest huntington's disease

Cited by 4 publications

References 26 publications

Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes

Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes

TensorGraphicalModels: A Julia toolbox for multiway covariance models and ensemble Kalman filter

Comprehensive shape analysis of the cortex in Huntington's disease

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