Neurite Density and Free Water in the Gray and White Matter of Early Psychosis Patients
Bibliographic record
Abstract
Diffusion weighted imaging has been frequently used to characterize the white matter in patients with schizophrenia, but the most commonly used model, diffusion tensor imaging (DTI), is not specific to the histological nature of microstructural changes. This is particularly true in the more complex grey matter tissue. Furthermore, DTI changes have not been consistently reported in early schizophrenia populations, but this does not exclude more subtle changes that may not affect the model fit. Recently developed biophysical models of diffusion, such as the neurite orientation dispersion and density imaging (NODDI) model, may overcome these issues by quantifying specific tissue subcompartments, capturing diffusion profiles characteristic of intra-neurite, extra-neurite, and free water space. We applied the NODDI model to early schizophrenia patients (n=54) and healthy controls (n=51) from the Human Connectome Project - Early Psychosis dataset, investigating both the grey and white matter. We observed a diffuse, increased free water fraction throughout the grey matter, especially in the left insula, though there were not notable changes in the white matter. The spatial variation in the grey matter free water was not fully explained by the partial volume effects from the cerebrospinal fluid, indicating a role for tissue edema. The role of vasogenic processes in early stages of psychosis that may precede white matter anomalies documented in later disease stages warrant further investigation.
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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 | 0.001 |
| 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".