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Record W4388296073 · doi:10.1101/2023.10.31.23297406

Cortical network mechanisms in subcallosal cingulate deep brain stimulation for depression

2023· preprint· en· W4388296073 on OpenAlexafffund
Maximilian Scherer, IE Harmsen, Nardin Samuel, GJB Elias, Jürgen Germann, Alexandre Boutet, C. E. MacLeod, Peter Giacobbe, NC Rowland, Andrés M. Lozano, Luka Milosevic

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsKrembil FoundationSunnybrook Health Science CentreToronto Western HospitalMcMaster UniversityUniversity Health NetworkUniversity of TorontoOntario Brain Institute
FundersNational Institutes of HealthCanadian Institutes of Health ResearchAlexander von Humboldt-Stiftung
KeywordsStimulationNeuroscienceDefault mode networkMagnetoencephalographyDeep brain stimulationPsychologyAnterior cingulate cortexBrain activity and meditationInferior frontal gyrusBrain stimulationFunctional magnetic resonance imagingMedicineElectroencephalographyCognitionInternal medicine

Abstract

fetched live from OpenAlex

Abstract Identifying functional biomarkers of clinical success can contribute to therapy optimization, and provide insights into the pathophysiology of treatment-resistant depression and mechanisms underlying the potential restorative effects of subcallosal cingulate deep brain stimulation. Magnetoencephalography data were obtained from 15 individuals who underwent subcallosal cingulate deep brain stimulation for treatment-resistant depression and 25 healthy subjects. The first objective herein was to identify region-specific oscillatory modulations for the identification of discriminative network nodes expressing (i) pathological differences in TRD (responders and non-responders, stimulation-OFF) compared to healthy subjects, which (ii) were counteracted by stimulation in a responder-specific manner. The second objective of this work was to further explore the mechanistic effects of stimulation intensity and frequency. Oscillatory power analyses led to the identification of discriminative regions that differentiated responders from non-responders based on modulations of increased alpha (8-12 Hz) and decreased gamma (32-116 Hz) power within nodes of the default mode, central executive, and somatomotor networks, Broca’s area, and lingual gyrus. Within these nodes, it was also found that low stimulation frequency had stronger effects on oscillatory modulation than increased stimulation intensity. The identified discriminative network profile implies modulation of pathological activities in brain regions involved in emotional control/processing, motor control, and the interaction between speech, vision, and memory, which have all been implicated in depression. This modulated network profile may represent a functional substrate for therapy optimization. Stimulation parameter analyses revealed that oscillatory modulations can be strengthened by increasing stimulation intensity or, to an even greater extent, by reducing frequency.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.321
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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