<i>In Vivo</i> Characterization of Traumatic Brain Injury Neuropathology with Structural and Functional Neuroimaging
Bibliographic record
Abstract
Quantitative neuroimaging is increasingly used to study the effects of traumatic brain injury (TBI) on brain structure and function. This paper reviews quantitative structural and functional neuroimaging studies of patients with TBI, with an emphasis on the effects of diffuse axonal injury (DAI), the primary neuropathology in TBI. Quantitative structural neuroimaging has evolved from simple planometric measurements through targeted region-of-interest analyses to whole-brain analysis of quantified tissue compartments. Recent studies converge to indicate widespread volume loss of both gray and white matter in patients with moderate-to-severe TBI. These changes can be documented even when patients with focal lesions are excluded. Broadly speaking, performance on standard neuropsychological tests of speeded information processing are related to these changes, but demonstration of specific brain-behavior relationships requires more refined experimental behavioral measures. The functional consequences of these structural changes can be imaged with activation functional neuroimaging. Although this line of research is at an early stage, results indicate that TBI causes a more widely dispersed activation in frontal and posterior cortices. Further progress in analysis of the consequences of TBI on neural structure and function will require control of variability in neuropathology and behavior.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".