Diffusion Tensor Imaging to Analyze White Matter Tract Abnormalities in Major Psychiatric Disorders
Bibliographic record
Abstract
Abstract Diffusion tensor imaging (DTI) has emerged as a valuable tool in studying depression-related brain changes. DTI is a type of magnetic resonance (MR) imaging that maps the diffusion of water molecules to visualize white matter tracts in the brain. By measuring the diffusion of water molecules in brain tissue, DTI provides information about the structural organization of white matter fibers and the integrity of neural pathways. A total of 30 cases with the first episode of severe depression were included in the study. Age- and gender-matched 30 healthy controls were also included. A structured clinical interview was conducted using the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) criteria for the diagnosis of major depression and the Hamilton Rating Scale for Depression was calculated. A 1.5 Tesla (T) MR System was used to determine the fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values of 10 main white matter fiber tracts. Mann–Whitney U test was applied and p-values of all the 10 white matter fiber tracts were calculated. DTI analysis found that FA values were significantly lower (p < 0.05) in patients compared with healthy controls in the fornix (p = 0), cingulum hippocampus (p = 0.011), inferior fronto-occipital fasciculus (p = 0.013), and superior longitudinal fasciculus (p = 0.026). ADC values were significantly higher in the fornix (p = 0.040) and reduced in anterior thalamic radiation (p = 0.011). We concluded that various microstructural changes occur in these white matter tracts, mainly in the fronto-limbic system and play a key role in the pathophysiology of depression. This information can help in developing methods of preventing such alterations, which can reduce the morbidity of depressive patients.
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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 | 0.001 | 0.001 |
| 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".