Intravenous and Non-invasive Drug Delivery to the Mouse Basal Forebrain Using MRI-guided Focused Ultrasound
Bibliographic record
Abstract
Basal forebrain cholinergic neurons (BFCNs) regulate circuit dynamics underlying cognitive processing, including attention, memory, and cognitive flexibility. In Alzheimer's disease and related neurodegenerative conditions, the degeneration of BFCNs has long been considered a key player in cognitive decline. The cholinergic system thus represents a key therapeutic target. A long-standing obstacle for the development of effective cholinergic-based therapies is not only the production of biologically active compounds but also a platform for safe and efficient drug delivery to the basal forebrain. The blood-brain barrier (BBB) presents a significant challenge for drug delivery to the brain, excluding approximately 98% of small-molecule biologics and nearly 100% of large-molecule therapeutic agents from entry into the brain parenchyma. Current modalities to achieve effective drug delivery to deep brain structures, such as the basal forebrain, are particularly limited. Direct intracranial injection via a needle or catheter carries risks associated with invasive neurosurgery. Intra-arterial injection of hyperosmotic solutions or therapeutics modified to penetrate the BBB using endogenous transport systems lack regional specificity, which may not always be desirable. Intranasal, intrathecal, and intraventricular administration have limited drug distribution beyond the brain surface. Here, we present a protocol for non-invasively, locally, and transiently increasing BBB permeability using MRI-guided focused ultrasound (MRIgFUS) in the murine basal forebrain for delivery of therapeutic agents targeting the cholinergic system. Ongoing work in preclinical models and clinical trials supports the safety and feasibility of MRIgFUS-mediated BBB modulation as a promising drug delivery modality for the treatment of debilitating neurological diseases.
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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".