Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the same way that beauty lies in the eye of the beholder, what a stimulus does to the brain is determined not simply by the nature of the stimulus, but by the nature of the brain receiving the stimulus, at that instant of time. Therapeutic brain stimulation, over the past decades, typically applied open-loop fixed protocols and has largely ignored this principle. Only recent neuro-technological advancements enabled us to predict the nature of the brain (i.e., the electrophysiological brain state in the next instant of time) with sufficient temporal precision in the range of milliseconds, using feedforward algorithms applied to EEG time series data. This allows stimulation exclusively whenever the targeted brain area is in a prespecified excitability or connectivity state. Preclinical studies showed that repetitive stimulation during a particular brain state (e.g., high-excitability state), but not during other states, results in lasting modification (e.g., long-term potentiation) of the stimulated circuits. Here we survey the evidence that this is also possible at the systems level of human cortex, using EEG-informed transcranial magnetic stimulation (EEG-TMS). We discuss critically opportunities and difficulties to develop brain state-dependent stimulation for more effective long-term modification of pathological brain networks (e.g., in major depressive disorder) than achievable with conventional fixed protocols. The same real-time EEG-TMS technology will allow closing the loop by recording the effects of stimulation. This information may serve for stimulation protocol adaptation to maximize the treatment response. This way, brain states control brain stimulation, introducing a paradigm-shift from open-loop to closed-loop stimulation. In the same way that beauty lies in the eye of the beholder, what a stimulus does to the brain is determined not simply by the nature of the stimulus, but by the nature of the brain receiving the stimulus, at that instant of time. Therapeutic brain stimulation, over the past decades, typically applied open-loop fixed protocols and has largely ignored this principle. Only recent neuro-technological advancements enabled us to predict the nature of the brain (i.e., the electrophysiological brain state in the next instant of time) with sufficient temporal precision in the range of milliseconds, using feedforward algorithms applied to EEG time series data. This allows stimulation exclusively whenever the targeted brain area is in a prespecified excitability or connectivity state. Preclinical studies showed that repetitive stimulation during a particular brain state (e.g., high-excitability state), but not during other states, results in lasting modification (e.g., long-term potentiation) of the stimulated circuits. Here we survey the evidence that this is also possible at the systems level of human cortex, using EEG-informed transcranial magnetic stimulation (EEG-TMS). We discuss critically opportunities and difficulties to develop brain state-dependent stimulation for more effective long-term modification of pathological brain networks (e.g., in major depressive disorder) than achievable with conventional fixed protocols. The same real-time EEG-TMS technology will allow closing the loop by recording the effects of stimulation. This information may serve for stimulation protocol adaptation to maximize the treatment response. This way, brain states control brain stimulation, introducing a paradigm-shift from open-loop to closed-loop stimulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it