1269 – Reduced Fractional Anisotropy In The Uncinate Fasciculus In Patients With Major Depression Carrying The Met-allele Of The Val66met Brain-derived Neurotrophic Factor Genotype
Bibliographic record
Abstract
Objective Experimental studies support a neurotrophic hypothesis of major depressive disorder (MDD). The aim of this study was to determine the effect of Val66Met brain derived neurotrophic factor (BDNF) polymorphism on the white matter fibre tracts connecting hippocampus and amygdala with the prefrontal lobe in a sample of patients with MDD and healthy controls. Methods Thirty seven patients with MDD and 42 healthy volunteers were recruited. Diffusion tensor imaging (DTI) data with 61 diffusion directions were obtained with MRI 3 Tesla scanner. Deterministic tractography was applied with ExploreDTI and Val66Met BDNF SNP (rs6265) was genotyped. Fibre tracts connecting the hippocampus and amygdala with the prefrontal lobe, namely uncinate fasciculus, fornix and cingulum were analysed. Results A significant interaction was found in the uncinate fasciculus (UF) between BDNF alleles and diagnosis. Patients carrying the BDNF met-allele had smaller fractional anisotropy (FA) in the UF compared to those patients homozygous for valallele and compared to healthy subjects carrying the met-allele. A significant 3-way interaction was detected between region of the cingulum (dorsal, rostral and parahippocampal regions), brain hemisphere and BDNF genotype. Larger FA was detectable in the left rostral cingulum for met-allele carriers when compared to val/val alelle carriers. Conclusions We provide evidence for the importance of the neurotrophic involvement in limbic and prefrontal connections. The met-allele of the BDNF polymorphism seems to render subjects more vulnerable for dysfunctions associated with the UF, a tract known to be related to negative emotional-cognitive processing bias, declarative memory problems, and autonoetic self awareness.
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 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 | 0.000 | 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) | 0.003 | 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".