Two schizophrenia imaging signatures and their associations with cognition, psychopathology, and genetics in the general population

Abstract

The prevalence and significance of schizophrenia-related phenotypes at the population-level are debated in the literature. Here we assess whether two recently reported neuroanatomical signatures of schizophrenia, signature 1 with widespread reduction of gray matter volume, and signature 2 with increased striatal volume, could be replicated in an independent schizophrenia sample, and investigate whether expression of these signatures can be detected at the population-level and how they relate to cognition, psychosis spectrum symptoms, and schizophrenia genetic risk. This cross-sectional study used an independent schizophrenia-control sample (n=347; age 16-57 years) for replication of imaging signatures, and then examined two independent population-level datasets: Philadelphia Neurodevelopmental Cohort [PNC; n=359 typically developing (TD) and psychosis-spectrum symptoms (PS) youth] and UK Biobank (UKBB; n=836; age 44-50 years) adults. We quantified signature expression using support-vector machine learning, and compared cognition, psychopathology, and polygenic risk between signatures. Two neuroanatomical signatures of schizophrenia were replicated. Signature 1 but not signature 2 was significantly more common in youth with PS than TD youth, whereas signature 2 frequency was similar. In both youth and adults, signature 1 had worse cognitive performance than signature 2. Compared to adults with neither signature, adults expressing signature 1 had elevated schizophrenia polygenic risk scores, but this was not seen for signature 2. We successfully replicate two neuroanatomical signatures of schizophrenia, and describe their prevalence in population-based samples of youth and adults. We further demonstrate distinct relationships of these signatures with psychosis symptoms, cognition, and genetic risk, potentially reflecting underlying neurobiological vulnerability.

Two distinct subtypes of obsessive compulsive disorder revealed by heterogeneity through discriminative analysis

“…The optimal number of clusters was determined when ARI was the largest. As done in previous studies (Chand et al, 2020(Chand et al, , 2022Varol et al, 2017), we identified optimal subtypes of OCD from 1 to 6 using 10-fold cross validation where age, sex, and education level were included as covariates. The workflow is shown in Figure 1.…”

Amyloid-beta biomarkers in Braak stages and their predictive relationships with cognitive impairment: Support vector machine and deep learning approaches

“…One challenge to the appropriate quantification of PET images is the partial volume effect [35] which was accounted for in our study by using a state-of-the-art parallel level set (PLS) regularization-based partial volume correction method [36]. To harmonize multi-scanner datasets, we relied on our previously used and validated approach [27,32] in which we used data from all study participants to compute the coefficients accounting for the effect of factors such as site, age and sex and apply them to each individual participant [37]. After correcting for the site, age, and sex covariates, we produced the harmonized DVR and used that in our analyses.…”

Immune-Related Genomic Schizophrenic Subtyping Identified in DLPFC Transcriptome

“…In comparison to other schizophrenics, those with more negative symptoms exhibit more widespread cortical volume decreases [16][17][18]. However, other neuroanatomical studies have identified subtypes based on brain structure differences not otherwise detectible by clinical phenotyping [3,4,19]. The biologic etiology of schizophrenia remains unclear, and the clinical heterogeneity associated with this disorder may have frustrated attempts to gain insights into the pathophysiology.…”

“…Many recent studies have provided evidence that schizophrenia may be etiologically heterogeneous and can be divided into at least two classes with different pathophysiological signatures via behavioral and cognitive symptoms [ 1 , 2 ], cortical structure [ 3 , 4 ], gene expression patterns [ 5 , 6 , 7 , 8 ], and serum inflammation markers [ 9 , 10 , 11 , 12 ]. Despite this, most studies continue to evaluate schizophrenics as a pooled group [ 13 , 14 , 15 ].…”

“…In comparison to other schizophrenics, those with more negative symptoms exhibit more widespread cortical volume decreases [ 16 , 17 , 18 ]. However, other neuroanatomical studies have identified subtypes based on brain structure differences not otherwise detectible by clinical phenotyping [ 3 , 4 , 19 ].…”