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Research data keyboard_double_arrow_right Dataset 2008 EnglishICPSR - Interuniversity Consortium for Political and Social Research WT, NIH | Cascades of Network Struc..., NIH | Economic Evaluation of Ad... +207 projectsWT ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Economic Evaluation of Adolescent Alcohol Use and the Impact of Social Networks ,EC| ENGAGE ,NIH| Consortium for Neuropsychiatric Phenomics-Coordinating Center (1 of 8) ,NIH| Administrative and Research Support Core ,NIH| PATHOLOGY MONITORING--F344 RAT COLONY ,NIH| GENETICS OF ADOLESCENT ANTISOCIAL DRUG DEPENDENCE ,NIH| NYS FAMILY STUDY: PROBLEM ALCOHOL USE &PROBLEM BEHAVIOR ,NIH| Neuropharmacology of Response Inhibition in Comorbid ADHD and Nicotine Dependence ,NIH| Genome-Wide Associations Environmental Interactions in the Lung Health Study ,NIH| BIOBEHAVIORAL FACTORS IN CORONARY HEART DISEASE ,EC| DEPRIVEDHOODS ,NIH| Genome Wide Association Coordinating Center ,NIH| Health Disparities Among a Vulnerable Population: A Longitudinal Analysis ,EC| SOCIOGENOME ,NIH| ECONOMICS OF AGING TRAINING PROGRAM -- EXTENSION ,NIH| Center for Family and Demographic Research ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Computational Methods to Detect Epistasis ,NIH| OPTIMIZING BEHAVIORAL INTERVENTIONS FOR DRUG ABUSE PREVENTION &TREATMENT ,NIH| The Collaborative Genetic Study of Nicotine Dependence ,NIH| Alcohol Contextual Influences: Effects on Health Disparities and Mortality ,NIH| The University of Iowa Prevention Research Center ,NIH| Adolescent Alcohol Use: Disentangling Friend Selection &Influence ,NSF| Science Achievement and Health Behavior: High School Curriculum, Social Context, and Opportunity to Learn ,NIH| Dietary Etiologies of Heart Disease ,NIH| Birth Outcomes Among Adolescents ,NIH| Socioeconomic Disparities in Young Adult Health ,NIH| Genetic Risk to Stroke in Smokers and Nonsmokers in Two Ethnic Groups ,AKA| Impact of childhood growth patterns and latent cardiovascular risk factors on the microcirculation in adult life: Cardiovascular risk in Young Finns Study ,NIH| GWA for Gene-Environment Interaction Effects Influencing CHD ,NIH| Training in Developmental Science to Improve Child Health and Well-Being ,NIH| Statistical Methods for Network Epidemiology ,NIH| Genetics of Alcohol Dependence in African-Americans ,NIH| The Genetic Epidemiology of Nicotine Dependence ,NIH| Carolina Population Center ,NIH| Study of Addiction: Genetics and Environment ,NIH| Linkage Disequilibrium Studies of Alcohol Dependence ,NIH| Molecular Epidemiology of Alcoholism 2- Big Sibships ,NIH| Mentoring Clinical Investigators in Adolescent-onset Substance Use Disorders Research ,NIH| Social Demographic Moderation of Genome Wide Associations for Body Mass Index ,NIH| FLUORIDE AND OTHER FACTORS IN CHILDHOOD BONE DEVELOPMENT ,NIH| Genetic Epidemiology of Lung Cancer ,NIH| Role of Romantic Relationships in the Sexual Behavior of Obese and Non-Obese Girl ,NIH| A Nurse-Community Health Worker-Family Partnership Model to Increase COVID-19 Testing in Urban Underserved and Vulnerable Communities ,AKA| MSDs@LIFECOURSE CONSORTIU Subproject: Shared Risk Factors Study Group Turku University Central Hospital / Consortium: MSDs@LIFE ,ARC| Quantitative and Molecular Genetic Analysis of Cognition ,NIH| Institute for Clinical and Translational Research (UL1) ,NIH| High Density Association Analysis of Lung Cancer ,NIH| Propensity Scores and Preventive Drug Use in the Elderly ,NIH| Genetic Risk, Pathways to Adulthood, and Health Inequalities ,NIH| Washington University Institute of Clinical and Translational Sciences (UL1) ,WT| Familial and other risk factors for adolescent substance use and abuse. ,NIH| From GWAS loci to blood pressure genes, variants & mechanisms ,NIH| Genome-Wide Association for Loci Influencing CHD and Other Heart, Lung and Blood ,NIH| COSTS AND BENEFITS OF ALCOHOL SERVICES &INTERVENTIONS ,NSF| Social Inequality and Status Attainment ,NIH| Containing Bioterroist and Emerging Infectious Diseases ,NIH| UNC Interdisciplinary Obesity Training (IDOT)(RMI) ,EC| DYNANETS ,NIH| BEHAVIORAL PHARMACOGENETICS OF DRUG AND ALCOHOL ABUSE ,NIH| Variants in CHRNA5/CHRNA3/CHRNB4 and nicotine dependence ,NIH| Statistical Methods for Gene Environment Interactions In Lung Cancer ,NIH| Economic Evaluation Methods: Development and Application ,NIH| Model-Based Clustering Methods of Medical Image ,NIH| Nicotinic receptor genes &substance abuse: Functional studies of associated SNPs ,NSF| CAREER: Model Fluid-Solid Interactions, Networks REUs, and BioCalculus ,NIH| Genes, early adversity, and sensitive periods in social-emotional development ,NIH| Health Communication and Health Literacy Core ,NIH| HUMAN GENETIC VARIATION IN SMOKING AND ADDICTION RISK ,NIH| Data Core ,NIH| Genetics of Adolescent Antisocial Drug Dependence ,NIH| Cancer Center Support Grant ,NIH| NATURAL HISTORY OF ALCOHOL USE &ABUSE--GENETIC MODELS ,EC| NBHCHOICE ,NIH| The Role of Peer Networks in Youth Drug Use ,NIH| Inflammation Genes and Lung Cancer Risk ,NIH| Fine Mapping Susceptibility Loci for Nicotine Dependence ,NIH| Longitudinal Study of Trauma, HIV Risk, and Criminal Justice Involvement ,NSF| Health Lifestyles and the Reproduction of Inequality ,NIH| GENETICS OF COCAINE DEPENDENCE ,EC| TODO ,NIH| SOCIAL DEMOGRAPHY ,NIH| UIC Program for Interdisciplinary Careers in Womens Health Research ,NIH| LONGITUDINAL STUDY OF SUBSTANCE USE, INCARCERATION, AND STI IN THE US ,NIH| GENETICS OF VULNERABILITY TO NICOTINE ADDICTION ,NIH| Community Assist of Southern Arizona ,NIH| Human Genetics of Addiction: A Study of Common and Specific Factors ,NIH| The effects of heavy alcohol use on weight gain in college freshmen: Examining an overlooked calorie source ,NSF| National Science Foundation Alan T. Waterman Award ,NIH| The Social Marginalization of Adolescents in High School ,NIH| Refining Phenotypic Measures of Nicotine Withdrawal ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Individual differences and health outcomes: A secondary data analysis in cognitiv ,NIH| Understanding memory consolidation by studying pharmacologically enhanced naps ,NIH| A Center for GEI Association Studies ,NIH| Longtudinal Relations Between Internalizing Disorders and Substance Use Problems ,NIH| JH/CIDR Genotyping for Genome-Wide Association Studies ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| MOTS: Modeling Obesity Through Simulation ,NIH| Comprehensive Mapping of a Blood Pressure QTL on Chromosome 17 ,NIH| Deciphering genes and pathways in nicotine dependence ,NIH| MECHANISMS AND PREVENTION OF ENVIRONMENTAL DISEASE ,NHMRC| Experience-dependent cellular plasticity and cognitive deficits in mouse models of schizophrenia ,NIH| FINANCIAL STATUS--RETIREMENT SAVING PROGRAMS ,NIH| Genetics of Early Onset-Stroke ,NIH| Human Development: Interdisciplinary Research Training ,SSHRC ,NIH| DIET, HORMONES AND RISK OF COLORECTAL CANCERS ,NIH| Population Research Institute ,NIH| GENETIC EPIDEMIOLOGY OF LUNG CANCER ,NIH| NICHD Population Center ,NIH| Population Research Training ,NIH| Data Mgmt &Analysis Core - The NINDS International Stroke Genetics Consortium St ,NIH| Obesity and the Environment: The Transition to Adulthood ,EC| ADDICTION ,NIH| Age at First Sex, Genes, Religion, and Other Social and Demographic Context ,NIH| Novel Use of Gwas for Improved Understanding of Nicotine Dependence ,NIH| Racial disparities in cancer outcomes: quantifying modifiable mechanisms ,NIH| Response Inhibition and Dopamine Neurotransmission (RI) (4 of 8) ,ARC| Locating genes for elementary and complex cognitive abilities using genetic linkage and association analysis ,NIH| Synthetic Information Systems for Better Informing Public Health Policymakers ,NSF| GSE/RES Gender Differences in Science and Math: Diversity and the Role of Social Context ,NIH| University of Minnesota Clinical and Translational Science Institute (UMN CTSI) ,NIH| Molecular Epidemiology of Alcoholism 3 - EDAC Families ,NIH| Identifying essential network properties for disease spread ,NIH| Health Disparities in Obesity: Partner Violence and its Psychosocial Pathways ,NIH| Childhood Family Instability, Adult Stress Reactivity, and Consequences for Health ,NIH| Outreach Core ,NIH| Social Support as a Facilitator of Adherence to HIV Pre-Exposure Prophylaxis Among Young MSM of Color ,CIHR ,NIH| GENETICS OF NICOTINE AND OTHER ABUSED SUBSTANCES ,NIH| Substance Abuse & Treatment Gaps in Asians, Pacific Islanders & Multiple-race Ind ,NIH| Colorado Adoption/Twin Study of Lifespan behavioral development & cognitive aging (CATSLife) ,NIH| The Genetics of Vulnerability to Nicotine Addictions ,NIH| PROSTATE, LUNG, COLORECTAL &OVARIAN CANCER ,NIH| HEALTHY YOUTH DEVELOPMENT PREVENTION RESEARCH AND TRAINING CENTER ,NIH| GENETICS AND CONSEQUENCES OF NICOTINE ADDICTION ,NIH| PROSTATE, LUNG, COLORECTAL, AND OVARIAN (PLCO) CANCER ,NIH| Population Research Center ,NIH| Mid Southern Primary Care Networks Node ,NIH| University of Washington Reproductive, Perinatal and Pediatric Epidemiology ,NIH| Carolina Population Center ,NIH| Mapping Genes for comorbidity of SUDs and Depression ,NIH| Genetic &environmental pathways to drug use, abuse &dependence ,NIH| Epidemiology of Venous Thrombosis &Pulmonary Embolism ,AKA| Role of early life risk factors in associations between work, cardiovascular disease and depression: A life course approach based on two prospective cohorts. / Consortium: ELRFWCDD ,ARC| Elucidating the genetics of attention deficit hyperactivity disorder by integrating pathway and prediction analyses ,NIH| Administrative Core ,NIH| TRAINING GRANT IN CANCER EPIDEMIOLOGY ,NIH| Physical Environment Dynamics, Inequality and Obesity ,NIH| Transitions to Adulthood and Health Risk Among U.S. Young Adults ,NIH| CUPC Admin Core ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Translational studies of nicotinic receptor genes: alcohol and nicotine behaviors ,NIH| Sexual Behavior Trajectories from Adolescence to Adulthood ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Washington University Institute of Clinical and Translational Sciences (KL2) ,NIH| PROSPECTIVE LONGITUDINAL STUDY OF ADOLESCENT HEALTH ,NIH| Identifying Mediated Pathways of Risk for Substance Use in Sexual Minority Girls ,EC| GMI ,NIH| Intergenerational Research on Obesity Prevention: From Correlates to Intervention ,NIH| Smoking/Nicotine Dependence in Attention Deficit Hyperactivity Disorder (ADHD) ,NIH| University of Colorado Population Center ,NIH| The Washington University Center for Diabetes Translation Research ,NIH| Genes, Environments & Interventions: Understanding and Addressing Alcohol Misuse ,NIH| Center on Antisocial Drug Dependence: The Genetics of HIV Risk Behaviors ,AKA| Public Health Genomics to Practice in Cardiovascular Diseases / Consortium: PUBGENSENS ,NIH| SUBSTANCE USE AND DISORDERED WEIGHT BEHAVIORS IN SEXUAL MINORITY YOUTH CONTEXTS ,NIH| BEYOND RACE--EXPLAINING INEQUALITY MANIFESTED AS OBESITY ,NIH| Research Training Program in the Behavioral and Biomedical Sciences ,NIH| Genetics of Opioid Dependence ,NIH| HIV in Young Adulthood: Pathways and Prevention ,NIH| Research and Mentoring on Integrating Psychiatric Genetics and Neuroscience ,NIH| Vitamin D metabolism related genetic variations and developmental origins of card ,NIH| Systematic Error and Confounding: Meta-Analyses of Alcohol and Disease ,AKA| The Cardiovascular Risk in Young Finns Study - the 27-year follow-up. ,NIH| Obesity and Metabolic Risk Disparities: Underlying Food Environment Factors ,NIH| VARIATION IN THE EFFECTS OF ALCOHOL ON LIVER FUNCTION ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN CANCER ,NIH| The University of Colorado Population Center ,NIH| Genes and Environment Initiatives in Type 2 Diabetes ,NIH| Pharmacogenetics of Nicotine Addiction Treatment ,NIH| Interracial Friendship and Romance Among Adolescents ,NIH| Phenotypic refinement of externalizing pathways to alcohol-related behaviors ,ARC| Maximising knowledge from dense SNP (single nucleotide polymorphisms) data using multi-locus analysis ,NIH| PERSISTENCE &CHANGE IN DRINKING HABITS: TWIN STUDY ,AKA| Center of Excellence in Complex Disease Genetics ,NIH| Exome Variants Underlying Weight Gain from Adolescence to Adulthood ,NIH| Adolescent Health and Academic Achievement ,NIH| YOUNG ADULT SUBSTANCE USE--PREDICTORS AND CONSEQUENCES ,NIH| CORE--ADIPOSE TISSUE BIOLOGY AND BASIC MECHANISMS ,NIH| MDMA and Other Hallucinogen Use: Onset and Abuse/Dependence ,NIH| Molecular Genetics and Behavior: Alcohol and Tobacco Use ,NIH| Adenocarinoma of the Lung in Women ,NIH| Do active communities support activity or support active people? ,NSF| Neighborhoods and Schools, Education, and Heritability ,NIH| Sexual Orientation and Obesity: Test of a Gendered Biopsychosocial Model ,NSF| Machine learning techniques to model the impact of relational communication on distributed team effectiveness ,NIH| DIETARY ETIOLOGIES OF HEART DISEASE AND CANCER ,NSF| The Genetic Basis of Social Networks and Civic Engagement ,NIH| Social and Demographic Context and Heritability ,NIH| High Risk Drug Use &HIV-Learning from the NYC Epidemic ,AKA| Sustainable Innovative Materials in High Tech Applications. An Interdisciplinary Approach to Design,Engineering Technology and Chemistry of Environmentally Sound Products and Production. ,AKA| Roles of inflammation, oxidation, sex hormones and genetic variation in vascular aging and the development of atherosclerosis over the life-course. ,NIH| The Pathobiology of Nephrolithiasis ,NIH| GENETIC INTERACTIONS CONTRIBUTING TO ALCOHOL AND NICOTINE DEPENDENCE ,NIH| Modeling HIV and STD in Drug User and Social Networks ,NIH| Innovations in Pediatric Pain ResearchAuthors: Harris, Kathleen Mullan; Udry, J. Richard;Harris, Kathleen Mullan; Udry, J. Richard;doi: 10.3886/icpsr21600.v16 , 10.3886/icpsr21600.v21 , 10.3886/icpsr21600.v15 , 10.3886/icpsr21600.v18 , 10.3886/icpsr21600.v19 , 10.3886/icpsr21600.v5 , 10.3886/icpsr21600.v3 , 10.3886/icpsr21600.v6 , 10.3886/icpsr21600.v1 , 10.3886/icpsr21600.v17 , 10.3886/icpsr21600.v10 , 10.3886/icpsr21600.v14 , 10.3886/icpsr21600.v8 , 10.3886/icpsr21600.v12 , 10.3886/icpsr21600.v2 , 10.3886/icpsr21600.v20 , 10.3886/icpsr21600.v4 , 10.3886/icpsr21600.v22 , 10.3886/icpsr21600.v9 , 10.3886/icpsr21600.v11 , 10.3886/icpsr21600.v7 , 10.3886/icpsr21600.v13
doi: 10.3886/icpsr21600.v16 , 10.3886/icpsr21600.v21 , 10.3886/icpsr21600.v15 , 10.3886/icpsr21600.v18 , 10.3886/icpsr21600.v19 , 10.3886/icpsr21600.v5 , 10.3886/icpsr21600.v3 , 10.3886/icpsr21600.v6 , 10.3886/icpsr21600.v1 , 10.3886/icpsr21600.v17 , 10.3886/icpsr21600.v10 , 10.3886/icpsr21600.v14 , 10.3886/icpsr21600.v8 , 10.3886/icpsr21600.v12 , 10.3886/icpsr21600.v2 , 10.3886/icpsr21600.v20 , 10.3886/icpsr21600.v4 , 10.3886/icpsr21600.v22 , 10.3886/icpsr21600.v9 , 10.3886/icpsr21600.v11 , 10.3886/icpsr21600.v7 , 10.3886/icpsr21600.v13
A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2008 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample.; Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I.; Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later.; Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. ; For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent; Wave II: 88.6 percent; Wave III: 77.4 percent; Wave IV: 80.3 percent; Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States. audio computer-assisted self interview (ACASI) computer-assisted personal interview (CAPI) computer-assisted self interview (CASI) paper and pencil interview (PAPI) face-to-face interview
Inter-university Con... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu65 citations 65 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!more_vert Inter-university Con... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2019 EnglishElsevier BV SSHRC, WT | Functional architectures ...SSHRC ,WT| Functional architectures in the brain.Authors: Badcock, Paul B.; Friston, Karl J.; Ramstead, Maxwell J.D.;Badcock, Paul B.; Friston, Karl J.; Ramstead, Maxwell J.D.;pmc: PMC6941235
pmid: 30704846
This article presents a unifying theory of the embodied, situated human brain called the Hierarchically Mechanistic Mind (HMM). The HMM describes the brain as a complex adaptive system that actively minimises the decay of our sensory and physical states by producing self-fulfilling action-perception cycles via dynamical interactions between hierarchically organised neurocognitive mechanisms. This theory synthesises the free-energy principle (FEP) in neuroscience with an evolutionary systems theory of psychology that explains our brains, minds, and behaviour by appealing to Tinbergen's four questions: adaptation, phylogeny, ontogeny, and mechanism. After leveraging the FEP to formally define the HMM across different spatiotemporal scales, we conclude by exploring its implications for theorising and research in the sciences of the mind and behaviour. Highlights • We present an interdisciplinary theory of the embodied, situated human brain called the Hierarchically Mechanistic Mind (HMM). • We describe the HMM as a model of neural architecture. • We explore how the HMM synthesises the free-energy principle in neuroscience with an evolutionary systems theory of psychology. • We translate our model into a new heuristic for theorising and research in neuroscience and psychology.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu40 citations 40 popularity Top 10% influence Average impulse Top 1% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=PMC6941235&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2021 GermanyMDPI AG WT | Functional architectures ..., SSHRC, WT | Wellcome Centre for Human... +1 projectsWT| Functional architectures in the brain. ,SSHRC ,WT| Wellcome Centre for Human Neuroimaging ,WTThomas Parr; Lancelot Da Costa; Conor Heins; Maxwell J. D. Ramstead; Karl J. Friston;In theoretical biology, we are often interested in random dynamical systems—like the brain—that appear to model their environments. This can be formalized by appealing to the existence of a (possibly non-equilibrium) steady state, whose density preserves a conditional independence between a biological entity and its surroundings. From this perspective, the conditioning set, or Markov blanket, induces a form of vicarious synchrony between creature and world—as if one were modelling the other. However, this results in an apparent paradox. If all conditional dependencies between a system and its surroundings depend upon the blanket, how do we account for the mnemonic capacity of living systems? It might appear that any shared dependence upon past blanket states violates the independence condition, as the variables on either side of the blanket now share information not available from the current blanket state. This paper aims to resolve this paradox, and to demonstrate that conditional independence does not preclude memory. Our argument rests upon drawing a distinction between the dependencies implied by a steady state density, and the density dynamics of the system conditioned upon its configuration at a previous time. The interesting question then becomes: What determines the length of time required for a stochastic system to ‘forget’ its initial conditions? We explore this question for an example system, whose steady state density possesses a Markov blanket, through simple numerical analyses. We conclude with a discussion of the relevance for memory in cognitive systems like us.
Europe PubMed Centra... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu15 citations 15 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 50visibility views 50 download downloads 55 Powered bymore_vert Europe PubMed Centra... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Preprint , Article 2020MDPI AG SSHRC, WT | Functional architectures ...SSHRC ,WT| Functional architectures in the brain.Authors: Maxwell J. D. Ramstead; Karl J. Friston; Inês Hipólito;Maxwell J. D. Ramstead; Karl J. Friston; Inês Hipólito;is most appropriate. We focus on non-realist (deflationary and fictionalist-instrumentalist) approaches. We consider a deflationary account of mental representation, according to which the explanatorily relevant contents of neural representations are mathematical, rather than cognitive, and a fictionalist or instrumentalist account, according to which representations are scientifically useful fictions that serve explanatory (and other) aims. After reviewing the free-energy principle and active inference, we argue that the model of adaptive phenotypes under the free-energy principle can be used to furnish a formal semantics, enabling us to assign semantic content to specific phenotypic states (the internal states of a Markovian system that exists far from equilibrium). We propose a modified fictionalist account&mdash our position is thus coherent with, but rests on distinct assumptions from, the realist position. We argue that the free-energy principle thereby explains the aboutness or intentionality in living systems and hence their capacity to parse their sensory stream using an ontology or set of semantic factors. an organism-centered fictionalism or instrumentalism. We argue that, under the free-energy principle, pursuing even a deflationary account of the content of neural representations licenses the appeal to the kind of semantic content involved in the &lsquo The aim of this paper is twofold: (1) to assess whether the construct of neural representations plays an explanatory role under the variational free-energy principle and its corollary process theory, active inference in relation to the ontological and epistemological status of representations&mdash and (2) if so, to assess which philosophical stance&mdash or intentionality of cognitive systems aboutness&rsquo
Entropy arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu50 citations 50 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert Entropy arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2008 EnglishICPSR - Interuniversity Consortium for Political and Social Research NIH | Center for Family and Dem..., NIH | PROSTATE, LUNG, COLORECTA..., NIH | Computational Methods to ... +195 projectsNIH| Center for Family and Demographic Research ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Computational Methods to Detect Epistasis ,NIH| OPTIMIZING BEHAVIORAL INTERVENTIONS FOR DRUG ABUSE PREVENTION &TREATMENT ,NIH| The Collaborative Genetic Study of Nicotine Dependence ,NIH| Socioeconomic Disparities in Young Adult Health ,NIH| Genetic Risk to Stroke in Smokers and Nonsmokers in Two Ethnic Groups ,AKA| Impact of childhood growth patterns and latent cardiovascular risk factors on the microcirculation in adult life: Cardiovascular risk in Young Finns Study ,NIH| GWA for Gene-Environment Interaction Effects Influencing CHD ,NIH| Study of Addiction: Genetics and Environment ,NIH| Linkage Disequilibrium Studies of Alcohol Dependence ,NIH| A Nurse-Community Health Worker-Family Partnership Model to Increase COVID-19 Testing in Urban Underserved and Vulnerable Communities ,AKA| MSDs@LIFECOURSE CONSORTIU Subproject: Shared Risk Factors Study Group Turku University Central Hospital / Consortium: MSDs@LIFE ,ARC| Quantitative and Molecular Genetic Analysis of Cognition ,NIH| Institute for Clinical and Translational Research (UL1) ,NIH| High Density Association Analysis of Lung Cancer ,NIH| Propensity Scores and Preventive Drug Use in the Elderly ,NIH| Genetic Risk, Pathways to Adulthood, and Health Inequalities ,NIH| Washington University Institute of Clinical and Translational Sciences (UL1) ,WT| Familial and other risk factors for adolescent substance use and abuse. ,NIH| From GWAS loci to blood pressure genes, variants & mechanisms ,NIH| Genome-Wide Association for Loci Influencing CHD and Other Heart, Lung and Blood ,NIH| COSTS AND BENEFITS OF ALCOHOL SERVICES &INTERVENTIONS ,NSF| Social Inequality and Status Attainment ,NIH| Containing Bioterroist and Emerging Infectious Diseases ,NIH| UNC Interdisciplinary Obesity Training (IDOT)(RMI) ,EC| DYNANETS ,NIH| BEHAVIORAL PHARMACOGENETICS OF DRUG AND ALCOHOL ABUSE ,NIH| Variants in CHRNA5/CHRNA3/CHRNB4 and nicotine dependence ,NIH| Statistical Methods for Gene Environment Interactions In Lung Cancer ,NIH| Economic Evaluation Methods: Development and Application ,NIH| Model-Based Clustering Methods of Medical Image ,NIH| Nicotinic receptor genes &substance abuse: Functional studies of associated SNPs ,NSF| CAREER: Model Fluid-Solid Interactions, Networks REUs, and BioCalculus ,NIH| Genes, early adversity, and sensitive periods in social-emotional development ,NIH| Health Communication and Health Literacy Core ,NIH| HUMAN GENETIC VARIATION IN SMOKING AND ADDICTION RISK ,NIH| Data Core ,NIH| Genetics of Adolescent Antisocial Drug Dependence ,NIH| BIOBEHAVIORAL FACTORS IN CORONARY HEART DISEASE ,EC| DEPRIVEDHOODS ,NIH| Genome Wide Association Coordinating Center ,EC| SOCIOGENOME ,NIH| ECONOMICS OF AGING TRAINING PROGRAM -- EXTENSION ,NIH| Cancer Center Support Grant ,NIH| NATURAL HISTORY OF ALCOHOL USE &ABUSE--GENETIC MODELS ,EC| NBHCHOICE ,NIH| The Role of Peer Networks in Youth Drug Use ,NIH| Inflammation Genes and Lung Cancer Risk ,NIH| Fine Mapping Susceptibility Loci for Nicotine Dependence ,NIH| Longitudinal Study of Trauma, HIV Risk, and Criminal Justice Involvement ,NIH| GENETICS OF COCAINE DEPENDENCE ,EC| TODO ,NIH| SOCIAL DEMOGRAPHY ,NIH| LONGITUDINAL STUDY OF SUBSTANCE USE, INCARCERATION, AND STI IN THE US ,NIH| GENETICS OF VULNERABILITY TO NICOTINE ADDICTION ,NIH| Community Assist of Southern Arizona ,NIH| Human Genetics of Addiction: A Study of Common and Specific Factors ,NIH| The effects of heavy alcohol use on weight gain in college freshmen: Examining an overlooked calorie source ,NSF| National Science Foundation Alan T. Waterman Award ,NIH| The Social Marginalization of Adolescents in High School ,NIH| Refining Phenotypic Measures of Nicotine Withdrawal ,NIH| NICHD Population Center ,NIH| Population Research Training ,NIH| Data Mgmt &Analysis Core - The NINDS International Stroke Genetics Consortium St ,NIH| Obesity and the Environment: The Transition to Adulthood ,EC| ADDICTION ,NIH| Age at First Sex, Genes, Religion, and Other Social and Demographic Context ,NIH| Novel Use of Gwas for Improved Understanding of Nicotine Dependence ,NIH| Racial disparities in cancer outcomes: quantifying modifiable mechanisms ,NIH| Response Inhibition and Dopamine Neurotransmission (RI) (4 of 8) ,ARC| Locating genes for elementary and complex cognitive abilities using genetic linkage and association analysis ,NSF| GSE/RES Gender Differences in Science and Math: Diversity and the Role of Social Context ,NIH| University of Minnesota Clinical and Translational Science Institute (UMN CTSI) ,NIH| Molecular Epidemiology of Alcoholism 3 - EDAC Families ,NIH| Identifying essential network properties for disease spread ,NIH| Health Disparities in Obesity: Partner Violence and its Psychosocial Pathways ,NIH| Outreach Core ,NIH| Social Support as a Facilitator of Adherence to HIV Pre-Exposure Prophylaxis Among Young MSM of Color ,NIH| BEYOND RACE--EXPLAINING INEQUALITY MANIFESTED AS OBESITY ,NIH| Genetics of Opioid Dependence ,NIH| HIV in Young Adulthood: Pathways and Prevention ,NIH| Research and Mentoring on Integrating Psychiatric Genetics and Neuroscience ,NIH| Vitamin D metabolism related genetic variations and developmental origins of card ,WT ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Economic Evaluation of Adolescent Alcohol Use and the Impact of Social Networks ,NIH| MECHANISMS AND PREVENTION OF ENVIRONMENTAL DISEASE ,NIH| FINANCIAL STATUS--RETIREMENT SAVING PROGRAMS ,NIH| Genetics of Early Onset-Stroke ,NIH| Human Development: Interdisciplinary Research Training ,NIH| CORE--ADIPOSE TISSUE BIOLOGY AND BASIC MECHANISMS ,NIH| MDMA and Other Hallucinogen Use: Onset and Abuse/Dependence ,NIH| Molecular Genetics and Behavior: Alcohol and Tobacco Use ,CIHR ,NIH| PROSPECTIVE LONGITUDINAL STUDY OF ADOLESCENT HEALTH ,NIH| Identifying Mediated Pathways of Risk for Substance Use in Sexual Minority Girls ,EC| GMI ,NIH| Intergenerational Research on Obesity Prevention: From Correlates to Intervention ,NIH| Smoking/Nicotine Dependence in Attention Deficit Hyperactivity Disorder (ADHD) ,NIH| The Washington University Center for Diabetes Translation Research ,NIH| Genes, Environments & Interventions: Understanding and Addressing Alcohol Misuse ,NIH| Center on Antisocial Drug Dependence: The Genetics of HIV Risk Behaviors ,AKA| Public Health Genomics to Practice in Cardiovascular Diseases / Consortium: PUBGENSENS ,NIH| SUBSTANCE USE AND DISORDERED WEIGHT BEHAVIORS IN SEXUAL MINORITY YOUTH CONTEXTS ,NIH| Alcohol Contextual Influences: Effects on Health Disparities and Mortality ,NIH| Adolescent Alcohol Use: Disentangling Friend Selection &Influence ,NSF| Science Achievement and Health Behavior: High School Curriculum, Social Context, and Opportunity to Learn ,NIH| Dietary Etiologies of Heart Disease ,NIH| Birth Outcomes Among Adolescents ,NIH| GENETICS OF NICOTINE AND OTHER ABUSED SUBSTANCES ,NIH| Substance Abuse & Treatment Gaps in Asians, Pacific Islanders & Multiple-race Ind ,NIH| Colorado Adoption/Twin Study of Lifespan behavioral development & cognitive aging (CATSLife) ,NIH| The Genetics of Vulnerability to Nicotine Addictions ,NIH| PROSTATE, LUNG, COLORECTAL &OVARIAN CANCER ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Individual differences and health outcomes: A secondary data analysis in cognitiv ,NIH| Understanding memory consolidation by studying pharmacologically enhanced naps ,NIH| A Center for GEI Association Studies ,NIH| Longtudinal Relations Between Internalizing Disorders and Substance Use Problems ,NIH| JH/CIDR Genotyping for Genome-Wide Association Studies ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| MOTS: Modeling Obesity Through Simulation ,NIH| Comprehensive Mapping of a Blood Pressure QTL on Chromosome 17 ,NIH| Deciphering genes and pathways in nicotine dependence ,NIH| Systematic Error and Confounding: Meta-Analyses of Alcohol and Disease ,AKA| The Cardiovascular Risk in Young Finns Study - the 27-year follow-up. ,NIH| Obesity and Metabolic Risk Disparities: Underlying Food Environment Factors ,NIH| VARIATION IN THE EFFECTS OF ALCOHOL ON LIVER FUNCTION ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN CANCER ,NIH| The University of Colorado Population Center ,NIH| HEALTHY YOUTH DEVELOPMENT PREVENTION RESEARCH AND TRAINING CENTER ,NIH| GENETICS AND CONSEQUENCES OF NICOTINE ADDICTION ,NIH| PROSTATE, LUNG, COLORECTAL, AND OVARIAN (PLCO) CANCER ,NIH| Population Research Center ,NIH| Mid Southern Primary Care Networks Node ,NIH| University of Washington Reproductive, Perinatal and Pediatric Epidemiology ,NIH| Carolina Population Center ,NIH| Mapping Genes for comorbidity of SUDs and Depression ,NIH| Genetic &environmental pathways to drug use, abuse &dependence ,NIH| Epidemiology of Venous Thrombosis &Pulmonary Embolism ,AKA| Role of early life risk factors in associations between work, cardiovascular disease and depression: A life course approach based on two prospective cohorts. / Consortium: ELRFWCDD ,ARC| Elucidating the genetics of attention deficit hyperactivity disorder by integrating pathway and prediction analyses ,NIH| Administrative Core ,NIH| TRAINING GRANT IN CANCER EPIDEMIOLOGY ,NIH| Physical Environment Dynamics, Inequality and Obesity ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Translational studies of nicotinic receptor genes: alcohol and nicotine behaviors ,NIH| Sexual Behavior Trajectories from Adolescence to Adulthood ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Washington University Institute of Clinical and Translational Sciences (KL2) ,NIH| Genes and Environment Initiatives in Type 2 Diabetes ,NIH| Pharmacogenetics of Nicotine Addiction Treatment ,NIH| Interracial Friendship and Romance Among Adolescents ,NIH| The University of Iowa Prevention Research Center ,NIH| Training in Developmental Science to Improve Child Health and Well-Being ,NIH| Statistical Methods for Network Epidemiology ,NIH| Genetics of Alcohol Dependence in African-Americans ,NIH| The Genetic Epidemiology of Nicotine Dependence ,NIH| Carolina Population Center ,NIH| Molecular Epidemiology of Alcoholism 2- Big Sibships ,NIH| Mentoring Clinical Investigators in Adolescent-onset Substance Use Disorders Research ,NIH| Social Demographic Moderation of Genome Wide Associations for Body Mass Index ,NIH| FLUORIDE AND OTHER FACTORS IN CHILDHOOD BONE DEVELOPMENT ,NIH| Genetic Epidemiology of Lung Cancer ,NIH| Role of Romantic Relationships in the Sexual Behavior of Obese and Non-Obese Girl ,EC| ENGAGE ,NIH| Consortium for Neuropsychiatric Phenomics-Coordinating Center (1 of 8) ,NIH| Administrative and Research Support Core ,NIH| PATHOLOGY MONITORING--F344 RAT COLONY ,NIH| GENETICS OF ADOLESCENT ANTISOCIAL DRUG DEPENDENCE ,NIH| NYS FAMILY STUDY: PROBLEM ALCOHOL USE &PROBLEM BEHAVIOR ,NIH| Neuropharmacology of Response Inhibition in Comorbid ADHD and Nicotine Dependence ,NIH| Genome-Wide Associations Environmental Interactions in the Lung Health Study ,NIH| Adenocarinoma of the Lung in Women ,NIH| Do active communities support activity or support active people? ,NSF| Neighborhoods and Schools, Education, and Heritability ,NIH| Sexual Orientation and Obesity: Test of a Gendered Biopsychosocial Model ,NSF| Machine learning techniques to model the impact of relational communication on distributed team effectiveness ,NIH| DIETARY ETIOLOGIES OF HEART DISEASE AND CANCER ,NSF| The Genetic Basis of Social Networks and Civic Engagement ,NIH| Social and Demographic Context and Heritability ,NIH| High Risk Drug Use &HIV-Learning from the NYC Epidemic ,AKA| Sustainable Innovative Materials in High Tech Applications. An Interdisciplinary Approach to Design,Engineering Technology and Chemistry of Environmentally Sound Products and Production. ,AKA| Roles of inflammation, oxidation, sex hormones and genetic variation in vascular aging and the development of atherosclerosis over the life-course. ,NIH| The Pathobiology of Nephrolithiasis ,NIH| GENETIC INTERACTIONS CONTRIBUTING TO ALCOHOL AND NICOTINE DEPENDENCE ,NIH| Modeling HIV and STD in Drug User and Social Networks ,ARC| Maximising knowledge from dense SNP (single nucleotide polymorphisms) data using multi-locus analysis ,NIH| PERSISTENCE &CHANGE IN DRINKING HABITS: TWIN STUDY ,AKA| Center of Excellence in Complex Disease Genetics ,NIH| Exome Variants Underlying Weight Gain from Adolescence to Adulthood ,NIH| Adolescent Health and Academic Achievement ,NIH| YOUNG ADULT SUBSTANCE USE--PREDICTORS AND CONSEQUENCES ,SSHRC ,NIH| DIET, HORMONES AND RISK OF COLORECTAL CANCERS ,NIH| Population Research Institute ,NIH| GENETIC EPIDEMIOLOGY OF LUNG CANCERAuthors: Harris, Kathleen Mullan; Udry, J. Richard;Harris, Kathleen Mullan; Udry, J. Richard;Downloads of Add Health require submission of the following information, which is shared with the original producer of Add Health: supervisor name, supervisor email, and reason for download. A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2018 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Wave V data collection took place from 2016 to 2018, when the original Wave I respondents were 33 to 43 years old. For the first time, a mixed mode survey design was used. In addition, several experiments were embedded in early phases of the data collection to test response to various treatments. A similar range of data was collected on social, environmental, economic, behavioral, and health circumstances of respondents, with the addition of retrospective child health and socio-economic status questions. Physical measurements and biospecimens were again collected at Wave V, and included most of the same measures as at Wave IV. Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights DS32: Wave V: Mixed-Mode Survey, Public Use Sample DS33: Wave V: Mixed-Mode Survey, Public Use Sample (Section 16B: Pregnancy, Live Births, Children and Parenting) DS34: Wave V: Biomarkers, Anthropometrics DS35: Wave V: Biomarkers, Cardiovascular Measures DS36: Wave V: Biomarkers, Demographics DS37: Wave V: Biomarkers, Measures of Glucose Homeostasis DS38: Wave V: Biomarkers, Measures of Inflammation and Immune Function DS39: Wave V: Biomarkers, Lipids DS40: Wave V: Biomarkers, Medication Use DS41: Wave V: Biomarkers, Renal Function DS42: Wave V: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample. Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I. Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later. Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. Wave V: All Wave I respondents who were still living were eligible at Wave V, yielding a pool of 19,828 persons. This pool was split into three stratified random samples for the purposes of survey design testing. For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. audio computer-assisted self interview (ACASI); computer-assisted personal interview (CAPI); computer-assisted self interview (CASI); face-to-face interview; mixed mode; paper and pencil interview (PAPI); telephone interviewWave V data files were minimally processed by ICPSR. For value labeling, missing value designation, and question text (where applicable), please see the available P.I. Codebook/Questionnaires. The study-level documentation (Data Guide, User Guide) does not include Wave V datasets.Documentation for Waves prior to Wave V may use an older version of the study title.Users should be aware that version history notes dated prior to 2015-11-09 do not apply to the current organization of the datasets.Please note that dates present in the Summary and Time Period fields are taken from the Add Health Study Design page. The Date of Collection field represents the range of interview dates present in the data files for each wave.Wave I and Wave II field work was conducted by the National Opinion Research Center at the University of Chicago.Wave III, Wave IV, and Wave V field work was conducted by the Research Triangle Institute.For the most updated list of related publications, please see the Add Health Publications Web site.Additional information on the National Longitudinal Study of Adolescent to Adult Health (Add Health) series can be found on the Add Health Web site. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Wave V aimed to track the emergence of chronic disease as the cohort aged into their 30s and early 40s. Add health is a school-based longitudinal study of a nationally-representative sample of adolescents in grates 7-12 in the United States in 1945-45. Over more than 20 years of data collection, data have been collected from adolescents, their fellow students, school administrators, parents, siblings, friends, and romantic partners through multiple data collection components. In addition, existing databases with information about respondents' neighborhoods and communities have been merged with Add Health data, including variables on income poverty, unemployment, availability and utilization of health services, crime, church membership, and social programs and policies. The data files are not weighted. However, the collection features a number of weight variables contained within the following datasets: DS4: Wave I: Public Use Grand Sample Weights DS7: Wave II: Public Use Grand Sample Weights DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS21: Wave III: Public In-Home Weights DS31: Wave IV: Public Use Weights DS42: Wave V: Public Use Weights Please note that these weights files do not apply to the Biomarker data files. For additional information on the application of weights for data analysis, please see the ICPSR User Guide, or the Guidelines for Analyzing Add Health Data. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent Wave II: 88.6 percent Wave III: 77.4 percent Wave IV: 80.3 percent Wave V: 71.8 percent Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3886/icpsr21600.v23&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu8 citations 8 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2017 WT | Functional architectures ..., SSHRCWT| Functional architectures in the brain. ,SSHRCAuthors: Ramstead, Maxwell James Désormeau; Badcock, Paul Benjamin; Friston, Karl John;Ramstead, Maxwell James Désormeau; Badcock, Paul Benjamin; Friston, Karl John;pmc: PMC5857288
The free-energy principle (FEP) is a formal model of neuronal processes that is widely recognised in neuroscience as a unifying theory of the brain and biobehaviour. More recently, however, it has been extended beyond the brain to explain the dynamics of living systems, and their unique capacity to avoid decay. The aim of this review is to synthesise these advances with a meta-theoretical ontology of biological systems called variational neuroethology, which integrates the FEP with Tinbergen's four research questions to explain biological systems across spatial and temporal scales. We exemplify this framework by applying it to Homo sapiens, before translating variational neuroethology into a systematic research heuristic that supplies the biological, cognitive, and social sciences with a computationally tractable guide to discovery. Highlights • We describe a meta-theoretical ontology of life based on the free energy principle. • We propose a multiscale formulation of the free energy principle. • We translate our ontology into a systematic research heuristic for life sciences. • We apply this meta-theoretical ontology and research heuristic to Homo sapiens.
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For further information contact us at helpdesk@openaire.eu84 citations 84 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=PMC5857288&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2020 United Kingdom, Canada, FranceCambridge University Press (CUP) EC | LIFESPAN, SSHRC, CIHR +3 projectsEC| LIFESPAN ,SSHRC ,CIHR ,UKRI| Pathways to self-harm: Biological mechanisms and genetic contribution ,WT| Reducing cognitive vulnerability to suicidal ideation in recurrent major depression: using translational research to elucidate risk mechanisms and their modification ,WT| The Avon Longitudinal Study of Parents and Children (ALSPAC): A multi-generation, longitudinal resource focusing on life course health and well-being.Massimiliano Orri; Abigail Emma Russell; Becky Mars; Gustavo Turecki; David Gunnell; Jon Heron; Richard E. Tremblay; Michel Boivin; Anne Monique Nuyt; Sylvana M. Côté; Marie-Claude Geoffroy;AbstractBackgroundWe aimed to identify groups of children presenting distinct perinatal adversity profiles and test the association between profiles and later risk of suicide attempt.MethodsData were from the Québec Longitudinal Study of Child Development (QLSCD, N = 1623), and the Avon Longitudinal Study of Parents and Children (ALSPAC, N = 5734). Exposures to 32 perinatal adversities (e.g. fetal, obstetric, psychosocial, and parental psychopathology) were modeled using latent class analysis, and associations with a self-reported suicide attempt by age 20 were investigated with logistic regression. We investigated to what extent childhood emotional and behavioral problems, victimization, and cognition explained the associations.ResultsIn both cohorts, we identified five profiles: No perinatal risk, Poor fetal growth, Socioeconomic adversity, Delivery complications, Parental mental health problems (ALSPAC only). Compared to children with No perinatal risk, children in the Poor fetal growth (pooled estimate QLSCD-ALSPAC, OR 1.89, 95% CI 1.04–3.44), Socioeconomic adversity (pooled-OR 1.42, 95% CI 1.08–1.85), and Parental mental health problems (OR 1.74, 95% CI 1.27–2.40), but not Delivery complications, profiles were more likely to attempt suicide. The proportion of this effect mediated by the putative mediators was larger for the Socioeconomic adversity profile compared to the others.ConclusionsPerinatal adversities associated with suicide attempt cluster in distinct profiles. Suicide prevention may begin early in life and requires a multidisciplinary approach targeting a constellation of factors from different domains (psychiatric, obstetric, socioeconomic), rather than a single factor, to effectively reduce suicide vulnerability. The way these factors cluster together also determined the pathways leading to a suicide attempt, which can guide decision-making on personalized suicide prevention strategies.
Papyrus : Dépôt inst... arrow_drop_down Papyrus : Dépôt institutionnel - Université de Montréal; Psychological MedicineOther literature type . Article . 2020License: Cambridge Core User Agreementadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu15 citations 15 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 32visibility views 32 download downloads 0 Powered bymore_vert Papyrus : Dépôt inst... arrow_drop_down Papyrus : Dépôt institutionnel - Université de Montréal; Psychological MedicineOther literature type . Article . 2020License: Cambridge Core User Agreementadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2019 EnglishSpringer US SSHRC, WT | Functional architectures ...SSHRC ,WT| Functional architectures in the brain.Badcock, Paul B.; Friston, Karl J.; Ramstead, Maxwell J. D.; Ploeger, Annemie; Hohwy, Jakob;pmc: PMC6861365
pmid: 31115833
The purpose of this review was to integrate leading paradigms in psychology and neuroscience with a theory of the embodied, situated human brain, called the Hierarchically Mechanistic Mind (HMM). The HMM describes the brain as a complex adaptive system that functions to minimize the entropy of our sensory and physical states via action-perception cycles generated by hierarchical neural dynamics. First, we review the extant literature on the hierarchical structure of the brain. Next, we derive the HMM from a broader evolutionary systems theory that explains neural structure and function in terms of dynamic interactions across four nested levels of biological causation (i.e., adaptation, phylogeny, ontogeny, and mechanism). We then describe how the HMM aligns with a global brain theory in neuroscience called the free-energy principle, leveraging this theory to mathematically formulate neural dynamics across hierarchical spatiotemporal scales. We conclude by exploring the implications of the HMM for psychological inquiry.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu33 citations 33 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2021 NetherlandsFrontiers Media SA SSHRC, WT | Functional architectures ...SSHRC ,WT| Functional architectures in the brain.Authors: Thomas Parr; Dimitrije Marković; Maxwell James D. Ramstead; Maxwell James D. Ramstead; +6 AuthorsThomas Parr; Dimitrije Marković; Maxwell James D. Ramstead; Maxwell James D. Ramstead; Maxwell James D. Ramstead; Maxwell James D. Ramstead; Ryan Smith; Casper Hesp; Casper Hesp; Karl Friston;NARCIS arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3389/frai.2021.710179&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert NARCIS arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3389/frai.2021.710179&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2018 SSHRC, WT | Functional architectures ...SSHRC ,WT| Functional architectures in the brain.Authors: Maxwell J D, Ramstead; Axel, Constant; Paul B, Badcock; Karl J, Friston;Maxwell J D, Ramstead; Axel, Constant; Paul B, Badcock; Karl J, Friston;pmid: 30655223
pmc: PMC6941227
This paper addresses the challenges faced by multiscale formulations of the variational (free energy) approach to dynamics that obtain for large-scale ensembles. We review a framework for modelling complex adaptive control systems for multiscale free energy bounding organism–niche dynamics, thereby integrating the modelling strategies and heuristics of variational neuroethology with a broader perspective on the ecological nestedness of biotic systems. We extend the multiscale variational formulation beyond the action–perception loops of individual organisms by appealing to the variational approach to niche construction to explain the dynamics of coupled systems constituted by organisms and their ecological niche. We suggest that the statistical robustness of living systems is inherited, in part, from their eco-niches, as niches help coordinate dynamical patterns across larger spatiotemporal scales. We call this approach variational ecology. We argue that, when applied to cultural animals such as humans, variational ecology enables us to formulate not just a physics of individual minds, but also a physics of interacting minds across spatial and temporal scales – a physics of sentient systems that range from cells to societies. Highlights • We extend the multiscale variational free energy approach to large-scale ensembles. • We integrate multiscale modelling with a broad perspective on ecological nestedness. • We argue that the statistical robustness of living systems is ecologically inherited. • We propose variational ecology as a physics of sentient systems.
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For further information contact us at helpdesk@openaire.eu26 citations 26 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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Research data keyboard_double_arrow_right Dataset 2008 EnglishICPSR - Interuniversity Consortium for Political and Social Research WT, NIH | Cascades of Network Struc..., NIH | Economic Evaluation of Ad... +207 projectsWT ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Economic Evaluation of Adolescent Alcohol Use and the Impact of Social Networks ,EC| ENGAGE ,NIH| Consortium for Neuropsychiatric Phenomics-Coordinating Center (1 of 8) ,NIH| Administrative and Research Support Core ,NIH| PATHOLOGY MONITORING--F344 RAT COLONY ,NIH| GENETICS OF ADOLESCENT ANTISOCIAL DRUG DEPENDENCE ,NIH| NYS FAMILY STUDY: PROBLEM ALCOHOL USE &PROBLEM BEHAVIOR ,NIH| Neuropharmacology of Response Inhibition in Comorbid ADHD and Nicotine Dependence ,NIH| Genome-Wide Associations Environmental Interactions in the Lung Health Study ,NIH| BIOBEHAVIORAL FACTORS IN CORONARY HEART DISEASE ,EC| DEPRIVEDHOODS ,NIH| Genome Wide Association Coordinating Center ,NIH| Health Disparities Among a Vulnerable Population: A Longitudinal Analysis ,EC| SOCIOGENOME ,NIH| ECONOMICS OF AGING TRAINING PROGRAM -- EXTENSION ,NIH| Center for Family and Demographic Research ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Computational Methods to Detect Epistasis ,NIH| OPTIMIZING BEHAVIORAL INTERVENTIONS FOR DRUG ABUSE PREVENTION &TREATMENT ,NIH| The Collaborative Genetic Study of Nicotine Dependence ,NIH| Alcohol Contextual Influences: Effects on Health Disparities and Mortality ,NIH| The University of Iowa Prevention Research Center ,NIH| Adolescent Alcohol Use: Disentangling Friend Selection &Influence ,NSF| Science Achievement and Health Behavior: High School Curriculum, Social Context, and Opportunity to Learn ,NIH| Dietary Etiologies of Heart Disease ,NIH| Birth Outcomes Among Adolescents ,NIH| Socioeconomic Disparities in Young Adult Health ,NIH| Genetic Risk to Stroke in Smokers and Nonsmokers in Two Ethnic Groups ,AKA| Impact of childhood growth patterns and latent cardiovascular risk factors on the microcirculation in adult life: Cardiovascular risk in Young Finns Study ,NIH| GWA for Gene-Environment Interaction Effects Influencing CHD ,NIH| Training in Developmental Science to Improve Child Health and Well-Being ,NIH| Statistical Methods for Network Epidemiology ,NIH| Genetics of Alcohol Dependence in African-Americans ,NIH| The Genetic Epidemiology of Nicotine Dependence ,NIH| Carolina Population Center ,NIH| Study of Addiction: Genetics and Environment ,NIH| Linkage Disequilibrium Studies of Alcohol Dependence ,NIH| Molecular Epidemiology of Alcoholism 2- Big Sibships ,NIH| Mentoring Clinical Investigators in Adolescent-onset Substance Use Disorders Research ,NIH| Social Demographic Moderation of Genome Wide Associations for Body Mass Index ,NIH| FLUORIDE AND OTHER FACTORS IN CHILDHOOD BONE DEVELOPMENT ,NIH| Genetic Epidemiology of Lung Cancer ,NIH| Role of Romantic Relationships in the Sexual Behavior of Obese and Non-Obese Girl ,NIH| A Nurse-Community Health Worker-Family Partnership Model to Increase COVID-19 Testing in Urban Underserved and Vulnerable Communities ,AKA| MSDs@LIFECOURSE CONSORTIU Subproject: Shared Risk Factors Study Group Turku University Central Hospital / Consortium: MSDs@LIFE ,ARC| Quantitative and Molecular Genetic Analysis of Cognition ,NIH| Institute for Clinical and Translational Research (UL1) ,NIH| High Density Association Analysis of Lung Cancer ,NIH| Propensity Scores and Preventive Drug Use in the Elderly ,NIH| Genetic Risk, Pathways to Adulthood, and Health Inequalities ,NIH| Washington University Institute of Clinical and Translational Sciences (UL1) ,WT| Familial and other risk factors for adolescent substance use and abuse. ,NIH| From GWAS loci to blood pressure genes, variants & mechanisms ,NIH| Genome-Wide Association for Loci Influencing CHD and Other Heart, Lung and Blood ,NIH| COSTS AND BENEFITS OF ALCOHOL SERVICES &INTERVENTIONS ,NSF| Social Inequality 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NBHCHOICE ,NIH| The Role of Peer Networks in Youth Drug Use ,NIH| Inflammation Genes and Lung Cancer Risk ,NIH| Fine Mapping Susceptibility Loci for Nicotine Dependence ,NIH| Longitudinal Study of Trauma, HIV Risk, and Criminal Justice Involvement ,NSF| Health Lifestyles and the Reproduction of Inequality ,NIH| GENETICS OF COCAINE DEPENDENCE ,EC| TODO ,NIH| SOCIAL DEMOGRAPHY ,NIH| UIC Program for Interdisciplinary Careers in Womens Health Research ,NIH| LONGITUDINAL STUDY OF SUBSTANCE USE, INCARCERATION, AND STI IN THE US ,NIH| GENETICS OF VULNERABILITY TO NICOTINE ADDICTION ,NIH| Community Assist of Southern Arizona ,NIH| Human Genetics of Addiction: A Study of Common and Specific Factors ,NIH| The effects of heavy alcohol use on weight gain in college freshmen: Examining an overlooked calorie source ,NSF| National Science Foundation Alan T. Waterman Award ,NIH| The Social Marginalization of Adolescents in High School ,NIH| Refining Phenotypic Measures of Nicotine Withdrawal ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Individual differences and health outcomes: A secondary data analysis in cognitiv ,NIH| Understanding memory consolidation by studying pharmacologically enhanced naps ,NIH| A Center for GEI Association Studies ,NIH| Longtudinal Relations Between Internalizing Disorders and Substance Use Problems ,NIH| JH/CIDR Genotyping for Genome-Wide Association Studies ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| MOTS: Modeling Obesity Through Simulation ,NIH| Comprehensive Mapping of a Blood Pressure QTL on Chromosome 17 ,NIH| Deciphering genes and pathways in nicotine dependence ,NIH| MECHANISMS AND PREVENTION OF ENVIRONMENTAL DISEASE ,NHMRC| Experience-dependent cellular plasticity and cognitive deficits in mouse models of schizophrenia ,NIH| FINANCIAL STATUS--RETIREMENT SAVING PROGRAMS ,NIH| Genetics of Early Onset-Stroke ,NIH| Human Development: Interdisciplinary Research Training ,SSHRC ,NIH| DIET, HORMONES AND RISK OF COLORECTAL CANCERS ,NIH| Population Research Institute ,NIH| GENETIC EPIDEMIOLOGY OF LUNG CANCER ,NIH| NICHD Population Center ,NIH| Population Research Training ,NIH| Data Mgmt &Analysis Core - The NINDS International Stroke Genetics Consortium St ,NIH| Obesity and the Environment: The Transition to Adulthood ,EC| ADDICTION ,NIH| Age at First Sex, Genes, Religion, and Other Social and Demographic Context ,NIH| Novel Use of Gwas for Improved Understanding of Nicotine Dependence ,NIH| Racial disparities in cancer outcomes: quantifying modifiable mechanisms ,NIH| Response Inhibition and Dopamine Neurotransmission (RI) (4 of 8) ,ARC| Locating genes for elementary and complex cognitive abilities using genetic linkage and association analysis ,NIH| Synthetic Information Systems for Better Informing Public Health Policymakers ,NSF| GSE/RES Gender Differences in Science and Math: Diversity and the Role of Social Context ,NIH| University of Minnesota Clinical and Translational Science Institute (UMN CTSI) ,NIH| Molecular Epidemiology of Alcoholism 3 - EDAC Families ,NIH| Identifying essential network properties for disease spread ,NIH| Health Disparities in Obesity: Partner Violence and its Psychosocial Pathways ,NIH| Childhood Family Instability, Adult Stress Reactivity, and Consequences for Health ,NIH| Outreach Core ,NIH| Social Support as a Facilitator of Adherence to HIV Pre-Exposure Prophylaxis Among Young MSM of Color ,CIHR ,NIH| GENETICS OF NICOTINE AND OTHER ABUSED SUBSTANCES ,NIH| Substance Abuse & Treatment Gaps in Asians, Pacific Islanders & Multiple-race Ind ,NIH| Colorado Adoption/Twin Study of Lifespan behavioral development & cognitive aging (CATSLife) ,NIH| The Genetics of Vulnerability to Nicotine Addictions ,NIH| PROSTATE, LUNG, COLORECTAL &OVARIAN CANCER ,NIH| HEALTHY YOUTH DEVELOPMENT PREVENTION RESEARCH AND TRAINING CENTER ,NIH| GENETICS AND CONSEQUENCES OF NICOTINE ADDICTION ,NIH| PROSTATE, LUNG, COLORECTAL, AND OVARIAN (PLCO) CANCER ,NIH| Population Research Center ,NIH| Mid Southern Primary Care Networks Node ,NIH| University of Washington Reproductive, Perinatal and Pediatric Epidemiology ,NIH| Carolina Population Center ,NIH| Mapping Genes for comorbidity of SUDs and Depression ,NIH| Genetic &environmental pathways to drug use, abuse &dependence ,NIH| Epidemiology of Venous Thrombosis &Pulmonary Embolism ,AKA| Role of early life risk factors in associations between work, cardiovascular disease and depression: A life course approach based on two prospective cohorts. / Consortium: ELRFWCDD ,ARC| Elucidating the genetics of attention deficit hyperactivity disorder by integrating pathway and prediction analyses ,NIH| Administrative Core ,NIH| TRAINING GRANT IN CANCER EPIDEMIOLOGY ,NIH| Physical Environment Dynamics, Inequality and Obesity ,NIH| Transitions to Adulthood and Health Risk Among U.S. Young Adults ,NIH| CUPC Admin Core ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Translational studies of nicotinic receptor genes: alcohol and nicotine behaviors ,NIH| Sexual Behavior Trajectories from Adolescence to Adulthood ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Washington University Institute of Clinical and Translational Sciences (KL2) ,NIH| PROSPECTIVE LONGITUDINAL STUDY OF ADOLESCENT HEALTH ,NIH| Identifying Mediated Pathways of Risk for Substance Use in Sexual Minority Girls ,EC| GMI ,NIH| Intergenerational Research on Obesity Prevention: From Correlates to Intervention ,NIH| Smoking/Nicotine Dependence in Attention Deficit Hyperactivity Disorder (ADHD) ,NIH| University of Colorado Population Center ,NIH| The Washington University Center for Diabetes Translation Research ,NIH| Genes, Environments & Interventions: Understanding and Addressing Alcohol Misuse ,NIH| Center on Antisocial Drug Dependence: The Genetics of HIV Risk Behaviors ,AKA| Public Health Genomics to Practice in Cardiovascular Diseases / Consortium: PUBGENSENS ,NIH| SUBSTANCE USE AND DISORDERED WEIGHT BEHAVIORS IN SEXUAL MINORITY YOUTH CONTEXTS ,NIH| BEYOND RACE--EXPLAINING INEQUALITY MANIFESTED AS OBESITY ,NIH| Research Training Program in the Behavioral and Biomedical Sciences ,NIH| Genetics of Opioid Dependence ,NIH| HIV in Young Adulthood: Pathways and Prevention ,NIH| Research and Mentoring on Integrating Psychiatric Genetics and Neuroscience ,NIH| Vitamin D metabolism related genetic variations and developmental origins of card ,NIH| Systematic Error and Confounding: Meta-Analyses of Alcohol and Disease ,AKA| The Cardiovascular Risk in Young Finns Study - the 27-year follow-up. ,NIH| Obesity and Metabolic Risk Disparities: Underlying Food Environment Factors ,NIH| VARIATION IN THE EFFECTS OF ALCOHOL ON LIVER FUNCTION ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN CANCER ,NIH| The University of Colorado Population Center ,NIH| Genes and Environment Initiatives in Type 2 Diabetes ,NIH| Pharmacogenetics of Nicotine Addiction Treatment ,NIH| Interracial Friendship and Romance Among Adolescents ,NIH| Phenotypic refinement of externalizing pathways to alcohol-related behaviors ,ARC| Maximising knowledge from dense SNP (single nucleotide polymorphisms) data using multi-locus analysis ,NIH| PERSISTENCE &CHANGE IN DRINKING HABITS: TWIN STUDY ,AKA| Center of Excellence in Complex Disease Genetics ,NIH| Exome Variants Underlying Weight Gain from Adolescence to Adulthood ,NIH| Adolescent Health and Academic Achievement ,NIH| YOUNG ADULT SUBSTANCE USE--PREDICTORS AND CONSEQUENCES ,NIH| CORE--ADIPOSE TISSUE BIOLOGY AND BASIC MECHANISMS ,NIH| MDMA and Other Hallucinogen Use: Onset and Abuse/Dependence ,NIH| Molecular Genetics and Behavior: Alcohol and Tobacco Use ,NIH| Adenocarinoma of the Lung in Women ,NIH| Do active communities support activity or support active people? ,NSF| Neighborhoods and Schools, Education, and Heritability ,NIH| Sexual Orientation and Obesity: Test of a Gendered Biopsychosocial Model ,NSF| Machine learning techniques to model the impact of relational communication on distributed team effectiveness ,NIH| DIETARY ETIOLOGIES OF HEART DISEASE AND CANCER ,NSF| The Genetic Basis of Social Networks and Civic Engagement ,NIH| Social and Demographic Context and Heritability ,NIH| High Risk Drug Use &HIV-Learning from the NYC Epidemic ,AKA| Sustainable Innovative Materials in High Tech Applications. An Interdisciplinary Approach to Design,Engineering Technology and Chemistry of Environmentally Sound Products and Production. ,AKA| Roles of inflammation, oxidation, sex hormones and genetic variation in vascular aging and the development of atherosclerosis over the life-course. ,NIH| The Pathobiology of Nephrolithiasis ,NIH| GENETIC INTERACTIONS CONTRIBUTING TO ALCOHOL AND NICOTINE DEPENDENCE ,NIH| Modeling HIV and STD in Drug User and Social Networks ,NIH| Innovations in Pediatric Pain ResearchAuthors: Harris, Kathleen Mullan; Udry, J. Richard;Harris, Kathleen Mullan; Udry, J. Richard;doi: 10.3886/icpsr21600.v16 , 10.3886/icpsr21600.v21 , 10.3886/icpsr21600.v15 , 10.3886/icpsr21600.v18 , 10.3886/icpsr21600.v19 , 10.3886/icpsr21600.v5 , 10.3886/icpsr21600.v3 , 10.3886/icpsr21600.v6 , 10.3886/icpsr21600.v1 , 10.3886/icpsr21600.v17 , 10.3886/icpsr21600.v10 , 10.3886/icpsr21600.v14 , 10.3886/icpsr21600.v8 , 10.3886/icpsr21600.v12 , 10.3886/icpsr21600.v2 , 10.3886/icpsr21600.v20 , 10.3886/icpsr21600.v4 , 10.3886/icpsr21600.v22 , 10.3886/icpsr21600.v9 , 10.3886/icpsr21600.v11 , 10.3886/icpsr21600.v7 , 10.3886/icpsr21600.v13
doi: 10.3886/icpsr21600.v16 , 10.3886/icpsr21600.v21 , 10.3886/icpsr21600.v15 , 10.3886/icpsr21600.v18 , 10.3886/icpsr21600.v19 , 10.3886/icpsr21600.v5 , 10.3886/icpsr21600.v3 , 10.3886/icpsr21600.v6 , 10.3886/icpsr21600.v1 , 10.3886/icpsr21600.v17 , 10.3886/icpsr21600.v10 , 10.3886/icpsr21600.v14 , 10.3886/icpsr21600.v8 , 10.3886/icpsr21600.v12 , 10.3886/icpsr21600.v2 , 10.3886/icpsr21600.v20 , 10.3886/icpsr21600.v4 , 10.3886/icpsr21600.v22 , 10.3886/icpsr21600.v9 , 10.3886/icpsr21600.v11 , 10.3886/icpsr21600.v7 , 10.3886/icpsr21600.v13
A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2008 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample.; Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I.; Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later.; Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. ; For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent; Wave II: 88.6 percent; Wave III: 77.4 percent; Wave IV: 80.3 percent; Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States. audio computer-assisted self interview (ACASI) computer-assisted personal interview (CAPI) computer-assisted self interview (CASI) paper and pencil interview (PAPI) face-to-face interview
Inter-university Con... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu65 citations 65 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!more_vert Inter-university Con... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2019 EnglishElsevier BV SSHRC, WT | Functional architectures ...SSHRC ,WT| Functional architectures in the brain.Authors: Badcock, Paul B.; Friston, Karl J.; Ramstead, Maxwell J.D.;Badcock, Paul B.; Friston, Karl J.; Ramstead, Maxwell J.D.;pmc: PMC6941235
pmid: 30704846
This article presents a unifying theory of the embodied, situated human brain called the Hierarchically Mechanistic Mind (HMM). The HMM describes the brain as a complex adaptive system that actively minimises the decay of our sensory and physical states by producing self-fulfilling action-perception cycles via dynamical interactions between hierarchically organised neurocognitive mechanisms. This theory synthesises the free-energy principle (FEP) in neuroscience with an evolutionary systems theory of psychology that explains our brains, minds, and behaviour by appealing to Tinbergen's four questions: adaptation, phylogeny, ontogeny, and mechanism. After leveraging the FEP to formally define the HMM across different spatiotemporal scales, we conclude by exploring its implications for theorising and research in the sciences of the mind and behaviour. Highlights • We present an interdisciplinary theory of the embodied, situated human brain called the Hierarchically Mechanistic Mind (HMM). • We describe the HMM as a model of neural architecture. • We explore how the HMM synthesises the free-energy principle in neuroscience with an evolutionary systems theory of psychology. • We translate our model into a new heuristic for theorising and research in neuroscience and psychology.
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For further information contact us at helpdesk@openaire.eu40 citations 40 popularity Top 10% influence Average impulse Top 1% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2021 GermanyMDPI AG WT | Functional architectures ..., SSHRC, WT | Wellcome Centre for Human... +1 projectsWT| Functional architectures in the brain. ,SSHRC ,WT| Wellcome Centre for Human Neuroimaging ,WTThomas Parr; Lancelot Da Costa; Conor Heins; Maxwell J. D. Ramstead; Karl J. Friston;In theoretical biology, we are often interested in random dynamical systems—like the brain—that appear to model their environments. This can be formalized by appealing to the existence of a (possibly non-equilibrium) steady state, whose density preserves a conditional independence between a biological entity and its surroundings. From this perspective, the conditioning set, or Markov blanket, induces a form of vicarious synchrony between creature and world—as if one were modelling the other. However, this results in an apparent paradox. If all conditional dependencies between a system and its surroundings depend upon the blanket, how do we account for the mnemonic capacity of living systems? It might appear that any shared dependence upon past blanket states violates the independence condition, as the variables on either side of the blanket now share information not available from the current blanket state. This paper aims to resolve this paradox, and to demonstrate that conditional independence does not preclude memory. Our argument rests upon drawing a distinction between the dependencies implied by a steady state density, and the density dynamics of the system conditioned upon its configuration at a previous time. The interesting question then becomes: What determines the length of time required for a stochastic system to ‘forget’ its initial conditions? We explore this question for an example system, whose steady state density possesses a Markov blanket, through simple numerical analyses. We conclude with a discussion of the relevance for memory in cognitive systems like us.
Europe PubMed Centra... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu15 citations 15 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 50visibility views 50 download downloads 55 Powered bymore_vert Europe PubMed Centra... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Preprint , Article 2020MDPI AG SSHRC, WT | Functional architectures ...SSHRC ,WT| Functional architectures in the brain.Authors: Maxwell J. D. Ramstead; Karl J. Friston; Inês Hipólito;Maxwell J. D. Ramstead; Karl J. Friston; Inês Hipólito;is most appropriate. We focus on non-realist (deflationary and fictionalist-instrumentalist) approaches. We consider a deflationary account of mental representation, according to which the explanatorily relevant contents of neural representations are mathematical, rather than cognitive, and a fictionalist or instrumentalist account, according to which representations are scientifically useful fictions that serve explanatory (and other) aims. After reviewing the free-energy principle and active inference, we argue that the model of adaptive phenotypes under the free-energy principle can be used to furnish a formal semantics, enabling us to assign semantic content to specific phenotypic states (the internal states of a Markovian system that exists far from equilibrium). We propose a modified fictionalist account&mdash our position is thus coherent with, but rests on distinct assumptions from, the realist position. We argue that the free-energy principle thereby explains the aboutness or intentionality in living systems and hence their capacity to parse their sensory stream using an ontology or set of semantic factors. an organism-centered fictionalism or instrumentalism. We argue that, under the free-energy principle, pursuing even a deflationary account of the content of neural representations licenses the appeal to the kind of semantic content involved in the &lsquo The aim of this paper is twofold: (1) to assess whether the construct of neural representations plays an explanatory role under the variational free-energy principle and its corollary process theory, active inference in relation to the ontological and epistemological status of representations&mdash and (2) if so, to assess which philosophical stance&mdash or intentionality of cognitive systems aboutness&rsquo
Entropy arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu50 citations 50 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert Entropy arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2008 EnglishICPSR - Interuniversity Consortium for Political and Social Research NIH | Center for Family and Dem..., NIH | PROSTATE, LUNG, COLORECTA..., NIH | Computational Methods to ... +195 projectsNIH| Center for Family and Demographic Research ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Computational Methods to Detect Epistasis ,NIH| OPTIMIZING BEHAVIORAL INTERVENTIONS FOR DRUG ABUSE PREVENTION &TREATMENT ,NIH| The Collaborative Genetic Study of Nicotine Dependence ,NIH| Socioeconomic Disparities in Young Adult Health ,NIH| Genetic Risk to Stroke in Smokers and Nonsmokers in Two Ethnic Groups ,AKA| Impact of childhood growth patterns and latent cardiovascular risk factors on the microcirculation in adult life: Cardiovascular risk in Young Finns Study ,NIH| GWA for Gene-Environment Interaction Effects Influencing CHD ,NIH| Study of Addiction: Genetics and Environment ,NIH| Linkage Disequilibrium Studies of Alcohol Dependence ,NIH| A Nurse-Community Health Worker-Family Partnership Model to Increase COVID-19 Testing in Urban Underserved and Vulnerable Communities ,AKA| MSDs@LIFECOURSE CONSORTIU Subproject: Shared Risk Factors Study Group Turku University Central Hospital / Consortium: MSDs@LIFE ,ARC| Quantitative and Molecular Genetic Analysis of Cognition ,NIH| Institute for Clinical and Translational Research (UL1) ,NIH| High Density Association Analysis of Lung Cancer ,NIH| Propensity Scores and Preventive Drug Use in the Elderly ,NIH| Genetic Risk, Pathways to Adulthood, and Health Inequalities ,NIH| Washington University Institute of Clinical and Translational Sciences (UL1) ,WT| Familial and other risk factors for adolescent substance use and abuse. ,NIH| From GWAS loci to blood pressure genes, variants & mechanisms ,NIH| Genome-Wide Association for Loci Influencing CHD and Other Heart, Lung and Blood ,NIH| COSTS AND BENEFITS OF ALCOHOL SERVICES &INTERVENTIONS ,NSF| Social Inequality and Status Attainment ,NIH| Containing Bioterroist and Emerging Infectious Diseases ,NIH| UNC Interdisciplinary Obesity Training (IDOT)(RMI) ,EC| DYNANETS ,NIH| BEHAVIORAL PHARMACOGENETICS OF DRUG AND ALCOHOL ABUSE ,NIH| Variants in CHRNA5/CHRNA3/CHRNB4 and nicotine dependence ,NIH| Statistical Methods for Gene Environment Interactions In Lung Cancer ,NIH| Economic Evaluation Methods: Development and Application ,NIH| Model-Based Clustering Methods of Medical Image ,NIH| Nicotinic receptor genes &substance abuse: Functional studies of associated SNPs ,NSF| CAREER: Model Fluid-Solid Interactions, Networks REUs, and BioCalculus ,NIH| Genes, early adversity, and sensitive periods in social-emotional development ,NIH| Health Communication and Health Literacy Core ,NIH| HUMAN GENETIC VARIATION IN SMOKING AND ADDICTION RISK ,NIH| Data Core ,NIH| Genetics of Adolescent Antisocial Drug Dependence ,NIH| BIOBEHAVIORAL FACTORS IN CORONARY HEART DISEASE ,EC| DEPRIVEDHOODS ,NIH| Genome Wide Association Coordinating Center ,EC| SOCIOGENOME ,NIH| ECONOMICS OF AGING TRAINING PROGRAM -- EXTENSION ,NIH| Cancer Center Support Grant ,NIH| NATURAL HISTORY OF ALCOHOL USE &ABUSE--GENETIC MODELS ,EC| NBHCHOICE ,NIH| The Role of Peer Networks in Youth Drug Use ,NIH| Inflammation Genes and Lung Cancer Risk ,NIH| Fine Mapping Susceptibility Loci for Nicotine Dependence ,NIH| Longitudinal Study of Trauma, HIV Risk, and Criminal Justice Involvement ,NIH| GENETICS OF COCAINE DEPENDENCE ,EC| TODO ,NIH| SOCIAL DEMOGRAPHY ,NIH| LONGITUDINAL STUDY OF SUBSTANCE USE, INCARCERATION, AND STI IN THE US ,NIH| GENETICS OF VULNERABILITY TO NICOTINE ADDICTION ,NIH| Community Assist of Southern Arizona ,NIH| Human Genetics of Addiction: A Study of Common and Specific Factors ,NIH| The effects of heavy alcohol use on weight gain in college freshmen: Examining an overlooked calorie source ,NSF| National Science Foundation Alan T. Waterman Award ,NIH| The Social Marginalization of Adolescents in High School ,NIH| Refining Phenotypic Measures of Nicotine Withdrawal ,NIH| NICHD Population Center ,NIH| Population Research Training ,NIH| Data Mgmt &Analysis Core - The NINDS International Stroke Genetics Consortium St ,NIH| Obesity and the Environment: The Transition to Adulthood ,EC| ADDICTION ,NIH| Age at First Sex, Genes, Religion, and Other Social and Demographic Context ,NIH| Novel Use of Gwas for Improved Understanding of Nicotine Dependence ,NIH| Racial disparities in cancer outcomes: quantifying modifiable mechanisms ,NIH| Response Inhibition and Dopamine Neurotransmission (RI) (4 of 8) ,ARC| Locating genes for elementary and complex cognitive abilities using genetic linkage and association analysis ,NSF| GSE/RES Gender Differences in Science and Math: Diversity and the Role of Social Context ,NIH| University of Minnesota Clinical and Translational Science Institute (UMN CTSI) ,NIH| Molecular Epidemiology of Alcoholism 3 - EDAC Families ,NIH| Identifying essential network properties for disease spread ,NIH| Health Disparities in Obesity: Partner Violence and its Psychosocial Pathways ,NIH| Outreach Core ,NIH| Social Support as a Facilitator of Adherence to HIV Pre-Exposure Prophylaxis Among Young MSM of Color ,NIH| BEYOND RACE--EXPLAINING INEQUALITY MANIFESTED AS OBESITY ,NIH| Genetics of Opioid Dependence ,NIH| HIV in Young Adulthood: Pathways and Prevention ,NIH| Research and Mentoring on Integrating Psychiatric Genetics and Neuroscience ,NIH| Vitamin D metabolism related genetic variations and developmental origins of card ,WT ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Economic Evaluation of Adolescent Alcohol Use and the Impact of Social Networks ,NIH| MECHANISMS AND PREVENTION OF ENVIRONMENTAL DISEASE ,NIH| FINANCIAL STATUS--RETIREMENT SAVING PROGRAMS ,NIH| Genetics of Early Onset-Stroke ,NIH| Human Development: Interdisciplinary Research Training ,NIH| CORE--ADIPOSE TISSUE BIOLOGY AND BASIC MECHANISMS ,NIH| MDMA and Other Hallucinogen Use: Onset and Abuse/Dependence ,NIH| Molecular Genetics and Behavior: Alcohol and Tobacco Use ,CIHR ,NIH| PROSPECTIVE LONGITUDINAL STUDY OF ADOLESCENT HEALTH ,NIH| Identifying Mediated Pathways of Risk for Substance Use in Sexual Minority Girls ,EC| GMI ,NIH| Intergenerational Research on Obesity Prevention: From Correlates to Intervention ,NIH| Smoking/Nicotine Dependence in Attention Deficit Hyperactivity Disorder (ADHD) ,NIH| The Washington University Center for Diabetes Translation Research ,NIH| Genes, Environments & Interventions: Understanding and Addressing Alcohol Misuse ,NIH| Center on Antisocial Drug Dependence: The Genetics of HIV Risk Behaviors ,AKA| Public Health Genomics to Practice in Cardiovascular Diseases / Consortium: PUBGENSENS ,NIH| SUBSTANCE USE AND DISORDERED WEIGHT BEHAVIORS IN SEXUAL MINORITY YOUTH CONTEXTS ,NIH| Alcohol Contextual Influences: Effects on Health Disparities and Mortality ,NIH| Adolescent Alcohol Use: Disentangling Friend Selection &Influence ,NSF| Science Achievement and Health Behavior: High School Curriculum, Social Context, and Opportunity to Learn ,NIH| Dietary Etiologies of Heart Disease ,NIH| Birth Outcomes Among Adolescents ,NIH| GENETICS OF NICOTINE AND OTHER ABUSED SUBSTANCES ,NIH| Substance Abuse & Treatment Gaps in Asians, Pacific Islanders & Multiple-race Ind ,NIH| Colorado Adoption/Twin Study of Lifespan behavioral development & cognitive aging (CATSLife) ,NIH| The Genetics of Vulnerability to Nicotine Addictions ,NIH| PROSTATE, LUNG, COLORECTAL &OVARIAN CANCER ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Individual differences and health outcomes: A secondary data analysis in cognitiv ,NIH| Understanding memory consolidation by studying pharmacologically enhanced naps ,NIH| A Center for GEI Association Studies ,NIH| Longtudinal Relations Between Internalizing Disorders and Substance Use Problems ,NIH| JH/CIDR Genotyping for Genome-Wide Association Studies ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| MOTS: Modeling Obesity Through Simulation ,NIH| Comprehensive Mapping of a Blood Pressure QTL on Chromosome 17 ,NIH| Deciphering genes and pathways in nicotine dependence ,NIH| Systematic Error and Confounding: Meta-Analyses of Alcohol and Disease ,AKA| The Cardiovascular Risk in Young Finns Study - the 27-year follow-up. ,NIH| Obesity and Metabolic Risk Disparities: Underlying Food Environment Factors ,NIH| VARIATION IN THE EFFECTS OF ALCOHOL ON LIVER FUNCTION ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN CANCER ,NIH| The University of Colorado Population Center ,NIH| HEALTHY YOUTH DEVELOPMENT PREVENTION RESEARCH AND TRAINING CENTER ,NIH| GENETICS AND CONSEQUENCES OF NICOTINE ADDICTION ,NIH| PROSTATE, LUNG, COLORECTAL, AND OVARIAN (PLCO) CANCER ,NIH| Population Research Center ,NIH| Mid Southern Primary Care Networks Node ,NIH| University of Washington Reproductive, Perinatal and Pediatric Epidemiology ,NIH| Carolina Population Center ,NIH| Mapping Genes for comorbidity of SUDs and Depression ,NIH| Genetic &environmental pathways to drug use, abuse &dependence ,NIH| Epidemiology of Venous Thrombosis &Pulmonary Embolism ,AKA| Role of early life risk factors in associations between work, cardiovascular disease and depression: A life course approach based on two prospective cohorts. / Consortium: ELRFWCDD ,ARC| Elucidating the genetics of attention deficit hyperactivity disorder by integrating pathway and prediction analyses ,NIH| Administrative Core ,NIH| TRAINING GRANT IN CANCER EPIDEMIOLOGY ,NIH| Physical Environment Dynamics, Inequality and Obesity ,NIH| PROSTATE, LUNG, COLORECTAL AND OVARIAN (PLCO) CANCER ,NIH| Translational studies of nicotinic receptor genes: alcohol and nicotine behaviors ,NIH| Sexual Behavior Trajectories from Adolescence to Adulthood ,NIH| Cascades of Network Structure and Function: Pathways to Adolescent Substance Use ,NIH| Washington University Institute of Clinical and Translational Sciences (KL2) ,NIH| Genes and Environment Initiatives in Type 2 Diabetes ,NIH| Pharmacogenetics of Nicotine Addiction Treatment ,NIH| Interracial Friendship and Romance Among Adolescents ,NIH| The University of Iowa Prevention Research Center ,NIH| Training in Developmental Science to Improve Child Health and Well-Being ,NIH| Statistical Methods for Network Epidemiology ,NIH| Genetics of Alcohol Dependence in African-Americans ,NIH| The Genetic Epidemiology of Nicotine Dependence ,NIH| Carolina Population Center ,NIH| Molecular Epidemiology of Alcoholism 2- Big Sibships ,NIH| Mentoring Clinical Investigators in Adolescent-onset Substance Use Disorders Research ,NIH| Social Demographic Moderation of Genome Wide Associations for Body Mass Index ,NIH| FLUORIDE AND OTHER FACTORS IN CHILDHOOD BONE DEVELOPMENT ,NIH| Genetic Epidemiology of Lung Cancer ,NIH| Role of Romantic Relationships in the Sexual Behavior of Obese and Non-Obese Girl ,EC| ENGAGE ,NIH| Consortium for Neuropsychiatric Phenomics-Coordinating Center (1 of 8) ,NIH| Administrative and Research Support Core ,NIH| PATHOLOGY MONITORING--F344 RAT COLONY ,NIH| GENETICS OF ADOLESCENT ANTISOCIAL DRUG DEPENDENCE ,NIH| NYS FAMILY STUDY: PROBLEM ALCOHOL USE &PROBLEM BEHAVIOR ,NIH| Neuropharmacology of Response Inhibition in Comorbid ADHD and Nicotine Dependence ,NIH| Genome-Wide Associations Environmental Interactions in the Lung Health Study ,NIH| Adenocarinoma of the Lung in Women ,NIH| Do active communities support activity or support active people? ,NSF| Neighborhoods and Schools, Education, and Heritability ,NIH| Sexual Orientation and Obesity: Test of a Gendered Biopsychosocial Model ,NSF| Machine learning techniques to model the impact of relational communication on distributed team effectiveness ,NIH| DIETARY ETIOLOGIES OF HEART DISEASE AND CANCER ,NSF| The Genetic Basis of Social Networks and Civic Engagement ,NIH| Social and Demographic Context and Heritability ,NIH| High Risk Drug Use &HIV-Learning from the NYC Epidemic ,AKA| Sustainable Innovative Materials in High Tech Applications. An Interdisciplinary Approach to Design,Engineering Technology and Chemistry of Environmentally Sound Products and Production. ,AKA| Roles of inflammation, oxidation, sex hormones and genetic variation in vascular aging and the development of atherosclerosis over the life-course. ,NIH| The Pathobiology of Nephrolithiasis ,NIH| GENETIC INTERACTIONS CONTRIBUTING TO ALCOHOL AND NICOTINE DEPENDENCE ,NIH| Modeling HIV and STD in Drug User and Social Networks ,ARC| Maximising knowledge from dense SNP (single nucleotide polymorphisms) data using multi-locus analysis ,NIH| PERSISTENCE &CHANGE IN DRINKING HABITS: TWIN STUDY ,AKA| Center of Excellence in Complex Disease Genetics ,NIH| Exome Variants Underlying Weight Gain from Adolescence to Adulthood ,NIH| Adolescent Health and Academic Achievement ,NIH| YOUNG ADULT SUBSTANCE USE--PREDICTORS AND CONSEQUENCES ,SSHRC ,NIH| DIET, HORMONES AND RISK OF COLORECTAL CANCERS ,NIH| Population Research Institute ,NIH| GENETIC EPIDEMIOLOGY OF LUNG CANCERAuthors: Harris, Kathleen Mullan; Udry, J. Richard;Harris, Kathleen Mullan; Udry, J. Richard;Downloads of Add Health require submission of the following information, which is shared with the original producer of Add Health: supervisor name, supervisor email, and reason for download. A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2018 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Wave V data collection took place from 2016 to 2018, when the original Wave I respondents were 33 to 43 years old. For the first time, a mixed mode survey design was used. In addition, several experiments were embedded in early phases of the data collection to test response to various treatments. A similar range of data was collected on social, environmental, economic, behavioral, and health circumstances of respondents, with the addition of retrospective child health and socio-economic status questions. Physical measurements and biospecimens were again collected at Wave V, and included most of the same measures as at Wave IV. Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights DS32: Wave V: Mixed-Mode Survey, Public Use Sample DS33: Wave V: Mixed-Mode Survey, Public Use Sample (Section 16B: Pregnancy, Live Births, Children and Parenting) DS34: Wave V: Biomarkers, Anthropometrics DS35: Wave V: Biomarkers, Cardiovascular Measures DS36: Wave V: Biomarkers, Demographics DS37: Wave V: Biomarkers, Measures of Glucose Homeostasis DS38: Wave V: Biomarkers, Measures of Inflammation and Immune Function DS39: Wave V: Biomarkers, Lipids DS40: Wave V: Biomarkers, Medication Use DS41: Wave V: Biomarkers, Renal Function DS42: Wave V: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample. Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I. Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later. Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. Wave V: All Wave I respondents who were still living were eligible at Wave V, yielding a pool of 19,828 persons. This pool was split into three stratified random samples for the purposes of survey design testing. For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. audio computer-assisted self interview (ACASI); computer-assisted personal interview (CAPI); computer-assisted self interview (CASI); face-to-face interview; mixed mode; paper and pencil interview (PAPI); telephone interviewWave V data files were minimally processed by ICPSR. For value labeling, missing value designation, and question text (where applicable), please see the available P.I. Codebook/Questionnaires. The study-level documentation (Data Guide, User Guide) does not include Wave V datasets.Documentation for Waves prior to Wave V may use an older version of the study title.Users should be aware that version history notes dated prior to 2015-11-09 do not apply to the current organization of the datasets.Please note that dates present in the Summary and Time Period fields are taken from the Add Health Study Design page. The Date of Collection field represents the range of interview dates present in the data files for each wave.Wave I and Wave II field work was conducted by the National Opinion Research Center at the University of Chicago.Wave III, Wave IV, and Wave V field work was conducted by the Research Triangle Institute.For the most updated list of related publications, please see the Add Health Publications Web site.Additional information on the National Longitudinal Study of Adolescent to Adult Health (Add Health) series can be found on the Add Health Web site. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Wave V aimed to track the emergence of chronic disease as the cohort aged into their 30s and early 40s. Add health is a school-based longitudinal study of a nationally-representative sample of adolescents in grates 7-12 in the United States in 1945-45. Over more than 20 years of data collection, data have been collected from adolescents, their fellow students, school administrators, parents, siblings, friends, and romantic partners through multiple data collection components. In addition, existing databases with information about respondents' neighborhoods and communities have been merged with Add Health data, including variables on income poverty, unemployment, availability and utilization of health services, crime, church membership, and social programs and policies. The data files are not weighted. However, the collection features a number of weight variables contained within the following datasets: DS4: Wave I: Public Use Grand Sample Weights DS7: Wave II: Public Use Grand Sample Weights DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS21: Wave III: Public In-Home Weights DS31: Wave IV: Public Use Weights DS42: Wave V: Public Use Weights Please note that these weights files do not apply to the Biomarker data files. For additional information on the application of weights for data analysis, please see the ICPSR User Guide, or the Guidelines for Analyzing Add Health Data. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent Wave II: 88.6 percent Wave III: 77.4 percent Wave IV: 80.3 percent Wave V: 71.8 percent Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States.
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For further information contact us at helpdesk@openaire.eu8 citations 8 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2017 WT | Functional architectures ..., SSHRCWT| Functional architectures in the brain. ,SSHRCAuthors: Ramstead, Maxwell James Désormeau; Badcock, Paul Benjamin; Friston, Karl John;Ramstead, Maxwell James Désormeau; Badcock, Paul Benjamin; Friston, Karl John;pmc: PMC5857288
The free-energy principle (FEP) is a formal model of neuronal processes that is widely recognised in neuroscience as a unifying theory of the brain and biobehaviour. More recently, however, it has been extended beyond the brain to explain the dynamics of living systems, and their unique capacity to avoid decay. The aim of this review is to synthesise these advances with a meta-theoretical ontology of biological systems called variational neuroethology, which integrates the FEP with Tinbergen's four research questions to explain biological systems across spatial and temporal scales. We exemplify this framework by applying it to Homo sapiens, before translating variational neuroethology into a systematic research heuristic that supplies the biological, cognitive, and social sciences with a computationally tractable guide to discovery. Highlights • We describe a meta-theoretical ontology of life based on the free energy principle. • We propose a multiscale formulation of the free energy principle. • We translate our ontology into a systematic research heuristic for life sciences. • We apply this meta-theoretical ontology and research heuristic to Homo sapiens.
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For further information contact us at helpdesk@openaire.eu84 citations 84 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2020 United Kingdom, Canada, FranceCambridge University Press (CUP) EC | LIFESPAN, SSHRC, CIHR +3 projectsEC| LIFESPAN ,SSHRC ,CIHR ,UKRI| Pathways to self-harm: Biological mechanisms and genetic contribution ,WT| Reducing cognitive vulnerability to suicidal ideation in recurrent major depression: using translational research to elucidate risk mechanisms and their modification ,WT| The Avon Longitudinal Study of Parents and Children (ALSPAC): A multi-generation, longitudinal resource focusing on life course health and well-being.Massimiliano Orri; Abigail Emma Russell; Becky Mars; Gustavo Turecki; David Gunnell; Jon Heron; Richard E. Tremblay; Michel Boivin; Anne Monique Nuyt; Sylvana M. Côté; Marie-Claude Geoffroy;AbstractBackgroundWe aimed to identify groups of children presenting distinct perinatal adversity profiles and test the association between profiles and later risk of suicide attempt.MethodsData were from the Québec Longitudinal Study of Child Development (QLSCD, N = 1623), and the Avon Longitudinal Study of Parents and Children (ALSPAC, N = 5734). Exposures to 32 perinatal adversities (e.g. fetal, obstetric, psychosocial, and parental psychopathology) were modeled using latent class analysis, and associations with a self-reported suicide attempt by age 20 were investigated with logistic regression. We investigated to what extent childhood emotional and behavioral problems, victimization, and cognition explained the associations.ResultsIn both cohorts, we identified five profiles: No perinatal risk, Poor fetal growth, Socioeconomic adversity, Delivery complications, Parental mental health problems (ALSPAC only). Compared to children with No perinatal risk, children in the Poor fetal growth (pooled estimate QLSCD-ALSPAC, OR 1.89, 95% CI 1.04–3.44), Socioeconomic adversity (pooled-OR 1.42, 95% CI 1.08–1.85), and Parental mental health problems (OR 1.74, 95% CI 1.27–2.40), but not Delivery complications, profiles were more likely to attempt suicide. The proportion of this effect mediated by the putative mediators was larger for the Socioeconomic adversity profile compared to the others.ConclusionsPerinatal adversities associated with suicide attempt cluster in distinct profiles. Suicide prevention may begin early in life and requires a multidisciplinary approach targeting a constellation of factors from different domains (psychiatric, obstetric, socioeconomic), rather than a single factor, to effectively reduce suicide vulnerability. The way these factors cluster together also determined the pathways leading to a suicide attempt, which can guide decision-making on personalized suicide prevention strategies.
Papyrus : Dépôt inst... arrow_drop_down Papyrus : Dépôt institutionnel - Université de Montréal; Psychological MedicineOther literature type . Article . 2020License: Cambridge Core User Agreementadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu15 citations 15 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 32visibility views 32 download downloads 0 Powered bymore_vert Papyrus : Dépôt inst... arrow_drop_down Papyrus : Dépôt institutionnel - Université de Montréal; Psychological MedicineOther literature type . Article . 2020License: Cambridge Core User Agreementadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2019 EnglishSpringer US SSHRC, WT | Functional architectures ...SSHRC ,WT| Functional architectures in the brain.Badcock, Paul B.; Friston, Karl J.; Ramstead, Maxwell J. D.; Ploeger, Annemie; Hohwy, Jakob;pmc: PMC6861365
pmid: 31115833
The purpose of this review was to integrate leading paradigms in psychology and neuroscience with a theory of the embodied, situated human brain, called the Hierarchically Mechanistic Mind (HMM). The HMM describes the brain as a complex adaptive system that functions to minimize the entropy of our sensory and physical states via action-perception cycles generated by hierarchical neural dynamics. First, we review the extant literature on the hierarchical structure of the brain. Next, we derive the HMM from a broader evolutionary systems theory that explains neural structure and function in terms of dynamic interactions across four nested levels of biological causation (i.e., adaptation, phylogeny, ontogeny, and mechanism). We then describe how the HMM aligns with a global brain theory in neuroscience called the free-energy principle, leveraging this theory to mathematically formulate neural dynamics across hierarchical spatiotemporal scales. We conclude by exploring the implications of the HMM for psychological inquiry.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu33 citations 33 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2021 NetherlandsFrontiers Media SA SSHRC, WT | Functional architectures ...SSHRC ,WT| Functional architectures in the brain.Authors: Thomas Parr; Dimitrije Marković; Maxwell James D. Ramstead; Maxwell James D. Ramstead; +6 AuthorsThomas Parr; Dimitrije Marković; Maxwell James D. Ramstead; Maxwell James D. Ramstead; Maxwell James D. Ramstead; Maxwell James D. Ramstead; Ryan Smith; Casper Hesp; Casper Hesp; Karl Friston;NARCIS arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3389/frai.2021.710179&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert NARCIS arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3389/frai.2021.710179&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2018 SSHRC, WT | Functional architectures ...SSHRC ,WT| Functional architectures in the brain.Authors: Maxwell J D, Ramstead; Axel, Constant; Paul B, Badcock; Karl J, Friston;Maxwell J D, Ramstead; Axel, Constant; Paul B, Badcock; Karl J, Friston;pmid: 30655223
pmc: PMC6941227
This paper addresses the challenges faced by multiscale formulations of the variational (free energy) approach to dynamics that obtain for large-scale ensembles. We review a framework for modelling complex adaptive control systems for multiscale free energy bounding organism–niche dynamics, thereby integrating the modelling strategies and heuristics of variational neuroethology with a broader perspective on the ecological nestedness of biotic systems. We extend the multiscale variational formulation beyond the action–perception loops of individual organisms by appealing to the variational approach to niche construction to explain the dynamics of coupled systems constituted by organisms and their ecological niche. We suggest that the statistical robustness of living systems is inherited, in part, from their eco-niches, as niches help coordinate dynamical patterns across larger spatiotemporal scales. We call this approach variational ecology. We argue that, when applied to cultural animals such as humans, variational ecology enables us to formulate not just a physics of individual minds, but also a physics of interacting minds across spatial and temporal scales – a physics of sentient systems that range from cells to societies. Highlights • We extend the multiscale variational free energy approach to large-scale ensembles. • We integrate multiscale modelling with a broad perspective on ecological nestedness. • We argue that the statistical robustness of living systems is ecologically inherited. • We propose variational ecology as a physics of sentient systems.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=PMC6941227&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu26 citations 26 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=PMC6941227&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu