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1. A comparison of methods to harmonize cortical thickness measurements across scanners and sites

Author

Sun, Delin, Rakesh, Gopalkumar, Haswell, Courtney C, Logue, Mark, Baird, C Lexi, O'Leary, Erin N, Cotton, Andrew S, Xie, Hong, Tamburrino, Marijo, Chen, Tian, Dennis, Emily L, Jahanshad, Neda, Salminen, Lauren E, Thomopoulos, Sophia I, Rashid, Faisal, Ching, Christopher RK, Koch, Saskia BJ, Frijling, Jessie L, Nawijn, Laura, van Zuiden, Mirjam, Zhu, Xi, Suarez-Jimenez, Benjamin, Sierk, Anika, Walter, Henrik, Manthey, Antje, Stevens, Jennifer S, Fani, Negar, van Rooij, Sanne JH, Stein, Murray, Bomyea, Jessica, Koerte, Inga K, Choi, Kyle, van der Werff, Steven JA, Vermeiren, Robert RJM, Herzog, Julia, Lebois, Lauren AM, Baker, Justin T, Olson, Elizabeth A, Straube, Thomas, Korgaonkar, Mayuresh S, Andrew, Elpiniki, Zhu, Ye, Li, Gen, Ipser, Jonathan, Hudson, Anna R, Peverill, Matthew, Sambrook, Kelly, Gordon, Evan, Baugh, Lee, Forster, Gina, Simons, Raluca M, Simons, Jeffrey S, Magnotta, Vincent, Maron-Katz, Adi, du Plessis, Stefan, Disner, Seth G, Davenport, Nicholas, Grupe, Daniel W, Nitschke, Jack B, deRoon-Cassini, Terri A, Fitzgerald, Jacklynn M, Krystal, John H, Levy, Ifat, Olff, Miranda, Veltman, Dick J, Wang, Li, Neria, Yuval, De Bellis, Michael D, Jovanovic, Tanja, Daniels, Judith K, Shenton, Martha, van de Wee, Nic JA, Schmahl, Christian, Kaufman, Milissa L, Rosso, Isabelle M, Sponheim, Scott R, Hofmann, David Bernd, Bryant, Richard A, Fercho, Kelene A, Stein, Dan J, Mueller, Sven C, Hosseini, Bobak, Phan, K Luan, McLaughlin, Katie A, Davidson, Richard J, Larson, Christine L, May, Geoffrey, Nelson, Steven M, Abdallah, Chadi G, Gomaa, Hassaan, Etkin, Amit, Seedat, Soraya, Harpaz-Rotem, Ilan, Liberzon, Israel, van Erp, Theo GM, Quidé, Yann, Wang, Xin, Thompson, Paul M, and Morey, Rajendra A

Subjects
Good Health and Well Being, Adolescent, Adult, Aged, Aged, 80 and over, Case-Control Studies, Child, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Stress Disorders, Post-Traumatic, Young Adult, Data Harmonization, Scanner Effects, Site Effects, Cortical Thickness, ComBat, ComBat-GAM, Linear Mixed-Effects Model, General Additive Model, PTSD, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants' demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2-81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3-85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.

Published
2022

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

Author

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, and Morey, Rajendra A.

Published
2023
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3. Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Author

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, and Rajendra A. Morey

Subjects
Posttraumatic stress disorder, Multimodal MRI, Machine learning, Deep learning, Classification, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

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 methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

Published
2023
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4. Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder: Results From the ENIGMA-PGC Posttraumatic Stress Disorder Consortium

Author

Sun, Delin, Rakesh, Gopalkumar, Clarke-Rubright, Emily K., Haswell, Courtney C., Logue, Mark W., O’Leary, Erin N., Cotton, Andrew S., Xie, Hong, Dennis, Emily L., Jahanshad, Neda, Salminen, Lauren E., Thomopoulos, Sophia I., Rashid, Faisal M., Ching, Christopher R.K., Koch, Saskia B.J., Frijling, Jessie L., Nawijn, Laura, van Zuiden, Mirjam, Zhu, Xi, Suarez-Jimenez, Benjamin, Sierk, Anika, Walter, Henrik, Manthey, Antje, Stevens, Jennifer S., Fani, Negar, van Rooij, Sanne J.H., Stein, Murray B., Bomyea, Jessica, Koerte, Inga, Choi, Kyle, van der Werff, Steven J.A., Vermeiren, Robert R.J.M., Herzog, Julia I., Lebois, Lauren A.M., Baker, Justin T., Ressler, Kerry J., Olson, Elizabeth A., Straube, Thomas, Korgaonkar, Mayuresh S., Andrew, Elpiniki, Zhu, Ye, Li, Gen, Ipser, Jonathan, Hudson, Anna R., Peverill, Matthew, Sambrook, Kelly, Gordon, Evan, Baugh, Lee A., Forster, Gina, Simons, Raluca M., Simons, Jeffrey S., Magnotta, Vincent A., Maron-Katz, Adi, du Plessis, Stefan, Disner, Seth G., Davenport, Nicholas D., Grupe, Dan, Nitschke, Jack B., deRoon-Cassini, Terri A., Fitzgerald, Jacklynn, Krystal, John H., Levy, Ifat, Olff, Miranda, Veltman, Dick J., Wang, Li, Neria, Yuval, De Bellis, Michael D., Jovanovic, Tanja, Daniels, Judith K., Shenton, Martha E., van de Wee, Nic J.A., Schmahl, Christian, Kaufman, Milissa L., Rosso, Isabelle M., Sponheim, Scott R., Hofmann, David Bernd, Bryant, Richard A., Fercho, Kelene A., Stein, Dan J., Mueller, Sven C., Phan, K. Luan, McLaughlin, Katie A., Davidson, Richard J., Larson, Christine, May, Geoffrey, Nelson, Steven M., Abdallah, Chadi G., Gomaa, Hassaan, Etkin, Amit, Seedat, Soraya, Harpaz-Rotem, Ilan, Liberzon, Israel, Wang, Xin, Thompson, Paul M., and Morey, Rajendra A.

Published
2022
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5. A comparison of methods to harmonize cortical thickness measurements across scanners and sites

Author

Delin Sun, Gopalkumar Rakesh, Courtney C. Haswell, Mark Logue, C. Lexi Baird, Erin N. O'Leary, Andrew S. Cotton, Hong Xie, Marijo Tamburrino, Tian Chen, Emily L. Dennis, Neda Jahanshad, Lauren E. Salminen, Sophia I. Thomopoulos, Faisal Rashid, Christopher R.K. Ching, Saskia B.J. Koch, Jessie L. Frijling, Laura Nawijn, Mirjam van Zuiden, Xi Zhu, Benjamin Suarez-Jimenez, Anika Sierk, Henrik Walter, Antje Manthey, Jennifer S. Stevens, Negar Fani, Sanne J.H. van Rooij, Murray Stein, Jessica Bomyea, Inga K. Koerte, Kyle Choi, Steven J.A. van der Werff, Robert R.J.M. Vermeiren, Julia Herzog, Lauren A.M. Lebois, Justin T. Baker, Elizabeth A. Olson, Thomas Straube, Mayuresh S. Korgaonkar, Elpiniki Andrew, Ye Zhu, Gen Li, Jonathan Ipser, Anna R. Hudson, Matthew Peverill, Kelly Sambrook, Evan Gordon, Lee Baugh, Gina Forster, Raluca M. Simons, Jeffrey S. Simons, Vincent Magnotta, Adi Maron-Katz, Stefan du Plessis, Seth G. Disner, Nicholas Davenport, Daniel W. Grupe, Jack B. Nitschke, Terri A. deRoon-Cassini, Jacklynn M. Fitzgerald, John H. Krystal, Ifat Levy, Miranda Olff, Dick J. Veltman, Li Wang, Yuval Neria, Michael D. De Bellis, Tanja Jovanovic, Judith K. Daniels, Martha Shenton, Nic J.A. van de Wee, Christian Schmahl, Milissa L. Kaufman, Isabelle M. Rosso, Scott R. Sponheim, David Bernd Hofmann, Richard A. Bryant, Kelene A. Fercho, Dan J. Stein, Sven C. Mueller, Bobak Hosseini, K. Luan Phan, Katie A. McLaughlin, Richard J. Davidson, Christine L. Larson, Geoffrey May, Steven M. Nelson, Chadi G. Abdallah, Hassaan Gomaa, Amit Etkin, Soraya Seedat, Ilan Harpaz-Rotem, Israel Liberzon, Theo G.M. van Erp, Yann Quidé, Xin Wang, Paul M. Thompson, and Rajendra A. Morey

Subjects
Data Harmonization, Scanner Effects, Site Effects, Cortical Thickness, ComBat, ComBat-GAM, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2–81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3–85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.

Published
2022
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6. Towards clinical grating-interferometry mammography

Author

Arboleda, Carolina, Wang, Zhentian, Jefimovs, Konstantins, Koehler, Thomas, Van Stevendaal, Udo, Kuhn, Norbert, David, Bernd, Prevrhal, Sven, Lång, Kristina, Forte, Serafino, Kubik-Huch, Rahel Antonia, Leo, Cornelia, Singer, Gad, Marcon, Magda, Boss, Andreas, Roessl, Ewald, and Stampanoni, Marco

Published
2020
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8. Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Author

MGGZ, Brain, 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., MGGZ, Brain, 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, and Morey, Rajendra A.

Published
2023

9. Correction to: Towards clinical grating-interferometry mammography

Author

Arboleda, Carolina, Wang, Zhentian, Jefimovs, Konstantins, Koehler, Thomas, Van Stevendaal, Udo, Kuhn, Norbert, David, Bernd, Prevrhal, Sven, Lång, Kristina, Forte, Serafino, Kubik-Huch, Rahel Antonia, Leo, Cornelia, Singer, Gad, Marcon, Magda, Boss, Andreas, Roessl, Ewald, and Stampanoni, Marco

Published
2020
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10. Multimodal Imaging-Based Classification of PTSD Using Data-Driven Computational Approaches: A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

Author

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, and Rajendra A. Morey

Abstract

BackgroundCurrent clinical assessments of Posttraumatic stress disorder (PTSD) rely solely on subjective symptoms and experiences reported by the patient, rather than objective biomarkers of the illness. Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. Here we aimed to classify individuals with PTSD versus controls using heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.MethodsWe analyzed brain MRI data from 3,527 structural-MRI; 2,502 resting state-fMRI; and 1,953 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls (TEHC and HC) using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality.ResultsWe found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history across all three modalities (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance.ConclusionOur findings highlight the promise offered by machine learning methods for the diagnosis of patients with PTSD. The utility of brain biomarkers across three MRI modalities and the contribution of DVAE models for improving generalizability offers new insights into neural mechanisms involved in PTSD.Significance⍰Classifying PTSD from trauma-unexposed healthy controls (HC) using three imaging modalities performed well (∼75% AUC), but performance suffered markedly when classifying PTSD from trauma-exposed healthy controls (TEHC) using three imaging modalities (∼60% AUC).⍰Using deep learning for feature reduction (denoising variational auto-encoder; DVAE) dramatically reduced the number of features with no concomitant performance degradation.⍰Utilizing denoising variational autoencoder (DVAE) models improves generalizability across heterogeneous multi-site data compared with the traditional machine learning frameworks

Published
2022
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11. Multimodal Imaging-Based Classification of PTSD Using Data-Driven Computational Approaches: A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

Author

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

Published
2022
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12. A comparison of methods to harmonize cortical thickness measurements across scanners and sites

Author

Sun, Delin, primary, Rakesh, Gopalkumar, additional, Haswell, Courtney C., additional, Logue, Mark, additional, Baird, C. Lexi, additional, O'Leary, Erin N., additional, Cotton, Andrew S., additional, Xie, Hong, additional, Tamburrino, Marijo, additional, Chen, Tian, additional, Dennis, Emily L., additional, Jahanshad, Neda, additional, Salminen, Lauren E., additional, Thomopoulos, Sophia I., additional, Rashid, Faisal, additional, Ching, Christopher R.K., additional, Koch, Saskia B.J., additional, Frijling, Jessie L., additional, Nawijn, Laura, additional, van Zuiden, Mirjam, additional, Zhu, Xi, additional, Suarez-Jimenez, Benjamin, additional, Sierk, Anika, additional, Walter, Henrik, additional, Manthey, Antje, additional, Stevens, Jennifer S., additional, Fani, Negar, additional, van Rooij, Sanne J.H., additional, Stein, Murray, additional, Bomyea, Jessica, additional, Koerte, Inga K., additional, Choi, Kyle, additional, van der Werff, Steven J.A., additional, Vermeiren, Robert R.J.M., additional, Herzog, Julia, additional, Lebois, Lauren A.M., additional, Baker, Justin T., additional, Olson, Elizabeth A., additional, Straube, Thomas, additional, Korgaonkar, Mayuresh S., additional, Andrew, Elpiniki, additional, Zhu, Ye, additional, Li, Gen, additional, Ipser, Jonathan, additional, Hudson, Anna R., additional, Peverill, Matthew, additional, Sambrook, Kelly, additional, Gordon, Evan, additional, Baugh, Lee, additional, Forster, Gina, additional, Simons, Raluca M., additional, Simons, Jeffrey S., additional, Magnotta, Vincent, additional, Maron-Katz, Adi, additional, du Plessis, Stefan, additional, Disner, Seth G., additional, Davenport, Nicholas, additional, Grupe, Daniel W., additional, Nitschke, Jack B., additional, deRoon-Cassini, Terri A., additional, Fitzgerald, Jacklynn M., additional, Krystal, John H., additional, Levy, Ifat, additional, Olff, Miranda, additional, Veltman, Dick J., additional, Wang, Li, additional, Neria, Yuval, additional, De Bellis, Michael D., additional, Jovanovic, Tanja, additional, Daniels, Judith K., additional, Shenton, Martha, additional, van de Wee, Nic J.A., additional, Schmahl, Christian, additional, Kaufman, Milissa L., additional, Rosso, Isabelle M., additional, Sponheim, Scott R., additional, Hofmann, David Bernd, additional, Bryant, Richard A., additional, Fercho, Kelene A., additional, Stein, Dan J., additional, Mueller, Sven C., additional, Hosseini, Bobak, additional, Phan, K. Luan, additional, McLaughlin, Katie A., additional, Davidson, Richard J., additional, Larson, Christine L., additional, May, Geoffrey, additional, Nelson, Steven M., additional, Abdallah, Chadi G., additional, Gomaa, Hassaan, additional, Etkin, Amit, additional, Seedat, Soraya, additional, Harpaz-Rotem, Ilan, additional, Liberzon, Israel, additional, van Erp, Theo G.M., additional, Quidé, Yann, additional, Wang, Xin, additional, Thompson, Paul M., additional, and Morey, Rajendra A., additional

Published
2022
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13. Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder: Results from the ENIGMA-PGC PTSD Consortium

Author

Sun, Delin, Rakesh, Gopalkumar, Clarke-Rubright, Emily K, Haswell, Courtney C, Logue, Mark, O'Leary, Erin N, Cotton, Andrew S, Xie, Hong, Dennis, Emily L, Jahanshad, Neda, Salminen, Lauren E, Thomopoulos, Sophia I, Rashid, Faisal, Ching, Christopher R K, Koch, Saskia B J, Frijling, Jessie L, Nawijn, Laura, van Zuiden, Mirjam, Zhu, Xi, Suarez-Jimenez, Benjamin, Sierk, Anika, Walter, Henrik, Manthey, Antje, Stevens, Jennifer S, Fani, Negar, van Rooij, Sanne J H, Stein, Murray, Bomyea, Jessica, Koerte, Inga, Choi, Kyle, van de Werff, Steven J A, Vermeiren, Robert R J M, Herzog, Julia, Lebois, Lauren A M, Baker, Justin T, Ressler, Kerry J, Olson, Elizabeth A, Straube, Thomas, Korgaonkar, Mayuresh S, Andrew, Elpiniki, Zhu, Ye, Li, Gen, Ipser, Jonathan, Hudson, Anna, Peverill, Matthew, Sambrook, Kelly, Gordon, Evan, Baugh, Lee, Forster, Gina, Simons, Raluca, Simons, Jeffrey, Magnotta, Vincent, Maron-Katz, Adi, du Plessis, Stefan, Disner, Seth, Davenport, Nicholas, Grupe, Dan, Nitschke, Jack, deRoon-Cassini, Terri A, Fitzgerald, Jacklynn, Krystal, John H, Levy, Ifat, Olff, Miranda, Veltman, Dick J, Wang, Li, Neria, Yuval, De Bellis, Michael D, Jovanovic, Tanja, Daniels, Judith K, Shenton, Martha, van de Wee, Nic J A, Schmahl, Christian, Kaufman, Milissa L, Rosso, Isabelle M, Sponheim, Scott R, Hofmann, David Bernd, Bryant, Richard A, Fercho, Kelene A, Stein, Dan J, Mueller, Sven C, Phan, Luan, McLaughlin, Katie A, Davidson, Richard J, Larson, Christine, May, Geoffrey, Nelson, Steven M, Abdallah, Chadi G, Gomaa, Hassaan, Etkin, Amit, Seedat, Soraya, Harpaz-Rotem, Ilan, Liberzon, Israel, Wang, Xin, Thompson, Paul M, Morey, Rajendra A, and Clinical Psychology and Experimental Psychopathology

Abstract

BACKGROUND: Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA). METHODS: Neuroimaging and clinical data were aggregated from 29 research sites in >1,300 PTSD cases and >2,000 trauma-exposed controls (age 6.2-85.2 years) by the ENIGMA-PGC PTSD working group. Cortical regions in the network were rank-ordered by effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2 to 148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared to the mean SC of 5,000 randomly generated n-region networks. RESULTS: Patients with PTSD, relative to non-PTSD controls, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex and age modulated covariance differences of PTSD-related structural networks. CONCLUSIONS: Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The structural covariance networks that are perturbed in PTSD comport with converging evidence from resting state functional connectivity networks and networks impacted by inflammatory processes, and stress hormones in PTSD.

Published
2022

14. Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder: Results From the ENIGMA-PGC Posttraumatic Stress Disorder Consortium

Author

Delin Sun, Gopalkumar Rakesh, Emily K. Clarke-Rubright, Courtney C. Haswell, Mark W. Logue, Erin N. O’Leary, Andrew S. Cotton, Hong Xie, Emily L. Dennis, Neda Jahanshad, Lauren E. Salminen, Sophia I. Thomopoulos, Faisal M. Rashid, Christopher R.K. Ching, Saskia B.J. Koch, Jessie L. Frijling, Laura Nawijn, Mirjam van Zuiden, Xi Zhu, Benjamin Suarez-Jimenez, Anika Sierk, Henrik Walter, Antje Manthey, Jennifer S. Stevens, Negar Fani, Sanne J.H. van Rooij, Murray B. Stein, Jessica Bomyea, Inga Koerte, Kyle Choi, Steven J.A. van der Werff, Robert R.J.M. Vermeiren, Julia I. Herzog, Lauren A.M. Lebois, Justin T. Baker, Kerry J. Ressler, Elizabeth A. Olson, Thomas Straube, Mayuresh S. Korgaonkar, Elpiniki Andrew, Ye Zhu, Gen Li, Jonathan Ipser, Anna R. Hudson, Matthew Peverill, Kelly Sambrook, Evan Gordon, Lee A. Baugh, Gina Forster, Raluca M. Simons, Jeffrey S. Simons, Vincent A. Magnotta, Adi Maron-Katz, Stefan du Plessis, Seth G. Disner, Nicholas D. Davenport, Dan Grupe, Jack B. Nitschke, Terri A. deRoon-Cassini, Jacklynn Fitzgerald, John H. Krystal, Ifat Levy, Miranda Olff, Dick J. Veltman, Li Wang, Yuval Neria, Michael D. De Bellis, Tanja Jovanovic, Judith K. Daniels, Martha E. Shenton, Nic J.A. van de Wee, Christian Schmahl, Milissa L. Kaufman, Isabelle M. Rosso, Scott R. Sponheim, David Bernd Hofmann, Richard A. Bryant, Kelene A. Fercho, Dan J. Stein, Sven C. Mueller, K. Luan Phan, Katie A. McLaughlin, Richard J. Davidson, Christine Larson, Geoffrey May, Steven M. Nelson, Chadi G. Abdallah, Hassaan Gomaa, Amit Etkin, Soraya Seedat, Ilan Harpaz-Rotem, Israel Liberzon, Xin Wang, Paul M. Thompson, Rajendra A. Morey, Adult Psychiatry, APH - Mental Health, ANS - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Global Health, Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Neurology, Pediatric surgery, Anatomy and neurosciences, Amsterdam Neuroscience - Brain Imaging, and APH - Personalized Medicine

Subjects
Adult, Aged, 80 and over, Adolescent, Depression, Cognitive Neuroscience, Surface area, Neuroimaging, PTSD, Middle Aged, Brain network, Magnetic Resonance Imaging, Cortical thickness, Structural covariance, Stress Disorders, Post-Traumatic, Young Adult, Case-Control Studies, Connectome, Humans, Radiology, Nuclear Medicine and imaging, Neurology (clinical), Child, Biological Psychiatry, Aged
Abstract

Background: Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA). Methods: Neuroimaging and clinical data were aggregated from 29 research sites in >1300 PTSD cases and >2000 trauma-exposed control subjects (ages 6.2–85.2 years) by the ENIGMA-PGC (Enhancing Neuro Imaging Genetics through Meta Analysis–Psychiatric Genomics Consortium) PTSD working group. Cortical regions in the network were rank ordered by the effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2–148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared with the mean SC of 5000 randomly generated n-region networks. Results: Patients with PTSD, relative to non-PTSD control subjects, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex, and age modulated covariance differences of PTSD-related structural networks. Conclusions: Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The SC networks that are perturbed in PTSD comport with converging evidence from resting-state functional connectivity networks and networks affected by inflammatory processes and stress hormones in PTSD.

Published
2022
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15. Fundamentals and applications of magnetic particle imaging

Author

Borgert, Jörn, Schmidt, Joachim D., Schmale, Ingo, Rahmer, Jürgen, Bontus, Claas, Gleich, Bernhard, David, Bernd, Eckart, Rainer, Woywode, Oliver, Weizenecker, Jürgen, Schnorr, Jörg, Taupitz, Matthias, Haegele, Julian, Vogt, Florian M., and Barkhausen, Jörg

Published
2012
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16. A Comparison of Methods to Harmonize Cortical Thickness Measurements Across Scanners and Sites

Author

Sun, Delin, primary, Rakesh, Gopalkumar, additional, Haswell, Courtney C., additional, Logue, Mark, additional, Lexi Baird, C., additional, O’Leary, Brian M., additional, Cotton, Andrew S., additional, Xie, Hong, additional, Tamburrino, Marijo, additional, Chen, Tian, additional, Dennis, Emily L., additional, Jahanshad, Neda, additional, Salminen, Lauren E., additional, Thomopoulos, Sophia I., additional, Rashid, Faisal, additional, Ching, Christopher R. K., additional, Koch, Saskia B. J., additional, Frijling, Jessie L., additional, Nawijn, Laura, additional, van Zuiden, Mirjam, additional, Zhu, Xi, additional, Suarez-Jimenez, Benjamin, additional, Sierk, Anika, additional, Walter, Henrik, additional, Manthey, Antje, additional, Stevens, Jennifer S., additional, Fani, Negar, additional, van Rooij, Sanne J.H., additional, Stein, Murray, additional, Bomyea, Jessica, additional, Koerte, Inga K., additional, Choi, Kyle, additional, van der Werff, Steven J.A., additional, Vermeiren, Robert R. J. M., additional, Herzog, Julia, additional, Lebois, Lauren A. M., additional, Baker, Justin T., additional, Olson, Elizabeth A., additional, Straube, Thomas, additional, Korgaonkar, Mayuresh S., additional, Andrew, Elpiniki, additional, Zhu, Ye, additional, Li, Gen, additional, Ipser, Jonathan, additional, Hudson, Anna R., additional, Peverill, Matthew, additional, Sambrook, Kelly, additional, Gordon, Evan, additional, Baugh, Lee, additional, Forster, Gina, additional, Simons, Raluca M., additional, Simons, Jeffrey S., additional, Magnotta, Vincent, additional, Maron-Katz, Adi, additional, du Plessis, Stefan, additional, Disner, Seth G., additional, Davenport, Nicholas, additional, Grupe, Daniel W., additional, Nitschke, Jack B., additional, deRoon-Cassini, Terri A., additional, Fitzgerald, Jacklynn M., additional, Krystal, John H., additional, Levy, Ifat, additional, Olff, Miranda, additional, Veltman, Dick J., additional, Wang, Li, additional, Neria, Yuval, additional, De Bellis, Michael D., additional, Jovanovic, Tanja, additional, Daniels, Judith K., additional, Shenton, Martha, additional, van de Wee, Nic J.A., additional, Schmahl, Christian, additional, Kaufman, Milissa L., additional, Rosso, Isabelle M., additional, Sponheim, Scott R., additional, Hofmann, David Bernd, additional, Bryant, Richard A., additional, Fercho, Kelene A., additional, Stein, Dan J., additional, Mueller, Sven C., additional, Hosseini, Bobak, additional, Luan Phan, K., additional, McLaughlin, Katie A., additional, Davidson, Richard J., additional, Larson, Christine L., additional, May, Geoffrey, additional, Nelson, Steven M., additional, Abdallah, Chadi G., additional, Gomaa, Hassaan, additional, Etkin, Amit, additional, Seedat, Soraya, additional, Harpaz-Rotem, Ilan, additional, Liberzon, Israel, additional, van Erp, Theo G.M., additional, Wang, Xin, additional, Thompson, Paul M., additional, and Morey, Rajendra A., additional

Published
2021
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17. Structural Covariance Networks in Post-Traumatic Stress Disorder: A Multisite ENIGMA-PGC Study

Author

Rakesh, Gopalkumar, primary, Logue, Mark, additional, Clarke-Rubright, Emily, additional, O’Leary, Brian M., additional, Haswell, Courtney C., additional, Xie, Hong, additional, Thompson, Paul M., additional, Dennis, Emily L., additional, Jahanshad, Neda, additional, Koch, Saskia B.J., additional, Frijling, Jessie L., additional, Nawijn, Laura, additional, Olff, Miranda, additional, van Zuiden, Mirjam, additional, Rashid, Faisal M., additional, Zhu, Xi, additional, De Bellis, Michael D., additional, Daniels, Judith K., additional, Sierk, Anika, additional, Manthey, Antje, additional, Stevens, Jennifer S., additional, Jovanovic, Tanja, additional, Stein, Murray B., additional, Shenton, Martha, additional, van de Werff, Steven J.A., additional, van de Wee, Nic J.A., additional, Vermeiren, Robert R.J.M., additional, Schmahl, Christian, additional, Herzog, Julia, additional, Kaufman, Milissa L., additional, O’Connor, Lauren, additional, Lebois, Lauren A.M., additional, Baker, Justin T., additional, Gruber, Staci A., additional, Wolff, Jonathan D., additional, Wolf, Erika J., additional, Wintemitz, Sherry R., additional, Gönenc, Atilla, additional, Ressler, Kerry J., additional, Hofmann, David Bernd, additional, Bryant, Richard A., additional, Korgaonkar, Mayuresh, additional, Andrew, Elpiniki, additional, Wang, Li, additional, Zhu, Ye, additional, Li, Gen, additional, Stein, Dan J., additional, Ipser, Jonathan, additional, Koopowitz, Sheri, additional, Mueller, Sven, additional, Hudson, Anna, additional, Phan, Luan, additional, Hosseini, Bobak, additional, Angstadt, K. Mike, additional, King, Anthony P., additional, Tamburrino, Marijo, additional, Skilliter, Brynn C., additional, Geuze, Elbert, additional, van Rooij, Sanne J.H., additional, Varkevisser, Tim, additional, McLaughlin, Katie A., additional, Sheridan, Margaret A., additional, Peverill, Matthew, additional, Sambrook, Kelly, additional, Veltman, Dick J., additional, Thomaes, Kathleen, additional, Nelson, Steven M., additional, May, Geoffrey, additional, Baugh, Lee, additional, Forster, Gina, additional, Simons, Raluca, additional, Simons, Jeffrey, additional, Magnotta, Vincent, additional, Fercho, Kelene A, additional, Maron-Katz, Adi, additional, du Plessis, Stefan, additional, Disner, Seth, additional, Davenport, Nicholas, additional, Thomopoulos, Sophia I., additional, Suarez-Jimenez, Benjamin, additional, Wager, Tor D., additional, Neria, Yuval, additional, Fani, Negar, additional, Walter, Henrik, additional, Koerte, Inga, additional, Bomyea, Jessica, additional, Choi, Kyle, additional, Simmons, Alan N., additional, Olson, Elizabeth, additional, Rosso, Isabelle, additional, Straube, Thomas, additional, van Erp, Theo G.M., additional, Chen, Tian, additional, Cotton, Andrew S., additional, Wall, John, additional, Davidson, Richard J., additional, deRoon-Cassini, Terri, additional, Fitzgerald, Jacklynn, additional, Larson, Christine, additional, Gordon, Evan, additional, Grupe, Dan, additional, Sponheim, Scott R., additional, Etkin, Amit, additional, Seedat, Soraya, additional, Harpaz-Rotem, Ilan, additional, Wrocklage, Kristen, additional, Abdallah, Chadi G., additional, Krystal, John H., additional, Levy, Ifat, additional, Gomaa, Hassaan, additional, McMahon, Mary Agnes B., additional, Liberzon, Israel, additional, Wang, Xin, additional, Sun, Delin, additional, and Morey, Rajendra A., additional

Published
2021
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20. Integrated high-Tc SQUID magnetometer

Author

David, Bernd, Grundler, Dirk, Krumme, Jens-Peter, and Doessel, Olaf

Subjects
Superconducting quantum interference devices -- Design and construction, Magnetometer -- Design and construction, Business, Electronics, Electronics and electrical industries
Abstract

An integrated superconducting quantum interference device (SQUID) magnetometer based on Y1Ba2Cu3O7 step-edge Josephson junctions is developed. A coupling coefficent k of 0.9 allows tight coupling between the input coil and the SQUID washer. A nearly frequency independent flux noise is observed while bias reversal completely suppresses 1/f-noise contributions at lower temperatures. The magnetometer's field resolution is estimated with the use of field sensitivity and flux noise measurements.

Published
1995

21. Multilevel devices of YBa2Cu3O7 with NdGaO3 barrier

Author

Grundler, Dirk, Krumme, Jens-Peter, David, Bernd, and Doessel, Olaf

Subjects
Josephson junction -- Research, High temperature superconductors -- Research, Business, Electronics, Electronics and electrical industries
Abstract

The electrical behavior of ramp-type YBa2Cu3O7 Josephson junctions with NdGaO3 barrier layers is investigated at varying temperatures by current-voltage measurements. Results show that the barrier layers create a weak coupling of the junctions' superconducting electrodes and exhibit excellent Josephson properties. The measured data also indicate that the junctions have a considerable intrinsic capacitance and high resistivity.

Published
1995

22. Towards clinical grating-interferometry mammography

Author

Arboleda, Carolina, primary, Wang, Zhentian, additional, Jefimovs, Konstantins, additional, Koehler, Thomas, additional, Van Stevendaal, Udo, additional, Kuhn, Norbert, additional, David, Bernd, additional, Prevrhal, Sven, additional, Lång, Kristina, additional, Forte, Serafino, additional, Kubik-Huch, Rahel Antonia, additional, Leo, Cornelia, additional, Singer, Gad, additional, Marcon, Magda, additional, Boss, Andreas, additional, Roessl, Ewald, additional, and Stampanoni, Marco, additional

Published
2019
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23. Experimental investigation of the kinetic inductance in YBa2Cu3O7 square washer superconducting quantum interference devices.

Author

Grundler, Dirk, David, Bernd, and Doessel, Olaf

Subjects
*ELECTRIC inductance, *SUPERCONDUCTORS
Abstract

Presents a study that measured the magnetic inductance of an yttrium-based square washer superconducting quantum interference devices. Methodology; Analysis of the temperature dependance of inductance; Impact of inductance on device performance.

Published
1995
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26. Perspectives on clinical magnetic particle imaging

Author

Borgert, Jörn, primary, Schmidt, Joachim D., additional, Schmale, Ingo, additional, Bontus, Claas, additional, Gleich, Bernhard, additional, David, Bernd, additional, Weizenecker, Jürgen, additional, Jockram, Jonas, additional, Lauruschkat, Christian, additional, Mende, Oliver, additional, Heinrich, Mathias, additional, Halkola, Aleksi, additional, Bergmann, Jörn, additional, Woywode, Oliver, additional, and Rahmer, Jürgen, additional

Published
2013
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28. Towards MR-guided EP interventions using an RF-safe approach

Author

Krueger, Sascha, primary, Lips, Oliver, additional, David, Bernd, additional, Wirtz, Daniel, additional, Weiss, Steffen, additional, Pedersen, Steen F, additional, Caulfield, Dennis, additional, Bostock, Julian, additional, Razavi, Reza, additional, and Schaeffter, Tobias, additional

Published
2009
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29. Liquid-metal anode x-ray tube

Author

David, Bernd R., primary, Barschdorf, Hans, additional, Doormann, Volker, additional, Eckart, Rainer, additional, Harding, Geoffrey, additional, Schlomka, Jens-Peter, additional, Thran, Axel, additional, Bachmann, Peter, additional, and Flisikowski, Peter, additional

Published
2004
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35. Microbubbles as a contrast agent in grating interferometry mammography: an ex vivo proof-of-mechanism study

Author

Lång, Kristina, Arboleda, Carolina, Forte, Serafino, Wang, Zhentian, Prevrhal, Sven, Koehler, Thomas, Kuhn, Norbert, David, Bernd, Jefimovs, Konstantins, Kubik-Huch, Rahel A., and Stampanoni, Marco

Subjects
Interferometry, Microbubbles, Contrast media, education, Phantoms (imaging), 3. Good health, Mammography
Abstract

Grating interferometry mammography (GIM) is an experimental breast imaging method at the edge of being clinically implemented. Besides attenuation, GIM can measure the refraction and scattering of x-rays resulting in differential phase contrast (DPC) and dark-field (DF) images. In this exploratory study, we assessed the feasibility of using microbubbles as a contrast agent in GIM. Two millilitres of microbubbles and iodine were respectively injected into ex vivo breast phantoms, consisting of fresh chicken breasts. Native and postcontrast images were acquired with a clinically compatible GIM setup, operated at 38 kVp, 14-s acquisition time, and with a dose of 1.3 mGy. The visibility of the contrast agents was analysed in a side-by-side comparison by three radiologists. The contrast-to-noise-ratio (CNR) was calculated for each contrast agent. We found that both contrast agents were judged to be visible by the readers. The mean CNR was 3.1 ± 1.9 for microbubbles in DF and 24.2 ± 6.5 for iodine in attenuation. In conclusion, this is a first proof-of-mechanism study that microbubbles could be used as a contrast agent in clinically compatible GIM, due to their scattering properties, which implies the potential use of a contrast agent with a high safety profile in x-ray-based breast imaging., European Radiology Experimental, 3 (1), ISSN:2509-9280

36. Towards clinical grating-interferometry mammography

Author

Arboleda, Carolina, Wang, Zhentian, Jefimovs, Konstantins, Koehler, Thomas, van Stevendaal, Udo, Kuhn, Norbert, David, Bernd, Prevrhal, Sven, Lång, Kristina, Forte, Serafino, Kubik-Huch, Rahel Antonia, Leo, Cornelia, Singer, Gad, Marcon, Magda, Boss, Andreas, Roessl, Ewald, and Stampanoni, Marco

Subjects
Interferometry, Phase contrast, 3. Good health, Mammography
Abstract

Objectives Grating-interferometry-based mammography (GIM) might facilitate breast cancer detection, as several research works have demonstrated in a pre-clinical setting, since it is able to provide attenuation, differential phase contrast, and scattering images simultaneously. In order to translate this technique to the clinics, it has to be adapted to cover a large field-of-view within a clinically acceptable exposure time and radiation dose. Methods We set up a grating interferometer that fits into a standard mammography system and fulfilled the aforementioned conditions. Here, we present the first mastectomy images acquired with this experimental device. Results and conclusion Our system performs at a mean glandular dose of 1.6 mGy for a 5-cm-thick, 18%-dense breast, and a field-of-view of 26 × 21 cm2. It seems to be well-suited as basis for a clinical-environment device. Further, dark-field signals seem to support an improved lesion visualization. Evidently, the effective impact of such indications must be evaluated and quantified within the context of a proper reader study. Key Points • Grating-interferometry-based mammography (GIM) might facilitate breast cancer detection, since it is sensitive to refraction and scattering and thus provides additional tissue information. • The most straightforward way to do grating-interferometry in the clinics is to modify a standard mammography device. • In a first approximation, the doses given with this technique seem to be similar to those of conventional mammography., European Radiology, 30, ISSN:0938-7994, ISSN:1432-1084

41. Perspectives on clinical magnetic particle imaging.

Author

Borgert J, Schmidt JD, Schmale I, Bontus C, Gleich B, David B, Weizenecker J, Jockram J, Lauruschkat C, Mende O, Heinrich M, Halkola A, Bergmann J, Woywode O, and Rahmer J

Subjects
Contrast Media, Equipment Design, Equipment Failure Analysis, Image Enhancement instrumentation, Image Enhancement methods, Magnetic Resonance Imaging instrumentation, Magnetic Resonance Imaging methods, Magnetite Nanoparticles, Molecular Imaging instrumentation, Molecular Imaging methods
Abstract

After realizing the worlds' first preclinical magnetic particle imaging (MPI) demonstrator, Philips is now realizing the worlds' first whole-body clinical prototype to prove the feasibility of MPI for clinical imaging. After a brief introduction of the basic MPI imaging process, this contribution presents an overview on the determining factors for key properties, i.e., spatial resolution, acquisition speed, sensitivity, and quantitativeness, and how these properties are influenced by scaling up from preclinical to clinical instrumentation. Furthermore, it is discussed how this scale up affects the physiological compatibility of the method as well as hardware parameters such as power requirements for drive field generation, selection and focus field generation, and the design of the receive chain of the MPI device.

Published
2013
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