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  • 1.
    Paul, Elisabeth
    et al.
    Linköping University, Department of Biomedical and Clinical Sciences, Center for Social and Affective Neuroscience. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Östman Vasko, Lars
    Linköping University, Department of Biomedical and Clinical Sciences, Center for Social and Affective Neuroscience. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Psykiatricentrum, Psykiatriska kliniken i Linköping.
    Heilig, Markus
    Linköping University, Department of Biomedical and Clinical Sciences, Center for Social and Affective Neuroscience. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Psykiatricentrum, Psykiatriska kliniken i Linköping.
    Mayberg, Helen S.
    Icahn Sch Med Mt Sinai, NY USA.
    Hamilton, J. Paul
    Univ Bergen, Norway.
    Towards a multilevel model of major depression: genes, immuno-metabolic function, and cortico-striatal signaling2023In: Translational Psychiatry, E-ISSN 2158-3188, Vol. 13, no 1, article id 171Article, review/survey (Refereed)
    Abstract [en]

    Biological assay and imaging techniques have made visible a great deal of the machinery of mental illness. Over fifty years of investigation of mood disorders using these technologies has identified several biological regularities in these disorders. Here we present a narrative connecting genetic, cytokine, neurotransmitter, and neural-systems-level findings in major depressive disorder (MDD). Specifically, we connect recent genome-wide findings in MDD to metabolic and immunological disturbance in this disorder and then detail links between immunological abnormalities and dopaminergic signaling within cortico-striatal circuitry. Following this, we discuss implications of reduced dopaminergic tone for cortico-striatal signal conduction in MDD. Finally, we specify some of the flaws in the current model and propose ways forward for advancing multilevel formulations of MDD most efficiently.

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