Neurite Density and Kurtosis in the Gray Matter of People With Early Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Classic models of diffusion-weighted imaging, especially diffusion tensor imaging, are unsuitable for application to the cortical gray matter given its high microstructural complexity. As such, most neuroimaging studies have focused on gross structural effects of schizophrenia, such as cortical thickness differences. More recently developed models, such as neurite orientation dispersion and density imaging (NODDI) and diffusion kurtosis imaging (DKI), incorporate higher-resolution data and may provide more sensitive descriptions of schizophrenia pathology with more specific interpretations. METHODS: We applied the NODDI and DKI models to the cortical gray matter of people with early schizophrenia (n = 54) and healthy control participants (n = 51) from the Human Connectome Project for Early Psychosis dataset. Comparisons between groups were made using region-of-interest and clustering approaches. The effect sizes of these approaches were compared with those of cortical thickness differences. We also investigated the relationship between these parameters and lifetime antipsychotic usage. RESULTS: Cortical thickness differences were most prominent between groups in terms of global effect size and spatial extent. We also observed a diffuse, right hemisphere-dominant increase in mean kurtosis and isotropic diffusion fraction throughout the gray matter, which was not fully explained by partial volume effects. Additionally, a lower neurite density index (NDI) correlated with greater lifetime antipsychotic usage. CONCLUSIONS: Increases in mean kurtosis and isotropic diffusion fraction are both markers of schizophrenia, consistent with inflammation models of the gray matter in schizophrenia. NDI reduction, reflecting intraneurite pathology, becomes prominent only in individuals with greater disease burden.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".