Microstructural Findings in White Matter Associated with Cannabis and Alcohol Use in Early-Phase Psychosis: A Diffusion Tensor Imaging and Relaxometry Study
Bibliographic record
Abstract
Accumulating evidence suggests that brain white matter (WM) abnormalities may be central to the pathophysiology of psychotic disorders. In addition, there is evidence that cannabis use and alcohol use each is associated with WM abnormalities. However, there are very limited data on the effects of these substances on WM microstructure in patients with psychosis, especially for those at the early phase of illness. This project aimed to examine the impact of cannabis use and alcohol use on WM tissue in early-phase psychosis (EPP). WM was investigated in 21 patients with EPP using diffusion tensor imaging (DTI) and transverse relaxation time of tissue water (T2), with the primary outcomes being mean fractional anisotropy (FA) and T2. DTI analyses were performed at the full-brain level using tract-based spatial statistics with both DTI and T2 analysis done within a WM volume of interest (VOI) implicated in psychosis (containing the left superior longitudinal fasciculus). Our findings revealed that younger age of onset of regular alcohol use (more than one drink per week) was associated with lower FA values in the left thalamic radiation and left parahippocampal and left amygdalar WM. More frequent lifetime cannabis use was correlated with increased mean full-brain FA. There was no significant relationship found between FA and alcohol or cannabis use within the VOI. Relaxometry analysis revealed trend-level evidence of shortened T2 with later onset of regular alcohol use and with more frequent cannabis use. This study provides novel data demonstrating cortical and subcortical WM findings related to alcohol use in EPP and is the first to combine DTI and relaxometry, relating to this patient population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".