The Amish Connectome Project (ACP) will extend the Human Connectome Project (HCP) to mental illnesses by characterizing brain circuitry and its relation to psychiatric behavior and symptom dimensions. We propose to collect HCP connectomics and whole genome sequencing data in Old Order Amish (OOA) adults recruited from multigenerational families with multiplex mental disorders spanning diagnostic boundaries. Large nuclear families with two or more members with major DSM-5 disorder will be recruited for phenotyping using the full HCP lifespan imaging and behavioral protocol expanded to Research Domain Criteria (RDoC) standard. The advent of non-invasive connectivity-oriented neuroimaging methods has shed new light onto the inner workings of the brain. The brain's functional and structural connectome plays key roles in regulating the pathway from genes to neural systems to mental illnesses. Extending the gene -->connectome HCP approach to gene -->connectome -->mental disorder in HCP-Human Disease adds tremendous opportunity to identify genetic underpinning of heritable mental disorders. ACP offers a uniquely efficient and powerful study design that is critical for a successful breakthrough. The ACP will study large, multigenerational families from a population isolate, which is a powerful statistical design for discovery of genetic linkage between connectomic traits and mental disorders. The OOA sample is unique in its relative genetic uniformity, with ancestry recorded in the NIH database and traceable back fourteen generations to limited founders. This sample is also unique because of its relative uniformity in educational background, life and work conditions, socioeconomic status and much reduced influence by illicit drugs. These unique characteristics of OOA community members, combined with their large family size, makes the OOA a powerful population sample to study the genetic factors that alter cerebral connectivity in mental disorders with familial pattern of inheritance. We will expand upon HCP protocol with the psychiatric diagnosis, broad symptom and RDoC dimensional assessments of behavior and cognition. Whole genome data will be obtained for all participants through next generation sequencing and family-based imputation of GWAS data. These data will be shared with the large research community while protecting the privacy, confidentiality and welfare of participants, with special attention given to protect the welfare of the OOA community. Medical genetic discoveries in OOA have routinely been replicated in general population samples and translated to clinical practice. We are inspired to create opportunities to allow repetitions of suh success in brain connectome and in mental illness through shared data effort, and have assembled an efficient team to lead this endeavor.

Public Health Relevance

The Amish Connectome Project will collect data from large, multi-generational Old Order Amish families with high prevalence of mental disorders. The project aims to extend the ongoing Human Connectome Project with state of the art cerebral connectomics and whole genome sequencing data to study the underpinning of heritable mental disorders.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZRG1-ETTN-H (55))
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Churchill, James D
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University of Maryland Baltimore
Schools of Medicine
United States
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Du, Xiaoming; Hong, L Elliot (2018) Test-retest reliability of short-interval intracortical inhibition and intracortical facilitation in patients with schizophrenia. Psychiatry Res 267:575-581
Du, Xiaoming; Rowland, Laura M; Summerfelt, Ann et al. (2018) TMS evoked N100 reflects local GABA and glutamate balance. Brain Stimul 11:1071-1079
Du, Xiaoming; Rowland, Laura M; Summerfelt, Ann et al. (2018) Cerebellar-Stimulation Evoked Prefrontal Electrical Synchrony Is Modulated by GABA. Cerebellum :
Kochunov, Peter; Dickie, Erin W; Viviano, Joseph D et al. (2018) Integration of routine QA data into mega-analysis may improve quality and sensitivity of multisite diffusion tensor imaging studies. Hum Brain Mapp 39:1015-1023
Chiappelli, Joshua; Notarangelo, Francesca M; Pocivavsek, Ana et al. (2018) Influence of plasma cytokines on kynurenine and kynurenic acid in schizophrenia. Neuropsychopharmacology 43:1675-1680
Ryan, Meghann C; Sherman, Paul; Rowland, Laura M et al. (2018) Miniature pig model of human adolescent brain white matter development. J Neurosci Methods 296:99-108
Adhikari, Bhim M; Jahanshad, Neda; Shukla, Dinesh et al. (2018) Comparison of heritability estimates on resting state fMRI connectivity phenotypes using the ENIGMA analysis pipeline. Hum Brain Mapp 39:4893-4902
van Erp, Theo G M; Walton, Esther; Hibar, Derrek P et al. (2018) Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium. Biol Psychiatry 84:644-654
Kelly, S; Jahanshad, N; Zalesky, A et al. (2018) Widespread white matter microstructural differences in schizophrenia across 4322 individuals: results from the ENIGMA Schizophrenia DTI Working Group. Mol Psychiatry 23:1261-1269
Chavez, Sofia; Viviano, Joseph; Zamyadi, Mojdeh et al. (2018) A novel DTI-QA tool: Automated metric extraction exploiting the sphericity of an agar filled phantom. Magn Reson Imaging 46:28-39

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