Editorial: New insights into schizophrenia-related neural and behavioral phenotypes

Deciphering the Heterogeneity of Schizophrenia: A Multimodal and Multivariate Neuroimaging Framework for Unveiling Brain-Symptom Relationships and Underlying Subtypes

BACKGROUND AND HYPOTHESIS

Schizophrenia manifests large heterogeneities in either symptoms or brain abnormalities. However, the neurobiological basis of symptomatic diversity remains poorly understood. We hypothesized that schizophrenia's diverse symptoms arise from the interplay of structural and functional alterations across multiple brain regions, rather than isolated abnormalities in a single area.

STUDY DESIGN

A total of 495 schizophrenia patients and 507 healthy controls from 8 sites were recruited. Five symptomatic dimensions of schizophrenia patients were derived from the Positive and Negative Syndrome Scale. Multivariate canonical correlation analysis was introduced to identify symptom-related multimodal magnetic resonance imaging composite indicators (MRICIs) derived from gray matter volume, functional connectivity strength, and white matter fractional anisotropy. The intergroup differences in MRICIs were compared, and the paired-wise correlations between symptom dimensions and MRICIs were resolved. Finally, K-means clustering was used to identify the underlying biological subtypes of schizophrenia based on MRICIs.

STUDY RESULTS

Canonical correlation analysis identified 15 MRICIs in schizophrenia that were specifically contributed by the neuroimaging measures of multiple regions, respectively. These MRICIs can effectively characterize the complexity of symptoms, showing correlations within and across symptom dimensions, and were consistent across both first-episode and chronic patients. Additionally, some of these indicators could moderately differentiate schizophrenia patients from healthy controls. K-means clustering identified 2 schizophrenia subtypes with distinct MRICI profiles and symptom severity.

CONCLUSIONS

Symptom-guided multimodal and multivariate MRICIs could decode the symptom heterogeneity of schizophrenia patients and might be considered as potential biomarkers for schizophrenia.

Emergence of Extracellular Vesicles as "Liquid Biopsy" for Neurological Disorders: Boom or Bust

Extracellular vesicles (EVs) have emerged as an attractive liquid biopsy approach in the diagnosis and prognosis of multiple diseases and disorders. The feasibility of enriching specific subpopulations of EVs from biofluids based on their unique surface markers has opened novel opportunities to gain molecular insight from various tissues and organs, including the brain. Over the past decade, EVs in bodily fluids have been extensively studied for biomarkers associated with various neurological disorders, such as Alzheimer's disease, Parkinson's disease, schizophrenia, bipolar disorder, major depressive disorders, substance use disorders, human immunodeficiency virus-associated neurocognitive disorder, and cancer/treatment-induced neurodegeneration. These studies have focused on the isolation and cargo characterization of either total EVs or brain cells, such as neuron-, astrocyte-, microglia-, oligodendrocyte-, pericyte-, and endothelial-derived EVs from biofluids to achieve early diagnosis and molecular characterization and to predict the treatment and intervention outcomes. The findings of these studies have demonstrated that EVs could serve as a repetitive and less invasive source of valuable molecular information for these neurological disorders, supplementing existing costly neuroimaging techniques and relatively invasive measures, like lumbar puncture. However, the initial excitement surrounding blood-based biomarkers for brain-related diseases has been tempered by challenges, such as lack of central nervous system specificity in EV markers, lengthy protocols, and the absence of standardized procedures for biological sample collection, EV isolation, and characterization. Nevertheless, with rapid advancements in the EV field, supported by improved isolation methods and sensitive assays for cargo characterization, brain cell-derived EVs continue to offer unparallel opportunities with significant translational implications for various neurological disorders. SIGNIFICANCE STATEMENT: Extracellular vesicles present a less invasive liquid biopsy approach in the diagnosis and prognosis of various neurological disorders. Characterizing these vesicles in biofluids holds the potential to yield valuable molecular information, thereby significantly impacting the development of novel biomarkers for various neurological disorders. This paper has reviewed the methodology employed to isolate extracellular vesicles derived from various brain cells in biofluids, their utility in enhancing the molecular understanding of neurodegeneration, and the potential challenges in this research field.