The genetic and neuroimaging correlates of persistent negative symptoms in first-episode psychosis
Bibliographic record
Abstract
Background: About one third of first-episode psychosis (FEP) patients will develop persistent negative symptoms (PNS) which prevent remission from the disorder and diminish patients' functional outcomes. In addition, there is mounting evidence that negative symptoms and PNS are not unique to schizophrenia, and genome-wide association studies (GWAS) have uncovered a number of shared risk genes between schizophrenia and bipolar disorder (BPD). Although GWAS have identified an array of risk genes for psychotic disorders, information about the phenotypic effects of such genes are scant. Additionally, while imaging studies have identified neuroanatomical associations with negative symptoms and PNS, few studies have integrated both genetics and imaging. Objectives: The overall objective of this thesis research was to examine the genetic and neuroimaging correlates of PNS in a first-episode of psychosis (FEP) cohort. The following studies were performed: 1. The first of two studies sought to i) examine whether previously-identified BPD risk genes are associated with negative symptoms in schizophrenia, and ii) to examine whether such genes influence brain morphology. 2. The second study focused on PNS and aimed to i) assess whether previously-identified risk genes for schizophrenia and bipolar disorder might have a phenotypic association with PNS; and ii) examine any neuroanatomical associations of these genes. Methods: 1. Patients experiencing FES (n=133) were genotyped for a number previously-identified BPD risk genes (n=21), and a series of ANCOVAs examined the association between negative symptom severity – as measured by the Scale for the Assessment of Negative Symptoms (SANS) – and genotype. A subset of participants (n=61) underwent a structural 1.5T MRI T1 scan, analyzed for surface area changes via the CIVET pipeline and LPBA40 atlas. 2. FEP participants (affective and non-affective) were recruited from PEPP-Montreal at the Douglas Institute (N=192). Participants underwent thorough symptom evaluations and were classified as either PNS (n=33) or non-PNS (n=148), and were genotyped for 44 risk alleles associated with schizophrenia and bipolar disorder. Additionally, a subset of 90 participants underwent a T1 structural MRI scan. A stepwise regression was performed to identify SNPs predicting PNS, and significant SNPs were included in a series of MANCOVAs to examine associated surface area and cortical thickness abnormalities. Results: 1. We observed a significant association between negative symptom severity and the BPD risk gene FOXO6 (rs4660531). Individuals with the CC genotype presented significantly higher negative symptoms (Cohen's d=0.46, F=5.854, p=.017) and significantly smaller surface area within the right middle orbitofrontal gyrus (Cohen's d=0.69, F=7.289, p=.009) compared to carriers of allele A. 2. The minor alleles of SNPs within three genes were predictive of PNS: CNNM2, C9orf5, and FLJ16124, and all three SNPs were associated with surface area and/or cortical thickness reductions in regions previously associated with PNS. Conclusion: 1. Lacking the FOXO6 minor allele was associated with an increase in negative symptoms and surface area reduction in the right orbitofrontal gyrus – an area previously associated with negative symptoms – suggesting that presence of the FOXO6 minor allele confers resistance against negative symptoms and associated neuroanatomical changes in FES. 2. These results suggest that certain SNPs previously-associated with risk for schizophrenia and bipolar disorder are associated with the PNS phenotype in first-episode psychosis, and these SNPs are also associated with neuroanatomical abnormalities previously-associated with negative symptoms and PNS. Together, these two studies provide encouraging evidence that the emergence of negative symptoms may be traced to specific biomarkers, which may ultimately inform future treatment of these currently debilitating symptoms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".