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Persistent brain network signatures in psychosis: implications for diagnosis, prognosis, and treatment

Neuropsychopharmacology volume 50, pages 871–872 (2025)Cite this article

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Psychotic disorders result in large societal burden given their early onset and chronicity of highly disabling symptoms. For most individuals, the first episode of psychosis is followed by recurring relapses over the lifetime. This chronic course is associated with deteriorating response to treatment and accumulation of disability. Furthermore, during the chronic phase of the illness, individuals with psychotic disorders are at greater risk of dementia, medical comorbidities, and premature mortality. There is clear agreement in the field that comprehensive specialized treatment is critical during the early psychosis phase – typically referring to the 5 years following the first diagnosis – to mitigate disability during the rest of the course of illness, also known as the chronic phase. Similarly, understanding the neurobiological underpinnings of the changes occurring over the course of illness, especially transitioning from the early into the chronic phase, is necessary to develop targeted preventive and treatment strategies. This issue of Neuropsychopharmacology contains work by Foster et al. [1] comparing functional brain networks implicated in psychotic disorders between the early and the chronic phases.

Previous studies of chronic psychosis show functional hypoconnectivity within and between the default mode, frontoparietal, ventral attention, and somatosensory networks, as well as subcortical structures, compared to healthy controls as noteworthy features [2]. Similar changes in functional connectivity have also been reported in first episode psychosis and early psychosis [2]. However, these patterns of functional disruptions have not yet been directly compared across early and chronic stages. Prior analyses have largely relied on univariate, seed-based analyses. In recent years, data-driven, brain-based predictive models, including connectome-based predictive models, have increasingly been used in neuroscience and psychiatry. These models capture multivariate, whole-brain associations between imaging-derived brain measures and behavioral outcomes, advancing our mechanistic understanding of the neural bases of behaviors and illnesses [3].

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References

  1. Foster M, Ye J, Powers AR, Dvornek N, Scheinost D. Connectome-based predictive modeling of early and chronic psychosis symptoms. Neuropsychopharmacology. 2025 https://doi.org/10.1038/s41386-025-02064-9.

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Funding

This work was supported by the following sources: Northwell Health Advancing Women in Science and Medicine (Career Development Award to ED and Educational Achievement Award to ED), Feinstein Institutes for Medical Research (Emerging Scientist Award to ED), Brain and Behavior Research Foundation (Young Investigator Grant to ED), National Institutes of Mental Health (K23MH127300 to JR). Any opinions or recommendations expressed here are those of the authors and do not necessarily reflect the views of the funders.

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Authors and Affiliations

  1. Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, USA

    Elvisha Dhamala & Jose M. Rubio

  2. Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Uniondale, NY, USA

    Elvisha Dhamala & Jose M. Rubio

Authors
  1. Elvisha Dhamala
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  2. Jose M. Rubio
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ED and JR drafted and edited this manuscript.

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Correspondence to Elvisha Dhamala.

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Competing interests

JR has received research support from Alkermes, advisory board compensation from TEVA, Karuna, Janssen, royalties from UpToDate. ED reports no disclosures.

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Dhamala, E., Rubio, J.M. Persistent brain network signatures in psychosis: implications for diagnosis, prognosis, and treatment. Neuropsychopharmacol. 50, 871–872 (2025). https://doi.org/10.1038/s41386-025-02079-2

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