Bibliographic record
Abstract
Brain stimulation therapies have a long history, beginning with the development of electroconvulsive (also known as electroshock) therapy in the first half of the previous century, progressing with the development of other, more refined, methods to induce electrical currents in the living brain. Those methods are used to treat various psychiatric diseases, ranging from depression and bipolar disorder to posttraumatic stress disorder and schizophrenia. Obviously, a better understanding of the mechanism of action of these treatments will allow optimization of stimulation parameters to enhance and optimize the therapeutic effects. Such understanding would also be an important milestone in development of new versions of stimulation tools, to improve and adapt existing protocols individually to each patient, and to develop objective, quantifiable measures of therapeutic outcome. An appealing possibility to explain the effects of electricity-based therapies is their ability to change the brain network activity over time; that is, to enhance or suppress global neuronal activity or specifically to encourage the creation and strengthening of existing connections.The 2 papers presented in this issue by Dr Zafiris J Daskalakis' group (see Voineskos et al1 and Rajji et al2) draw a comprehensive and consistent theory on the relation between schizophrenia, brain plasticity, and effectiveness of brain stimulation therapies. In this sense, schizophrenia is an interesting touchstone to examine the issue of brain stimulation plasticity-based treatments because it encompasses significant deficits in higher cognitive functions, including learning-related plastic changes.The first article1 is a comprehensive review demonstrating neuroplastic impairments in schizophrenia as reflected in transcranial magnetic stimulation (TMS) studies that include measurements of neural excitability and inhibition levels by various paradigms of TMS. Dr Daskalakis and colleagues' nicely introduce the concept of brain plasticity impairments in schizophrenia, and suggest that cognitive deficits accompanying this disease, especially in executive attention and working memory, are caused by reduced ability of the brain to perform adaptation of its activity in response to experience and training. Patients with schizophrenia, compared with healthy subjects, do not show adaptation of their response to motor cortex magnetic stimulation following a motor training protocol. They do not show acquisition and strengthening of association between peripheral motor stimulation and motor responses induced by TMS in a paired associative stimulation (PAS) paradigm. In these studies, patients with schizophrenia, unlike healthy subjects, show no increase or decrease in activity as a result of repetitive stimulation of the motor cortex or adjacent areas. The authors' suggest alterations in glutamate (via N-methyl-D-aspartate receptors), gammaaminobutyric acid, and dopamine neurotransmission for explaining these neuroplastic impairments as these systems had been linked with long-term plasticity processes in basic electrophysiological studies and with morbidity of schizophrenia in neuropharmacological studies.The second article, by Dr Tarek K Rajji and colleagues,2 describes current knowledge on various brain stimulation (TMS and direct electrical) approaches used in the treatment of schizophrenia. Search engines were scanned for relevant articles, and the results, concerning repetitive TMS, thetaburst stimulation, PAS, and transcranial direct current stimulation, are summarized comprehensively. …
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".