Brain heterogeneity in 1,792 individuals with schizophrenia: effects of illness stage, sites of origin and pathophysiology
Bibliographic record
Abstract
Abstract Importance Schizophrenia is characterized with greater variability beyond the mean differences in brain structures. This variability is often assumed to be static, reflecting the presence of heterogeneous subgroups, but this assumption and alternative explanations remain untested. Objective To test if gray matter volume (GMV) variability is more less in later stages of schizophrenia, and evaluate if a putative ‘spreading pattern’ with GMV deficits originating in one part of the brain and diffusing elsewhere explain the variability of schizophrenia. Design, settings, and participants This study evaluated the regional GMV variability using MRI of 1,792 individuals with schizophrenia and 1,523 healthy controls (HCs), and the association of GMV variability with neurotransmitter and transcriptomic gene data in the human brain. Main outcomes and measures Regional variability was evaluated by comparing the relative variability of patients to controls, using the relative mean-scaled log variability ratio (lnCVR). A network diffusion model (NDM) was employed to simulate the possible processes of GMV alteration across brain regions. Results Compared with HCs, greater lnCVR ( p FDR <0.05) was found in 50 regions in the whole patient group (n=1792; 762 females; mean[SD] age, 29.9[11.9] years), at a much greater frequency ( p=5.0 ×10 −13 ) in the first-episode drug-naïve subsample (73 regions) (n=478; mean[SD] illness duration, 0.548[0.459] years), compared to the chronic medicated subsample (28 regions) (n=398; mean[SD] illness duration, 14.0[10.4] years). The average lnCVR across all regions was greater in the first-episode than chronic subsample ( t =10.8, p= 1.7×10 −7 ). The areas with largest lnCVR were located at frontotemporal cortex and thalamus (first-episode), or hippocampus and caudate (chronic); there was a significant correlation with case-control mean difference ( r =0.367, p= 6.7×10 −4 ). We determined a gene expression map that correlated with the lnCVR map in schizophrenia ( r =0.491, p =0.003). The NDM performed consistently (72.1% patients, p spin <0.001) in replicating GMV changes when simulated and observed values were compared. Conclusion and relevance Brain-based heterogeneity is unlikely to be a static feature of schizophrenia; it is more pronounced at the onset of the disorder but reduced over the long term. Differences in the site of ‘origin’ of GMV changes in individual-level may explain the observed anatomical variability in schizophrenia. Key Points Question No two individuals with schizophrenia have the same anatomical change in the brain. Is this variability a fixed feature of schizophrenia or does it become more pronounced at later stages? Is this variability explained by a putative ‘spreading pattern’ of gray matter deficits originating in one part of the brain and diffusing elsewhere? Findings In 1,792 individuals with schizophrenia, neuroanatomical variability is not a fixed feature; it is more pronounced at the illness onset but less prominent in later stages. The neuroanatomical variability is associated with various molecular and neurobiological processes implicated in the neurodevelopmental etiology of schizophrenia. Differences in the site of ‘origin’ of gray matter deficits in each individual with schizophrenia explains most of the observed variability. Meaning Our work finds support for a space-time interaction along a shared pathophysiological continuum (network-based trans-neuronal diffusion), as a possible explanatory model for inter-subject variability. These findings contribute to the understanding that inter-individual variability in schizophrenia may arise from a common cohesive process that varies in its state (across time) and space (across brain regions). This also raises the question of what dynamic processes contribute to the reducing heterogeneity over time in schizophrenia. Answering this question will be a key test to the neurobiological validity of the concept of schizophrenia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".