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51. Remember this: Age moderation of genetic and environmental contributions to verbal episodic memory from midlife through late adulthood

Author

Susan E. Luczak, Christopher R. Beam, Shandell Pahlen, Morgan Lynch, Matthew Pilgrim, Chandra A. Reynolds, Matthew S. Panizzon, Vibeke S. Catts, Kaare Christensen, Deborah Finkel, Carol E. Franz, William S. Kremen, Teresa Lee, Matt McGue, Marianne Nygaard, Brenda L. Plassman, Keith E. Whitfield, Nancy L. Pedersen, and Margaret Gatz

Subjects
Arts and Humanities (miscellaneous), Developmental and Educational Psychology, Experimental and Cognitive Psychology
Published
2023

52. Lifestyle and the aging brain: interactive effects of modifiable lifestyle behaviors and cognitive ability in men from midlife to old age

Author

Mark Sanderson-Cimino, Ruth McKenzie, Matthew S. Panizzon, Carol E. Franz, Christine Fennema-Notestine, Jeremy A. Elman, McKenna E. Williams, William S. Kremen, Hong Xian, Anders M. Dale, Chandra A. Reynolds, Sean N. Hatton, Xin M. Tu, Michael C. Neale, Rahul C. Pearce, Nathan Whitsel, Michael J. Lyons, Donald J. Hagler, Richard L. Hauger, Lisa T. Eyler, Rosemary Toomey, Teresa Warren, Nathan A. Gillespie, and Olivia K. Puckett

Subjects
Male, Gerontology, Aging, Disease, Neurodegenerative, Alzheimer's disease brain signature, Alzheimer's Disease, Cognition, Cognitive Reserve, Medicine, Aging brain, Young adult, skin and connective tissue diseases, Cognitive reserve, General Neuroscience, Age Factors, Modifiable lifestyle behavior, Middle Aged, White Matter, Neurological, Biomedical Imaging, Independent Living, Adult, White matter abnormalities, Clinical Sciences, General cognitive ability, Affect (psychology), Article, Young Adult, Alzheimer Disease, Clinical Research, Behavioral and Social Science, Acquired Cognitive Impairment, Humans, Dementia, Healthy Lifestyle, Life Style, Aged, Behavior, Neurology & Neurosurgery, business.industry, Prevention, Neurosciences, Mild cognitive impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), medicine.disease, Twin study, Brain Disorders, Brain aging, Neurology (clinical), Geriatrics and Gerontology, business, Developmental Biology
Abstract

We examined the influence of lifestyle on brain aging after nearly 30 years, and tested the hypothesis that young adult general cognitive ability (GCA) would moderate these effects. In the community-dwelling Vietnam Era Twin Study of Aging (VETSA), 431 largely non-Hispanic white men completed a test of GCA at mean age 20. We created a modifiable lifestyle behavior composite from data collected at mean age 40. During VETSA, MRI-based measures at mean age 68 included predicted brain age difference (PBAD), Alzheimer's disease (AD) brain signature, and abnormal white matter scores. There were significant main effects of young adult GCA and lifestyle on PBAD and the AD signature (ps ≤ 0.012), and a GCA-by-lifestyle interaction on both (ps ≤ 0.006). Regardless of GCA level, having more favorable lifestyle behaviors predicted less advanced brain age and less AD-like brain aging. Unfavorable lifestyles predicted advanced brain aging in those with lower age 20 GCA, but did not affect brain aging in those with higher age 20 GCA. Targeting early lifestyle modification may promote dementia risk reduction, especially among lower reserve individuals.

Published
2021

53. Rare variant association study of veteran twin whole-genomes links severe depression with a nonsynonymous change in the neuronal gene BHLHE22

Author

William S. Kremen, Yan V. Sun, Daniel Hupalo, Viola Vaccarino, Christopher W. Forsberg, Murray B. Stein, Harvey B. Pollard, Michael J. Lyons, Carol E. Franz, Coralie Viollet, Matthew D. Wilkerson, Anthony R. Soltis, Nicholas L. Smith, Jack Goldberg, Robert J. Ursano, and Clifton L. Dalgard

Subjects
Whole genome sequencing, Genetics, Nonsynonymous substitution, education.field_of_study, Population, Genome-wide association study, Biology, medicine.disease, Genome, Psychiatry and Mental health, Exact test, medicine, Major depressive disorder, Human genome, education, Biological Psychiatry
Abstract

OBJECTIVES Major Depressive Disorder (MDD) is a complex neuropsychiatric disease with known genetic associations, but without known links to rare variation in the human genome. Here we aim to identify rare genetic variants associated with MDD using deep whole-genome sequencing data in an independent population. METHODS We report the sequencing of 1,688 whole genomes in a large sample of male-male Veteran twins. Depression status was classified based on a structured diagnostic interview according to DSM-III-R diagnostic criteria. Searching only rare variants in genomic regions from recent GWAS on MDD, we used the optimised sequence kernel association test and Fisher's Exact test to fine map loci associated with severe depression. RESULTS Our analysis identified one gene associated with severe depression, basic helix loop helix e22 (PAdjusted = 0.03) via SKAT-O test between unrelated severely depressed cases compared to unrelated non-depressed controls. The same gene BHLHE22 had a non-silent variant rs13279074 (PAdjusted = 0.032) based on a single variant Fisher's Exact test between unrelated severely depressed cases compared to unrelated non-depressed controls. CONCLUSION The gene BHLHE22 shows compelling genetic evidence of directly impacting the severe depression phenotype. Together these results advance understanding of the genetic contribution to major depressive disorder in a new cohort and link a rare variant to severe forms of the disorder.

Published
2021

54. Long‐term associations of cigarette smoking in early mid‐life with predicted brain aging from mid‐ to late life

Author

Michael C. Neale, Rahul C. Pearce, Matthew S. Panizzon, Tyler Bell, William S. Kremen, Mark Sanderson-Cimino, Daniel E. Gustavson, Sean N. Hatton, Christine Fennema-Notestine, Chandra A. Reynolds, Anders M. Dale, Hong Xian, Rosemary Toomey, Xin M. Tu, Erik J. Buchholz, Linda K. McEvoy, Jeremy A. Elman, Nathan Whitsel, Lisa T. Eyler, Ruth McKenzie, Shandell Pahlen, Mc Kenna E. Williams, Carol E. Franz, Richard L. Hauger, Donald J. Hagler, Nathan A. Gillespie, Michael J. Lyons, and Olivia K. Puckett

Subjects
Adult, Male, Aging, longitudinal, Adolescent, Population, Medicine (miscellaneous), Medical and Health Sciences, smoking, Cigarette Smoking, Substance Misuse, Young Adult, Atrophy, Cigarette smoking, Clinical Research, Tobacco, Humans, Medicine, Dementia, Prospective Studies, education, Prospective cohort study, Brain aging, Aged, education.field_of_study, Tobacco Smoke and Health, alcohol, business.industry, Prevention, Psychology and Cognitive Sciences, Neurosciences, Substance Abuse, imaging, Brain, PBAD, Middle Aged, medicine.disease, Confidence interval, Brain Disorders, Psychiatry and Mental health, Good Health and Well Being, Neurological, Female, Observational study, business, Demography
Abstract

Background and aims Smoking is associated with increased risk for brain aging/atrophy and dementia. Few studies have examined early associations with brain aging. This study aimed to measure whether adult men with a history of heavier smoking in early mid-life would have older than predicted brain age 16-28 years later. Design Prospective cohort observational study, utilizing smoking pack years data from average age 40 (early mid-life) predicting predicted brain age difference scores (PBAD) at average ages 56, 62 (later mid-life) and 68 years (early old age). Early mid-life alcohol use was also evaluated. Setting Population-based United States sample. Participants/cases Participants were male twins of predominantly European ancestry who served in the United States military between 1965 and 1975. Structural magnetic resonance imaging (MRI) began at average age 56. Subsequent study waves included most baseline participants; attrition replacement subjects were added at later waves. Measurements Self-reported smoking information was used to calculate pack years smoked at ages 40, 56, 62, and 68. MRIs were processed with the Brain-Age Regression Analysis and Computation Utility software (BARACUS) program to create PBAD scores (chronological age-predicted brain age) acquired at average ages 56 (n = 493; 2002-08), 62 (n = 408; 2009-14) and 68 (n = 499; 2016-19). Findings In structural equation modeling, age 40 pack years predicted more advanced age 56 PBAD [β = -0.144, P = 0.012, 95% confidence interval (CI) = -0.257, -0.032]. Age 40 pack years did not additionally predict PBAD at later ages. Age 40 alcohol consumption, but not a smoking × alcohol interaction, predicted more advanced PBAD at age 56 (β = -0.166, P = 0.001, 95% CI = -0.261, -0.070) with additional influences at age 62 (β = -0.115, P = 0.005, 95% CI = -0.195, -0.036). Age 40 alcohol did not predict age 68 PBAD. Within-twin-pair analyses suggested some genetic mechanism partially underlying effects of alcohol, but not smoking, on PBAD. Conclusions Heavier smoking and alcohol consumption by age 40 appears to predict advanced brain aging by age 56 in men.

Published
2021

55. Altered lateralization of the cingulum in deployment‐related traumatic brain injury: An <scp>ENIGMA</scp> military‐relevant brain injury study

Author

Emily L Dennis, Mary R Newsome, Hannah M Lindsey, Maheen M Adams, Tara A Austin, Seth G Disner, Blessen C Eapen, Carrie Esopenko, Carol E Franz, Elbert Geuze, Courtney Haswell, Sidney R Hinds, Cooper B Hodges, Andrei Irimia, Kimbra Kenney, Inga K Koerte, William S Kremen, Harvey S Levin, Rajendra A Morey, John Ollinger, Jared A Rowland, Randall S Scheibel, Martha E Shenton, Danielle R Sullivan, Leah D Talbert, Sophia I Thomopoulos, Maya Troyanskaya, William C Walker, Xin Wang, Ashley L Ware, J Kent Werner, Wright Williams, Paul M Thompson, David F Tate, and Elisabeth A Wilde

Subjects
Adult, Traumatic, Physical Injury - Accidents and Adverse Effects, 6.6 Psychological and behavioural, Neuropsychological Tests, Traumatic Brain Injury (TBI), Humans, Radiology, Nuclear Medicine and imaging, military, Traumatic Head and Spine Injury, Radiological and Ultrasound Technology, traumatic brain injury, Neurosciences, Brain, Evaluation of treatments and therapeutic interventions, Experimental Psychology, White Matter, Brain Disorders, Mental Health, Neurology, DTI, Brain Injuries, Neurological, Biomedical Imaging, Cognitive Sciences, Neurology (clinical), Anatomy
Abstract

Traumatic brain injury (TBI), a significant concern in military populations, is associated with alterations in brain structure and function, cognition, as well as physical and psychological dysfunction. Diffusion magnetic resonance imaging (dMRI) is particularly sensitive to changes in brain structure following TBI, as alterations in white matter (WM) microstructure are common. However, dMRI studies in mild TBI (mTBI) are conflicting, likely due to relatively small samples, sample heterogeneity (demographics, pre- and comorbidities) and injury characteristics (mechanism; chronicity). Furthermore, few studies account for brain asymmetry, which may impact cognitive functions subserved by WM tracts. Examining brain asymmetry in large samples may increase sensitivity to detect heterogeneous areas of subtle WM alteration in mTBI.Through the Enhancing Neuroimaging and Genetics through Meta-analysis (ENIGMA) Military-Relevant Brain Injury working group, we conducted a mega-analysis of neuroimaging and clinical data from 16 cohorts of Active Duty Service Members and Veterans (n=2,598; 2,321 males/277 females; age 19-85 years). 1,080 reported a deployment-related TBI, 480 had a history of only non-military-related TBI, 823 reported no history of TBI, and 215 did not differentiate between military and non-military TBI. dMRI data were processed in a harmonized manner along with harmonized demographic, injury, psychiatric, and cognitive measures. Hemispheric asymmetry of fractional anisotropy (FA, a common proxy for myelin organization) was calculated for 19 WM tracts and compared between those with and without TBI history.FA in the cingulum showed greater asymmetry in individuals with a history of deployment-related TBI; this effect was driven by greater left lateralization in the group with TBI. There was a trend towards lower FA of the right cingulum in the TBI group. These results remained significant after accounting for potentially confounding variables including posttraumatic stress disorder, depression, and handedness and were driven primarily by individuals who had sustained their worst TBI before age 40. We further found that alterations in the cingulum were associated with slower processing speed and poorer set shifting.The results indicate an enhancement of the previously reported natural left laterality, possibly due to vulnerability of the non-dominant hemisphere or compensatory mechanisms in the dominant hemisphere. The cingulum is one of the last WM tracts to mature, reaching peak FA around 42 years old. This effect was primarily detected in individuals whose worst injury occurred before age 40, suggesting that the protracted development of the cingulum may lead to increased vulnerability to insults, such as TBI.

Published
2022

56. Rostral-middle locus coeruleus integrity and subjective cognitive decline in early old age

Author

Tyler Reed Bell, Jeremy A. Elman, Asad Beck, Christine Fennema-Notestine, Daniel E. Gustavson, Donald J. Hagler, Amy J. Jack, Michael J. Lyons, Olivia K. Puckett, Rosemary Toomey, Carol E. Franz, and William S. Kremen

Subjects
Psychiatry and Mental health, Clinical Psychology, General Neuroscience, Neurology (clinical)
Abstract

Objectives: Abnormal tau, a hallmark Alzheimer’s disease (AD) pathology, may appear in the locus coeruleus (LC) decades before AD symptom onset. Reports of subjective cognitive decline are also often present prior to formal diagnosis. Yet, the relationship between LC structural integrity and subjective cognitive decline has remained unexplored. Here, we aimed to explore these potential associations. Methods: We examined 381 community-dwelling men (mean age = 67.58; SD = 2.62) in the Vietnam Era Twin Study of Aging who underwent LC-sensitive magnetic resonance imaging and completed the Everyday Cognition scale to measure subjective cognitive decline along with their selected informants. Mixed models examined the associations between rostral-middle and caudal LC integrity and subjective cognitive decline after adjusting for depressive symptoms, physical morbidities, and family. Models also adjusted for current objective cognitive performance and objective cognitive decline to explore attenuation. Results: For participant ratings, lower rostral-middle LC contrast to noise ratio (LCCNR) was associated with significantly greater subjective decline in memory, executive function, and visuospatial abilities. For informant ratings, lower rostral-middle LCCNR was associated with significantly greater subjective decline in memory only. Associations remained after adjusting for current objective cognition and objective cognitive decline in respective domains. Conclusions: Lower rostral-middle LC integrity is associated with greater subjective cognitive decline. Although not explained by objective cognitive performance, such a relationship may explain increased AD risk in people with subjective cognitive decline as the LC is an important neural substrate important for higher order cognitive processing, attention, and arousal and one of the first sites of AD pathology.

Published
2022

57. Genome-wide Association Study Meta-analysis of Neurofilament light (NfL) levels in blood reveals novel loci related to neurodegeneration

Author

Shahzad Ahmad, Mohammad Aslam Imtiaz, Aniket Mishra, Ruiqi Wang, Marisol Herrera-Rivero, Joshua C Bis, Myriam Fornage, Gennady Roshchupkin, Edith Hofer, Mark Logue, WT Longstreth, Rui Xia, Vincent Bouteloup, Thomas Mosley, Lenore Launer, Michael Khalil, Jens Kuhle, Robert A. Rissman, Genevieve Chene, Carole Dufouil, Luc Djoussé, Michael J. Lyons, Kenneth J. Mukamal, William S. Kremen, Carol E. Franz, Reinhold Schmidt, Stephanie Debette, Monique M.B. Breteler, Klaus Berger, Qiong Yang, Sudha Seshadri, N. Ahmad Aziz, Mohsen Ghanbari, and M. Arfan Ikram

Abstract

BackgroundNeurofilament light chain (NfL) levels in circulation have been established as a sensitive biomarker of neuro-axonal damage across a range of neurodegenerative disorders. Elucidation of the genetic architecture of blood NfL levels and its genetic correlation with neurological traits could therefore provide new insights into shared molecular mechanisms underlying neurodegenerative disorders.MethodsTo identify the genetic variations underlying blood NfL levels, we conducted an ancestry-specific meta-analyses of genome-wide association studies (GWAS) based on 18,532 participants from 11 cohorts of European and 1142 participants (3 cohorts) of African-American ancestry. In the post-GWAS analyses, we performed expression quantitative trait loci (eQTL) analysis, LD-regression, and genetic risk score (GRS) association analysis with neurological traits.ResultsIn the European ancestry GWAS meta-analysis, we identified two genome-wide significant (P< 5x10−8) loci at 16p12 (UMOD), and 17q24 (SLC39A11). In the African-American ancestry GWAS meta-analysis, we identified three novel loci at 1q43 (FMN2), 12q14, and 12q21. Genetic correlation based on the European ancestry meta-analysis with neurological traits showed a strong genetic correlation of NfL with Alzheimer’s disease(AD) (rg= 0.32,P= 1.74x10−6), total-tau (rg= 2.01,P= 1.03x10−6), amyloid-beta (Aβ)-40 (rg= 0.80,P= 6.92x10−6), and Aβ-42 (rg= 1.03,P= 4.39x10−5). A higher genetic risk score based on NfL-associated genetic variants was also related to increased plasma levels of total-tau (P= 1.97x10−4), Aβ-40 (P= 2.24x10−5), Aβ-42 (P= 2.92x10−4) in the Rotterdam Study.ConclusionThis large-scale GWAS meta-analysis revealed multiple novel genetic loci of NFL levels in blood in participants from European and African-American ancestry. Significant genetic correlation of genes underlying NfL with AD, Aβ-42, and total-tau may indicate a common underlying pathway of neurodegeneration.

Published
2022

60. Executive Functions and Episodic Memory are Associated with Extracellular White Matter Microstructure in Early Old Age

Author

Daniel E. Gustavson, Derek B Archer, Jeremy A. Elman, Christine Fennema‐Notestine, Donald J. Hagler, Matthew S. Panizzon, Niranjana Shashikumar, Timothy J. Hohman, Angela L. Jefferson, Michael J. Lyons, Carol E Franz, and William S. Kremen

Subjects
Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
Published
2022

61. A Million Veteran Program GWAS of Alzheimer’s Disease and Related Dementias in African Americans Identifies Multiple Genome‐Wide Significant Dementia Risk Loci

Author

Richard Sherva, Rui Zhang, Lindsay A. Farrer, Nathan Sahelijo, Gyungah R Jun, Tori Anglin, Catherine Chanfreau, Kelly Cho, Jennifer Fonda, J. Michael Gaziano, Kelly Harrington, Yuk‐Lam Ho, William S. Kremen, Elizabeth M Litkowski, Julie Lynch, Zoe Neale, Panos Roussos, David E Marra, Jesse B. Mez, Mark Miller, David H Salat, Debby W Tsuang, Erika Wolf, Qing Zeng, Matthew S. Panizzon, Victoria Merritt, Richard L. Hauger, and Mark W. Logue

Subjects
Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
Published
2022

62. The etiology of blood‐based biomarkers for Alzheimer’s Disease in a population‐based sample of mid to late‐age males

Author

Nathan A. Gillespie, Robert A. Rissman, Jeremy A. Elman, Chandra A. Reynolds, Matthew S. Panizzon, Michael J. Lyons, Michael C. Neale, Carol E Franz, and William S. Kremen

Subjects
Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
Published
2022

64. Longitudinal genetic and environmental relationships between structural‐ and diffusion‐based Alzheimer’s disease neuroimaging signatures

Author

McKenna E. Williams, Nathan A. Gillespie, Tyler Reed Bell, Anders M. Dale, Jeremy A. Elman, Lisa T. Eyler, Christine Fennema‐Notestine, Carol E Franz, Donald J. Hagler, Michael J. Lyons, Linda K. McEvoy, Michael C. Neale, Matthew S. Panizzon, Chandra A. Reynolds, Mark E. Sanderson‐Cimino, and William S. Kremen

Subjects
Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
Published
2022

68. Genomic Structural Equation Modeling Reveals Latent Phenotypes in the Human Cortex with Distinct Genetic Architecture

Author

Rajendra A. Morey, Yuanchao Zheng, Delin Sun, Melanie E. Garrett, Marianna Gasperi, Adam X. Maihofer, Lexi Baird, Katrina L. Grasby, Ashley Huggins, Courtney C. Haswell, C. Paul M. Thompson, Sarah Medland, Daniel E. Gustavson, Matthew S. Panizzon, William S. Kremen, Caroline M. Nievergelt, Allison E. Ashley-Koch, and Mark W. Logue

Abstract

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from anatomical, functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to model the genetic architecture of cortical surface area (SA) and cortical thickness (CT) of 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance. Genomic SEM can fit a multivariate GWAS from summary statistics, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA was explained by 6 latent factors and CT was explained by 4 latent factors. The multivariate GWAS of these latent factors identified 74 genome-wide significant (GWS) loci (p−8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of latent factor GWAS results found that SA-derived factors had a positive genetic correlation with bipolar disorder (BPD), and major depressive disorder (MDD), and a negative genetic correlation with attention deficit hyperactivity disorder (ADHD), MDD, and insomnia, while CT factors displayed a negative genetic correlation with alcohol dependence. Jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across phenotypes offers a new vantage point for mapping genetically informed cortical networks.HIGHLIGHTSGenomic SEM can examine genetic correlation across cortical regions.We inferred regional genetic networks of cortical thickness and surface area.Network-associated variants have been implicated in multiple traits.These networks are genetically correlated with several psychiatric disorders including MDD, bipolar, ADHD, and alcohol dependence.

Published
2022

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

Author

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

Abstract

BackgroundThe cerebellum critically contributes to higher-order cognitive and emotional functions such fear learning and memory. Prior research on cerebellar volume in PTSD is scant and has neglected neuroanatomical subdivisions of the cerebellum that differentially map on to motor, cognitive, and affective functions.MethodsWe quantified cerebellar lobule volumes using structural magnetic resonance imaging in 4,215 adults (PTSD n= 1640; Control n=2575) across 40 sites from the from the ENIGMA-PGC PTSD working group. Using a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation, we obtained 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 total and subregional volume in PTSD compared to healthy controls. The Benjamini-Hochberg procedure was used to control the false discovery rate (p-FDR< .05).ResultsPTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume. In addition, people with PTSD showed reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), but also the vermis (VI, VIII), flocculonodular lobe (lobule X), and cerebellar white matter (allp-FDR< 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status.ConclusionsThese findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in high-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
2022

72. Associations between MRI-assessed locus coeruleus integrity and cortical gray matter microstructure

Author

Jeremy A Elman, Olivia K Puckett, Donald J Hagler, Rahul C Pearce, Christine Fennema-Notestine, Sean N Hatton, Michael J Lyons, Linda K McEvoy, Matthew S Panizzon, Emilie T Reas, Anders M Dale, Carol E Franz, and William S Kremen

Subjects
Male, Aging, Cognitive Neuroscience, neuromelanin MRI, Neurodegenerative, Alzheimer's Disease, Cellular and Molecular Neuroscience, Norepinephrine, Acquired Cognitive Impairment, Humans, Psychology, Gray Matter, Aged, diffusion, Neurosciences, Water, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Experimental Psychology, Magnetic Resonance Imaging, Brain Disorders, restriction spectrum imaging, Neurological, Biomedical Imaging, Original Article, Locus Coeruleus, Dementia, Cognitive Sciences, Alzheimer’s disease
Abstract

The locus coeruleus (LC) is one of the earliest sites of tau pathology, making it a key structure in early Alzheimer’s disease (AD) progression. As the primary source of norepinephrine for the brain, reduced LC integrity may have negative consequences for brain health, yet macrostructural brain measures (e.g. cortical thickness) may not be sensitive to early stages of neurodegeneration. We therefore examined whether LC integrity was associated with differences in cortical gray matter microstructure among 435 men (mean age = 67.5; range = 62–71.7). LC structural integrity was indexed by contrast-to-noise ratio (LCCNR) from a neuromelanin-sensitive MRI scan. Restriction spectrum imaging (RSI), an advanced multi-shell diffusion technique, was used to characterize cortical microstructure, modeling total diffusion in restricted, hindered, and free water compartments. Higher LCCNR (greater integrity) was associated with higher hindered and lower free water diffusion in multiple cortical regions. In contrast, no associations between LCCNR and cortical thickness survived correction. Results suggest lower LC integrity is associated with patterns of cortical microstructure that may reflect a reduction in cytoarchitectural barriers due to broader neurodegenerative processes. These findings highlight the potential utility for LC imaging and advanced diffusion measures of cortical microstructure in assessing brain health and early identification of neurodegenerative processes.

Published
2022

73. The genetic and environmental etiology of blood-based biomarkers related to risk of Alzheimer’s Disease in a population-based sample of early old-age men

Author

Nathan A. Gillespie, Robert A. Rissman, Jeremy A. Elman, Ruth E. McKenzie, Xin M. Tu, Hong Xian, Chandra A. Reynolds, Matthew S. Panizzon, Michael J. Lyons, Graham M.L. Eglit, Michael C. Neale, Carol Franz, and William S. Kremen

Abstract

The amyloid-tau-neurodegeneration (ATN) framework has led to an increased focus on Alzheimer’s disease (AD) biomarkers. The cost and invasiveness of obtaining biomarkers via cerebrospinal fluid has motivated efforts to develop sensitive blood-based biomarkers. Although AD is highly heritable, the biometric genetic and environmental etiology of blood-based biomarkers has never been explored. We therefore, analyzed plasma beta-amyloid (Aβ40, Aβ42, Aβ42/40), total tautau (t-tautau), and neurofilament light (NFL) biomarkers in a sample of 1,050 men aged 60 to 73 years (m=68.2, SD=2.5) from the Vietnam Era Twin Study of Aging (VETSA). Unlike Aβ and tautau, NFL does not define AD; however, as a biomarker of neurodegeneration it serves as the N component in the ATN framework. Univariate estimates suggest that familial aggregation in Aβ42, Aβ42/40, t-tau, and NFL is entirely explained by additive genetic influences accounting for 40%-58% of the total variance. All remaining variance is associated with unshared or unique environmental influences. For Aβ40, a additive genetic (31%), shared environmental (44%), and unshared environmental (25%) influences contribute to the total variance. In the more powerful multivariate analysis of Aβ42, Aβ40, t-tau, and NFL, heritability estimates range from 32% to 58%. Aβ40 and Aβ42 are statistically genetically identical (rg = 1.00, 95%CI = 0.92,1.00) and are also moderately environmentally correlated (re = 0.66, 95%CI = 0.59, 0.73). All other genetic and environmental associations were non-significant or small. Our results suggest that plasma biomarkers are heritable and that Aβ40 and Aβ42 share the same genetic influences, whereas the genetic influences on plasma t-tau and NFL are mostly unique and uncorrelated with plasma Aβ in early old-age men.

Published
2022

76. Does sleep duration moderate genetic and environmental contributions to cognitive performance?

Author

Tina T Vo, Shandell Pahlen, William S Kremen, Matt McGue, Anna Dahl Aslan, Marianne Nygaard, Kaare Christensen, and Chandra A Reynolds

Subjects
Adult, Aged, 80 and over, Aging, Twins, Monozygotic, Middle Aged, Cognition, Physiology (medical), Twins, Dizygotic, Humans, Female, Neurology (clinical), Sleep, Sleep, Health, and Disease, Aged
Abstract

While prior research has demonstrated a relationship between sleep and cognitive performance, how sleep relates to underlying genetic and environmental etiologies contributing to cognitive functioning, regardless of the level of cognitive function, is unclear. The present study assessed whether the importance of genetic and environmental contributions to cognition vary depending on an individual’s aging-related sleep characteristics. The large sample consisted of twins from six studies within the Interplay of Genes and Environment across Multiple Studies (IGEMS) consortium spanning mid- to late-life (Average age [Mage] = 57.6, range = 27–91 years, N = 7052, Female = 43.70%, 1525 complete monozygotic [MZ] pairs, 2001 complete dizygotic [DZ] pairs). Quantitative genetic twin models considered sleep duration as a primary moderator of genetic and environmental contributions to cognitive performance in four cognitive abilities (Semantic Fluency, Spatial-Visual Reasoning, Processing Speed, and Episodic Memory), while accounting for age moderation. Results suggested genetic and both shared and nonshared environmental contributions for Semantic Fluency and genetic and shared environmental contributions for Episodic Memory vary by sleep duration, while no significant moderation was observed for Spatial-Visual Reasoning or Processing Speed. Results for Semantic Fluency and Episodic Memory illustrated patterns of higher genetic influences on cognitive function at shorter sleep durations (i.e. 4 hours) and higher shared environmental contributions to cognitive function at longer sleep durations (i.e. 10 hours). Overall, these findings may align with associations of upregulation of neuroinflammatory processes and ineffective beta-amyloid clearance in short sleep contexts and common reporting of mental fatigue in long sleep contexts, both associated with poorer cognitive functioning.

Published
2022

77. Genetic and Environmental Influences on Structural and Diffusion-Based Alzheimer’s Disease Neuroimaging Signatures Across Midlife and Early Old Age

Author

McKenna E. Williams, Nathan A. Gillespie, Tyler R. Bell, Anders M. Dale, Jeremy A. Elman, Lisa T. Eyler, Christine Fennema-Notestine, Carol E. Franz, Donald J. Hagler, Michael J. Lyons, Linda K. McEvoy, Michael C. Neale, Matthew S. Panizzon, Chandra A. Reynolds, Mark Sanderson-Cimino, and William S. Kremen

Subjects
Cognitive Neuroscience, Radiology, Nuclear Medicine and imaging, Neurology (clinical), Biological Psychiatry
Abstract

Composite scores of MRI-derived metrics in brain regions associated with Alzheimer's disease (AD), commonly termed 'AD signatures,' have been developed to distinguish early AD-related atrophy from normal age-associated changes. Diffusion-based gray matter signatures may be more sensitive to early AD-related changes compared to thickness/volume-based signatures, demonstrating their potential clinical utility. The timing of early (i.e., midlife) changes in AD signatures from different modalities, and whether diffusion- and thickness/volume-based signatures each capture unique, AD-related phenotypic or genetic information, remains unknown.Our validated thickness/volume signature, our novel mean diffusivity (MD) signature, and an MRI-derived measure of brain age were used in biometrical analyses to examine genetic and environmental influences on the measures, as well as phenotypic and genetic relationships between measures over 12 years. Participants were 736 men from three waves of the Vietnam Era Twin Study of Aging (VETSA; baseline age=56.1, SD=2.6, range=51.1-60.2). Subsequent waves were at approximately 5.7-year intervals.MD and thickness/volume signatures were highly heritable (56-72%). Baseline MD signatures predicted thickness/volume signatures over a decade later, but baseline thickness/volume signatures showed a significantly weaker relationship with future MD signatures. AD signatures and brain age were correlated, but each measure captured unique phenotypic and genetic variance.Cortical MD and thickness/volume AD signatures are heritable, and each signature captures unique variance that is also not explained by brain age. Moreover, results are in line with changes in MD emerging before changes in cortical thickness, underscoring the utility of MD as a very early predictor of AD risk.

Published
2022

78. African Ancestry GWAS of Dementia in a Large Military Cohort Identifies Significant Risk Loci

Author

Richard Sherva, Rui Zhang, Nathan Sahelijo, Gyungah Jun, Tori Anglin, Catherine Chanfreau, Kelly Cho, Jennifer R. Fonda, J. Michael Gaziano, Kelly M. Harrington, Yuk-Lam Ho, William S. Kremen, Elizabeth Litkowski, Julie Lynch, Zoe Neale, Panos Roussos, David Marra, Jesse Mez, Mark W. Miller, David H. Salat, Debby Tsuang, Erika Wolf, Qing Zeng, Matthew S. Panizzon, Victoria C. Merritt, Lindsay A. Farrer, Richard L. Hauger, and Mark W. Logue

Subjects
Cellular and Molecular Neuroscience, Psychiatry and Mental health, Molecular Biology
Abstract

We conducted the largest genome-wide association study (GWAS) of Alzheimer’s disease and related dementia (ADRD) in individuals of African-ancestry (AFR) to date using participants from the Million Veteran Program (MVP; 4,012 ADRD cases and 18,435 controls). A proxy GWAS based on survey-reported parental dementia (n=6,641 proxy cases, 45,970 controls) was also performed. The MVP AFR ADRD GWAS and proxy GWAS results were meta-analyzed and combined with the Alzheimer’s Disease Genetics Consortium’s (ADGC) AFR AD GWAS results. The MVP meta-analysis yielded genome-wide significant associations in or near APOE, ROBO1, and RP11-340A13.2. The MVP/ADGC meta-analysis yielded additional genome-wide significant variants near known risk genes TREM2, CD2AP, and ABCA7. We examined differences in expression of the implicated genes in a cohort of AD case and control brains. This study provides insight into dementia pathophysiology in historically understudied individuals of AFR and may help to address health disparities.

Published
2022

79. Brain charts for the human lifespan

Author

Armin Raznahan, Eric Courchesne, Andrea Parolin Jackowski, Kamen A. Tsvetanov, Cameron T. Ellis, R.C. Gur, Bin Bae J, Park Mtm, Pedro A. Valdes-Sosa, Simon N. Vandekar, Jacob W. Vogel, Juan Zhou, Machteld Marcelis, Kiho Im, Patricia Ellen Grant, Minhui Ouyang, Blesa Cabez M, Michael V. Lombardo, Sarah E. Morgan, James P. Boardman, Adamson C, Calhoun Vd, Delarue M, James H. Cole, Pichet Binette A, Roberto Toro, David H. Rowitch, Nynke A. Groenewold, Kevin M. Anderson, David T.W. Jones, Michael Schöll, Wang Ys, Aiden Corvin, R.E. Gur, Damien A. Fair, Gareth Ball, Herma Lina Schaare, Andrew Zalesky, Evdokia Anagnostou, Michael J. Meaney, Taki Y, Gareth J. Sullivan, Warrier, Petra E. Vértes, Chixiang Chen, Lisa T. Eyler, Wei Liao, Tomáš Paus, Jeremy A. Elman, Phillip McGuire, Hisham Ziauddeen, William S. Kremen, Etienne Vachon-Presseau, E.T. Bullmore, Christophe Tzourio, White, Hammill Cf, Mothersill D, Richard N. Henson, Jiang Qiu, Duncan E. Astle, Fabrice Crivello, Paul C. Fletcher, Chertavian C, Kim K, Jennifer Crosbie, Russell Schachar, Gabriel A. Devenyi, Manfred G. Kitzbichler, Tianye Jia, Trey Hedden, Sang Jae Lee, Ross D. Markello, Silke Kern, Ian M. Goodyer, Keith A. Johnson, Frauke Beyer, Bernard Mazoyer, A. Heinz, Sylvane Desrivières, Rosenberg, Gary Donohoe, Ong Mq, Alexander D. Edwards, Dan J. Stein, Nenad Medic, Zuo Xn, Travis T. Mallard, Peter Fonagy, Lindsay W. Victoria, Ingmar Skoog, Avram J. Holmes, Jason P. Lerch, Jed T. Elison, Jianfu Li, John H. Gilmore, Rosemary Holt, Caitlin K. Rollins, Carol E. Franz, Pedro Mario Pan, Saashi A Bedford, Yang N, Jonathan C Ipser, Richard A. I. Bethlehem, Tuulari Jj, Stolicyn A, Hua Huang, Bratislav Misic, Conor Liston, Ayub M, Lisa Ronan, Yeo Bt, Sophie Adler, Charles J. Lynch, Faith M. Gunning, Konrad Wagstyl, M. Mallar Chakravarty, John Suckling, Theodore D. Satterthwaite, Bharath Holla, Yap Seng Chong, Jinglei Lv, Jakob Seidlitz, Niall J Bourke, Xinlei Qian, Simon Baron-Cohen, Cynthia M. Ortinau, Deirel Paz Linares, Thyreau B, René S. Kahn, Aaron P. Schultz, Vanessa Cropley, Eric Westman, Mitchell Valdés-Sosa, Rik Ossenkoppele, André Zugman, Hasse Karlsson, Sylvia Villeneuve, Katja Heuer, Di Biase Ma, Margaret L. Westwater, Sofie L. Valk, David J. Sharp, Brigitte Landeau, Matthew Borzage, Kirsten A. Donald, Timothy Rittman, Richard Beare, Giovanni Abrahão Salum, Gunter Schumann, Ryuta Kawashima, Romero-Garcia R, John Blangero, Yun Hj, Russel T. Shinohara, Nicolas Crossley, Simon K. Warfield, Karen Pierce, George S. Alexopoulos, Katharine Dunlop, David C. Glahn, Francois Lalonde, Anqi Qiu, Lana Vasung, Gaël Chételat, Lídice Galán-García, Clifford R. Jack, Reisa A. Sperling, Anna Zettergren, Elizabeth Kelley, Arno Villringer, Andrea Mechelli, Benegal, Aaron Alexander-Bloch, Nicholas B. Turk-Browne, van Amelsvoort T, John D. Lewis, Heather C. Whalley, A. V. Witte, Zdenka Pausova, Joel T. Nigg, Heather J. Zar, Raymond J. Dolan, Christopher D. Smyser, Jay N. Giedd, Lena Palaniyappan, Ali Gholipour, Areces-Gonzalez A, Peter B. Jones, Jacqueline Hoare, Oskar Hansson, Linnea Karlsson, C Pantelis, Paly L, Bonnie Auyeung, Jorge Bosch-Bayard, Bethlehem, Richard [0000-0002-0714-0685], White, Simon [0000-0001-8642-7037], Astle, Duncan [0000-0002-7042-5392], Baron-Cohen, Simon [0000-0001-9217-2544], Henson, Rik [0000-0002-0712-2639], Jones, Peter [0000-0002-0387-880X], Kitzbichler, Manfred [0000-0002-4494-0753], Rittman, Timothy [0000-0003-1063-6937], Rowitch, David [0000-0002-0079-0060], Tsvetanov, Kamen A. [0000-0002-3178-6363], Westwater-Wozniak, Margaret [0000-0002-2918-0979], Ziauddeen, Hisham [0000-0003-4044-1719], Apollo - University of Cambridge Repository, British Academy, Autism Research Trust, National Institute of Mental Health (US), UK Research and Innovation, Medical Research Council (UK), National Institute for Health and Care Research (US), Wellcome Trust, University of Cambridge, Cambridge Biomedical Research Centre, University of Cambridge [UK] (CAM), University of Pennsylvania, Yale University [New Haven], Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Physiopathologie et imagerie des troubles neurologiques (PhIND), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Génétique humaine et fonctions cognitives - Human Genetics and Cognitive Functions (GHFC (UMR_3571 / U-Pasteur_1)), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Child and Adolescent Psychiatry Department [AP- HP Hôpital Robert Debré], AP-HP Hôpital universitaire Robert-Debré [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Département de Neuroscience - Department of Neuroscience, Centre de Recherche Interdisciplinaire / Center for Research and Interdisciplinarity [Paris, France] (CRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Psychiatrie & Neuropsychologie, RS: MHeNs - R2 - Mental Health, MUMC+: MA Med Staf Spec Psychiatrie (9), Neurology, Amsterdam Neuroscience - Neurodegeneration, 3R-BRAIN, AIBL, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Repository Without Borders Investigators, CALM Team, Cam-CAN, CCNP, COBRE, cVEDA, ENIGMA Developmental Brain Age Working Group, Developing Human Connectome Project, FinnBrain, Harvard Aging Brain Study, IMAGEN, KNE96, The Mayo Clinic Study of Aging, NSPN, POND, The PREVENT-AD Research Group, VETSA, [Bethlehem, R. A. I.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Auyeung, B.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Baron-Cohen, S.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Bedford, S. A.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Holt, R.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Lombardo, M. V.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Bethlehem, R. A. I.] Univ Cambridge, Dept Psychiat, Brain Mapping Unit, Cambridge, England, [Kitzbichler, M. G.] Univ Cambridge, Dept Psychiat, Brain Mapping Unit, Cambridge, England, [Seidlitz, J.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Vogel, J. W.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Gur, R. E.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Gur, R. C.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Jackowski, A. P.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Satterthwaite, T. D.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Alexander-Bloch, A. F.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Seidlitz, J.] Childrens Hosp Philadelphia, Dept Child & Adolescent Psychiat & Behav Sci, Philadelphia, PA 19104 USA, [Alexander-Bloch, A. F.] Childrens Hosp Philadelphia, Dept Child & Adolescent Psychiat & Behav Sci, Philadelphia, PA 19104 USA, [Seidlitz, J.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [Chertavian, C.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [Gur, R. E.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [Gur, R. C.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [Alexander-Bloch, A. F.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [White, S. R.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Goodyer, I. M.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Henson, R. N.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Jones, P. B.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Kitzbichler, M. G.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Medic, N.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Morgan, S. E.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Romero-Garcia, R.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Ronan, L.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Suckling, J.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Vertes, P. E.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Warrier, V.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Westwater, M. L.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Ziauddeen, H.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Bullmore, E. T.] Univ Cambridge, Dept Psychiat, Cambridge, England, [White, S. R.] Univ Cambridge, MRC Biostat Unit, Cambridge, England, [Vogel, J. W.] Univ Penn, Lifespan Informat & Neuroimaging Ctr, Philadelphia, PA 19104 USA, [Satterthwaite, T. D.] Univ Penn, Lifespan Informat & Neuroimaging Ctr, Philadelphia, PA 19104 USA, [Anderson, K. M.] Yale Univ, Dept Psychol, New Haven, CT USA, [Ellis, C. T.] Yale Univ, Dept Psychol, New Haven, CT USA, [Turk-Browne, N. B.] Yale Univ, Dept Psychol, New Haven, CT USA, [Adamson, C.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Ball, G.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Beare, R.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Jackowski, A. P.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Adamson, C.] Monash Univ, Dept Med, Melbourne, Vic, Australia, [Beare, R.] Monash Univ, Dept Med, Melbourne, Vic, Australia, [Adler, S.] UCL Great Ormond St Inst Child Hlth, London, England, [Alexopoulos, G. S.] Weill Cornell Med, Dept Psychiat, Weill Cornell Inst Geriatr Psychiat, New York, NY USA, [Anagnostou, E.] Univ Toronto, Dept Pediat, Toronto, ON, Canada, [Anagnostou, E.] Holland Bloorview Kids Rehabil Hosp, Toronto, ON, Canada, [Pierce, K.] Holland Bloorview Kids Rehabil Hosp, Toronto, ON, Canada, [Areces-Gonzalez, A.] Univ Elect Sci & Technol China, MOE Key Lab NeuroInformat, Clin Hosp, Chengdu Brain Sci Inst, Chengdu, Peoples R China, [Paz-Linares, D.] Univ Elect Sci & Technol China, MOE Key Lab NeuroInformat, Clin Hosp, Chengdu Brain Sci Inst, Chengdu, Peoples R China, [Areces-Gonzalez, A.] Univ Pinar del Rio Hermanos Saiz Montes de Oca, Pinar Del Rio, Cuba, [Astle, D. E.] Univ Cambridge, MRC Cognit & Brain Sci Unit, Cambridge, England, [Henson, R. N.] Univ Cambridge, MRC Cognit & Brain Sci Unit, Cambridge, England, [Whalley, H. C.] Univ Cambridge, MRC Cognit & Brain Sci Unit, Cambridge, England, [Auyeung, B.] Univ Edinburgh, Sch Philosophy Psychol & Language Sci, Dept Psychol, Edinburgh, Midlothian, Scotland, [Pausova, Z.] Univ Edinburgh, Sch Philosophy Psychol & Language Sci, Dept Psychol, Edinburgh, Midlothian, Scotland, [Ayub, M.] Queens Univ, Dept Psychiat, Ctr Neurosci Studies, Kingston, ON, Canada, [Ayub, M.] UCL, Mental Hlth Neurosci Res Dept, Div Psychiat, London, England, [Bae, J.] Seoul Natl Univ, Bundang Hosp, Dept Neuropsychiat, Seongnam, South Korea, [Ball, G.] Univ Melbourne, Dept Paediat, Melbourne, Vic, Australia, [Baron-Cohen, S.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge Lifetime Asperger Syndrome Serv CLASS, Cambridge, England, [Benegal, V.] Natl Inst Mental Hlth & Neurosci NIMHANS, Ctr Addict Med, Bengaluru, India, [Beyer, F.] Max Planck Inst Human Cognit & Brain Sci, Dept Neurol, Leipzig, Germany, [Villringer, A.] Max Planck Inst Human Cognit & Brain Sci, Dept Neurol, Leipzig, Germany, [Witte, A. V.] Max Planck Inst Human Cognit & Brain Sci, Dept Neurol, Leipzig, Germany, [Blangero, J.] Univ Texas Rio Grande Valley, South Texas Diabet & Obes Inst, Dept Human Genet, Edinburg, TX USA, [Blesa Cabez, M.] Univ Edinburgh, MRC Ctr Reprod Hlth, Edinburgh, Midlothian, Scotland, [Boardman, J. P.] Univ Edinburgh, MRC Ctr Reprod Hlth, Edinburgh, Midlothian, Scotland, [Sullivan, G.] Univ Edinburgh, MRC Ctr Reprod Hlth, Edinburgh, Midlothian, Scotland, [Borzage, M.] Univ Southern Calif, Childrens Hosp Los Angeles, Keck Sch Med, Fetal & Neonatal Inst,Div Neonatol,Dept Pediat, Los Angeles, CA 90007 USA, [Bosch-Bayard, J. F.] Montreal Neurol Inst, Ludmer Ctr Neuroinformat & Mental Hlth, McGill Ctr Integrat Neurosci, Montreal, PQ, Canada, [Bosch-Bayard, J. F.] McGill Univ, Montreal, PQ, Canada, [Chakravarty, M. M.] McGill Univ, Montreal, PQ, Canada, [Bourke, N.] Imperial Coll London, Dept Brain Sci, London, England, [Sharp, D.] Imperial Coll London, Dept Brain Sci, London, England, [Alexander-Bloch, A. F.] Imperial Coll London, Dept Brain Sci, London, England, [Bourke, N.] Dementia Res Inst, Care Res & Technol Ctr, London, England, [Calhoun, V. D.] Georgia State Univ, Triinst Ctr Translat Res Neuroimaging & Data Sci, Georgia Inst Technol, Atlanta, GA 30303 USA, [Calhoun, V. D.] Emory Univ, Atlanta, GA 30322 USA, [Chakravarty, M. M.] Douglas Mental Hlth Univ Inst, Cerebral Imaging Ctr, Comp Brain Anat CoBrA Lab, Montreal, PQ, Canada, [Chen, C.] Univ Penn, Penn Stat Imaging & Visualizat Ctr, Dept Biostat Epidemiol & Informat, Perelman Sch Med, Philadelphia, PA 19104 USA, [Shinohara, R. T.] Univ Penn, Penn Stat Imaging & Visualizat Ctr, Dept Biostat Epidemiol & Informat, Perelman Sch Med, Philadelphia, PA 19104 USA, [Chetelat, G.] Normandie Univ, PhIND Physiopathol & Imaging Neurol Disorders, Inst Blood & Brain Caen Normandie, UNICAEN,INSERM,U1237, Caen, France, [Delarue, M.] Normandie Univ, PhIND Physiopathol & Imaging Neurol Disorders, Inst Blood & Brain Caen Normandie, UNICAEN,INSERM,U1237, Caen, France, [Landeau, B.] Normandie Univ, PhIND Physiopathol & Imaging Neurol Disorders, Inst Blood & Brain Caen Normandie, UNICAEN,INSERM,U1237, Caen, France, [Paly, L.] Normandie Univ, PhIND Physiopathol & Imaging Neurol Disorders, Inst Blood & Brain Caen Normandie, UNICAEN,INSERM,U1237, Caen, France, [Chong, Y. S.] Agcy Sci Technol & Res, Singapore Inst Clin Sci, Singapore, Singapore, [Chong, Y. S.] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Obstet & Gynaecol, Singapore, Singapore, [Cole, J. H.] UCL, Ctr Med Image Comp CMIC, London, England, [Cole, J. H.] UCL, Dementia Res Ctr DRC, London, England, [Corvin, A.] Trinity Coll Dublin, Dept Psychiat, Dublin, Ireland, [Costantino, M.] Douglas Mental Hlth Univ Inst, Cerebral Imaging Ctr, Verdun, PQ, Canada, [Costantino, M.] McGill Univ, Undergrad Program Neurosci, Montreal, PQ, Canada, [Courchesne, E.] Univ Calif San Diego, Dept Neurosci, San Diego, CA 92103 USA, [Courchesne, E.] Univ Calif San Diego, Autism Ctr Excellence, San Diego, CA 92103 USA, [Crivello, F.] Univ Bordeaux, Inst Neurodegenerat Disorders, CNRS UMR5293, CEA, Bordeaux, France, [Mazoyer, B.] Univ Bordeaux, Inst Neurodegenerat Disorders, CNRS UMR5293, CEA, Bordeaux, France, [Cropley, V. L.] Univ Melbourne, Melbourne Neuropsychiat Ctr, Melbourne, Vic, Australia, [Di Biase, M. A.] Univ Melbourne, Melbourne Neuropsychiat Ctr, Melbourne, Vic, Australia, [Lv, J.] Univ Melbourne, Melbourne Neuropsychiat Ctr, Melbourne, Vic, Australia, [Zalesky, A.] Univ Melbourne, Melbourne Neuropsychiat Ctr, Melbourne, Vic, Australia, [Hammill, C. F.] Hosp Sick Children, Toronto, ON, Canada, [Schachar, R. J.] Hosp Sick Children, Toronto, ON, Canada, [Crossley, N.] Pontificia Univ Catolica Chile, Sch Med, Dept Psychiat, Santiago, Chile, [Crossley, N.] Kings Coll London, Dept Psychosis Studies, Inst Psychiat Psychol & Neurosci, London, England, [McGuire, P.] Kings Coll London, Dept Psychosis Studies, Inst Psychiat Psychol & Neurosci, London, England, [Crossley, N.] Inst Milenio Intelligent Healthcare Engn, Santiago, Chile, [Delorme, R.] Robert Debre Univ Hosp, AP HP, Child & Adolescent Psychiat Dept, Paris, France, [Delorme, R.] Inst Pasteur, Human Genet & Cognit Funct, Paris, France, [Desrivieres, S.] Kings Coll London, Inst Psychiat Psychol & Neurosci, Social Genet & Dev Psychiat Ctr, London, England, [Devenyi, G. A.] Douglas Mental Hlth Univ Inst, McGill Dept Psychiat, Cerebral Imaging Ctr, Montreal, PQ, Canada, [Devenyi, G. A.] McGill Univ, Dept Psychiat, Montreal, PQ, Canada, [Di Biase, M. A.] Harvard Med Sch, Brigham & Womens Hosp, Dept Psychiat, Boston, MA 02115 USA, [Dolan, R.] UCL, Max Planck UCL Ctr Computat Psychiat & Ageing Res, London, England, [Dolan, R.] Wellcome Ctr Human Neuroimaging, London, England, [Wagstyl, K.] Wellcome Ctr Human Neuroimaging, London, England, [Donald, K. A.] Red Cross War Mem Childrens Hosp, Dept Paediat & Child Hlth, Div Dev Paediat, Cape Town, South Africa, [Donald, K. A.] Univ Cape Town, Neurosci Inst, Cape Town, South Africa, [Groenewold, N. A.] Univ Cape Town, Neurosci Inst, Cape Town, South Africa, [Donohoe, G.] Natl Univ Ireland Galway, Sch Psychol, Ctr Neuroimaging Cognit & Genom NICOG, Galway, Ireland, [Dunlop, K.] Weill Cornell Med, Dept Psychiat, Weil Family Brain & Mind Res Inst, New York, NY USA, [Lynch, C.] Weill Cornell Med, Dept Psychiat, Weil Family Brain & Mind Res Inst, New York, NY USA, [Edwards, A. D.] Kings Coll London, Ctr Dev Brain, London, England, [Edwards, A. D.] Evelina London Childrens Hosp, London, England, [Edwards, A. D.] MRC Ctr Neurodev Disorders, London, England, [Elison, J. T.] Univ Minnesota, Mason Inst Dev Brain, Dept Pediat, Inst Child Dev, Minneapolis, MN USA, [Fair, D. A.] Univ Minnesota, Mason Inst Dev Brain, Dept Pediat, Inst Child Dev, Minneapolis, MN USA, [Feczko, E.] Univ Minnesota, Mason Inst Dev Brain, Dept Pediat, Inst Child Dev, Minneapolis, MN USA, [Ellis, C. T.] Haskins Labs Inc, New Haven, CT USA, [Elman, J. A.] Univ Calif San Diego, Dept Psychiat, Ctr Behav Genet Aging, La Jolla, CA 92093 USA, [Franz, C. E.] Univ Calif San Diego, Dept Psychiat, Ctr Behav Genet Aging, La Jolla, CA 92093 USA, [Kremen, W. S.] Univ Calif San Diego, Dept Psychiat, Ctr Behav Genet Aging, La Jolla, CA 92093 USA, [Eyler, L.] VA San Diego Healthcare, Desert Pacific Mental Illness Res Educ & Clin Ctr, San Diego, CA USA, [Eyler, L.] Univ Calif San Diego, Dept Psychiat, Los Angeles, CA USA, [Fletcher, P. C.] Univ Cambridge, Dept Psychiat, Cambridge Biomed Campus, Cambridge, England, [Fletcher, P. C.] Wellcome Trust MRC Inst Metab Sci, Cambridge Biomed Campus, Cambridge, England, [Fletcher, P. C.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge, England, [Jones, P. B.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge, England, [Suckling, J.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge, England, [Ziauddeen, H.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge, England, [Fonagy, P.] UCL, Dept Clin Educ & Hlth Psychol, London, England, [Fonagy, P.] Anna Freud Natl Ctr Children & Families, London, England, [Galan-Garcia, L.] Cuban Ctr Neurosci, Havana, Cuba, [Valdes-Sosa, M. J.] Cuban Ctr Neurosci, Havana, Cuba, [Gholipour, A.] Boston Childrens Hosp, Computat Radiol Lab, Boston, MA USA, [Warfield, S. K.] Boston Childrens Hosp, Computat Radiol Lab, Boston, MA USA, [Giedd, J.] Univ Calif San Diego, Dept Child & Adolescent Psychiat, San Diego, CA 92103 USA, [Giedd, J.] Univ Calif San Diego, Dept Psychiat, San Diego, CA 92103 USA, [Gilmore, J. H.] Univ N Carolina, Dept Psychiat, Chapel Hill, NC 27515 USA, [Glahn, D. C.] Boston Childrens Hosp, Dept Psychiat, Boston, MA USA, [Im, K.] Boston Childrens Hosp, Dept Psychiat, Boston, MA USA, [Mathias, S. R.] Boston Childrens Hosp, Dept Psychiat, Boston, MA USA, [Rodrigue, A.] Boston Childrens Hosp, Dept Psychiat, Boston, MA USA, [Glahn, D. C.] Harvard Med Sch, Boston, MA 02115 USA, [Im, K.] Harvard Med Sch, Boston, MA 02115 USA, [Johnson, K. A.] Harvard Med Sch, Boston, MA 02115 USA, [Mathias, S. R.] Harvard Med Sch, Boston, MA 02115 USA, [Rodrigue, A.] Harvard Med Sch, Boston, MA 02115 USA, [Schultz, A. P.] Harvard Med Sch, Boston, MA 02115 USA, [Sperling, R. A.] Harvard Med Sch, Boston, MA 02115 USA, [Grant, P. E.] Harvard Med Sch, Fetal Neonatal Neuroimaging & Dev Sci Ctr, Boston Childrens Hosp, Div Newborn Med & Neuroradiol, Boston, MA 02115 USA, [Groenewold, N. A.] Univ Cape Town, SA MRC Unit Child & Adolescent Hlth, Red Cross War Mem Childrens Hosp, Dept Paediat & Child Hlth, Cape Town, South Africa, [Zar, H. J.] Univ Cape Town, SA MRC Unit Child & Adolescent Hlth, Red Cross War Mem Childrens Hosp, Dept Paediat & Child Hlth, Cape Town, South Africa, [Gunning, F. M.] Weill Cornell Med, Dept Psychiat, Weill Cornell Inst Geriatr Psychiat, New York, NY USA, [Victoria, L. W.] Weill Cornell Med, Dept Psychiat, Weill Cornell Inst Geriatr Psychiat, New York, NY USA, [Hammill, C. 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S.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Yang, N.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Yeo, B.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Zuo, X. N.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Wang, Y. S.] Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neuroscience Res Ctr, Beijing, Peoples R China, [Yang, N.] Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neuroscience Res Ctr, Beijing, Peoples R China, [Zuo, X. N.] Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neuroscience Res Ctr, Beijing, Peoples R China, [Wang, Y. S.] Natl Basic Sci Data Ctr, Beijing, Peoples R China, [Yang, N.] Natl Basic Sci Data Ctr, Beijing, Peoples R China, [Zuo, X. N.] Natl Basic Sci Data Ctr, Beijing, Peoples R China, [Wang, Y. S.] Chinese Acad Sci, Res Ctr Lifespan Dev Brain & Mind, Inst Psychol, Beijing, Peoples R China, [Yang, N.] Chinese Acad Sci, Res Ctr Lifespan Dev Brain & Mind, Inst Psychol, Beijing, Peoples R China, [Westman, E.] Karolinska Inst, Ctr Alzheimer Res, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Stockholm, Sweden, [Witte, A. V.] Univ Leipzig, CRC 1052 Obes Mech, Fac Med, Leipzig, Germany, [Zhou, J. H.] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Sleep & Cognit, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Translat MR Res, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, N1 Inst Hlth, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, Inst Digital Med, Singapore, Singapore, [Yun, H.] Natl Univ Singapore, Integrat Sci & Engn Programme ISEP, Singapore, Singapore, [Zar, H. J.] Univ Melbourne, Dept Biomed Engn, Melbourne, Vic, Australia, [Zhou, J. H.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Translat Magnet Resonance Res, Singapore, Singapore, [Ziauddeen, H.] Univ Cambridge, Wellcome Trust MRC Inst Metab Sci, Cambridge, England, [Zugman, A.] NIMH, NIH, Bethesda, MD 20892 USA, [Zugman, A.] Escola Paulista Med, Dept Psychiat, Sao Paulo, Brazil, [Zuo, X. N.] Nanning Normal Univ, Sch Educ Sci, Key Lab Brain & Educ, Nanning, Peoples R China, British Academy Postdoctoral fellowship, NIMH, UKRI Medical Research Council, NIHR Cambridge Biomedical Research Centre, NIHR Senior Investigator award, MRC research infrastructure award, Commonwealth Scientific and Industrial Research Organisation (CSIRO), and Ontario Brain Institute

Subjects
631/378/2649, OpenPain Project, KNE96, Growth, Psychiatric-disorders, DISEASE, 3R-BRAIN, Brain charts, MRI Brain, OASIS-3, Disease, CCNP, 631/378/2571, UMN BCP, Multidisciplinary, medicine.diagnostic_test, PSYCHIATRIC-DISORDERS, article, Brain, Human brain, ASSOCIATION, Magnetic Resonance Imaging, Harvard Aging Brain Study, The Mayo Clinic Study of Aging, NSPN, medicine.anatomical_structure, GROWTH, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ddc:500, BURDEN, WHITE-MATTER, FinnBrain, Harvard Aging Brain Study, Organization, Mri, MRI, medicine.medical_specialty, Concurrent validity, MODELS, Cam-CAN, Longevity, CALM Team, POND, Neuroimaging, Burden, ORGANIZATION, AIBL, The PREVENT-AD Research Group, VETSA, Cortical thickness, Association, Physical medicine and rehabilitation, FinnBrain, IMAGEN, KNE96, White-matter, medicine, Humans, ASRB, 631/378/1689, COBRE, business.industry, 631/378/2611, Brain morphometry, Neurosciences, Alzheimer’s Disease Repository Without Borders Investigators, Magnetic resonance imaging, Alzheimer’s Disease Neuroimaging Initiative, Anthropometry, Body Height, Brain growth, Birth, 59/57, Normative, IMAGEN, ENIGMA Developmental Brain Age working group, NSPN, business, CCNP, 3R-BRAIN, CORTICAL THICKNESS, Developing Human Connectome Project, ENIGMA Developmental Brain Age working group, The PREVENT-AD Research Group, VETSA, Bullmore, E.T
Abstract

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes., R.A.I.B. was supported by a British Academy Postdoctoral fellowship and by the Autism Research Trust. J. Seidlitz was supported by NIMH T32MH019112-29 and K08MH120564. S.R.W. was funded by UKRI Medical Research Council MC_UU_00002/2 and was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). E.T.B. was supported by an NIHR Senior Investigator award and the Wellcome Trust collaborative award for the Neuroscience in Psychiatry Network. A.F.A.-B. was supported by NIMH K08MH120564. Data were curated and analysed using a computational facility funded by an MRC research infrastructure award (MR/M009041/1) to the School of Clinical Medicine, University of Cambridge and supported by the mental health theme of the NIHR Cambridge Biomedical Research Centre.

Published
2022

80. Enhancing Discovery of Genetic Variants for Posttraumatic Stress Disorder Through Integration of Quantitative Phenotypes and Trauma Exposure Information

Author

Alma Dzubur Kulenovic, Michael J. Lyons, Elizabeth A. Bolger, Kenneth J. Ruggiero, Zhewu Wang, Laramie E. Duncan, Bozo Lugonja, Joanne Voisey, José Miguel Caldas-de-Almeida, Regina E. McGlinchey, Laura J. Bierut, Michael A. Hauser, Jean C. Beckham, Dan J. Stein, Alexander C. McFarlane, Elbert Geuze, Victoria B. Risbrough, Douglas Maurer, Christy A. Denckla, Seth G. Disner, William P. Milberg, Erika J. Wolf, Scott R. Sponheim, Caroline M. Nievergelt, Henry R. Kranzler, Clement C. Zai, Antonia V. Seligowski, Miro Jakovljević, Katharina Domschke, Paul A. Arbisi, Thomas Werge, Vasiliki Michopoulos, Joel Gelernter, Sarah D. Linnstaedt, Nastassja Koen, Sonya B. Norman, Nicholas G. Martin, Janine D. Flory, Meghan M Brashear, Melissa A. Polusny, Nathan A. Kimbrel, Douglas L. Delahanty, Milissa L. Kaufman, Peter Roy-Byrne, Magali Haas, Monica Uddin, Matig R. Mavissakalian, William S. Kremen, Ole A. Andreassen, Marco P. Boks, Matthew S. Panizzon, Christiaan H. Vinkers, Bart P. F. Rutten, Heather Lasseter, Richard A. Shaffer, Aferdita Goci, Jessica L. Maples-Keller, Israel Liberzon, Melanie E. Garrett, Alicia K. Smith, Catrin Lewis, Dewleen G. Baker, Murray B. Stein, Xuejun Qin, Nikolaos P. Daskalakis, Sherry Winternitz, Douglas E. Williamson, Alex O. Rothbaum, David Forbes, Leigh van den Heuvel, Scott D. Gordon, Edward J. Trapido, Marti Jett, Ole Mors, Adam X. Maihofer, Christina M. Sheerin, Lori A. Zoellner, A.C. Bustamante, David M. Hougaard, Alexandra Evans, Chia-Yen Chen, Robert H. Pietrzak, Rachel Yehuda, Allison C. Provost, Matthew Peverill, Aarti Gautam, Bruce R. Lawford, Derrick Silove, Bekh Bradley, Gerome Breen, Charles F. Gillespie, Allison E. Ashley-Koch, Kerry J. Ressler, Christiane Wolf, Renato Polimanti, Jonathan Ian Bisson, Adriana Lori, Lynn M. Almli, Norah C. Feeny, Jonas Bybjerg-Grauholm, Guia Guffanti, Søren Bo Andersen, Anders D. Børglum, Elizabeth Ketema, Andrea L. Roberts, Marie Bμkvad-Hansen, Ross McD. Young, Jürgen Deckert, Jonathan Sebat, Rajendra A. Morey, P. B. Mortensen, Lindsay A. Farrer, Yunpeng Wang, Karestan C. Koenen, Joseph R. Calabrese, Bizu Gelaye, Jurjen J. Luykx, Andrew Ratanatharathorn, Charles P. Morris, S. Bryn Austin, Miranda Van Hooff, Edward S. Peters, Katie A. McLaughlin, Anthony P. King, Jonathan R. I. Coleman, Holly K. Orcutt, Keith A. Young, Samuel A. McLean, Jennifer S. Stevens, Rasha Hammamieh, Robert J. Ursano, Mark W. Miller, Allison E. Aiello, Charles R. Marmar, Esmina Avdibegović, Katy Torres, Elliot C. Nelson, Rany M. Salem, Martin H. Teicher, Rebecca Mellor, Karen-Inge Karstoft, Aliza P. Wingo, Alaptagin Khan, Michelle A. Williams, Dick Schijven, Merete Nordentoft, Ananda B. Amstadter, Shareefa Dalvie, Michelle F. Dennis, Mark J. Daly, Mark W. Logue, Soraya Seedat, Julia S. Seng, Carol E. Franz, Stephan Ripke, Karmel W. Choi, Sandro Galea, Richard A. Bryant, Ian Jones, Anders M. Dale, Wesley K. Thompson, Lauren A.M. Lebois, Sixto E. Sanchez, Ronald C. Kessler, Tanja Jovanovic, Divya Mehta, Jordan W. Smoller, Eric O. Johnson, John P. Rice, Andrew C. Heath, Nancy L. Saccone, Barbara O. Rothbaum, Alan L. Peterson, Meaghan O'Donnell, Sian M. J. Hemmings, Eric Vermetten, Dragan Babić, Hongyu Zhao, Tianying Wu, Christopher R. Erbes, Ariane Rung, NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM), Centro de Estudos de Doenças Crónicas (CEDOC), Psychiatrie & Neuropsychologie, RS: MHeNs - R3 - Neuroscience, MUMC+: MA Psychiatrie (3), Anatomy and neurosciences, Psychiatry, and Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep

Subjects
Oncology, Multivariate analysis, LD SCORE REGRESSION, Genome-wide association study, THOUSANDS, Medical and Health Sciences, Stress Disorders, Post-Traumatic, GWAS, Stress Disorders, Psychiatry, Genome-Wide Association Study / methods, Traumatic stress, PROLIFERATION, PTSD, Single Nucleotide, Biological Sciences, Post-Traumatic Stress Disorder (PTSD), Anxiety Disorders, Mental Health, Phenotype, Cohort, Polymorphism, Single Nucleotide / genetics, medicine.medical_specialty, Stress Disorders, Post-Traumatic / genetics, Quantitative trait locus, Polymorphism, Single Nucleotide, Genetic correlation, behavioral disciplines and activities, Trauma, Heritability, Internal medicine, PSYCHIATRIC GENOMICS, mental disorders, medicine, Genetics, Humans, Genetic Predisposition to Disease, Polymorphism, GENOME-WIDE ASSOCIATION, METAANALYSIS, Biological Psychiatry, Genetic association, business.industry, Prevention, Human Genome, Psychology and Cognitive Sciences, PheWAS, Brain Disorders, Post-Traumatic, RISK-FACTORS, business, Genome-Wide Association Study
Abstract

Funding Information: This work was supported by the National Institute of Mental Health / U.S. Army Medical Research and Development Command (Grant No. R01MH106595 [to CMN, IL, MBS, KJRe, and KCK], National Institutes of Health (Grant No. 5U01MH109539 to the Psychiatric Genomics Consortium ), and Brain & Behavior Research Foundation (Young Investigator Grant [to KWC]). Genotyping of samples was provided in part through the Stanley Center for Psychiatric Genetics at the Broad Institute supported by Cohen Veterans Bioscience . Statistical analyses were carried out on the LISA/Genetic Cluster Computer ( https://userinfo.surfsara.nl/systems/lisa ) hosted by SURFsara. This research has been conducted using the UK Biobank resource (Application No. 41209). This work would have not been possible without the financial support provided by Cohen Veterans Bioscience, the Stanley Center for Psychiatric Genetics at the Broad Institute, and One Mind. Funding Information: MBS has in the past 3 years received consulting income from Actelion, Acadia Pharmaceuticals, Aptinyx, Bionomics, BioXcel Therapeutics, Clexio, EmpowerPharm, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals, and Roche/Genentech and has stock options in Oxeia Biopharmaceuticals and Epivario. In the past 3 years, NPD has held a part-time paid position at Cohen Veterans Bioscience, has been a consultant for Sunovion Pharmaceuticals, and is on the scientific advisory board for Sentio Solutions for unrelated work. In the past 3 years, KJRe has been a consultant for Datastat, Inc., RallyPoint Networks, Inc., Sage Pharmaceuticals, and Takeda. JLM-K has received funding and a speaking fee from COMPASS Pathways. MU has been a consultant for System Analytic. HRK is a member of the Dicerna scientific advisory board and a member of the American Society of Clinical Psychopharmacology Alcohol Clinical Trials Initiative, which during the past 3 years was supported by Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Dicerna, Ethypharm, Indivior, Lundbeck, Mitsubishi, and Otsuka. HRK and JG are named as inventors on Patent Cooperative Treaty patent application number 15/878,640, entitled “Genotype-guided dosing of opioid agonists,” filed January 24, 2018. RP and JG are paid for their editorial work on the journal Complex Psychiatry. OAA is a consultant to HealthLytix. All other authors report no biomedical financial interests or potential conflicts of interest. Funding Information: This work was supported by the National Institute of Mental Health/ U.S. Army Medical Research and Development Command (Grant No. R01MH106595 [to CMN, IL, MBS, KJRe, and KCK], National Institutes of Health (Grant No. 5U01MH109539 to the Psychiatric Genomics Consortium), and Brain & Behavior Research Foundation (Young Investigator Grant [to KWC]). Genotyping of samples was provided in part through the Stanley Center for Psychiatric Genetics at the Broad Institute supported by Cohen Veterans Bioscience. Statistical analyses were carried out on the LISA/Genetic Cluster Computer (https://userinfo.surfsara.nl/systems/lisa) hosted by SURFsara. This research has been conducted using the UK Biobank resource (Application No. 41209). This work would have not been possible without the financial support provided by Cohen Veterans Bioscience, the Stanley Center for Psychiatric Genetics at the Broad Institute, and One Mind. This material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting true views of the U.S. Department of the Army or the Department of Defense. We thank the investigators who comprise the PGC-PTSD working group and especially the more than 206,000 research participants worldwide who shared their life experiences and biological samples with PGC-PTSD investigators. We thank Mark Zervas for his critical input. Full acknowledgments are in Supplement 1. MBS has in the past 3 years received consulting income from Actelion, Acadia Pharmaceuticals, Aptinyx, Bionomics, BioXcel Therapeutics, Clexio, EmpowerPharm, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals, and Roche/Genentech and has stock options in Oxeia Biopharmaceuticals and Epivario. In the past 3 years, NPD has held a part-time paid position at Cohen Veterans Bioscience, has been a consultant for Sunovion Pharmaceuticals, and is on the scientific advisory board for Sentio Solutions for unrelated work. In the past 3 years, KJRe has been a consultant for Datastat, Inc. RallyPoint Networks, Inc. Sage Pharmaceuticals, and Takeda. JLM-K has received funding and a speaking fee from COMPASS Pathways. MU has been a consultant for System Analytic. HRK is a member of the Dicerna scientific advisory board and a member of the American Society of Clinical Psychopharmacology Alcohol Clinical Trials Initiative, which during the past 3 years was supported by Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Dicerna, Ethypharm, Indivior, Lundbeck, Mitsubishi, and Otsuka. HRK and JG are named as inventors on Patent Cooperative Treaty patent application number 15/878,640, entitled ?Genotype-guided dosing of opioid agonists,? filed January 24, 2018. RP and JG are paid for their editorial work on the journal Complex Psychiatry. OAA is a consultant to HealthLytix. All other authors report no biomedical financial interests or potential conflicts of interest. Publisher Copyright: © 2021 Society of Biological Psychiatry Background: Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs). Methods: A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms. Results: GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program. Conclusions: Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods. publishersversion published

Published
2022

81. Rare copy number variation in posttraumatic stress disorder

Author

Adam X, Maihofer, Worrawat, Engchuan, Guillaume, Huguet, Marieke, Klein, Jeffrey R, MacDonald, Omar, Shanta, Bhooma, Thiruvahindrapuram, Martineau, Jean-Louis, Zohra, Saci, Sebastien, Jacquemont, Stephen W, Scherer, Elizabeth, Ketema, Allison E, Aiello, Ananda B, Amstadter, Esmina, Avdibegović, Dragan, Babic, Dewleen G, Baker, Jonathan I, Bisson, Marco P, Boks, Elizabeth A, Bolger, Richard A, Bryant, Angela C, Bustamante, Jose Miguel, Caldas-de-Almeida, Graça, Cardoso, Jurgen, Deckert, Douglas L, Delahanty, Katharina, Domschke, Boadie W, Dunlop, Alma, Dzubur-Kulenovic, Alexandra, Evans, Norah C, Feeny, Carol E, Franz, Aarti, Gautam, Elbert, Geuze, Aferdita, Goci, Rasha, Hammamieh, Miro, Jakovljevic, Marti, Jett, Ian, Jones, Milissa L, Kaufman, Ronald C, Kessler, Anthony P, King, William S, Kremen, Bruce R, Lawford, Lauren A M, Lebois, Catrin, Lewis, Israel, Liberzon, Sarah D, Linnstaedt, Bozo, Lugonja, Jurjen J, Luykx, Michael J, Lyons, Matig R, Mavissakalian, Katie A, McLaughlin, Samuel A, McLean, Divya, Mehta, Rebecca, Mellor, Charles Phillip, Morris, Seid, Muhie, Holly K, Orcutt, Matthew, Peverill, Andrew, Ratanatharathorn, Victoria B, Risbrough, Albert, Rizzo, Andrea L, Roberts, Alex O, Rothbaum, Barbara O, Rothbaum, Peter, Roy-Byrne, Kenneth J, Ruggiero, Bart P F, Rutten, Dick, Schijven, Julia S, Seng, Christina M, Sheerin, Michael A, Sorenson, Martin H, Teicher, Monica, Uddin, Robert J, Ursano, Christiaan H, Vinkers, Joanne, Voisey, Heike, Weber, Sherry, Winternitz, Miguel, Xavier, Ruoting, Yang, Ross, McD Young, Lori A, Zoellner, Rany M, Salem, Richard A, Shaffer, Tianying, Wu, Kerry J, Ressler, Murray B, Stein, Karestan C, Koenen, Jonathan, Sebat, Caroline M, Nievergelt, MUMC+: MA Psychiatrie (3), Psychiatrie & Neuropsychologie, RS: MHeNs - R3 - Neuroscience, Anatomy and neurosciences, Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Mental Health, Centro de Estudos de Doenças Crónicas (CEDOC), and NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)

Subjects
Stress Disorders, Post-Traumatic, Cellular and Molecular Neuroscience, Psychiatry and Mental health, Genome, SDG 3 - Good Health and Well-being, DNA Copy Number Variations, Humans, Brain, Genetic Predisposition to Disease, Molecular Biology, Polymorphism, Single Nucleotide, Genome-Wide Association Study
Abstract

Funding Information: This work was supported by the National Institute of Mental Health/U.S. Army Medical Research and Development Command (Grant No. R01MH106595 [to CMN, MBS, KJRe, and KCK]), and National Institutes of Health (Grant No. 5U01MH109539 [to the Psychiatric Genomics Consortium] and Grant No. U19 MH069056 [to BWD])). Financial support for the PTSD PGC was provided by the Cohen Veterans Bioscience, Stanley Center for Psychiatric Research at the Broad Institute, and One Mind. Genotyping of samples was provided in part through the Stanley Center for Psychiatric Genetics at the Broad Institute supported by Cohen Veterans Bioscience Statistical analyses were carried out on the LISA/Genetic Cluster Computer ( https://userinfo.surfsara.nl/systems/lisa ) hosted by SURFsara. This research has been conducted using the UK Biobank resource (Application No. 41209). Posttraumatic stress disorder (PTSD) is a heritable (h2 = 24–71%) psychiatric illness. Copy number variation (CNV) is a form of rare genetic variation that has been implicated in the etiology of psychiatric disorders, but no large-scale investigation of CNV in PTSD has been performed. We present an association study of CNV burden and PTSD symptoms in a sample of 114,383 participants (13,036 cases and 101,347 controls) of European ancestry. CNVs were called using two calling algorithms and intersected to a consensus set. Quality control was performed to remove strong outlier samples. CNVs were examined for association with PTSD within each cohort using linear or logistic regression analysis adjusted for population structure and CNV quality metrics, then inverse variance weighted meta-analyzed across cohorts. We examined the genome-wide total span of CNVs, enrichment of CNVs within specified gene-sets, and CNVs overlapping individual genes and implicated neurodevelopmental regions. The total distance covered by deletions crossing over known neurodevelopmental CNV regions was significant (beta = 0.029, SE = 0.005, P = 6.3 × 10−8). The genome-wide neurodevelopmental CNV burden identified explains 0.034% of the variation in PTSD symptoms. The 15q11.2 BP1-BP2 microdeletion region was significantly associated with PTSD (beta = 0.0206, SE = 0.0056, P = 0.0002). No individual significant genes interrupted by CNV were identified. 22 gene pathways related to the function of the nervous system and brain were significant in pathway analysis (FDR q < 0.05), but these associations were not significant once NDD regions were removed. A larger sample size, better detection methods, and annotated resources of CNV are needed to explore this relationship further. publishersversion published

Published
2022

82. Erectile Function, Sexual Satisfaction, and Cognitive Decline in Men from Midlife to Older Adulthood

Author

Riki E Slayday, Tyler R Bell, Michael J Lyons, Teresa S Warren , BA, Rosemary Toomey, Richard Vandiver, Martin J Sliwinski, William S Kremen, and Carol E Franz

Subjects
General Medicine, Geriatrics and Gerontology, Gerontology
Abstract

Background and Objectives Vascular theories of cognitive aging have focused on macrovascular changes and cognitive decline. However, according to the artery-size hypothesis, microvascular changes, such as those that underlie changes in erectile function, may also play an important role in contributing to cognitive decline. Thus, we examined associations between erectile function, sexual satisfaction, and cognition starting in middle age because this represents a transition period where declines in these areas emerge. Research Design and Methods We examined 818 men from the Vietnam Era Twin Study of Aging across three waves at mean ages 56, 61, and 68. Erectile function and sexual satisfaction were measured using the International Index of Erectile Function. Cognitive performance was measured using factor scores for episodic memory, executive function, and processing speed. We tested multilevel models hierarchically, adjusting for demographics, frequency of sexual activity, and physical and mental health confounders to examine how changes in erectile function and sexual satisfaction related to changes in cognitive performance. Results Lower erectile function at baseline was related to poorer performance in all cognitive domains at baseline and faster declines in processing speed over time. However, baseline sexual satisfaction was unrelated to cognitive performance. Decreases in erectile function and sexual satisfaction were both associated with memory decline. Discussion and Implications Decreasing sexual health may signal an increased risk for cognitive decline. We discuss potential mechanisms, including microvascular changes and psychological distress. Discussing and tracking sexual health in middle-aged men may help to identify those likely to face memory decline.

Published
2022

83. Persistence of pain and cognitive impairment in older adults

Author

William S. Kremen, Carol E. Franz, and Tyler Bell

Subjects
Persistence (psychology), Male, cognition, subjective memory, Aging, Activities of daily living, Neurodegenerative, Basic Behavioral and Social Science, Medical and Health Sciences, Article, Clinical Research, 2.3 Psychological, Activities of Daily Living, Behavioral and Social Science, Medicine, Dementia, Humans, Cognitive Dysfunction, pain, Effects of sleep deprivation on cognitive performance, Longitudinal Studies, Aetiology, Generalized estimating equation, Pain Measurement, Aged, cognitive impairment, Memory Disorders, business.industry, Prevention, Pain Research, Chronic pain, Neurosciences, Cognition, Health and Retirement Study, medicine.disease, Brain Disorders, Geriatrics, Neurological, Female, Mental health, Independent Living, Self Report, Geriatrics and Gerontology, Chronic Pain, social and economic factors, business, Mind and Body, Clinical psychology
Abstract

BackgroundNo studies have examined the longitudinal association between the persistence of pain and its relationship to cognitive problems in older adults. The objective of this study was to examine how the persistent of pain associates with cognitive performance, cognitive impairment, and subjective memory decline.MethodsAcross 10 biennial waves, 8515 adults ages 65 and over were assessed from the Health and Retirement Study (Mage =74.17, SD=6.87, 59.2% female). At each wave, individuals were asked to report on pain presence, and if present, rate its intensity and interference with daily activities such as housework or chores. Using running frequencies or averages, we calculated the persistence of pain using these three pain measures. Cognition was assessed using cognitive performance and different cognitive impairment cutoffs. Incident subjective memory decline was additionally measured as new self-reported memory change in the last 2 years. General estimating equations examined concurrent associations between persistence of pain and cognitive variables, adjusting for demographics, depressive symptoms, and medical comorbidities.ResultsPersistence of pain presence was associated with an increased risk of cognitive impairment. Only persistence of pain interference, not pain intensity, was significantly associated with poorer cognitive performance or being classified as cognitively impaired. For every 2 years, persistence of pain interference was associated with 21% increased odds of cognitive impairment. Only one of three pain variables was related to incident subjective memory decline.ConclusionsPersistence of pain is associated with poorer cognitive performance in community-dwelling older adults, especially when involving ongoing interference in chores and work. Facilitating pain management might be important for helping to maintain later-life cognition and reduce dementia risk.

Published
2022

84. Discovery of genomic loci of the human cerebral cortex using genetically informed brain atlases

Author

Carolina Makowski, Dennis van der Meer, Weixiu Dong, Hao Wang, Yan Wu, Jingjing Zou, Cin Liu, Sara B. Rosenthal, Donald J. Hagler, Chun Chieh Fan, William S. Kremen, Ole A. Andreassen, Terry L. Jernigan, Anders M. Dale, Kun Zhang, Peter M. Visscher, Jian Yang, Chi-Hua Chen, Psychiatrie & Neuropsychologie, RS: MHeNs - R2 - Mental Health, and Metamedica

Subjects
Male, Multifactorial Inheritance, SEGMENTATION, Regulatory Sequences, Nucleic Acid, ANNOTATION, Cohort Studies, ELEMENTS, 80 and over, WIDE ASSOCIATION, 2.1 Biological and endogenous factors, Aetiology, Child, Aged, 80 and over, RISK, Cerebral Cortex, Pediatric, Multidisciplinary, Genome, Chromatin/genetics, HERITABILITY, Mental Disorders, Single Nucleotide, Middle Aged, Magnetic Resonance Imaging, Chromatin, Mental Health, Neurological, Female, Human, Adult, General Science & Technology, Polymorphism, Single Nucleotide, Article, SINGLE-CELL, Clinical Research, Genetics, Humans, POPULATION-STRUCTURE, Polymorphism, Genetic Association Studies, FUNCTIONAL IMPACT, Aged, Mental Disorders/genetics, Nucleic Acid, Genome, Human, COMPLEX TRAITS, Prevention, Human Genome, Neurosciences, Genetic Variation, Molecular Sequence Annotation, Cerebral Cortex/anatomy & histology, Gene Ontology, Genetic Loci, Regulatory Sequences, Genome-Wide Association Study
Abstract

To determine the impact of genetic variants on the brain, we used genetically informed brain atlases in genome-wide association studies of regional cortical surface area and thickness in 39,898 adults and 9136 children. We uncovered 440 genome-wide significant loci in the discovery cohort and 800 from a post hoc combined meta-analysis. Loci in adulthood were largely captured in childhood, showing signatures of negative selection, and were linked to early neurodevelopment and pathways associated with neuropsychiatric risk. Opposing gradations of decreased surface area and increased thickness were associated with common inversion polymorphisms. Inferior frontal regions, encompassing Broca’s area, which is important for speech, were enriched for human-specific genomic elements. Thus, a mixed genetic landscape of conserved and human-specific features is concordant with brain hierarchy and morphogenetic gradients.

Published
2022

85. Genetic influences on hippocampal volume differ as a function of testosterone level in middle-aged men.

Author

Matthew S. Panizzon, Richard L. Hauger, Lindon J. Eaves, Chi-Hua Chen, Anders M. Dale, Lisa T. Eyler, Bruce Fischl, Christine Fennema-Notestine, Carol E. Franz, Michael D. Grant, Kristen C. Jacobson, Amy J. Jak, Michael J. Lyons 0002, Sally P. Mendoza, Michael C. Neale, Elizabeth Prom-Wormley, Larry J. Seidman, Ming T. Tsuang, Hong Xian, and William S. Kremen

Published
2012
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86. Genetic and environmental influences of white and gray matter signal contrast: A new phenotype for imaging genetics?

Author

Matthew S. Panizzon, Christine Fennema-Notestine, Thomas S. Kubarych, Chi-Hua Chen, Lisa T. Eyler, Bruce Fischl, Carol E. Franz, Michael D. Grant, Samar Hamza, Amy J. Jak, Terry L. Jernigan, Michael J. Lyons 0002, Michael C. Neale, Elizabeth Prom-Wormley, Larry J. Seidman, Ming T. Tsuang, Hao Wu 0055, Hong Xian, Anders M. Dale, and William S. Kremen

Published
2012
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87. Genetic and environmental influences on the size of specific brain regions in midlife: The VETSA MRI study.

Author

William S. Kremen, Elizabeth Prom-Wormley, Matthew S. Panizzon, Lisa T. Eyler, Bruce Fischl, Michael C. Neale, Carol E. Franz, Michael J. Lyons 0002, Jennifer L. Pacheco, Michele E. Perry, Allison Stevens, J. Eric Schmitt, Michael D. Grant, Larry J. Seidman, Heidi W. Thermenos, Ming T. Tsuang, Seth A. Eisen, Anders M. Dale, and Christine Fennema-Notestine

Published
2010
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88. Salivary cortisol and prefrontal cortical thickness in middle-aged men: A twin study.

Author

William S. Kremen, Robert C. O'Brien, Matthew S. Panizzon, Elizabeth Prom-Wormley, Lindon J. Eaves, Seth A. Eisen, Lisa T. Eyler, Richard L. Hauger, Christine Fennema-Notestine, Bruce Fischl, Michael D. Grant, Dirk H. Hellhammer, Amy J. Jak, Kristen C. Jacobson, Terry L. Jernigan, Sonia J. Lupien, Michael J. Lyons 0002, Sally P. Mendoza, Michael C. Neale, Larry J. Seidman, Heidi W. Thermenos, Ming T. Tsuang, Anders M. Dale, and Carol E. Franz

Published
2010
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89. Corrigendum to 'Genetic and environmental influences on the size of specific brain regions in midlife: The VETSA MRI study': [NeuroImage 49 (2010) 1213-1223].

Author

William S. Kremen, Elizabeth Prom-Wormley, Matthew S. Panizzon, Lisa T. Eyler, Bruce Fischl, Michael C. Neale, Carol E. Franz, Michael J. Lyons 0002, Jennifer L. Pacheco, Michele E. Perry, Allison Stevens, J. Eric Schmitt, Michael D. Grant, Larry J. Seidman, Heidi W. Thermenos, Ming T. Tsuang, Seth A. Eisen, Anders M. Dale, and Christine Fennema-Notestine

Published
2010
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90. Amyloid-β Positivity Predicts Cognitive Decline but Cognition Predicts Progression to Amyloid-β Positivity

Author

William S. Kremen, Jeremy A. Elman, Mark E Sanderson-Cimino, Daniel E. Gustavson, Michael J. Lyons, Alzheimer’s Disease Neuroimaging Initiative, Carol E. Franz, and Matthew S. Panizzon

Subjects
0301 basic medicine, Oncology, Apolipoprotein E, Aging, Disease, Neuropsychological Tests, Neurodegenerative, Alzheimer's Disease, Medical and Health Sciences, Cognition, 0302 clinical medicine, 2.1 Biological and endogenous factors, Medicine, Aetiology, Cognitive decline, Psychiatry, β-amyloid, beta-amyloid, Biological Sciences, Biomarker trajectories, Neurological, Disease Progression, Biomarker (medicine), Alzheimer’s disease, medicine.medical_specialty, tau Proteins, 03 medical and health sciences, Neuroimaging, Alzheimer Disease, Internal medicine, mental disorders, Acquired Cognitive Impairment, Humans, Cognitive Dysfunction, Effects of sleep deprivation on cognitive performance, Pathological, Biological Psychiatry, Amyloid beta-Peptides, business.industry, Psychology and Cognitive Sciences, Neurosciences, Mild cognitive impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), AD, Alzheimer’s Disease Neuroimaging Initiative, MCI, Brain Disorders, 030104 developmental biology, Dementia, business, Amyloid accumulation, Biomarkers, 030217 neurology & neurosurgery
Abstract

Background Stage 1 of the National Institute on Aging–Alzheimer’s Association’s proposed Alzheimer’s disease continuum is defined as amyloid-β (Aβ) positive but cognitively normal. Identifying at-risk individuals before Aβ reaches pathological levels could have great benefits for early intervention. Although Aβ levels become abnormal long before severe cognitive impairments appear, increasing evidence suggests that subtle cognitive changes may begin early, potentially before Aβ surpasses the threshold for abnormality. We examined whether baseline cognitive performance would predict progression from normal to abnormal levels of Aβ. Methods We examined the association of baseline cognitive composites (Preclinical Alzheimer Cognitive Composite, Alzheimer’s Disease Neuroimaging Initiative (ADNI) memory factor composite) with progression to Aβ positivity in 292 nondemented, Aβ-negative ADNI participants. Additional analyses included continuous cerebrospinal fluid biomarker levels to examine the effects of subthreshold pathology. Results Forty participants progressed to Aβ positivity during follow-up. Poorer baseline performance on both cognitive measures was significantly associated with increased odds of progression. More abnormal levels of baseline cerebrospinal fluid phosphorylated tau and subthreshold Aβ were associated with increased odds of progression to Aβ positivity. Nevertheless, baseline ADNI memory factor composite performance predicted progression even after controlling for baseline biomarker levels and APOE genotype (Preclinical Alzheimer Cognitive Composite was trend level). Survival analyses were largely consistent: controlling for baseline biomarker levels, baseline Preclinical Alzheimer Cognitive Composite still significantly predicted progression time to Aβ positivity (ADNI memory factor composite was trend level). Conclusions The possibility of intervening before Aβ reaches pathological levels is of obvious benefit. Low-cost, noninvasive cognitive measures can be informative for determining who is likely to progress to Aβ positivity, even after accounting for baseline subthreshold biomarker levels.

Published
2020

91. Integrating verbal fluency with executive functions: Evidence from twin studies in adolescence and middle age

Author

William S. Kremen, Naomi P. Friedman, Robin P. Corley, Michael J. Lyons, Matthew S. Panizzon, Daniel E. Gustavson, Chandra A. Reynolds, Carol E. Franz, and John K. Hewitt

Subjects
Male, Adolescent, Aptitude, Short-term memory, Experimental and Cognitive Psychology, Article, 050105 experimental psychology, Developmental psychology, Executive Function, Fluency, Nonverbal communication, Developmental Neuroscience, Humans, Verbal fluency test, 0501 psychology and cognitive sciences, Longitudinal Studies, General Psychology, Language, Working memory, 05 social sciences, Cognition, Middle Aged, Executive functions, Twin study, Female, Psychology
Abstract

The relationship of verbal fluency to executive functions (EFs) remains somewhat unclear. Verbal fluency is sometimes considered an EF ability, but is not often included in the same models as other well-studied EFs (inhibition, shifting, and working memory updating). We examined the associations between verbal fluency and EFs at 2 ages with the unity/diversity model, which includes common and domain-specific EF factors. Participants were 813 adolescent twins from the Colorado Longitudinal Twin Sample (mean age 17 years) and 1,290 middle-aged twins from the Vietnam Era Twin Study of Aging (mean age 56 years) who completed multiple measures of EFs, verbal fluency, vocabulary, and nonverbal cognitive ability. Results revealed that, in both samples, a General Fluency factor (i.e., comprising both phonemic and semantic fluency measures) was associated with the Common EF factor, but also with variance unique to working memory updating, working memory span, and set-shifting. In adolescents, semantic fluency also had unique associations with shifting beyond its shared variance with phonemic fluency and Common EF. After accounting for EFs and other cognitive abilities, there were unique genetic and environmental influences on the General Fluency and Semantic-Specific latent factors. These results suggest that verbal fluency ability may best be viewed as an amalgamation of general EF variance (i.e., Common EF ability), variance shared with other EFs (e.g., Updating-Specific ability), and multiple sources of unique genetic/environmental variance (i.e., General Fluency and Semantic-Specific abilities). These associations between verbal fluency and EFs generalize to populations that differ in age by approximately 40 years. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Published
2019

92. Age-dependent white matter disruptions after military traumatic brain injury: Multivariate analysis results from ENIGMA brain injury

Author

Heather C. Bouchard, Delin Sun, Emily L. Dennis, Mary R. Newsome, Seth G. Disner, Jeremy Elman, Annelise Silva, Carmen Velez, Andrei Irimia, Nicholas D. Davenport, Scott R. Sponheim, Carol E. Franz, William S. Kremen, Michael J. Coleman, M. Wright Williams, Elbert Geuze, Inga K. Koerte, Martha E. Shenton, Maheen M. Adamson, Raul Coimbra, Gerald Grant, Lori Shutter, Mark S. George, Ross D. Zafonte, Thomas W. McAllister, Murray B. Stein, Paul M. Thompson, Elisabeth A. Wilde, David F. Tate, Aristeidis Sotiras, and Rajendra A. Morey

Subjects
Traumatic, Physical Injury - Accidents and Adverse Effects, Traumatic Brain Injury (TBI), diffusion MRI, Stress Disorders, Post-Traumatic, mTBI, Clinical Research, Brain Injuries, Traumatic, Humans, Radiology, Nuclear Medicine and imaging, military, Traumatic Head and Spine Injury, Brain Concussion, Stress Disorders, Veterans, Radiological and Ultrasound Technology, traumatic brain injury, ENIGMA, Neurosciences, nonnegative matrix factorization, Brain, Experimental Psychology, White Matter, Brain Disorders, Mental Health, Military Personnel, Neurology, Brain Injuries, Multivariate Analysis, Post-Traumatic, Biomedical Imaging, Cognitive Sciences, Neurology (clinical), Anatomy
Abstract

Mild Traumatic brain injury (mTBI) is a signature wound in military personnel, and repetitive mTBI has been linked to age-related neurogenerative disorders that affect white matter (WM) in the brain. However, findings of injury to specific WM tracts have been variable and inconsistent. This may be due to the heterogeneity of mechanisms, etiology, and comorbid disorders related to mTBI. Non-negative matrix factorization (NMF) is a data-driven approach that detects covarying patterns (components) within high-dimensional data. We applied NMF to diffusion imaging data from military Veterans with and without a self-reported TBI history. NMF identified 12 independent components derived from fractional anisotropy (FA) in a large dataset (n= 1,475) gathered through the ENIGMA (Enhancing Neuroimaging Genetics through Meta-Analysis) Military Brain Injury working group. Regressions were used to examine TBI- and mTBI-related associations in NMF-derived components while adjusting for age, sex, post-traumatic stress disorder, depression, and data acquisition site/scanner. We found significantly stronger age-dependent effects of lower FA in Veterans with TBI than Veterans without in four components (q&nbsp

Published
2021

93. Associations between MRI‐assessed locus coeruleus integrity and restriction spectrum imaging of cortical gray matter microstructure

Author

Jeremy A. Elman, Olivia K. Puckett, Donald J. Hagler, Rahul C. Pearce, Christine Fennema‐Notestine, Sean N. Hatton, Anders M. Dale, Matthew S. Panizzon, Michael J. Lyons, Carol E. Franz, and William S. Kremen

Subjects
Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
Published
2021

95. Genetic and environmental influences on Alzheimer’s disease neuroimaging signatures

Author

McKenna E. Williams, Nathan A. Gillespie, Anders M. Dale, Jeremy A. Elman, Lisa T. Eyler, Christine Fennema‐Notestine, Donald J. Hagler, Linda K. McEvoy, Michael C. Neale, Matthew S. Panizzon, Mark E. Sanderson‐Cimino, Carol E. Franz, and William S. Kremen

Subjects
Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
Published
2021

100. Locus Coeruleus Integrity in Older Adults with and without Chronic Pain

Author

Olivia K. Puckett, Jeremy A. Elman, Christine Fennema-Notestine, William S. Kremen, Stephen M. Dorros, Donald J. Hagler, Michael J. Lyons, Lisa T. Eyler, Matthew S. Panizzon, Rahul C. Pearce, Tyler R. Bell, Asad Beck, and Carol E. Franz

Subjects
Moderate to severe, medicine.medical_specialty, business.industry, Chronic pain, medicine.disease, Twin study, Bodily pain, Opioid, Internal medicine, medicine, Locus coeruleus, Brainstem, business, Third wave, medicine.drug
Abstract

The locus coeruleus (LC) is a brainstem region involved in regulating pain. Chronic pain is common in older adulthood, but no studies have examined its association with the LC in humans. We used neuromelanin-sensitive imaging to study differences in LC integrity in older adults with and without chronic pain. Chronic pain was assessed in community-dwelling men from the Vietnam Era Twin Study of Aging (VETSA) in 3 study waves covering an average of 12 years. Pain was self-reported on the SF-36 Bodily Pain Scale. Chronic pain was defined as moderate to severe pain severity at the current and at least one prior wave; 17% had chronic pain (n=80). At the third wave, 481 participants (mean age=67.57) underwent neuromelanin-sensitive MRI scans from which we calculated an LC contrast-to-noise ratio (LCCNR) – an index of LC integrity. We examined associations between chronic pain and LCCNR (in the rostral LC and caudal regions) with generalized estimating equations after adjusting for age, race, education, depressive symptoms, medical comorbidities, and opioid medication use. Individuals with chronic pain had .35 standard deviation lower rostral LCCNR (95% CI: -.62 to -.05) compared to those without chronic pain. No differences in the caudal LCCNR were detected. Chronic pain was associated with decreased rostral LC integrity in older adults. Differences in the rostral LC, rather than caudal LC, suggest the association between lower LC integrity and chronic pain may be related to pain processing in cortical regions where rostral LC projections typically connect.

Published
2021

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