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2. Smaller total and subregional cerebellar volumes in posttraumatic stress disorder: a mega-analysis by the ENIGMA-PGC PTSD workgroup

Author

Huggins, Ashley A, Baird, C Lexi, Briggs, Melvin, Laskowitz, Sarah, Hussain, Ahmed, Fouda, Samar, Haswell, Courtney, Sun, Delin, Salminen, Lauren E, Jahanshad, Neda, Thomopoulos, Sophia I, Veltman, Dick J, Frijling, Jessie L, Olff, Miranda, van Zuiden, Mirjam, Koch, Saskia BJ, Nawjin, Laura, Wang, Li, Zhu, Ye, Li, Gen, Stein, Dan J, Ipser, Jonathan, Seedat, Soraya, du Plessis, Stefan, van den Heuvel, Leigh L, Suarez-Jimenez, Benjamin, Zhu, Xi, Kim, Yoojean, He, Xiaofu, Zilcha-Mano, Sigal, Lazarov, Amit, Neria, Yuval, Stevens, Jennifer S, Ressler, Kerry J, Jovanovic, Tanja, van Rooij, Sanne JH, Fani, Negar, Hudson, Anna R, Mueller, Sven C, Sierk, Anika, Manthey, Antje, Walter, Henrik, Daniels, Judith K, Schmahl, Christian, Herzog, Julia I, Říha, Pavel, Rektor, Ivan, Lebois, Lauren AM, Kaufman, Milissa L, Olson, Elizabeth A, Baker, Justin T, Rosso, Isabelle M, King, Anthony P, Liberzon, Isreal, Angstadt, Mike, Davenport, Nicholas D, Sponheim, Scott R, Disner, Seth G, Straube, Thomas, Hofmann, David, Qi, Rongfeng, Lu, Guang Ming, Baugh, Lee A, Forster, Gina L, Simons, Raluca M, Simons, Jeffrey S, Magnotta, Vincent A, Fercho, Kelene A, Maron-Katz, Adi, Etkin, Amit, Cotton, Andrew S, O’Leary, Erin N, Xie, Hong, Wang, Xin, Quidé, Yann, El-Hage, Wissam, Lissek, Shmuel, Berg, Hannah, Bruce, Steven, Cisler, Josh, Ross, Marisa, Herringa, Ryan J, Grupe, Daniel W, Nitschke, Jack B, Davidson, Richard J, Larson, Christine L, deRoon-Cassini, Terri A, Tomas, Carissa W, Fitzgerald, Jacklynn M, Blackford, Jennifer Urbano, Olatunji, Bunmi O, Kremen, William S, Lyons, Michael J, Franz, Carol E, Gordon, Evan M, May, Geoffrey, Nelson, Steven M, Abdallah, Chadi G, Levy, Ifat, and Harpaz-Rotem, Ilan

Subjects
Clinical and Health Psychology, Psychology, Mind and Body, Mental Illness, Anxiety Disorders, Neurosciences, Brain Disorders, Post-Traumatic Stress Disorder (PTSD), Behavioral and Social Science, Mental Health, Mental health, Humans, Stress Disorders, Post-Traumatic, Cerebellum, Female, Male, Adult, Magnetic Resonance Imaging, Middle Aged, White Matter, Gray Matter, Organ Size, Deep Learning, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p-FDR

Published
2024

4. 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, Angstadt, Michael, Averill, Christopher, Baird, C, Baugh, Lee, Blackford, Jennifer, Bomyea, Jessica, Bruce, Steven, Bryant, Richard, Cao, Zhihong, Choi, Kyle, Cisler, Josh, Cotton, Andrew, Daniels, Judith, Davenport, Nicholas, Davidson, Richard, DeBellis, Michael, Dennis, Emily, Densmore, Maria, deRoon-Cassini, Terri, Disner, Seth, Hage, Wissam, Etkin, Amit, Fani, Negar, Fercho, Kelene, Fitzgerald, Jacklynn, Forster, Gina, Frijling, Jessie, Geuze, Elbert, Gonenc, Atilla, Gordon, Evan, Gruber, Staci, Grupe, Daniel, Guenette, Jeffrey, Haswell, Courtney, Herringa, Ryan, Herzog, Julia, Hofmann, David, Hosseini, Bobak, Hudson, Anna, Huggins, Ashley, Ipser, Jonathan, Jahanshad, Neda, Jia-Richards, Meilin, Jovanovic, Tanja, Kaufman, Milissa, Kennis, Mitzy, King, Anthony, Kinzel, Philipp, Koch, Saskia, Koerte, Inga, Koopowitz, Sheri, Korgaonkar, Mayuresh, Krystal, John, Lanius, Ruth, Larson, Christine, Lebois, Lauren, Li, Gen, Liberzon, Israel, Lu, Guang, Luo, Yifeng, Magnotta, Vincent, Manthey, Antje, Maron-Katz, Adi, May, Geoffery, McLaughlin, Katie, Mueller, Sven, Nawijn, Laura, Nelson, Steven, Neufeld, Richard, Nitschke, Jack, OLeary, Erin, Olatunji, Bunmi, Olff, Miranda, Peverill, Matthew, Phan, K, Qi, Rongfeng, Quidé, Yann, Rektor, Ivan, Ressler, Kerry, Riha, Pavel, Ross, Marisa, Rosso, Isabelle, Salminen, Lauren, Sambrook, Kelly, Schmahl, Christian, Shenton, Martha, Sheridan, Margaret, Shih, Chiahao, Sicorello, Maurizio, Sierk, Anika, and van Rooij, Sanne

Subjects
Classification, Deep learning, Machine learning, Multimodal MRI, Posttraumatic stress disorder, Humans, Stress Disorders, Post-Traumatic, Reproducibility of Results, Big Data, Neuroimaging, Magnetic Resonance Imaging, Brain
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

6. FIMP: Foundation Model-Informed Message Passing for Graph Neural Networks

Author

Rizvi, Syed Asad, Pallikkavaliyaveetil, Nazreen, Zhang, David, Lyu, Zhuoyang, Nguyen, Nhi, Lyu, Haoran, Christensen, Benjamin, Caro, Josue Ortega, Fonseca, Antonio H. O., Zappala, Emanuele, Bagherian, Maryam, Averill, Christopher, Abdallah, Chadi G., Karbasi, Amin, Ying, Rex, Brbic, Maria, Dhodapkar, Rahul Madhav, and van Dijk, David

Subjects
Computer Science - Machine Learning
Abstract

Foundation models have achieved remarkable success across many domains, relying on pretraining over vast amounts of data. Graph-structured data often lacks the same scale as unstructured data, making the development of graph foundation models challenging. In this work, we propose Foundation-Informed Message Passing (FIMP), a Graph Neural Network (GNN) message-passing framework that leverages pretrained non-textual foundation models in graph-based tasks. We show that the self-attention layers of foundation models can effectively be repurposed on graphs to perform cross-node attention-based message-passing. Our model is evaluated on a real-world image network dataset and two biological applications (single-cell RNA sequencing data and fMRI brain activity recordings) in both finetuned and zero-shot settings. FIMP outperforms strong baselines, demonstrating that it can effectively leverage state-of-the-art foundation models in graph tasks., Comment: 16 pages (12 + 4 pages appendix). 5 figures and 4 tables

Published
2022

7. Neural Integro-Differential Equations

Author

Zappala, Emanuele, Fonseca, Antonio Henrique de Oliveira, Moberly, Andrew Henry, Higley, Michael James, Abdallah, Chadi, Cardin, Jessica, and van Dijk, David

Subjects
Computer Science - Machine Learning
Abstract

Modeling continuous dynamical systems from discretely sampled observations is a fundamental problem in data science. Often, such dynamics are the result of non-local processes that present an integral over time. As such, these systems are modeled with Integro-Differential Equations (IDEs); generalizations of differential equations that comprise both an integral and a differential component. For example, brain dynamics are not accurately modeled by differential equations since their behavior is non-Markovian, i.e. dynamics are in part dictated by history. Here, we introduce the Neural IDE (NIDE), a novel deep learning framework based on the theory of IDEs where integral operators are learned using neural networks. We test NIDE on several toy and brain activity datasets and demonstrate that NIDE outperforms other models. These tasks include time extrapolation as well as predicting dynamics from unseen initial conditions, which we test on whole-cortex activity recordings in freely behaving mice. Further, we show that NIDE can decompose dynamics into their Markovian and non-Markovian constituents via the learned integral operator, which we test on fMRI brain activity recordings of people on ketamine. Finally, the integrand of the integral operator provides a latent space that gives insight into the underlying dynamics, which we demonstrate on wide-field brain imaging recordings. Altogether, NIDE is a novel approach that enables modeling of complex non-local dynamics with neural networks., Comment: 18 pages (including 8 pages Appendix), 8 figures and 6 tables. v4: Final version with reviewers' comments included, to appear in AAAI-23 (up to formatting differences)

Published
2022

10. Examining the association between posttraumatic stress disorder and disruptions in cortical networks identified using data-driven methods

Author

Yang, Jin, Huggins, Ashley A., Sun, Delin, Baird, C. Lexi, Haswell, Courtney C., Frijling, Jessie L., Olff, Miranda, van Zuiden, Mirjam, Koch, Saskia B. J., Nawijn, Laura, Veltman, Dick J., Suarez-Jimenez, Benjamin, Zhu, Xi, Neria, Yuval, Hudson, Anna R., Mueller, Sven C., Baker, Justin T., Lebois, Lauren A. M., Kaufman, Milissa L., Qi, Rongfeng, Lu, Guang Ming, Říha, Pavel, Rektor, Ivan, Dennis, Emily L., Ching, Christopher R. K., Thomopoulos, Sophia I., Salminen, Lauren E., Jahanshad, Neda, Thompson, Paul M., Stein, Dan J., Koopowitz, Sheri M., Ipser, Jonathan C., Seedat, Soraya, du Plessis, Stefan, van den Heuvel, Leigh L., Wang, Li, Zhu, Ye, Li, Gen, Sierk, Anika, Manthey, Antje, Walter, Henrik, Daniels, Judith K., Schmahl, Christian, Herzog, Julia I., Liberzon, Israel, King, Anthony, Angstadt, Mike, Davenport, Nicholas D., Sponheim, Scott R., Disner, Seth G., Straube, Thomas, Hofmann, David, Grupe, Daniel W., Nitschke, Jack B., Davidson, Richard J., Larson, Christine L., deRoon-Cassini, Terri A., Blackford, Jennifer U., Olatunji, Bunmi O., Gordon, Evan M., May, Geoffrey, Nelson, Steven M., Abdallah, Chadi G., Levy, Ifat, Harpaz-Rotem, Ilan, Krystal, John H., Morey, Rajendra A., and Sotiras, Aristeidis

Published
2024
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11. A Framework for Reconstructing COVID-19 Transmission Network to Inform Betweenness Centrality-Based Control Measures

Author

Najem, Sara, Monni, Stefano, Hatoum, Rola, Sweidan, Hawraa, Faour, Ghaleb, Abdallah, Chadi, Ghosn, Nada, Hassan, Hamad, and Touma, Jihad

Subjects
Physics - Physics and Society, Statistics - Applications
Abstract

In this paper, we propose a general framework for optimal control measures, which follows the evolution of COVID-19 infection counts collected by Surveillance Units on a country level. We employ an autoregressive model that allows to decompose the mean number of infections into three components that describe: intra-locality infections, inter-locality infections, and infections from other sources such as travelers arriving to a country from abroad. We identify the inter-locality term as a time-evolving network and when it drives the dynamics of the disease we focus on its properties. Tools from network analysis are then employed to get insight into its topology. Building on this, and particularly on the centrality of the nodes of the identified network, a strategy for intervention and disease control is devised.

Published
2022

12. 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

14. Cortical volume abnormalities in posttraumatic stress disorder: an ENIGMA-psychiatric genomics consortium PTSD workgroup mega-analysis

Author

Wang, Xin, Xie, Hong, Chen, Tian, Cotton, Andrew S, Salminen, Lauren E, Logue, Mark W, Clarke-Rubright, Emily K, Wall, John, Dennis, Emily L, O’Leary, Brian M, Abdallah, Chadi G, Andrew, Elpiniki, Baugh, Lee A, Bomyea, Jessica, Bruce, Steven E, Bryant, Richard, Choi, Kyle, Daniels, Judith K, Davenport, Nicholas D, Davidson, Richard J, DeBellis, Michael, deRoon-Cassini, Terri, Disner, Seth G, Fani, Negar, Fercho, Kelene A, Fitzgerald, Jacklynn, Forster, Gina L, Frijling, Jessie L, Geuze, Elbert, Gomaa, Hassaan, Gordon, Evan M, Grupe, Dan, Harpaz-Rotem, Ilan, Haswell, Courtney C, Herzog, Julia I, Hofmann, David, Hollifield, Michael, Hosseini, Bobak, Hudson, Anna R, Ipser, Jonathan, Jahanshad, Neda, Jovanovic, Tanja, Kaufman, Milissa L, King, Anthony P, Koch, Saskia BJ, Koerte, Inga K, Korgaonkar, Mayuresh S, Krystal, John H, Larson, Christine, Lebois, Lauren AM, Levy, Ifat, Li, Gen, Magnotta, Vincent A, Manthey, Antje, May, Geoffrey, McLaughlin, Katie A, Mueller, Sven C, Nawijn, Laura, Nelson, Steven M, Neria, Yuval, Nitschke, Jack B, Olff, Miranda, Olson, Elizabeth A, Peverill, Matthew, Phan, K Luan, Rashid, Faisal M, Ressler, Kerry, Rosso, Isabelle M, Sambrook, Kelly, Schmahl, Christian, Shenton, Martha E, Sierk, Anika, Simons, Jeffrey S, Simons, Raluca M, Sponheim, Scott R, Stein, Murray B, Stein, Dan J, Stevens, Jennifer S, Straube, Thomas, Suarez-Jimenez, Benjamin, Tamburrino, Marijo, Thomopoulos, Sophia I, van der Wee, Nic JA, van der Werff, Steven JA, van Erp, Theo GM, van Rooij, Sanne JH, van Zuiden, Mirjam, Varkevisser, Tim, Veltman, Dick J, Vermeiren, Robert RJM, Walter, Henrik, Wang, Li, Zhu, Ye, Zhu, Xi, Thompson, Paul M, Morey, Rajendra A, and Liberzon, Israel

Subjects
Clinical Research, Behavioral and Social Science, Mental Health, Post-Traumatic Stress Disorder (PTSD), Neurosciences, Brain Disorders, Mental health, Cerebral Cortex, Genomics, Humans, Magnetic Resonance Imaging, Stress Disorders, Post-Traumatic, Temporal Lobe, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Studies of posttraumatic stress disorder (PTSD) report volume abnormalities in multiple regions of the cerebral cortex. However, findings for many regions, particularly regions outside commonly studied emotion-related prefrontal, insular, and limbic regions, are inconsistent and tentative. Also, few studies address the possibility that PTSD abnormalities may be confounded by comorbid depression. A mega-analysis investigating all cortical regions in a large sample of PTSD and control subjects can potentially provide new insight into these issues. Given this perspective, our group aggregated regional volumes data of 68 cortical regions across both hemispheres from 1379 PTSD patients to 2192 controls without PTSD after data were processed by 32 international laboratories using ENIGMA standardized procedures. We examined whether regional cortical volumes were different in PTSD vs. controls, were associated with posttraumatic stress symptom (PTSS) severity, or were affected by comorbid depression. Volumes of left and right lateral orbitofrontal gyri (LOFG), left superior temporal gyrus, and right insular, lingual and superior parietal gyri were significantly smaller, on average, in PTSD patients than controls (standardized coefficients = -0.111 to -0.068, FDR corrected P values

Published
2021

15. Altered white matter microstructural organization in posttraumatic stress disorder across 3047 adults: results from the PGC-ENIGMA PTSD consortium

Author

Dennis, Emily L, Disner, Seth G, Fani, Negar, Salminen, Lauren E, Logue, Mark, Clarke, Emily K, Haswell, Courtney C, Averill, Christopher L, Baugh, Lee A, Bomyea, Jessica, Bruce, Steven E, Cha, Jiook, Choi, Kyle, Davenport, Nicholas D, Densmore, Maria, du Plessis, Stefan, Forster, Gina L, Frijling, Jessie L, Gonenc, Atilla, Gruber, Staci, Grupe, Daniel W, Guenette, Jeffrey P, Hayes, Jasmeet, Hofmann, David, Ipser, Jonathan, Jovanovic, Tanja, Kelly, Sinead, Kennis, Mitzy, Kinzel, Philipp, Koch, Saskia BJ, Koerte, Inga, Koopowitz, Sheri, Korgaonkar, Mayuresh, Krystal, John, Lebois, Lauren AM, Li, Gen, Magnotta, Vincent A, Manthey, Antje, May, Geoff J, Menefee, Deleene S, Nawijn, Laura, Nelson, Steven M, Neufeld, Richard WJ, Nitschke, Jack B, O’Doherty, Daniel, Peverill, Matthew, Ressler, Kerry J, Roos, Annerine, Sheridan, Margaret A, Sierk, Anika, Simmons, Alan, Simons, Raluca M, Simons, Jeffrey S, Stevens, Jennifer, Suarez-Jimenez, Benjamin, Sullivan, Danielle R, Théberge, Jean, Tran, Jana K, van den Heuvel, Leigh, van der Werff, Steven JA, van Rooij, Sanne JH, van Zuiden, Mirjam, Velez, Carmen, Verfaellie, Mieke, Vermeiren, Robert RJM, Wade, Benjamin SC, Wager, Tor, Walter, Henrik, Winternitz, Sherry, Wolff, Jonathan, York, Gerald, Zhu, Ye, Zhu, Xi, Abdallah, Chadi G, Bryant, Richard, Daniels, Judith K, Davidson, Richard J, Fercho, Kelene A, Franz, Carol, Geuze, Elbert, Gordon, Evan M, Kaufman, Milissa L, Kremen, William S, Lagopoulos, Jim, Lanius, Ruth A, Lyons, Michael J, McCauley, Stephen R, McGlinchey, Regina, McLaughlin, Katie A, Milberg, William, Neria, Yuval, Olff, Miranda, Seedat, Soraya, Shenton, Martha, Sponheim, Scott R, Stein, Dan J, Stein, Murray B, Straube, Thomas, Tate, David F, and van der Wee, Nic JA

Subjects
Biological Psychology, Psychology, Post-Traumatic Stress Disorder (PTSD), Clinical Research, Mental Illness, Biomedical Imaging, Physical Injury - Accidents and Adverse Effects, Neurosciences, Behavioral and Social Science, Brain Disorders, Mental Health, Anxiety Disorders, 2.1 Biological and endogenous factors, Mental health, Good Health and Well Being, Adolescent, Adult, Aged, Aged, 80 and over, Anisotropy, Brain, Diffusion Tensor Imaging, Female, Humans, Male, Middle Aged, Stress Disorders, Post-Traumatic, White Matter, Young Adult, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

A growing number of studies have examined alterations in white matter organization in people with posttraumatic stress disorder (PTSD) using diffusion MRI (dMRI), but the results have been mixed which may be partially due to relatively small sample sizes among studies. Altered structural connectivity may be both a neurobiological vulnerability for, and a result of, PTSD. In an effort to find reliable effects, we present a multi-cohort analysis of dMRI metrics across 3047 individuals from 28 cohorts currently participating in the PGC-ENIGMA PTSD working group (a joint partnership between the Psychiatric Genomics Consortium and the Enhancing NeuroImaging Genetics through Meta-Analysis consortium). Comparing regional white matter metrics across the full brain in 1426 individuals with PTSD and 1621 controls (2174 males/873 females) between ages 18-83, 92% of whom were trauma-exposed, we report associations between PTSD and disrupted white matter organization measured by lower fractional anisotropy (FA) in the tapetum region of the corpus callosum (Cohen's d = -0.11, p = 0.0055). The tapetum connects the left and right hippocampus, for which structure and function have been consistently implicated in PTSD. Results were consistent even after accounting for the effects of multiple potentially confounding variables: childhood trauma exposure, comorbid depression, history of traumatic brain injury, current alcohol abuse or dependence, and current use of psychotropic medications. Our results show that PTSD may be associated with alterations in the broader hippocampal network.

Published
2021

17. A robust and reproducible connectome fingerprint of ketamine is highly associated with the connectomic signature of antidepressants

Author

Abdallah, Chadi G, Ahn, Kyung-Heup, Averill, Lynnette A, Nemati, Samaneh, Averill, Christopher L, Fouda, Samar, Ranganathan, Mohini, Morgan, Peter T, D’Souza, Deepak C, Mathalon, Daniel H, Krystal, John H, and Driesen, Naomi R

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Psychology, Pharmacology and Pharmaceutical Sciences, Clinical Research, Clinical Trials and Supportive Activities, Depression, Neurosciences, Mental Health, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Generic health relevance, Mental health, Antidepressive Agents, Connectome, Depressive Disorder, Major, Humans, Ketamine, Magnetic Resonance Imaging, Reproducibility of Results, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological psychology
Abstract

Over the past decade, various N-methyl-D-aspartate modulators have failed in clinical trials, underscoring the challenges of developing novel rapid-acting antidepressants based solely on the receptor or regional targets of ketamine. Thus, identifying the effect of ketamine on the brain circuitry and networks is becoming increasingly critical. In this longitudinal functional magnetic resonance imaging study of data from 265 participants, we used a validated predictive model approach that allows the full assessment of brain functional connectivity, without the need for seed selection or connectivity summaries. First, we identified a connectome fingerprint (CFP) in healthy participants (Cohort A, n = 25) during intravenous infusion of a subanesthetic dose of ketamine, compared to normal saline. We then demonstrated the robustness and reproducibility of the discovered ketamine CFP in two separate healthy samples (Cohort B, n = 22; Cohort C, n = 18). Finally, we investigated the ketamine CFP connectivity at 1-week post treatment in major depressive disorder patients randomized to 8 weeks of sertraline or placebo (Cohort D, n = 200). We found a significant, robust, and reproducible ketamine CFP, consistent with reduced connectivity within the primary cortices and within the executive network, but increased connectivity between the executive network and the rest of the brain. Compared to placebo, the ketamine CFP connectivity changes at 1 week predicted response to sertraline at 8 weeks. In each of Cohorts A-C, ketamine significantly increased connectivity in a previously identified antidepressant CFP. Investigating the brain connectivity networks, we successfully identified a robust and reproducible ketamine biomarker that is related to the mechanisms of antidepressants.

Published
2021

19. International pooled patient-level meta-analysis of ketamine infusion for depression: In search of clinical moderators

Author

Price, Rebecca B., Kissel, Nicholas, Baumeister, Andrew, Rohac, Rebecca, Woody, Mary L., Ballard, Elizabeth D., Zarate, Jr, Carlos A., Deakin, William, Abdallah, Chadi G., Feder, Adriana, Charney, Dennis S., Grunebaum, Michael F., Mann, J. John, Mathew, Sanjay J., Gallagher, Bronagh, McLoughlin, Declan M., Murrough, James W., Muthukumaraswamy, Suresh, McMillan, Rebecca, Sumner, Rachael, Papakostas, George, Fava, Maurizio, Hock, Rebecca, Phillips, Jennifer L., Blier, Pierre, Shiroma, Paulo, Šóš, Peter, Su, Tung-Ping, Chen, Mu-Hong, Tiger, Mikael, Lundberg, Johan, Wilkinson, Samuel T., and Wallace, Meredith L.

Published
2022
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21. Water Balance and Demand for Different Environmental Changes and Management Scenarios in the Hasbani Basin Using a WEAP Model and Geospatial Data

Author

Merheb, Mohammad, Abdallah, Chadi, Kostianoy, Andrey, Series Editor, Carpenter, Angela, Editorial Board Member, Younos, Tamim, Editorial Board Member, Scozzari, Andrea, Editorial Board Member, Vignudelli, Stefano, Editorial Board Member, Kouraev, Alexei, Editorial Board Member, and Shaban, Amin, editor

Published
2022
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22. 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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24. Dose-related effects of ketamine for antidepressant-resistant symptoms of posttraumatic stress disorder in veterans and active duty military: a double-blind, randomized, placebo-controlled multi-center clinical trial

Author

Abdallah, Chadi G., Roache, John D., Gueorguieva, Ralitza, Averill, Lynnette A., Young-McCaughan, Stacey, Shiroma, Paulo R., Purohit, Prerana, Brundige, Antoinette, Murff, William, Ahn, Kyung-Heup, Sherif, Mohamed A., Baltutis, Eric J., Ranganathan, Mohini, D’Souza, Deepak, Martini, Brenda, Southwick, Steven M., Petrakis, Ismene L., Burson, Rebecca R., Guthmiller, Kevin B., López-Roca, Argelio L., Lautenschlager, Karl A., McCallin, III, John P., Hoch, Matthew B., Timchenko, Alexandar, Souza, Sergio E., Bryant, Charles E., Mintz, Jim, Litz, Brett T., Williamson, Douglas E., Keane, Terence M., Peterson, Alan L., and Krystal, John H.

Published
2022
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30. STRONG STAR and the Consortium to Alleviate PTSD: Shaping the future of combat PTSD and related conditions in military and veteran populations

Author

Peterson, Alan L., Young-McCaughan, Stacey, Roache, John D., Mintz, Jim, Litz, Brett T., Williamson, Douglas E., Resick, Patricia A., Foa, Edna B., McGeary, Donald D., Dondanville, Katherine A., Taylor, Daniel J., Wachen, Jennifer Schuster, Fox, Peter T., Bryan, Craig J., McLean, Carmen P., Pruiksma, Kristi E., Yarvis, Jeffrey S., Niles, Barbara L., Abdallah, Chadi G., Averill, Lynnette A., Back, Sudie E., Baker, Monty T., Blount, Tabatha H., Borah, Adam M., Borah, Elisa V., Brock, Matthew S., Brown, Lily A., Burg, Matthew M., Cigrang, Jeffrey A., DeBeer, Bryann B., DeVoe, Ellen R., Fina, Brooke A., Flanagan, Julianne C., Fredman, Steffany J., Gardner, Cubby L., Gatchel, Robert R., Goodie, Jeffrey L., Gueorguieva, Ralitza, Higgs, Jay B., Jacoby, Vanessa M., Kelly, Kevin M., Krystal, John H., Lapiz-Bluhm, M. Danet, López-Roca, Argelio L., Marx, Brian P., Maurer, Douglas M., McDevitt-Murphy, Meghan E., McGeary, Cindy A., Meyer, Eric C., Miles, Shannon R., Monson, Candice M., Morilak, David A., Moring, John C., Mysliwiec, Vincent, Nicholson, Karin L., Rauch, Sheila A.M., Riggs, David S., Rosen, Craig S., Rudd, M. David, Schobitz, Richard P., Schrader, Christian C., Shinn, Antoinette M., Shiroma, Paulo R., Sloan, Denise M., Stern, Stephen L., Strong, Randy, Vannoy, Steven D., Young, Keith A., and Keane, Terence M.

Published
2021
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33. Prefrontal Connectivity and Glutamate Transmission: Relevance to Depression Pathophysiology and Ketamine Treatment

Author

Abdallah, Chadi G, Averill, Christopher L, Salas, Ramiro, Averill, Lynnette A, Baldwin, Philip R, Krystal, John H, Mathew, Sanjay J, and Mathalon, Daniel H

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Psychology, Pharmacology and Pharmaceutical Sciences, Mental Health, Neurosciences, Serious Mental Illness, Depression, Major Depressive Disorder, Clinical Research, Brain Disorders, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Mental health, Functional MRI, Global brain connectivity, Glutamate, Ketamine, Rapid-acting antidepressants, Treatment-resistant depression, Treatment resistant depression, functional MRI, global brain connectivity, glutamate, ketamine, rapid acting antidepressants, Biological psychology, Clinical and health psychology
Abstract

BackgroundPrefrontal global brain connectivity with global signal regression (GBCr) was proposed as a robust biomarker of depression, and was associated with ketamine's mechanism of action. Here, we investigated prefrontal GBCr in treatment-resistant depression (TRD) at baseline and following treatment. Then, we conducted a set of pharmacological challenges in healthy subjects to investigate the glutamate neurotransmission correlates of GBCr.MethodsIn study A, we used functional magnetic resonance imaging (fMRI) to compare GBCr between 22 TRD and 29 healthy control. Then, we examined the effects of ketamine and midazolam on GBCr in TRD patients 24h post-treatment. In study B, we acquired repeated fMRI in 18 healthy subjects to determine the effects of lamotrigine (a glutamate release inhibitor), ketamine, and lamotrigine-by-ketamine interaction.ResultsIn study A, TRD patients showed significant reduction in dorsomedial and dorsolateral prefrontal GBCr compared to healthy control. In TRD patients, GBCr in the altered clusters significantly increased 24h following ketamine (effect size = 1.0 [0.3 1.8]), but not midazolam (effect size = 0.5 [-0.6 1.3]). In study B, oral lamotrigine reduced GBCr 2h post-administration, while ketamine increased medial prefrontal GBCr during infusion. Lamotrigine significantly reduced the ketamine-induced GBCr surge. Exploratory analyses showed elevated ventral prefrontal GBCr in TRD and significant reduction of ventral prefrontal GBCr during ketamine infusion in healthy subjects.ConclusionsThis study provides first replication of the ability of ketamine to normalize depression-related prefrontal dysconnectivity. It also provides indirect evidence that these effects may be triggered by the capacity of ketamine to enhance glutamate neurotransmission.

Published
2017

36. Medial prefrontal cortex neurotransmitter abnormalities in posttraumatic stress disorder with and without comorbidity to major depression.

Author

Swanberg, Kelley M., Prinsen, Hetty, Averill, Christopher L., Campos, Leonardo, Kurada, Abhinav V., Krystal, John H., Petrakis, Ismene L., Averill, Lynnette A., Rothman, Douglas L., Abdallah, Chadi G., and Juchem, Christoph

Subjects
POST-traumatic stress disorder, MENTAL depression, PREFRONTAL cortex, BRAIN abnormalities, GLUTAMINE
Abstract

Posttraumatic stress disorder (PTSD) is a chronic psychiatric condition that follows exposure to a traumatic stressor. Though previous in vivo proton (1H) MRS) research conducted at 4 T or lower has identified alterations in glutamate metabolism associated with PTSD predisposition and/or progression, no prior investigations have been conducted at higher field strength. In addition, earlier studies have not extensively addressed the impact of psychiatric comorbidities such as major depressive disorder (MDD) on PTSD‐associated 1H‐MRS‐visible brain metabolite abnormalities. Here we employ 7 T 1H MRS to examine concentrations of glutamate, glutamine, GABA, and glutathione in the medial prefrontal cortex (mPFC) of PTSD patients with MDD (PTSD+MDD+; N = 6) or without MDD (PTSD+MDD−; N = 5), as well as trauma‐unmatched controls without PTSD but with MDD (PTSD−MDD+; N = 9) or without MDD (PTSD−MDD−; N = 18). Participants with PTSD demonstrated decreased ratios of GABA to glutamine relative to healthy PTSD−MDD− controls but no single‐metabolite abnormalities. When comorbid MDD was considered, however, MDD but not PTSD diagnosis was significantly associated with increased mPFC glutamine concentration and decreased glutamate:glutamine ratio. In addition, all participants with PTSD and/or MDD collectively demonstrated decreased glutathione relative to healthy PTSD−MDD− controls. Despite limited findings in single metabolites, patterns of abnormality in prefrontal metabolite concentrations among individuals with PTSD and/or MDD enabled supervised classification to separate them from healthy controls with 80+% sensitivity and specificity, with glutathione, glutamine, and myoinositol consistently among the most informative metabolites for this classification. Our findings indicate that MDD can be an important factor in mPFC glutamate metabolism abnormalities observed using 1H MRS in cohorts with PTSD. [ABSTRACT FROM AUTHOR]

Published
2024
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37. DSM-5 Criterion-A-Based Trauma Types in Service Members and Veterans Seeking Treatment for Posttraumatic Stress Disorder.

Author

Benfer, Natasha, Grunthal, Breanna, Dondanville, Katherine A., Young-McCaughan, Stacey, Blankenship, Abby, Abdallah, Chadi G., Back, Sudie E., Flanagan, Julianne, Foa, Edna B., Fox, Peter T., Krystal, John H., Marx, Brian P., McGeary, Donald D., McLean, Carmen P., Pruiksma, Kristi E., Resick, Patricia A., Roache, John D., Shiroma, Paulo, Sloan, Denise M., and Taylor, Daniel J.

Published
2024
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44. Smaller total and subregional cerebellar volumes in posttraumatic stress disorder:a mega-analysis by the ENIGMA-PGC PTSD workgroup

Author

Huggins, Ashley A., Baird, C. Lexi, Briggs, Melvin, Laskowitz, Sarah, Hussain, Ahmed, Fouda, Samar, Haswell, Courtney, Sun, Delin, Salminen, Lauren E., Jahanshad, Neda, Thomopoulos, Sophia I., Veltman, Dick J., Frijling, Jessie L., Olff, Miranda, van Zuiden, Mirjam, Koch, Saskia B.J., Nawjin, Laura, Wang, Li, Zhu, Ye, Li, Gen, Stein, Dan J., Ipser, Jonathan, Seedat, Soraya, du Plessis, Stefan, van den Heuvel, Leigh L., Suarez-Jimenez, Benjamin, Zhu, Xi, Kim, Yoojean, He, Xiaofu, Zilcha-Mano, Sigal, Lazarov, Amit, Neria, Yuval, Stevens, Jennifer S., Ressler, Kerry J., Jovanovic, Tanja, van Rooij, Sanne J.H., Fani, Negar, Hudson, Anna R., Mueller, Sven C., Sierk, Anika, Manthey, Antje, Walter, Henrik, Daniels, Judith K., Schmahl, Christian, Herzog, Julia I., Říha, Pavel, Rektor, Ivan, Lebois, Lauren A.M., Kaufman, Milissa L., Olson, Elizabeth A., Baker, Justin T., Rosso, Isabelle M., King, Anthony P., Liberzon, Isreal, Angstadt, Mike, Davenport, Nicholas D., Sponheim, Scott R., Disner, Seth G., Straube, Thomas, Hofmann, David, Qi, Rongfeng, Lu, Guang Ming, Baugh, Lee A., Forster, Gina L., Simons, Raluca M., Simons, Jeffrey S., Magnotta, Vincent A., Fercho, Kelene A., Maron-Katz, Adi, Etkin, Amit, Cotton, Andrew S., O’Leary, Erin N., Xie, Hong, Wang, Xin, Quidé, Yann, El-Hage, Wissam, Lissek, Shmuel, Berg, Hannah, Bruce, Steven, Cisler, Josh, Ross, Marisa, Herringa, Ryan J., Grupe, Daniel W., Nitschke, Jack B., Davidson, Richard J., Larson, Christine L., deRoon-Cassini, Terri A., Tomas, Carissa W., Fitzgerald, Jacklynn M., Blackford, Jennifer Urbano, Olatunji, Bunmi O., Kremen, William S., Lyons, Michael J., Franz, Carol E., Gordon, Evan M., May, Geoffrey, Nelson, Steven M., Abdallah, Chadi G., Levy, Ifat, Harpaz-Rotem, Ilan, Krystal, John H., Dennis, Emily L., Tate, David F., Cifu, David X., Walker, William C., Wilde, Elizabeth A., Harding, Ian H., Kerestes, Rebecca, Thompson, Paul M., Morey, Rajendra, Huggins, Ashley A., Baird, C. Lexi, Briggs, Melvin, Laskowitz, Sarah, Hussain, Ahmed, Fouda, Samar, Haswell, Courtney, Sun, Delin, Salminen, Lauren E., Jahanshad, Neda, Thomopoulos, Sophia I., Veltman, Dick J., Frijling, Jessie L., Olff, Miranda, van Zuiden, Mirjam, Koch, Saskia B.J., Nawjin, Laura, Wang, Li, Zhu, Ye, Li, Gen, Stein, Dan J., Ipser, Jonathan, Seedat, Soraya, du Plessis, Stefan, van den Heuvel, Leigh L., Suarez-Jimenez, Benjamin, Zhu, Xi, Kim, Yoojean, He, Xiaofu, Zilcha-Mano, Sigal, Lazarov, Amit, Neria, Yuval, Stevens, Jennifer S., Ressler, Kerry J., Jovanovic, Tanja, van Rooij, Sanne J.H., Fani, Negar, Hudson, Anna R., Mueller, Sven C., Sierk, Anika, Manthey, Antje, Walter, Henrik, Daniels, Judith K., Schmahl, Christian, Herzog, Julia I., Říha, Pavel, Rektor, Ivan, Lebois, Lauren A.M., Kaufman, Milissa L., Olson, Elizabeth A., Baker, Justin T., Rosso, Isabelle M., King, Anthony P., Liberzon, Isreal, Angstadt, Mike, Davenport, Nicholas D., Sponheim, Scott R., Disner, Seth G., Straube, Thomas, Hofmann, David, Qi, Rongfeng, Lu, Guang Ming, Baugh, Lee A., Forster, Gina L., Simons, Raluca M., Simons, Jeffrey S., Magnotta, Vincent A., Fercho, Kelene A., Maron-Katz, Adi, Etkin, Amit, Cotton, Andrew S., O’Leary, Erin N., Xie, Hong, Wang, Xin, Quidé, Yann, El-Hage, Wissam, Lissek, Shmuel, Berg, Hannah, Bruce, Steven, Cisler, Josh, Ross, Marisa, Herringa, Ryan J., Grupe, Daniel W., Nitschke, Jack B., Davidson, Richard J., Larson, Christine L., deRoon-Cassini, Terri A., Tomas, Carissa W., Fitzgerald, Jacklynn M., Blackford, Jennifer Urbano, Olatunji, Bunmi O., Kremen, William S., Lyons, Michael J., Franz, Carol E., Gordon, Evan M., May, Geoffrey, Nelson, Steven M., Abdallah, Chadi G., Levy, Ifat, Harpaz-Rotem, Ilan, Krystal, John H., Dennis, Emily L., Tate, David F., Cifu, David X., Walker, William C., Wilde, Elizabeth A., Harding, Ian H., Kerestes, Rebecca, Thompson, Paul M., and Morey, Rajendra

Abstract

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p -FDR < 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status. These findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in higher-order cognitive and emotional processes, far beyond its historical association with vestibulomotor function. Further examination of the cerebellum in trauma-related psychopathology will help to clarify how cerebellar structure and function may disrupt cognitive and affective processes at the center of translational models for PTSD.

Published
2024

46. Correction to: Dose-related effects of ketamine for antidepressant-resistant symptoms of posttraumatic stress disorder in veterans and active duty military: a double-blind, randomized, placebo-controlled multi-center clinical trial

Author

Abdallah, Chadi G., Roache, John D., Gueorguieva, Ralitza, Averill, Lynnette A., Young-McCaughan, Stacey, Shiroma, Paulo R., Purohit, Prerana, Brundige, Antoinette, Murff, William, Ahn, Kyung-Heup, Sherif, Mohamed A., Baltutis, Eric J., Ranganathan, Mohini, D’Souza, Deepak, Martini, Brenda, Southwick, Steven M., Petrakis, Ismene L., Burson, Rebecca R., Guthmiller, Kevin B., López-Roca, Argelio L., Lautenschlager, Karl A., McCallin, III, John P., Hoch, Matthew B., Timchenko, Alexandar, Souza, Sergio E., Bryant, Charles E., Mintz, Jim, Litz, Brett T., Williamson, Douglas E., Keane, Terence M., Peterson, Alan L., and Krystal, John H.

Published
2022
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48. Repeated ketamine infusions for antidepressant-resistant PTSD: Methods of a multicenter, randomized, placebo-controlled clinical trial

Author

Abdallah, Chadi G., Roache, John D., Averill, Lynnette A., Young-McCaughan, Stacey, Martini, Brenda, Gueorguieva, Ralitza, Amoroso, Timothy, Southwick, Steven M., Guthmiller, Kevin, López-Roca, Argelio L., Lautenschlager, Karl, Mintz, Jim, Litz, Brett T., Williamson, Douglas E., Keane, Terence M., Peterson, Alan L., and Krystal, John H.

Published
2019
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50. Scenario simulation and analysis in the transboundary Yarmouk River basin using a WEAP model.

Author

Al Sabeh, Hassan, Abdallah, Chadi, Merheb, Mohammad, and Zeitoun, Mark

Subjects
*WATER management, *WATER shortages, *IRRIGATION efficiency, *HYDROLOGY, *ENVIRONMENTAL infrastructure, *WATERSHEDS
Abstract

Water is a finite resource but an essential one. The continuously increasing demand leads to competition and conflict over limited resources. Syria, Jordan, and Israel compete over the water resources in the transboundary Yarmouk River basin where two water agreements dictate the allocation of water. The two arrangements are far from being efficient and fair while little cooperation is being made over sharing the resources that are being over-exploited. In this study, water sustainability was investigated under projected developments and trends based on the current use and allocation regime in the watershed using several scenarios. A one bucket soil moisture model was adopted and optimized in order to fully represent the ever-changing hydrology of the basin using the Water Evaluation and Planning (WEAP) tool. Demands along with the existing water infrastructure and their operation were modelled despite the lack of many data and the huge uncertainty in some. Simulation of business as usual scenario showed that continuing with the current use cannot be sustainable in the short and long term. The growth of both agriculture and population produced huge water shortages in all demand sectors. Under climate change scenarios RCP 4.5 and RCP 8.5, surface water availability and the retention of dams reduced significantly. The share of Jordan from the Yarmouk River was the most vulnerable to climate change impacts. Enhancing irrigation efficiency and a more stable population growth based on the UN medium variant population projection showed improvements in water coverage within all demand sectors. Analysis of future scenarios suggests that water shortage is expected in all riparian states of the basin but can be mitigated by reducing demands. [ABSTRACT FROM AUTHOR]

Published
2024
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