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Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illness

Molecular Psychiatry volume 25, pages 2119–2129 (2020)Cite this article

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A Correction to this article was published on 16 January 2019

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Abstract

Neuroimaging studies of psychotic disorders have demonstrated abnormalities in structural and functional connectivity involving widespread brain networks. However, these group-level observations have failed to yield any biomarkers that can provide confirmatory evidence of a patient’s current symptoms, predict future symptoms, or predict a treatment response. Lack of precision in both neuroanatomical and clinical boundaries have likely contributed to the inability of even well-powered studies to resolve these key relationships. Here, we employed a novel approach to defining individual-specific functional connectivity in 158 patients diagnosed with schizophrenia (n = 49), schizoaffective disorder (n = 37), or bipolar disorder with psychosis (n = 72), and identified neuroimaging features that track psychotic symptoms in a dimension- or disorder-specific fashion. Using individually specified functional connectivity, we were able to estimate positive, negative, and manic symptoms that showed correlations ranging from r = 0.35 to r = 0.51 with the observed symptom scores. Comparing optimized estimation models among schizophrenia spectrum patients, positive and negative symptoms were associated with largely non-overlapping sets of cortical connections. Comparing between schizophrenia spectrum and bipolar disorder patients, the models for positive symptoms were largely non-overlapping between the two disorder classes. Finally, models derived using conventional region definition strategies performed at chance levels for most symptom domains. Individual-specific functional connectivity analyses revealed important new distinctions among cortical circuits responsible for the positive and negative symptoms, as well as key new information about how circuits underlying symptom expressions may vary depending on the underlying etiology and illness syndrome from which they manifest.

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Acknowledgements

This work was supported by the NIH grants K01MH111802, K23MH104515, 1R01NS091604, P50MH106435, Beijing Municipal Science and Technology Commission No. Z161100002616009, National Natural Science Foundation of China grants No. 81790652.

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Author notes

  1. These authors contributed equally: Danhong Wang, Meiling Li, Meiyun Wang.

  2. These authors jointly supervised this work: Justin T. Baker, Hesheng Liu.

Authors and Affiliations

  1. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA

    Danhong Wang, Meiling Li, Franziska Schoeppe, Jianxun Ren & Hesheng Liu

  2. Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China

    Meiling Li & Huafu Chen

  3. Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, China

    Meiyun Wang & Hesheng Liu

  4. Psychotic Disorders Division, McLean Hospital, Harvard Medical School, Belmont, MA, USA

    Dost Öngür, Roscoe O. Brady Jr. & Justin T. Baker

  5. Institute for Research and Medical Consultations, Imam Abdulahman Bin Faisal University, Dammam, Saudi Arabia

    Hesheng Liu

  6. Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China

    Hesheng Liu

  7. Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

    Roscoe O. Brady Jr.

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  1. Danhong Wang
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Correspondence to Justin T. Baker or Hesheng Liu.

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Wang, D., Li, M., Wang, M. et al. Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illness. Mol Psychiatry 25, 2119–2129 (2020). https://doi.org/10.1038/s41380-018-0276-1

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