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Denoising Variational Autoencoder as a Feature Reduction Pipeline for the diagnosis of Autism based on Resting-state fMRI

Autor: Zheng, Xinyuan, Ravid, Orren, Barry, Robert A. J., Kim, Yoojean, Wang, Qian, Kim, Young-geun, Zhu, Xi, He, Xiaofu

Autism spectrum disorders (ASDs) are developmental conditions characterized by restricted interests and difficulties in communication. The complexity of ASD has resulted in a deficiency of objective diagnostic biomarkers. Deep learning methods have g

Externí odkaz: http://arxiv.org/abs/2410.00068

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Akademický článek

Response to Replication Stress and Maintenance of Genome Stability by WRN, the Werner Syndrome Protein.

Autor: Orren, David K.^1,2 (AUTHOR) amach0@uky.edu, Machwe, Amrita^1,2 (AUTHOR)

Publikováno v: International Journal of Molecular Sciences. Aug2024, Vol. 25 Issue 15, p8300. 27p.

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Akademický článek

Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder

Autor: Young-geun Kim, Orren Ravid, Xinyuan Zheng, Yoojean Kim, Yuval Neria, Seonjoo Lee, Xiaofu He, Xi Zhu

Publikováno v: Frontiers in Psychiatry, Vol 15 (2024)

BackgroundResting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used extensively to study brain function in psychiatric disorders, yielding insights into brain organization. However, the high dimensionality of the rs-fMRI data p

Externí odkaz: https://doaj.org/article/94bd2518f0f845009992587a38163d70

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Akademický článek

Cigarette Smoke-Induced Epithelial-to-Mesenchymal Transition: Insights into Cellular Mechanisms and Signaling Pathways

Autor: Sarah Mohammed Alqithami, Amrita Machwe, David K. Orren

Publikováno v: Cells, Vol 13, Iss 17, p 1453 (2024)

This review delves into the molecular complexities underpinning the epithelial-to-mesenchymal transition (EMT) induced by cigarette smoke (CS) in human bronchial epithelial cells (HBECs). The complex interplay of pathways, including those related to

Externí odkaz: https://doaj.org/article/c56c9477fa734239bce183d3827ee32f

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Akademický článek

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Akademický článek

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Autor: Zhu, Xi, Kim, Yoojean, Ravid, Orren, He, Xiaofu, Suarez-Jimenez, Benjamin, Zilcha-Mano, Sigal, Lazarov, Amit, Lee, Seonjoo, Abdallah, Chadi G., Angstadt, Michael, Averill, Christopher L., Baird, C. Lexi, Baugh, Lee A., Blackford, Jennifer U., Bomyea, Jessica, Bruce, Steven E., Bryant, Richard A., Cao, Zhihong, Choi, Kyle, Cisler, Josh, Cotton, Andrew S., Daniels, Judith K., Davenport, Nicholas D., Davidson, Richard J., DeBellis, Michael D., Dennis, Emily L., Densmore, Maria, deRoon-Cassini, Terri, Disner, Seth G., Hage, Wissam El, Etkin, Amit, Fani, Negar, Fercho, Kelene A., Fitzgerald, Jacklynn, Forster, Gina L., Frijling, Jessie L., Geuze, Elbert, Gonenc, Atilla, Gordon, Evan M., Gruber, Staci, Grupe, Daniel W, Guenette, Jeffrey P., Haswell, Courtney C., Herringa, Ryan J., Herzog, Julia, Hofmann, David Bernd, Hosseini, Bobak, Hudson, Anna R., Huggins, Ashley A., Ipser, Jonathan C., Jahanshad, Neda, Jia-Richards, Meilin, Jovanovic, Tanja, Kaufman, Milissa L., Kennis, Mitzy, King, Anthony, Kinzel, Philipp, Koch, Saskia B.J., Koerte, Inga K., Koopowitz, Sheri M., Korgaonkar, Mayuresh S., Krystal, John H., Lanius, Ruth, Larson, Christine L., Lebois, Lauren A.M., Li, Gen, Liberzon, Israel, Lu, Guang Ming, Luo, Yifeng, Magnotta, Vincent A., Manthey, Antje, Maron-Katz, Adi, May, Geoffery, McLaughlin, Katie, Mueller, Sven C., Nawijn, Laura, Nelson, Steven M., Neufeld, Richard W.J., Nitschke, Jack B, O'Leary, Erin M., Olatunji, Bunmi O., Olff, Miranda, Peverill, Matthew, Phan, K. Luan, Qi, Rongfeng, Quidé, Yann, Rektor, Ivan, Ressler, Kerry, Riha, Pavel, Ross, Marisa, Rosso, Isabelle M., Salminen, Lauren E., Sambrook, Kelly, Schmahl, Christian, Shenton, Martha E., Sheridan, Margaret, Shih, Chiahao, Sicorello, Maurizio, Sierk, Anika, Simmons, Alan N., Simons, Raluca M., Simons, Jeffrey S., Sponheim, Scott R., Stein, Murray B., Stein, Dan J., Stevens, Jennifer S., Straube, Thomas, Sun, Delin, Théberge, Jean, Thompson, Paul M., Thomopoulos, Sophia I., van der Wee, Nic J.A., van der Werff, Steven J.A., van Erp, Theo G.M., van Rooij, Sanne J.H., van Zuiden, Mirjam, Varkevisser, Tim, Veltman, Dick J., Vermeiren, Robert R.J.M., Walter, Henrik, Wang, Li, Wang, Xin, Weis, Carissa, Winternitz, Sherry, Xie, Hong, Zhu, Ye, Wall, Melanie, Neria, Yuval, Morey, Rajendra A.

Publikováno v: In NeuroImage 1 December 2023 283

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Akademický článek

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Autor: Xi Zhu, Yoojean Kim, Orren Ravid, Xiaofu He, Benjamin Suarez-Jimenez, Sigal Zilcha-Mano, Amit Lazarov, Seonjoo Lee, Chadi G. Abdallah, Michael Angstadt, Christopher L. Averill, C. Lexi Baird, Lee A. Baugh, Jennifer U. Blackford, Jessica Bomyea, Steven E. Bruce, Richard A. Bryant, Zhihong Cao, Kyle Choi, Josh Cisler, Andrew S. Cotton, Judith K. Daniels, Nicholas D. Davenport, Richard J. Davidson, Michael D. DeBellis, Emily L. Dennis, Maria Densmore, Terri deRoon-Cassini, Seth G. Disner, Wissam El Hage, Amit Etkin, Negar Fani, Kelene A. Fercho, Jacklynn Fitzgerald, Gina L. Forster, Jessie L. Frijling, Elbert Geuze, Atilla Gonenc, Evan M. Gordon, Staci Gruber, Daniel W Grupe, Jeffrey P. Guenette, Courtney C. Haswell, Ryan J. Herringa, Julia Herzog, David Bernd Hofmann, Bobak Hosseini, Anna R. Hudson, Ashley A. Huggins, Jonathan C. Ipser, Neda Jahanshad, Meilin Jia-Richards, Tanja Jovanovic, Milissa L. Kaufman, Mitzy Kennis, Anthony King, Philipp Kinzel, Saskia B.J. Koch, Inga K. Koerte, Sheri M. Koopowitz, Mayuresh S. Korgaonkar, John H. Krystal, Ruth Lanius, Christine L. Larson, Lauren A.M. Lebois, Gen Li, Israel Liberzon, Guang Ming Lu, Yifeng Luo, Vincent A. Magnotta, Antje Manthey, Adi Maron-Katz, Geoffery May, Katie McLaughlin, Sven C. Mueller, Laura Nawijn, Steven M. Nelson, Richard W.J. Neufeld, Jack B Nitschke, Erin M. O'Leary, Bunmi O. Olatunji, Miranda Olff, Matthew Peverill, K. Luan Phan, Rongfeng Qi, Yann Quidé, Ivan Rektor, Kerry Ressler, Pavel Riha, Marisa Ross, Isabelle M. Rosso, Lauren E. Salminen, Kelly Sambrook, Christian Schmahl, Martha E. Shenton, Margaret Sheridan, Chiahao Shih, Maurizio Sicorello, Anika Sierk, Alan N. Simmons, Raluca M. Simons, Jeffrey S. Simons, Scott R. Sponheim, Murray B. Stein, Dan J. Stein, Jennifer S. Stevens, Thomas Straube, Delin Sun, Jean Théberge, Paul M. Thompson, Sophia I. Thomopoulos, Nic J.A. van der Wee, Steven J.A. van der Werff, Theo G.M. van Erp, Sanne J.H. van Rooij, Mirjam van Zuiden, Tim Varkevisser, Dick J. Veltman, Robert R.J.M. Vermeiren, Henrik Walter, Li Wang, Xin Wang, Carissa Weis, Sherry Winternitz, Hong Xie, Ye Zhu, Melanie Wall, Yuval Neria, Rajendra A. Morey

Publikováno v: NeuroImage, Vol 283, Iss , Pp 120412- (2023)

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodolo

Externí odkaz: https://doaj.org/article/064745d8320544298a997782e36ec231

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