The Modulation of the Blood-Brain Barrier by Focused Ultrasound Stimulates Oligodendrogenesis
Bibliographic record
Abstract
Focused ultrasound (FUS) combined with intravenous microbubbles (MB) enables precise and reversible modulation of the blood-brain barrier (BBB) to enhance the delivery of therapeutics from the blood to targeted brain areas. Beyond this application, we discovered over a decade ago that FUS-BBB modulation, without the addition of exogenous therapeutics, activates endogenous regenerative events, "most notably" hippocampal neurogenesis. Here, we investigate the effects of FUS on oligodendrogenesis, a key process for myelination and white matter integrity. In adult mice, we targeted FUS-BBB modulation unilaterally to the hippocampus. The proliferation of oligodendrocyte precursor cells (OPCs) was quantified at 1, 4, 7, and 10 days post-treatment and myelinating oligodendrocytes were assessed at 30 days. At 1 and 4 days post-sonication, the proliferation of hippocampal OPCs increased by 6.8-fold and 2.3-fold, respectively; this resulted in a 5.3-fold increase in myelinating oligodendrocytes one month later. Next, we tested the robustness of FUS-induced oligodendrogenesis using an independent experimental design and targeting the striatum in a separate cohort of mice. The proliferation of striatal OPCs increased by 3.9-fold at 7 days post-FUS. This led to a 5.2-fold increase in oligodendrogenesis 30 days post-treatment, as observed in the hippocampus. Finally, we found that treatments at the same FUS parameters but without MB and without altering the BBB, did not lead to the proliferation of OPCs or oligodendrogenesis. Therefore, with these FUS parameters, MB-induced BBB modulation emerged as a key factor that promoted oligodendrogenesis. Given the long-validated application of FUS-BBB modulation for drug delivery, the additional stimulation of oligodendrogenesis broadens the therapeutic potential of this modality for white matter repair.
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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 | 0.000 |
| 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) | 0.001 | 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".