Zobrazeno 1 - 10
of 5 089
pro vyhledávání: '"Thompson, Paul M."'
Autor:
Newlin, Nancy R., Schilling, Kurt, Koudoro, Serge, Chandio, Bramsh Qamar, Kanakaraj, Praitayini, Moyer, Daniel, Kelly, Claire E., Genc, Sila, Chen, Jian, Yang, Joseph Yuan-Mou, Wu, Ye, He, Yifei, Zhang, Jiawei, Zeng, Qingrun, Zhang, Fan, Adluru, Nagesh, Nath, Vishwesh, Pathak, Sudhir, Schneider, Walter, Gade, Anurag, Rathi, Yogesh, Hendriks, Tom, Vilanova, Anna, Chamberland, Maxime, Pieciak, Tomasz, Ciupek, Dominika, Vega, Antonio Tristán, Aja-Fernández, Santiago, Malawski, Maciej, Ouedraogo, Gani, Machnio, Julia, Ewert, Christian, Thompson, Paul M., Jahanshad, Neda, Garyfallidis, Eleftherios, Landman, Bennett A.
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2024)
White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructu
Externí odkaz:
http://arxiv.org/abs/2411.09618
Autor:
Gadewar, Shruti P., Zhu, Alyssa H., Gari, Iyad Ba, Somu, Sunanda, Thomopoulos, Sophia I., Thompson, Paul M., Nir, Talia M., Jahanshad, Neda
Neuroimaging consortia can enhance reliability and generalizability of findings by pooling data across studies to achieve larger sample sizes. To adjust for site and MRI protocol effects, imaging datasets are often harmonized based on healthy control
Externí odkaz:
http://arxiv.org/abs/2403.00093
Autor:
Belov, Vladimir, Erwin-Grabner, Tracy, Zeng, Ling-Li, Ching, Christopher R. K., Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Brosch, Katharina, Bülow, Robin, Colle, Romain, Connolly, Colm G., Corruble, Emmanuelle, Couvy-Duchesne, Baptiste, Cullen, Kathryn, Dannlowski, Udo, Davey, Christopher G., Dols, Annemiek, Ernsting, Jan, Evans, Jennifer W., Fisch, Lukas, Fuentes-Claramonte, Paola, Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke A., Grotegerd, Dominik, Hahn, Tim, Hamilton, J. Paul, Han, Laura K. M., Harrison, Ben J, Ho, Tiffany C., Jahanshad, Neda, Jamieson, Alec J., Karuk, Andriana, Kircher, Tilo, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Leenings, Ramona, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Meinert, Susanne, Melloni, Elisa, Mueller, Bryon A., Mwangi, Benson, Nenadić, Igor, Ojha, Amar, Okamoto, Yasumasa, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Radua, Joaquim, Rodríguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Schrantee, Anouk, Sim, Kang, Soares, Jair C., Solanes, Aleix, Stein, Dan J., Stein, Frederike, Stolicyn, Aleks, Thomopoulos, Sophia I., Toenders, Yara J., Uyar-Demir, Aslihan, Vieta, Eduard, Vives-Gilabert, Yolanda, Völzke, Henry, Walter, Martin, Whalley, Heather C., Whittle, Sarah, Winter, Nils, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., Goya-Maldonado, Roberto
Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due t
Externí odkaz:
http://arxiv.org/abs/2311.11046
A major challenge in imaging genetics and similar fields is to link high-dimensional data in one domain, e.g., genetic data, to high dimensional data in a second domain, e.g., brain imaging data. The standard approach in the area are mass univariate
Externí odkaz:
http://arxiv.org/abs/2309.07352
Autor:
Dhinagar, Nikhil J., Singh, Amit, Ozarkar, Saket, Buwa, Ketaki, Thomopoulos, Sophia I., Owens-Walton, Conor, Laltoo, Emily, Chen, Yao-Liang, Cook, Philip, McMillan, Corey, Tsai, Chih-Chien, Wang, J-J, Wu, Yih-Ru, Thompson, Paul M.
Transfer learning represents a recent paradigm shift in the way we build artificial intelligence (AI) systems. In contrast to training task-specific models, transfer learning involves pre-training deep learning models on a large corpus of data and mi
Externí odkaz:
http://arxiv.org/abs/2309.04651
Autor:
Kennedy, Eamonn, Vadlamani, Shashank, Lindsey, Hannah M, Peterson, Kelly S, OConnor, Kristen Dams, Murray, Kenton, Agarwal, Ronak, Amiri, Houshang H, Andersen, Raeda K, Babikian, Talin, Baron, David A, Bigler, Erin D, Caeyenberghs, Karen, Delano-Wood, Lisa, Disner, Seth G, Dobryakova, Ekaterina, Eapen, Blessen C, Edelstein, Rachel M, Esopenko, Carrie, Genova, Helen M, Geuze, Elbert, Goodrich-Hunsaker, Naomi J, Grafman, Jordan, Haberg, Asta K, Hodges, Cooper B, Hoskinson, Kristen R, Hovenden, Elizabeth S, Irimia, Andrei, Jahanshad, Neda, Jha, Ruchira M, Keleher, Finian, Kenney, Kimbra, Koerte, Inga K, Liebel, Spencer W, Livny, Abigail, Lovstad, Marianne, Martindale, Sarah L, Max, Jeffrey E, Mayer, Andrew R, Meier, Timothy B, Menefee, Deleene S, Mohamed, Abdalla Z, Mondello, Stefania, Monti, Martin M, Morey, Rajendra A, Newcombe, Virginia, Newsome, Mary R, Olsen, Alexander, Pastorek, Nicholas J, Pugh, Mary Jo, Razi, Adeel, Resch, Jacob E, Rowland, Jared A, Russell, Kelly, Ryan, Nicholas P, Scheibel, Randall S, Schmidt, Adam T, Spitz, Gershon, Stephens, Jaclyn A, Tal, Assaf, Talbert, Leah D, Tartaglia, Maria Carmela, Taylor, Brian A, Thomopoulos, Sophia I, Troyanskaya, Maya, Valera, Eve M, van der Horn, Harm Jan, Van Horn, John D, Verma, Ragini, Wade, Benjamin SC, Walker, Willian SC, Ware, Ashley L, Werner Jr, J Kent, Yeates, Keith Owen, Zafonte, Ross D, Zeineh, Michael M, Zielinski, Brandon, Thompson, Paul M, Hillary, Frank G, Tate, David F, Wilde, Elisabeth A, Dennis, Emily L
An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which
Externí odkaz:
http://arxiv.org/abs/2309.04607
Recent advancements in the acquisition of various brain data sources have created new opportunities for integrating multimodal brain data to assist in early detection of complex brain disorders. However, current data integration approaches typically
Externí odkaz:
http://arxiv.org/abs/2305.16222
Autor:
Gadewar, Shruti P., Nourollahimoghadam, Elnaz, Bhatt, Ravi R., Ramesh, Abhinaav, Javid, Shayan, Gari, Iyad Ba, Zhu, Alyssa H., Thomopoulos, Sophia, Thompson, Paul M., Jahanshad, Neda
Structural alterations of the midsagittal corpus callosum (midCC) have been associated with a wide range of brain disorders. The midCC is visible on most MRI contrasts and in many acquisitions with a limited field-of-view. Here, we present an automat
Externí odkaz:
http://arxiv.org/abs/2305.01107
Autor:
Wu, Jianfeng, Su, Yi, Chen, Yanxi, Zhu, Wenhui, Reiman, Eric M., Caselli, Richard J., Chen, Kewei, Thompson, Paul M., Wang, Junwen, Wang, Yalin
Background: Alzheimer's Disease (AD) is the most common type of age-related dementia, affecting 6.2 million people aged 65 or older according to CDC data. It is commonly agreed that discovering an effective AD diagnosis biomarker could have enormous
Externí odkaz:
http://arxiv.org/abs/2304.00134
Autor:
Dhinagar, Nikhil J., Santhalingam, Vignesh, Lawrence, Katherine E., Laltoo, Emily, Thompson, Paul M.
For machine learning applications in medical imaging, the availability of training data is often limited, which hampers the design of radiological classifiers for subtle conditions such as autism spectrum disorder (ASD). Transfer learning is one meth
Externí odkaz:
http://arxiv.org/abs/2303.08224