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• Genomic Studies of Bipolar Disorder in a Large Cohort from The Netherlands

  The transcriptomes of whole blood samples of a large cohort of individuals from the Netherlands were assayed using RNA-seq. These whole blood samples included patients diagnosed with schizophrenia or bipolar disorder, family members of patients, and healthy controls. Within the whole blood dataset, about 600 of the samples were sequenced to an average of 13.9 million mapped reads per sample, while about 2,000 were sequenced to an average of 5.9 million mapped reads per sample, in order to assess changes in power to detect eQTLs based on sample size. We find that expression, defined as the log transcripts per million reads (TPMs), is highly correlated when comparing the moderate (13.9M) and lower (5.9M) coverage datasets. Additionally, a subset of about 150 individuals provided fibroblast cell lines which were assessed for the levels of gene expression using RNA-seq at an average of 50 million mapped reads per sample. We use downsampling techniques to generate synthetic datasets derived from this real fibroblast RNA-Seq data to explore the relationship between coverage and number individuals in an eQTL analysis. Corresponding individual-level genotypes for the samples included in the expression matrix are also made available. The genotypes were derived from multiple other project cohorts, each using different genotyping platforms including OmniExpress Exome, Global Screening Array, Illumina550, and PsychChip. A Plink file set of these cohorts is provided here after merging the separate project file sets and extracting only the individuals who have expression data also provided here; quality control for SNP-missingness is recommended. We demonstrate in this study that given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample to allow for an a greater number of individuals sequenced. 

  Study phs002856

• MUSIC: Long-TerM OUtcomes after the Multisystem Inflammatory Syndrome In Children

  This study is an observational cohort study that will use routinely-collected clinical and cardiac (EKG, echocardiogram, CMR, exercise testing) data to assess the association between MIS-C (multisystem inflammatory syndrome in children) and cardiac outcomes within the first year after hospital discharge. Research funding will be available for EKGs, echocardiograms and MRIs in protocol windows that are not ordered by primary caregivers. Our principal goal is to determine the spectrum and early time course of coronary artery involvement, LV systolic function, and arrhythmias or conduction system abnormalities, and, using these data, to define associated clinical and laboratory factors. We will include all eligible patients, including retrospective cases beginning January 1, 2020, with follow-up (in-person or telehealth) up to one year and annual medical history forms until up to 5 years have elapsed since illness onset. Because many patients will have been identified by retrospective review, we will obtain consent at different times in their illness course. For this reason, it may be hard to reach some patients and their families. Waiver of consent will be obtained after three attempts have been made to locate the patient and family without success, as well as for the rare child who dies before informed consent can be obtained. We will include a HIPAA-compliant cryptographic algorithm to create a sharable “hashed” identifier from patient information. If blood work for research purposes is added to usual clinically-indicated blood work during follow-up visits, this will be covered by other informed consent forms. Our primary outcomes are the largest proximal Left Anterior Descending Coronary Artery (LAD) or Right Coronary Artery (RCA) z score and lowest Left Ventricular Ejection Fraction (LVEF).

  Study phs002770

• Predicting brain tumor recurrence: development and validation of a DNA-methylation based nomogram in meningioma

  Our work is the first to demonstrate the transformative utility of integrating clinical and molecular factors for use beyond simple classification into the realm of individualized prognostication for any brain tumor. Using our developed and validated tools that are publicly available, clinicians will be able combine clinical and molecular factors to determine an individualized probability of recurrence for patients with meningiomas. This represents a major advance in the field of personalized medicine for neuro-oncology, and the use of this tool can help clinicians overcome one of the most challenging limitations we face when treating patients with meningiomas.

  Study EGAS00001003490

• GAW16 Framingham and Simulated Data

  Important links to apply for individual-level data Genetic Analysis Workshop Instructions to Request Authorized Access Data Use Certification Requirements (DUC) Apply here for controlled access to individual level data Research Use Statement Questions regarding GAW16 should be directed to Vanessa Olmo at vanessa@business-endeavors.com. Problem 2: Description of the Framingham Heart Study In GAW16, we use data drawn from the Framingham Heart Study. The Framingham Heart Study — under the direction of National Heart, Lung, and Blood Institute; NHLBI — began in 1948 with the recruitment of adults from the town of Framingham, Massachusetts. At the time, little was known about the general causes of heart disease and stroke, but the death rates for cardiovascular disease (CVD) had been increasing steadily since the beginning of the 20th century and had become an American epidemic. The Framingham Heart Study is now conducted in collaboration with Boston University. The objective of the Framingham Heart Study was to identify the common factors or characteristics that contribute to CVD by following its development over a long period of time in a large group of participants who had not yet developed overt symptoms of CVD or suffered a heart attack or stroke. Between 1948 and 1953 the researchers recruited 5,209 subjects (2,336 men and 2,873 women) between the ages of 29 and 62 from the town of Framingham, Massachusetts and began the first round of extensive physical examinations and lifestyle interviews that they would later analyze for common patterns related to CVD development. Subjects were recruited from lists of addresses recorded for the town. Two out of every three households were approached for participation in the study. While there was no intention to recruit families for family studies, the plan was to recruit all household members in the ages 30-60 within each house that was selected for study. Hence, many biologically related individuals were recruited, including 1644 spouse pairs. Since 1948, these participants have returned to the study every two years for a detailed medical history, physical examination, and laboratory tests. Now in 2008 at 60 years of follow up, there remain about 500 participants from this cohort. Between 1971 and 1975 the study enrolled a second-generation group — 5,124 of the original participants' children and the spouses of these children — to participate in similar examinations. 2,616 subjects are offspring of the original spouse pairs and 34 are stepchildren. A total of 898 offspring are children of cohort members where only one parent was a study participant and 1,576 are spouses of the offspring. The Offspring Cohort has been followed every four years through 2001 (except between Exams 1 and 2 with an intervening 8 years) using protocols similar to those used for study of the Original Cohort. Between 2002 and 2005 the study enrolled the third generation (Gen3) of the Framingham Heart Study - 4095 offspring of the second generation. None of their spouses were recruited. An additional 103 parents of this third generation, who were not recruited between 1971 and 1975, were also recruited at this time. The latter group is not included in the GAW16 data. With the recruitment of this third generation, the study has increasingly focused on genetic factors associated with the development of cardiovascular disease and its associated risk factors. To date, there is only one examination of this generation of participants. A description of the recruitment of this third generation and comparison with the earlier generations at their initial recruitment is presented in Splansky GL et al., 2007. Further information on the Study can be found at http://www.nhlbi.nih.gov/about/framingham/index.html. Genome-wide Dense SNP Scan in Framingham Heart Study Genetic studies did not begin in the FHS until the 1990s. In the late 1980s and through the 1990s DNA was extracted from blood samples of surviving FHS participants. In 2007, the FHS entered a new phase with the conduct of genotyping for the FHS SHARe (SNP Health Association Resource) project, for which dense SNP genotyping was performed using approximately 550,000 SNPs (GeneChip® Human Mapping 500K Array Set and the 50K Human Gene Focused Panel) in 10,775 samples (some duplicates) from the three generations of subjects (including over 900 pedigrees). Affymetrix conducted all genotyping for the FHS SHARe project, using the 250K Sty, 250K Nsp, and the supplemental 50K platforms. Eighty-nine percent of the DNA samples were collected during the 1990s. To maximize the power of the study, we also extracted DNA from 1133 blood samples, drawn from subjects who had no DNA, to include in the SHARe project. These samples had been sitting in our refrigerators for some time, a few as far back as the 1970s. We refer to these DNA samples as the legacy samples. These samples had a higher failure rate in the genotyping process (40%) than the other eighty-nine percent (3%). Affymetrix invoked its own criteria for a sample to succeed in genotyping. All non-legacy samples must succeed on all three platforms, while legacy samples needed to pass on at least one platform. When a sample failed, additional attempts were made. Samples that repeatedly failed 2-4 times were called failures. Other samples failed due to issues of genotyped sex identification not matching our records or low SNP concordance among SNPs common across arrays or contamination. Eighty-nine percent of the legacy samples for which genotyping results are available passed all three platforms. The genotyping data from the 10,043 samples from 9354 subjects that passed the Affymetrix criteria were additionally checked for gender consistency and consistency with family structure, resulting in genotyping data for 9,274 participants in FHS SHARe. Genotype calls were made with the BRLMM algorithm. The SHARe database is housed at the National Center for Biotechnology Information database of genotypes and phenotypes (NCBI dbGaP) and contains all ~550,000 SNPs. This genome-wide dense SNP scan and a subset of phenotypes from the Framingham Heart Study are the focus of the Genetic Analysis Workshop 16. Further information on the specific variables in the Problem 2 dataset can be found by clicking on the Documents tab at the top of the page. Problem 3: Description of the Simulated Data Set The focus of this simulation is gene discovery in genome-wide association scans (GWAS). The Framingham Heart Study data set (distributed as "Problem 2") is the basis for the FHS* simulated data. The pedigree structures are derived from the data distributed for Problem 2, and we distribute an accompanying triplet file (triplet_sim) containing person ID, father ID, mother ID, to ensure the identical subjects, pedigrees, and singletons are used in the simulated data analysis. Consistent with standard practice, founders and singletons are designated as subjects with both fshare and mshare equal to zero (missing). The simulated data includes a total of 6,479 subjects with both phenotype and genotype data, in 942 pedigrees distributed among 3 generations and 188 singletons. Data inclusion is consistent with the subjects' consent for use by both for-profit and not-for-profit researchers. The genotypes for all Problem 3 replicates are fixed as measured and distributed for Problem 2 for both the genomewide scan and the additional candidate gene SNPs, for a total of approximately 550,000 SNPs (GeneChip® Human Mapping 500K Array Set and the 50K Human Gene Focused Panel). Thus, to analyze the Problem 3 simulated data you also will need to download the Problem 2 genotypes. Note that there are slight discrepancies in counts between Problems 2 and 3 due to a change in consents between the two datasets. Several phenotypes that contribute to coronary heart disease (CHD) were simulated for all individuals with genotypes across three different time points, 10 years apart. All genotyped individuals have complete data; the effects of missing values can be investigated by user-specified missing value patterns. There are 200 longitudinal datasets created, based on the generating model, and each replication is found in a separate dataset. We suggest that if only one replication is to be analyzed, that it be replication 1 to enable more precise comparisons among analytical approaches. The 'shareid' will allow you to merge the simulated phenotype data with the Problem 2 genotype data, and reconstruction of the pedigrees using the distributed 'triplet_sim' file or for larger families' relationships with the triplet distributed with the Problem 2. The simulated data problem is further described in the associated readme file, and a data dictionary is provided defining all the variables. For disclosure of the generating model for these data, please contact Jean MacCluer at jean@sfbrgenetics.org.

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• CONNECTS Master Protocol for Clinical Trials targeting Macro- and Micro-Immuno-Thrombosis, Vascular Hyperinflammation, and Hypercoagulability and Renin-Angiotensin-Aldosterone System (RAAS) in Hospitalized Patients with COVID-19 (ACTIV-4 Host Tissue)

  ACTIV 4 Host Tissue is a shared-placebo clinical trial platform evaluating therapies targeting the host response to COVID-19 in hospitalized patients. The overarching goal of the platform was to find effective strategies for inpatient management of patients with COVID-19. Therapeutic goals for patients hospitalized for COVID-19 include hastening recovery and preventing progression to critical illness, multiorgan failure, or death. Our objective is to determine whether modulating the host tissue response improves clinical outcomes among patients with COVID-19. Three trials were implemented to investigate three agents: TXA-127, TRV-027, and Fostamatinib. These agents all impact the host tissue response in COVID-19 via a number of unique mechanisms, including potential beneficial effects on the RAAS system and formation of neutrophil extracellular traps (NETs). Each trial evaluated the efficacy of these agents' ability to impact the host tissue response and improve outcomes in patients hospitalized with COVID-19. We found no evidence that any of the three agents significantly improved clinical outcomes as measured by the primary outcome, oxygen free days through day 28.

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• Study of Addiction: Genetics and Environment (SAGE)

  This study is part of the Gene Environment Association Studies initiative (GENEVA) funded by the National Human Genome Research Institute. The overarching goal is to identify novel genetic factors that contribute to addiction through a large-scale genome-wide association study of DSM-IV alcohol dependent (and frequently illicit drug dependent) cases and non-dependent, unrelated control subjects of European and African American descent. The focus of this proposal is a case-control design of unrelated individuals for a genetic association study of addiction. Cases are defined as individuals with DSM-IV alcohol dependence (lifetime) and potentially other illicit drug dependence. In addition to the categorical diagnosis, we have data on ordinal measurements of number of DSM-IV symptoms for alcohol, nicotine, marijuana, cocaine, opiates and other drugs so that we will able to construct quantitative measurements of addiction severity over a wide range of substances. Controls are defined as individuals who have been exposed to alcohol (and possibly to other drugs), but have never met lifetime diagnosis for alcohol dependence or dependence on other illicit substances. Analyses that include refinement of the phenotype and incorporation of important demographic and environmental factors into association studies will be pursued. Cases and controls were selected from three large, complementary datasets: the Collaborative Study on the Genetics of Alcoholism (COGA), the Family Study of Cocaine Dependence (FSCD), and the Collaborative Genetic Study of Nicotine Dependence (COGEND). COGA: COGA was initiated in 1989 and is a large-scale family study that has had as its primary aim the identification of genes that contribute to alcoholism susceptibility and related characteristics. COGA is funded through the National Institute on Alcohol Abuse and Alcoholism (NIAAA). Subjects were recruited from 7 sites across the U.S. Alcohol dependent probands were recruited from treatment facilities and assessed by personal interview. After securing permission, other family members were also assessed. A set of comparison families was drawn from the same communities as the families recruited through the alcohol dependent probands. Assessment involved a comprehensive personal interview developed for this project, the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA), which gathers detailed information on alcoholism related symptoms along with other drugs and psychiatric symptoms. Families with three or more first-degree relatives who were alcohol dependent were invited for more extensive testing, including neurophysiology evaluations (ERPs and EEGs) and a battery of neuropsychological assessments. Blood was obtained for genetic studies. Institutional Review Boards at all sites approved the protocols, including sharing in the NIAAA national repository. COGA has four Co-Principal Investigators Bernice Porjesz, Victor Hesselbrock, Howard Edenberg, and Laura Bierut. COGA includes nine different centers where data collection, analysis, and storage take place. The nine sites and Principal Investigators and Co-investigators are: University of Connecticut (Victor Hesselbrock); Indiana University (Howard Edenberg, John Nurnberger, Jr., Tatiana Foroud); University of Iowa (Samuel Kuperman); SUNY Downstate (Bernice Porjesz); Washington University in St. Louis (Laura Bierut, Alison Goate, John Rice); University of California at San Diego (Marc Schuckit); Howard University (Robert Taylor); Rutgers University (Jay Tischfield); Southwest Foundation (Laura Almasy). Q. Max Guo serves as the NIAAA Staff Collaborator. This national collaborative study is supported by the NIH Grant U10AA008401 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA). Family Study of Cocaine Dependence (FSCD): This project was initiated in 2000 as a case-control family study of cocaine dependence funded through the National Institute on Drug Abuse (NIDA; PI: Laura Bierut). The primary goal was to increase the understanding of the familial and non-familial antecedents and consequences of cocaine dependence. Cocaine dependent individuals were systematically recruited from chemical dependency treatment units (both public and private; residential and outpatient) in the greater St. Louis metropolitan area. Community based control subjects were identified through a Missouri Driver's License Registry (maintained at Washington University for research purposes) and matched by age, race, gender, and residential zip code. As a supplement to this project, blood samples were collected for future genetic analysis and were included in the NIDA Genetics Consortium. Phenotypic data, DNA, and cell lines are in the NIDA Center for Genetics Studies. Collaborative Genetic Study of Nicotine Dependence (COGEND): COGEND was initiated in 2001 as a three-part program project grant funded through the National Cancer Institute (NCI; PI: Laura Bierut). The three projects included a study of the familial transmission of nicotine dependence, a genetic study of nicotine dependence, and a study of the relationship of nicotine dependence with nicotine metabolism. The primary goal is to detect, localize, and characterize genes that predispose or protect an individual with respect to heavy tobacco consumption, nicotine dependence, and related phenotypes and to integrate these findings with the family transmission and nicotine metabolism findings. The primary design is a community based case-control family study. All subjects were recruited from Detroit and St. Louis. Nicotine dependent cases and non-dependent smoking controls were identified and recruited. In addition, one sibling for each case and control subject was recruited in a subset of the sample. Over 56,000 subjects aged 25-44 years were screened by telephone, over 3,100 subjects were personally interviewed, and over 2,900 donated blood samples for genetic studies. All three studies (COGA, COGEND, FSCD) include measures of basic socio-demographic variables, including age, sex, race/ethnicity, family income, educational attainment, religious participation, and family structure. Other important covariates and/or potential moderators of genetic effects include comorbid addictions and age at initiation of use for cigarettes, alcohol and drugs. The assessments also include measures of various life stressors, such as physical and sexual abuse, which have been implicated in gene-environment interactions for several disorders. Coding for both individual variables and indices has been standardized across studies. All subjects were assessed in person by trained research assistants. Funding support for genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR), was provided by the NIH GEI (U01HG004438), the National Institute on Alcohol Abuse and Alcoholism, the National Institute on Drug Abuse, and the NIH contract "High throughput genotyping for studying the genetic contributions to human disease"(HHSN268200782096C). Note for Publications Related to Study: The Study of Addiction: Genetics and Environment (SAGE) has not yet generated publications. Below is a listing of publications related to the three studies from which the SAGE sample was selected. COGA has over 228 publications listed at www.niaaagenetics.org This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to addiction through a large-scale genome-wide association study of DSM-IV alcohol dependent (and frequently illicit drug dependent) cases and non-dependent, unrelated control subjects of European and African American descent. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

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• PAX4 loss of function increases diabetes risk by altering human pancreatic endocrine cell development

  The coding variant (p.Arg192His) in the transcription factor PAX4 is associated with an altered risk for type 2 diabetes (T2D) in East Asian populations. In mice, Pax4 is essential for beta cell formation but its role on human beta cell development and/or function is unknown. Participants carrying the PAX4 p.His192 allele exhibited decreased pancreatic beta cell function compared to homozygotes for the p.192Arg allele in a cross-sectional study in which we carried out an intravenous glucose tolerance test and an oral glucose tolerance test. In a pedigree of a patient with young onset diabetes, several members carry a newly identified p.Tyr186X allele. In the human beta cell model, EndoC-βH1, PAX4 knockdown led to impaired insulin secretion, reduced total insulin content, and altered hormone gene expression. Deletion of PAX4 in human induced pluripotent stem cell (hiPSC)-derived islet-like cells resulted in derepression of alpha cell gene expression. In vitro differentiation of hiPSCs carrying PAX4 p.His192 and p.X186 risk alleles exhibited increased polyhormonal endocrine cell formation and reduced insulin content that can be reversed with gene correction. Together, we demonstrate the role of PAX4 in human endocrine cell development, beta cell function, and its contribution to T2D-risk.

  Dataset EGAD50000000516

• How are we funded?

  How are we funded? The EGA is provided infrastructure, administrative support, advisory input, and other relevant and necessary aspects by: CRG The EGA at the CRG is funded and supported by “La Caixa Foundation”, The Generalitat de Catalunya (Catalan Government), the Spanish Ministerio de Asuntos Económicos y Transformación Digital (Spanish Government) and the Instituto de Salud Carlos III. The Generalitat de Catalunya contributes with funding to the CRG and to the Barcelona Supercomputing Centre (BSC). The Barcelona Supercomputing Center is decisively contributing to EGA by providing the required compute, storage and networking resources, as well as key personnel for shared operations of EGA infrastructure. EMBL-EBI As part of the European Molecular Biology Laboratory (EMBL), the EGA at EMBL’s European Bioinformatics Institute receives funding from the governments of EMBL’s member states. The UK government, via UK Research and Innovation, also continues to support EMBL-EBI technical infrastructure, with EMBL supporting operational costs. A major source of funding for the EGA both at CRG and EMBL-EBI are competitive projects.The EGA resource continues to be funded through strategic engagement in collaborative partnerships and projects. Please see our projects page for more information. Other funders include the European Commission, the US National Institutes of Health, the Wellcome Trust, UK Research Councils, and our Industry Programme partners. Last updated on October, 2024.

  Documentation about/projects-and-funders/funders

• National Institute of Neurological Disorders and Stroke (NINDS), Family Study of Essential Tremor (FASET), Identification of Susceptibility Genes for Essential Tremor

  The Familial Study of Essential Tremor (FASET) was designed to identify susceptibility genes for Essential Tremor. ET is among the most common neurological diseases with a prevalence (age > 40 years) estimated to be 4.0% and prevalence in advanced age (> 90 years) exceeding 20%. ET, often referred to as "familial tremor", is generally regarded as a highly genetic disorder with families, with affected members over multiple generations, and twin studies show high concordance among monozygotic twins. Probands (affected with ET) and relatives were enrolled in a family study of ET at Columbia University, New York between 2011 - 2014. The study was approved by the Institutional Review Board at Columbia University and written informed consent was obtained from all enrollees. The criteria for enrollment were: 1) the proband had early-onset ET with age at onset < 50 years, 2) the proband had a diagnosis of definite or probable ET, 3) in addition to the proband, there were at least two affected relatives in the family, 4) additional affected and unaffected family members were willing to participate in the study, and 5) the families contained more than two affected individuals in different generations. Blood samples were also collected for genetic research. For the genetic analyses, we excluded enrollees that we or others had diagnosed with Parkinson's disease (PD) or dystonia. The final sample includes 52 families (52 probands [affected with ET]) and 155 relatives). The number of affected individuals enrolled per family ranged from 3 - 7 (mean = 4.1). Genetic samples from FASET were analyzed with whole genome SNP genotyping (for linkage analyses) and whole exome sequencing. It is hoped that this resource will better help researchers to understand the genetic causes of ET and underlying disease pathogenesis.

  Study phs000966

• Neurodevelopmental Genomics: Trajectories of Complex Phenotypes

  This study is a collaboration between the Center for Applied Genomics (CAG) at Children's Hospital of Philadelphia (CHOP) and the Brain Behavior Laboratory at the University of Pennsylvania (Penn). The cohort consists of youths aged 8-21 years who consulted the CHOP network and volunteered to participate in genomic studies of complex pediatric disorders. All participants underwent clinical assessment, including a neuropsychiatric structured interview and review of electronic medical records. They were also administered a neuroscience based computerized neurocognitive battery (CNB) and a subsample underwent neuroimaging. These are described separately below. Clinical Testing: GOASSESS, a computerized, structured screener developed from a modified version of the Kiddie-Schedule for Affective Disorders and Schizophrenia (K-SADS, Kaufman et al. 1997, PMID: 9204677). Components of the interview include a timeline of life events, demographics and medical history, Global Assessment of Functioning, and Interviewer Observations. A psychopathology symptom and criterion-related assessment of mood disorders (depression, mania/hypomania), anxiety disorders (overanxious/generalized-, separation-, social-anxiety, specific phobia, panic disorder, agoraphobia, obsessive compulsive disorder, post-traumatic stress disorder), behavioral disorders (attention deficit hyperactivity disorder, oppositional defiant disorder, conduct disorder), psychosis spectrum (psychosis and prodromal symptoms), eating disorders, suicidal thinking and behavior, and treatment history. This is also based on K-SADS. An abbreviated Family Interview for Genetics Studies (FIGS) to assess major domains of psychopathology in the proband's first-degree relatives. Computerized Neurocognitive Battery: The CNB, developed for large-scale studies, yields measures of accuracy and speed for domains of executive-control functions (abstraction, attention, working memory), episodic memory (verbal, facial, spatial), complex cognitive processing (language reasoning, nonverbal reasoning, spatial processing), social cognition (emotion identification, emotion intensity differentiation, age differentiation) and sensorimotor and motor speed. The following neurobehavioral domains were assessed: Penn Conditional Exclusion Test is a measure of abstraction and concept formation. Participants decide which of 4 objects does not belong with the other 3, based on one of three sorting principles, which change. Feedback is used. Attention: The Penn Continuous Performance Test. Participants respond to a set of 7-segment displays whenever they form a digit or letter. Working Memory: The Letter N-back Test displays sequences of uppercase letters with a stimulus duration of 500 ms (ISI 2,500 ms.) In the 0-back condition, participants respond to a single target (i.e., X). In the 1-back condition they respond if the letter is identical to that preceding it. In the 2-back condition, they respond if the letter is identical to that presented two trials back. Verbal Memory: The Penn Word Memory Test presents 20 target words that are then mixed with 20 distracters equated for frequency, length, concreteness and low imageability. A 20 min delayed recall procedure is also administered. Face Memory: The Penn Face Memory Test presents 20 digitized faces that are then mixed with 20 distracters equated for age, gender and ethnicity. The procedure is repeated at 20 min delay. Spatial Memory: The Visual Object Learning Test uses Euclidean shapes as stimuli with the same paradigm as the word and face. Language and Analogical Reasoning: The Penn Verbal Reasoning consists of verbal analogy problems. Spatial Processing: Penn Line Orientation Test presents two lines at an angle, and participants click on a button that makes one line rotate until it has the same angle as the other. Emotion Processing: Facial displays of 4 emotions (Happy, Sad, Anger, Fear) and Neutral faces, 8 each, are presented and the subject identifies the emotion in a multiple-choice format. The facial stimuli are balanced for gender, age, and ethnicity. Sensory-motor Processing Speed: The task requires moving the mouse and clicking on a green square that disappears after the click. The square gets increasingly small and appears in unpredictable locations. Neuroimaging Protocol: Studies were performed at Penn using a Siemens Trio (Erlangen, Germany) 3T scanner equipped with 40mT/m gradients and 200 mT/m/s slew-rates. RF transmission utilized a quadrature body-coil, and reception a 12-channel head coil optimized for parallel imaging. Total image acquisition time was about 45 min. Structural Imaging: The T1-weighted protocol utilized a 3D, inversion-recovery, and magnetization-prepared rapid acquisition gradient echo. Relevant imaging procedures include: Structural magnetic imaging Diffusion tensor imaging ASL perfusion BOLD fMRI Neuroimaging tasks: Fractal N-Back Task of Spatial Working Memory Face Emotion Identification Task Neuroimages: The current data release includes over 9700 MRI images that may be downloaded through Authorized Access.

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