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Related Concept Videos

Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
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Psychosis: Pathophysiology of Schizophrenia and Other Psychotic Disorders01:27

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Schizophrenia is a neurodevelopmental disorder whose origins are rooted in complex genetic components. Despite our burgeoning understanding, the pathophysiology of this disorder remains incompletely deciphered.
Researchers have identified genetic factors that increase susceptibility to schizophrenia, underscoring the intricate interplay between genetics and environment in disease development. At the core of schizophrenia's pathophysiology is excessive dopaminergic neurotransmission within...
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Mapping the Psychosis Spectrum - Imaging Neurosubtypes from Multi-Scale Functional Network Connectivity.

Ram Ballem1,2, Pablo Andrés-Camazón2,3, Kyle M Jensen1,2

  • 1Georgia State University, Atlanta, USA.

Biorxiv : the Preprint Server for Biology
|April 8, 2025
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Summary
This summary is machine-generated.

Researchers identified three distinct Psychosis Imaging Neurosubtypes (PINs) using brain imaging data. These subtypes show unique connectivity patterns and cognitive profiles, offering a more biologically grounded classification than current diagnostic systems.

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Area of Science:

  • Neuroimaging
  • Psychiatry
  • Computational Neuroscience

Background:

  • Current diagnostic systems for psychosis lack biological specificity.
  • Identifying homogeneous subgroups is crucial for understanding psychosis heterogeneity.

Purpose of the Study:

  • To identify Psychosis Imaging Neurosubtypes (PINs) using resting-state fMRI.
  • To characterize distinct neurobiological, cognitive, and clinical profiles of these subtypes.
  • To assess the construct validity and familial aggregation of identified neurosubtypes.

Main Methods:

  • Utilized resting-state fMRI data from 2103 participants (psychosis, relatives, controls).
  • Computed subject-specific multiscale functional network connectivity (msFNC).
  • Derived a low-dimensional neurobiological subspace (Latent Network Connectivity - LNC) and applied unsupervised learning to identify PINs.

Main Results:

  • Identified three distinct PINs (PIN-1, PIN-2, PIN-3) with unique connectivity patterns and cognitive/clinical profiles across all DSM diagnoses.
  • PIN-1 showed the most cognitive impairment with specific hypoconnectivity/hyperconnectivity patterns.
  • PIN-2 was most cognitively preserved with distinct hypoconnectivity.
  • PIN-3 exhibited intermediate cognitive function with mixed connectivity patterns.
  • Relatives showed significantly higher alignment with their affected family member's PIN than with DSM-based classifications, supporting biological validity.

Conclusions:

  • Cognitive performance and brain connectivity patterns reliably define distinct neurobiological subtypes of psychosis.
  • These imaging-derived neurosubtypes demonstrate familial aggregation, suggesting a genetic or biological basis.
  • PINs offer a more biologically informed approach to understanding the psychosis spectrum compared to traditional diagnostic categories.