Deep Brain Ultrasound Augments Human Attention
Bibliographic record
Abstract
Abstract Background Deep brain ultrasound offers a novel means of modulating human cognition by noninvasively targeting subcortical structures that were previously accessible only through invasive procedures. While decades of research have mapped cortical circuits of attention, the causal roles of deep hubs such as the basal ganglia and thalamus remain poorly understood in the healthy human brain. Objectives/Hypothesis To test whether low intensity transcranial ultrasound stimulation (TUS) of two nodes in the basal ganglia-thalamic network, the globus pallidus internus (GPi) and the pulvinar, causally alters visual attention. We hypothesized that TUS-induced modulations in attentional performance would be site specific, reflecting distinct circuit functions. Results Across sessions, focal TUS accelerated reaction time in a visual search task, indicating augmented attention. Reaction time improvements were observed after stimulation relative to baseline. A dissociation emerged across sites: both GPi and pulvinar enhanced reaction times, but pulvinar yielded more robust benefits for target present trials at peripheral eccentricities, and improved search efficiency in the same trials. Conclusions These findings provide causal evidence that human attentional control can be steered at deep subcortical sites. TUS offers a practical approach for dissecting circuit level contributions to cognition and a potential noninvasive avenue for enhancing attention and other cognitive or affective functions.
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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 | 0.000 | 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) | 0.005 | 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".