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 play key roles in regulating the pathways from genes to neural systems to mental illnesses. Along these pathways, we hypothesize that abnormal activity in the innate stress-immune pathways has a major contribution to the brain connectome disruption in early stage of schizophrenia spectrum disorder and to its clinical consequence. The project proposes to extend the Connectome Project in Mental Illness to characterize brain circuitry and its relation to stress-immune axis dysfunction in early stage of schizophrenia spectrum disorder in China. We propose a longitudinal study in a large sample of patients that aims to overcome the heterogeneity in the relationship between mental illness and stress-immune-connectome axis, a well-recognized barrier to advancing research and treatment. We will recruit 500 patients with schizophrenia spectrum disorders within five years of disease onset. They will be assessed using modern chronic stress and acute psychological stress laboratory paradigms to define the stress biomarkers at baseline. The patients will be compared with 250 age and sex matched healthy controls. The collaboration leverages the clinical stress research expertise by the U.S. partner and the the clinical immunology research expertise in schizophrenia by the Chinese partner. The proposed study also builds on our ongoing work using acute and chronic stress paradigms to understand how stress is linked to brain structural and functional connectome in schizophrenia spectrum disorders. This novel proposition in U.S. and Chinese mental health research field is strongly supported by preliminary data. The ability to apply the cutting edge connectome protocol using the advanced research designated scanner in Beijing will also enhance our ability to use multimodal imaging tools to aid biologically based heterogeneity reduction. Together, this study will generate actionable strategies to treat and prevent brain connectome deterioration and facilitate clinical recovery after psychosis onset.

Public Health Relevance

The U.S. - China Collaboration project will collect data from a large group of schizophrenia spectrum disorder patients in their early stage of disease. The project aims to study how stress and immune adaptive function or dysfunction may impact brain circuitry and disease progression or recovery, so that doctors can provide more effective prevention and treatment options to these patients in the future.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH112180-05
Application #
10057388
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Zalcman, Steven J
Project Start
2017-02-01
Project End
2021-11-30
Budget Start
2020-12-01
Budget End
2021-11-30
Support Year
5
Fiscal Year
2021
Total Cost
Indirect Cost
Name
University of Maryland Baltimore
Department
Psychiatry
Type
Schools of Medicine
DUNS #
188435911
City
Baltimore
State
MD
Country
United States
Zip Code
21201
Adhikari, Bhim M; Jahanshad, Neda; Shukla, Dinesh et al. (2018) Heritability estimates on resting state fMRI data using ENIGMA analysis pipeline. Pac Symp Biocomput 23:307-318
Yu, Ting; Li, Yanli; Fan, Fengmei et al. (2018) Decreased Gray Matter Volume of Cuneus and Lingual Gyrus in Schizophrenia Patients with Tardive Dyskinesia is Associated with Abnormal Involuntary Movement. Sci Rep 8:12884
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
Chen, Shuo; Xing, Yishi; Kang, Jian et al. (2018) Bayesian modeling of dependence in brain connectivity data. Biostatistics :

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