Microstructural Integrity of Hippocampal Subregions Is Impaired after Mild Traumatic Brain Injury
Bibliographic record
Abstract
Mild traumatic brain injury (mTBI) affects a large number of individuals and diffusion tensor imaging can be used to investigate microstructural integrity of brain tissue after mTBI. However, results have varied considerably between studies and gray matter (GM) integrity has been largely neglected in these investigations. Given impaired working memory processing after mTBI and its possible association with Alzheimer's disease, we investigated hippocampal integrity and parcellated this structure into five subregions: subiculum, cornu ammonis (CA) 1, CA 2/3, CA 4/dentate gyrus, and stratum radiatum/lacunosum-moleculare. We also employed shape analysis of bilateral hippocampi to explore whether morphological changes had occurred due to the traumatic injury and conducted neuropsychological memory tests. The sample comprised 15 subjects with mTBI (18-55 years, nine female) and 13 age- and sex-matched healthy control subjects (19-57 years, nine female). Voxelwise analyses showed significantly increased mean diffusivity in patients, compared with controls, in the right hippocampus and three of its five subregions (family-wise error corrected p < 0.05). Additionally, results from probabilistic tractography indicated impaired CA 1 connectivity after mTBI (Benjamini-Hochberg false discovery rate [FDR] corrected p < 0.05). Shape of bilateral hippocampi did not significantly differ between groups (Benjamini-Hochberg FDR corrected p > 0.05). Subjects with mTBI reported more symptoms and performed worse in a non-standard verbal working memory task. Based on these preliminary findings, we were able to demonstrate altered diffusivity of hippocampal subregions following mTBI, indicating impaired GM microstructural integrity. These differences highlight the potential of diffusion imaging for investigation of subtle yet relevant changes in GM microstructure not detected otherwise following mTBI.
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".