Neurotransmitters’ white matter mapping unveils the neurochemical fingerprints of stroke
Bibliographic record
Abstract
Distinctive patterns of brain neurotransmission frame determinant circuits for behavior. Understanding the relationship between their damage and the cognitive impairment provoked by brain lesions could provide insights into the pathophysiology and therapeutics of disabling disorders, like stroke. Yet, the challenges of neurotransmitter circuits mapping in vivo have hampered this investigation. Here, we developed an MRI white matter atlas of neurotransmitter circuits and created a method to chart how stroke damages neurotransmitter systems, which distinguishes pre and postsynaptic disruption. Our model, trained and tested in two large stroke patient samples, identified eight clusters with different neurochemical patterns. The associations with patients' cognitive profiles were scarce, denoting that a particular cognitive deficit might have finer underlying neurochemical disturbances that are unfit to the granularity of our analyses. These findings depict stroke neurochemical diaschisis patterns, provide insights into stroke cognitive deficits and potential treatments, and open a new window for tailored neurotransmitter modulation.
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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 | 0.001 | 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".