Magnus Neuromodulation System with Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT) as a Personalized Continuation Therapy for Depression: An Open-Label Pilot Trial
Bibliographic record
Abstract
Background: Brain oscillations, which reflect the organized activity of neuronal assemblies, play a crucial role in brain function and are altered in conditions such as depression, schizophrenia, and stroke.While transcranial electric currents have shown therapeutic potential in modulating these oscillations, the direct effects on brain dynamics during stimulation remain elusive due to the inability to record oscillatory changes concurrently.Moreover, inability to assess brain oscillations during stimulation renders closed-loop paradigms unfeasible.Here, we introduce a promising approach to overcome this limitation and highlight the possibility of influencing brain activity and behavior beyond the motor system.Objectives/Hypothesis: In a series of experiments involving healthy volunteers, we used closed-loop amplitude-modulated transcranial alternating current stimulation (CLAM-tACS) to modulate brain oscillations in motor regions and beyond.Specifically, we investigated whether phaselocked, state-dependent stimulation can enhance or suppress oscillatory activity and related brain functions across different paradigms.Methods: In a series of studies, we employed CLAM-tACS that combines artifact attenuation with real-time phase-tracking and stimulation of brain rhythms.Oscillations across different frequency bands, such as theta, frontoparietal alpha or central mu, were targeted.Results: Across multiple experiments, phase-dependent enhancement and suppression of oscillations were observed.We found consistent effects on behavioral outcome measures that correlated with specific stimulation phase angles.Conclusions: Our findings suggest that CLAM-tACS can be effectively applied beyond the motor system to modulate brain function and behavior.However, limitations of the approach have to be further explored and application in clinical populations tested.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".