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Record W4407387193 · doi:10.1093/ijnp/pyae059.296

IS THERE AN OPTIMAL ELECTRODE PLACEMENT FOR PATIENTS WITH SCHIZOPHRENIA UNDERGOING ELECTROCONVULSIVE THERAPY?

2025· article· en· W4407387193 on OpenAlexaboutno aff
Weng Jun Tan, Jenies Hui Xin Foo, Kimberly Wan Xin Choo

Bibliographic record

VenueThe International Journal of Neuropsychopharmacology · 2025
Typearticle
Languageen
FieldMedicine
TopicElectroconvulsive Therapy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsElectroconvulsive therapySchizophrenia (object-oriented programming)MedicinePsychiatryPsychology

Abstract

fetched live from OpenAlex

Abstract Background Electroconvulsive therapy (ECT) using the 3 common electrode placements, namely bitemporal, bifrontal and right unilateral (RUL) modalities, has been shown to be efficacious in symptom-reduction in patients with schizophrenia (Ali et al., 2019). However, the most efficacious modality for the treatment of schizophrenia has not been ascertained. Furthermore, the benefit of switching ECT modalities after poor response to the initial electrode placement has not been well-studied. Aims And Objectives We hypothesise that different patients with schizophrenia respond well to a particular ECT modality but not to another. These patients would benefit from switching ECT modalities once a lack of response to the initial modality is identified. Hence, we aim to illustrate the twin issues of the optimal ECT modality and the effect of switching ECT modalities after initial non-response in patients with schizophrenia. Method We report a case series of two distinct patients with schizophrenia who underwent multiple courses of both bifrontal and RUL ECT. Their response to ECT was objectively assessed by comparing their scores on the Brief Psychiatric Rating Scale (BPRS) and the Global Assessment of Functioning (GAF) scale prior to the commencement of ECT with their scores after the 6th and 12th sessions of ECT. The Montreal Cognitive Assessment (MoCA) was used to assess for cognitive side effects of ECT. Results Both patients showed good response to at least one previous course of bifrontal ECT. Subsequently, they were given 6 sessions of RUL ECT with the aim to minimise cognitive side effects, but their response to RUL ECT was poor. However, after they were switched back to bifrontal ECT, they showed marked improvement in their BPRS and GAF scores. Furthermore, one of the patients had a better MoCA score after he was switched back to bifrontal ECT than when he had received RUL ECT. Discussion And Conclusion As both patients had lacked response to RUL ECT but consistently responded well to bifrontal ECT, we believe that different patients with schizophrenia only respond well to a certain type of ECT modality. This is possibly because the pattern and degree of brain stimulation can be affected by the type of electrode placement which influences the strength and distribution of the electric field generated by the ECT stimulus (Bai et al., 2017; Bai et al., 2019; Lee et al., 2010), as well as anatomical differences such as head-size and skull-thickness (Bai et al., 2019; Fridgeirsson et al., 2021). In conclusion, there is no optimal ECT modality for the treatment of schizophrenia currently. Patients present with a variety of demographics, anatomy and severity of symptoms, and hence we believe that the prescription of ECT should be individualised, rather than employ a “one size fits all” approach. If response to a particular type of ECT modality is insufficient after 6 sessions, a switch to a different modality should be strongly considered as adequate response may only be achieved using a different ECT modality that is unique to the individual patient. References [1]Ali, SA. et al. (2019) ‘Electroconvulsive therapy and schizophrenia: a systematic review’, Mol Neuropsychiatry. 5(2), pp. 75-83. doi:10.1159/000497376. [2]Bai, S. et al. (2017) ‘Computational models of bitemporal, bifrontal and right unilateral ECT predict differential stimulation of brain regions associated with efficacy and cognitive side effects’, Eur Psychiatry. 41, pp. 21-29. doi:10.1016/j.eurpsy.2016.09.005. [3]Bai, S. et al. (2019) ‘Computational comparison of conventional and novel electroconvulsive therapy electrode placements for the treatment of depression’, Eur Psychiatry. 60, pp. 71-78. doi:10.1016/j.eurpsy.2019.05.006. [4]Lee, WH. et al. (2010) ‘Regional electric field induced by electroconvulsive therapy: a finite element simulation study’, Annu Int Conf IEEE Eng Med Biol Soc. 2010, pp. 2045-2048. doi:10.1109/IEMBS.2010.5626553. [5]Fridgeirsson, EA. et al. (2021) ‘Electric field strength induced by electroconvulsive therapy is associated with clinical outcome’, Neuroimage Clin. 30, pp. 102581. doi:10.1016/j.nicl.2021.102581.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.337
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
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