Diffusion basis spectrum imaging in post-hemorrhagic hydrocephalus of prematurity
Bibliographic record
Abstract
ABSTRACT Objective The debilitating neurological deficits of neonatal post-hemorrhagic hydrocephalus (PHH) have been linked to periventricular white matter injury. To improve understanding of the deleterious mechanisms underlying PHH-related brain injury, this study applied diffusion basis spectrum imaging (DBSI) for the first time in neonates, modeling white matter fibers to assess axonal and myelin integrity, fiber density, and extra-fiber pathologies including cellularity, edema, and inflammation. The objectives of the study were to characterize DBSI measures in key periventricular white matter tracts of PHH infants, associate those diffusion measures with ventricular size, and utilize postmortem white matter histology to compare with the MRI findings. Method A prospective cohort of very preterm infants (n=95) underwent MRI at term equivalent age, of which 68 were controls (VPT group), 15 had high-grade intraventricular hemorrhage without hydrocephalus (IVH group), and 12 had PHH (PHH group). DBSI metrics extracted from manually segmented corpus callosum (CC), corticospinal tracts (CST), and optic radiations (OPRA) included fiber level axial diffusivity (FAD), fiber radial diffusivity (FRD), fiber fractional anisotropy (FFA), fiber fraction (FF), restricted fractions (RF), and non-restricted fractions (NRF). All measures were contrasted across groups and correlated with frontal occipital horn ratio (FOHR), a measure of ventricular size. Postmortem immunohistochemistry was performed on the CC of 10 preterm infants (five VPT, three IVH, and two PHH) and two full-term infants who died from non-neurologic causes assessing white matter intra- and extra-fiber pathologies, as well as the integrity of the adjoining ventricular and subventricular zones. Results Except for FF in the CC, there were no differences in all measures between IVH and VPT infants. In the unmyelinated CC, PHH had the lowest FF, FAD, and FFA and the highest RF. In the CC, FOHR related negatively with FAD, FFA, and FF and positively with RF. In the myelinated CST, PHH had the lowest FAD, FFA, and FF and the highest FRD and RF. FOHR related negatively to FAD and FFA and positively with NRF and FRD. In the OPRA, PHH was associated with the lowest FF and the highest RF, NRF, and FAD. FOHR related positively with FAD and NRF and negatively with FF. On postmortem tissues, PHH was associated with the highest white matter cellularity counts, variable amounts of cytoplasmic vacuolation, and the lowest synaptophysin marker intensity. The adjoining ventricular and subventricular zones in PHH had poor cytoarchitecture on H&E staining and relatively increased expression of GFAP and IBA1. Conclusions This initial utilization of DBSI to investigate neonatal brain development and injury demonstrated that PHH was associated with diffuse periventricular white matter injury, with tract-specific microstructural patterns and severity of axonal injury, myelin injury, white matter fiber loss, hypercellularity, and inflammation. While axonal injury was present in the CST and unmyelinated CC, myelin injury occurred only in the CST. The OPRA predominantly showed inflammation with myelin preservation. White matter cellular infiltration occurred in all tracts. Postmortem immunohistochemistry confirmed the imaging findings of decreased axonal fiber density, sparser fiber architecture, and increased cellular infiltration. Larger ventricular size was associated with greater white matter disruption. Building upon these results, DBSI provides an innovative approach for investigating the complex neuropathological effects of PHH on periventricular white matter microstructure.
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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 | 0.001 | 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 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".