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Ben-Zion Z, Korem N, Fine NB, Katz S, Siddhanta M, Funaro MC, Duek O, Spiller TR, Danböck SK, Levy I, Harpaz-Rotem I. Structural Neuroimaging of Hippocampus and Amygdala Subregions in Posttraumatic Stress Disorder: A Scoping Review. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:120-134. [PMID: 38298789 PMCID: PMC10829655 DOI: 10.1016/j.bpsgos.2023.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/28/2023] [Accepted: 07/02/2023] [Indexed: 02/02/2024] Open
Abstract
Numerous studies have explored the relationship between posttraumatic stress disorder (PTSD) and the hippocampus and the amygdala because both regions are implicated in the disorder's pathogenesis and pathophysiology. Nevertheless, those key limbic regions consist of functionally and cytoarchitecturally distinct substructures that may play different roles in the etiology of PTSD. Spurred by the availability of automatic segmentation software, structural neuroimaging studies of human hippocampal and amygdala subregions have proliferated in recent years. Here, we present a preregistered scoping review of the existing structural neuroimaging studies of the hippocampus and amygdala subregions in adults diagnosed with PTSD. A total of 3513 studies assessing subregion volumes were identified, 1689 of which were screened, and 21 studies were eligible for this review (total N = 2876 individuals). Most studies examined hippocampal subregions and reported decreased CA1, CA3, dentate gyrus, and subiculum volumes in PTSD. Fewer studies investigated amygdala subregions and reported altered lateral, basal, and central nuclei volumes in PTSD. This review further highlights the conceptual and methodological limitations of the current literature and identifies future directions to increase understanding of the distinct roles of hippocampal and amygdalar subregions in posttraumatic psychopathology.
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Affiliation(s)
- Ziv Ben-Zion
- Yale School of Medicine, Yale University, New Haven, Connecticut
- US Department of Veterans Affairs National Center for PTSD, Clinical Neuroscience Division, VA Connecticut Healthcare System, West Haven, Connecticut
- Wu Tsai Institute, Yale University, New Haven, Connecticut
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Nachshon Korem
- Yale School of Medicine, Yale University, New Haven, Connecticut
- US Department of Veterans Affairs National Center for PTSD, Clinical Neuroscience Division, VA Connecticut Healthcare System, West Haven, Connecticut
| | - Naomi B Fine
- Sagol Brain Institute Tel-Aviv, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Faculty of Social Sciences, School of Psychological Science, Tel Aviv University, Tel Aviv, Israel
| | - Sophia Katz
- Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Megha Siddhanta
- Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Melissa C Funaro
- Harvey Cushing/John Hay Whitney Medical Library, Yale University, New Haven, Connecticut
| | - Or Duek
- Yale School of Medicine, Yale University, New Haven, Connecticut
- US Department of Veterans Affairs National Center for PTSD, Clinical Neuroscience Division, VA Connecticut Healthcare System, West Haven, Connecticut
- Department of Epidemiology, Biostatistics and Community Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Tobias R Spiller
- Yale School of Medicine, Yale University, New Haven, Connecticut
- US Department of Veterans Affairs National Center for PTSD, Clinical Neuroscience Division, VA Connecticut Healthcare System, West Haven, Connecticut
- Department of Consultation-Liaison Psychiatry and Psychosomatic Medicine, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Sarah K Danböck
- Yale School of Medicine, Yale University, New Haven, Connecticut
- Division of Clinical Psychology and Psychopathology, Department of Psychology, Paris London University of Salzburg, Salzburg, Austria
| | - Ifat Levy
- Yale School of Medicine, Yale University, New Haven, Connecticut
- Wu Tsai Institute, Yale University, New Haven, Connecticut
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Ilan Harpaz-Rotem
- Yale School of Medicine, Yale University, New Haven, Connecticut
- US Department of Veterans Affairs National Center for PTSD, Clinical Neuroscience Division, VA Connecticut Healthcare System, West Haven, Connecticut
- Wu Tsai Institute, Yale University, New Haven, Connecticut
- Department of Psychology, Yale University, New Haven, Connecticut
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Duan Z, Dai Y, Hwang A, Lee C, Xie K, Xiao C, Xu M, Girgenti MJ, Zhang J. iHerd: an integrative hierarchical graph representation learning framework to quantify network changes and prioritize risk genes in disease. PLoS Comput Biol 2023; 19:e1011444. [PMID: 37695793 PMCID: PMC10513318 DOI: 10.1371/journal.pcbi.1011444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 09/21/2023] [Accepted: 08/19/2023] [Indexed: 09/13/2023] Open
Abstract
Different genes form complex networks within cells to carry out critical cellular functions, while network alterations in this process can potentially introduce downstream transcriptome perturbations and phenotypic variations. Therefore, developing efficient and interpretable methods to quantify network changes and pinpoint driver genes across conditions is crucial. We propose a hierarchical graph representation learning method, called iHerd. Given a set of networks, iHerd first hierarchically generates a series of coarsened sub-graphs in a data-driven manner, representing network modules at different resolutions (e.g., the level of signaling pathways). Then, it sequentially learns low-dimensional node representations at all hierarchical levels via efficient graph embedding. Lastly, iHerd projects separate gene embeddings onto the same latent space in its graph alignment module to calculate a rewiring index for driver gene prioritization. To demonstrate its effectiveness, we applied iHerd on a tumor-to-normal GRN rewiring analysis and cell-type-specific GCN analysis using single-cell multiome data of the brain. We showed that iHerd can effectively pinpoint novel and well-known risk genes in different diseases. Distinct from existing models, iHerd's graph coarsening for hierarchical learning allows us to successfully classify network driver genes into early and late divergent genes (EDGs and LDGs), emphasizing genes with extensive network changes across and within signaling pathway levels. This unique approach for driver gene classification can provide us with deeper molecular insights. The code is freely available at https://github.com/aicb-ZhangLabs/iHerd. All other relevant data are within the manuscript and supporting information files.
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Affiliation(s)
- Ziheng Duan
- Department of Computer Science, University of California, Irvine, California, United States of America
| | - Yi Dai
- Department of Computer Science, University of California, Irvine, California, United States of America
| | - Ahyeon Hwang
- Department of Computer Science, University of California, Irvine, California, United States of America
| | - Cheyu Lee
- Department of Computer Science, University of California, Irvine, California, United States of America
| | - Kaichi Xie
- Department of Computer Science, University of California, Davis, California, United States of America
| | - Chutong Xiao
- Department of Computer Science, University of California, Irvine, California, United States of America
| | - Min Xu
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Matthew J. Girgenti
- Department of Psychiatry, School of Medicine, Yale University, New Haven, Connecticut, United States of America
- Clinical Neurosciences Division, National Center for PTSD, U.S. Department of Veterans Affairs, West Haven, Connecticut, United States of America
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, California, United States of America
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Fitzgerald JM, Webb EK, Weis CN, Huggins AA, Bennett KP, Miskovich TA, Krukowski JL, deRoon-Cassini TA, Larson CL. Hippocampal Resting-State Functional Connectivity Forecasts Individual Posttraumatic Stress Disorder Symptoms: A Data-Driven Approach. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:139-149. [PMID: 34478884 PMCID: PMC8825698 DOI: 10.1016/j.bpsc.2021.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 07/18/2021] [Accepted: 08/22/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND Posttraumatic stress disorder (PTSD) is a debilitating disorder, and there is no current accurate prediction of who develops it after trauma. Neurobiologically, individuals with chronic PTSD exhibit aberrant resting-state functional connectivity (rsFC) between the hippocampus and other brain regions (e.g., amygdala, prefrontal cortex, posterior cingulate), and these aberrations correlate with severity of illness. Previous small-scale research (n < 25) has also shown that hippocampal rsFC measured acutely after trauma is predictive of future severity using a region-of-interest-based approach. While this is a promising biomarker, to date, no study has used a data-driven approach to test whole-brain hippocampal FC patterns in forecasting the development of PTSD symptoms. METHODS A total of 98 adults at risk of PTSD were recruited from the emergency department after traumatic injury and completed resting-state functional magnetic resonance imaging (8 min) within 1 month; 6 months later, they completed the Clinician-Administered PTSD Scale for DSM-5 for assessment of PTSD symptom severity. Whole-brain rsFC values with bilateral hippocampi were extracted (using CONN) and used in a machine learning kernel ridge regression analysis (PRoNTo); a k-folds (k = 10) and 70/30 testing versus training split approach were used for cross-validation (1000 iterations to bootstrap confidence intervals for significance values). RESULTS Acute hippocampal rsFC significantly predicted Clinician-Administered PTSD Scale for DSM-5 scores at 6 months (r = 0.30, p = .006; mean squared error = 120.58, p = .006; R2 = 0.09, p = .025). In post hoc analyses, hippocampal rsFC remained significant after controlling for demographics, PTSD symptoms at baseline, and depression, anxiety, and stress severity at 6 months (B = 0.59, SE = 0.20, p = .003). CONCLUSIONS Findings suggest that functional connectivity of the hippocampus across the brain acutely after traumatic injury is associated with prospective PTSD symptom severity.
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Affiliation(s)
| | - Elisabeth Kate Webb
- University of Wisconsin-Milwaukee, Department of Psychology, Milwaukee, WI, USA
| | - Carissa N. Weis
- University of Wisconsin-Milwaukee, Department of Psychology, Milwaukee, WI, USA
| | - Ashley A. Huggins
- Medical University of South Carolina, Department of Psychiatry, Charleston, SC, USA
| | | | | | | | - Terri A. deRoon-Cassini
- Medical College of Wisconsin, Department of Surgery, Division of Trauma & Acute Care Surgery, Milwaukee, WI, USA
| | - Christine L. Larson
- University of Wisconsin-Milwaukee, Department of Psychology, Milwaukee, WI, USA
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Zheng Y, Garrett ME, Sun D, Clarke-Rubright EK, Haswell CC, Maihofer AX, Elman JA, Franz CE, Lyons MJ, Kremen WS, Peverill M, Sambrook K, McLaughlin KA, Davenport ND, Disner S, Sponheim SR, Andrew E, Korgaonkar M, Bryant R, Varkevisser T, Geuze E, Coleman J, Beckham JC, Kimbrel NA, Sullivan D, Miller M, Hayes J, Verfaellie M, Wolf E, Salat D, Spielberg JM, Milberg W, McGlinchey R, Dennis EL, Thompson PM, Medland S, Jahanshad N, Nievergelt CM, Ashley-Koch AE, Logue MW, Morey RA. Trauma and posttraumatic stress disorder modulate polygenic predictors of hippocampal and amygdala volume. Transl Psychiatry 2021; 11:637. [PMID: 34916497 PMCID: PMC8677780 DOI: 10.1038/s41398-021-01707-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 10/05/2021] [Accepted: 10/20/2021] [Indexed: 11/08/2022] Open
Abstract
The volume of subcortical structures represents a reliable, quantitative, and objective phenotype that captures genetic effects, environmental effects such as trauma, and disease effects such as posttraumatic stress disorder (PTSD). Trauma and PTSD represent potent exposures that may interact with genetic markers to influence brain structure and function. Genetic variants, associated with subcortical volumes in two large normative discovery samples, were used to compute polygenic scores (PGS) for the volume of seven subcortical structures. These were applied to a target sample enriched for childhood trauma and PTSD. Subcortical volume PGS from the discovery sample were strongly associated in our trauma/PTSD enriched sample (n = 7580) with respective subcortical volumes of the hippocampus (p = 1.10 × 10-20), thalamus (p = 7.46 × 10-10), caudate (p = 1.97 × 10-18), putamen (p = 1.7 × 10-12), and nucleus accumbens (p = 1.99 × 10-7). We found a significant association between the hippocampal volume PGS and hippocampal volume in control subjects from our sample, but was absent in individuals with PTSD (GxE; (beta = -0.10, p = 0.027)). This significant GxE (PGS × PTSD) relationship persisted (p < 1 × 10-19) in four out of five threshold peaks (0.024, 0.133, 0.487, 0.730, and 0.889) used to calculate hippocampal volume PGSs. We detected similar GxE (G × ChildTrauma) relationships in the amygdala for exposure to childhood trauma (rs4702973; p = 2.16 × 10-7) or PTSD (rs10861272; p = 1.78 × 10-6) in the CHST11 gene. The hippocampus and amygdala are pivotal brain structures in mediating PTSD symptomatology. Trauma exposure and PTSD modulate the effect of polygenic markers on hippocampal volume (GxE) and the amygdala volume PGS is associated with PTSD risk, which supports the role of amygdala volume as a risk factor for PTSD.
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Affiliation(s)
- Yuanchao Zheng
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Melanie E Garrett
- Department of Medicine, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
- VISN 6 MIRECC, Durham VA Health Care System, Durham, NC, USA
| | - Delin Sun
- VISN 6 MIRECC, Durham VA Health Care System, Durham, NC, USA
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
| | - Emily K Clarke-Rubright
- VISN 6 MIRECC, Durham VA Health Care System, Durham, NC, USA
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
| | - Courtney C Haswell
- VISN 6 MIRECC, Durham VA Health Care System, Durham, NC, USA
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
| | - Adam X Maihofer
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Jeremy A Elman
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Carol E Franz
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - William S Kremen
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
- Center of Excellence for Stress and Mental Health, Veterans Affairs San Diego Healthcare System, La Jolla, CA, USA
| | - Matthew Peverill
- Department of Psychology, University of Washington, Seattle, WA, USA
| | - Kelly Sambrook
- Department of Psychology, Harvard University, Boston, MA, USA
| | | | - Nicholas D Davenport
- Minneapolis VA Health Care System, Minneapolis, MN, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Seth Disner
- Minneapolis VA Health Care System, Minneapolis, MN, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Scott R Sponheim
- Minneapolis VA Health Care System, Minneapolis, MN, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | | | - Mayuresh Korgaonkar
- Brain Dynamics Centre, Westmead Institute of Medical Research, University of Sydney, Westmead, NSW, Australia
| | - Richard Bryant
- School of Psychology, University of New South Wales, Sydney, NSW, Australia
| | - Tim Varkevisser
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
- Brain Research and Innovation Centre, Ministry of Defence, Utrecht, The Netherlands
| | - Elbert Geuze
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
- Brain Research and Innovation Centre, Ministry of Defence, Utrecht, The Netherlands
| | - Jonathan Coleman
- King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- King's College London, NIHR Maudsley BRC, London, UK
| | - Jean C Beckham
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Nathan A Kimbrel
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Danielle Sullivan
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Mark Miller
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- VA Boston Healthcare System, Jamaica Plain, MA, USA
| | - Jasmeet Hayes
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Mieke Verfaellie
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Erika Wolf
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - David Salat
- VA Boston Healthcare System, Jamaica Plain, MA, USA
- Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Jeffrey M Spielberg
- VA Boston Healthcare System, Jamaica Plain, MA, USA
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
| | - William Milberg
- Translational Research Center for TBI and Stress Disorders, VA Boston Healthcare System, Boston, MA, USA
- Geriatric Research, Educational and Clinical Center, VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Regina McGlinchey
- Translational Research Center for TBI and Stress Disorders, VA Boston Healthcare System, Boston, MA, USA
- Geriatric Research, Educational and Clinical Center, VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Emily L Dennis
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarah Medland
- Queensland Institute for Medical Research, Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Caroline M Nievergelt
- Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Center of Excellence for Stress and Mental Health, Veterans Affairs San Diego Healthcare System, La Jolla, CA, USA
| | - Allison E Ashley-Koch
- Department of Medicine, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
- VISN 6 MIRECC, Durham VA Health Care System, Durham, NC, USA
| | - Mark W Logue
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Departments of Psychiatry and Biomedical Genetics, Boston University School of Medicine, Boston, MA, USA
| | - Rajendra A Morey
- VISN 6 MIRECC, Durham VA Health Care System, Durham, NC, USA.
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA.
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA.
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