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Record W3216030351 · doi:10.1016/j.brs.2021.10.281

Different effects of dopaminergic medications on subthalamic beta bursts and non-oscillatory fractal components in parkinson’s disease: a longitudinal study

2021· article· en· W3216030351 on OpenAlexaff
Ghazaleh Darmani, Neil M. Drummond, Hamidreza Ramezanpour, Ke Zeng, Kaviraja Udupa, Can Sarica, William D. Hutchison, Andrés M. Lozano, Alfonso Fasano, Robert Chen

Bibliographic record

VenueBrain stimulation · 2021
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsUniversity Health NetworkUniversity of TorontoYork UniversityToronto Western Hospital
Fundersnot available
KeywordsSubthalamic nucleusDopaminergicParkinson's diseaseLocal field potentialDeep brain stimulationLevodopaBeta RhythmBETA (programming language)NeuroscienceMedicinePsychologyDopamineInternal medicineElectroencephalographyDiseaseComputer science

Abstract

fetched live from OpenAlex

Despite a variety of different treatment options for major depressive disorder (MDD), many patients do not experience adequate symptom relief.Moving from the standard one-size-fits-all treatment prescription towards stratifying patients to different interventions by means of biomarkers, could aid in increasing clinical remission.We recently developed a clinically implementable and easily interpretable biomarker (Brainmarker-I) based on the individual alpha peak frequency (iAPF) measured during resting-state electroencephalography (EEG) in a large heterogeneous dataset (N¼4249), and conducted blinded out-ofsample validations in two independent samples, successfully predicting remission to different pharmaceutical and non-pharmaceutical interventions of attention-deficit/hyperactivity-disorder.Next, we applied Brainmarker-I to several datasets to predict remission to different MDD treatments including rTMS (10Hz left DLPFC and 1Hz right DLPFC) and pharmaceutical interventions (sertraline, escitalopram, venlafaxine).Positive predictive values (PPVs) were employed to indicate the direction of treatment stratification.Normalized PPVs were calculated to improve comparability of predicted increase in remission rates across datasets.As demonstrated in earlier work, an iAPF close to the stimulation frequency of 10Hz at the site of stimulation best predicted remission to 10Hz rTMS, with an increase in predicted normalized remission rate (normalized PPV) of 24%.A relatively lower iAPF suggested an increased likelihood of remission to sertraline, while individuals with a relatively higher iAPF were more likely to remit to 1Hz rTMS.Escitalopram and venlafaxine were exploratively examined in the same way, and results are discussed.Here we present a transdiagnostic treatment stratification biomarker that is capable of predicting differential treatment outcome in patient subgroups, and that is ready for implementation in clinical practice.Brainmarker-I represents a first step from a one-size-fits-all treatment approach towards personalized psychiatry in depression treatment.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.454

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 science0.0000.000
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 head0.025
GPT teacher head0.291
Teacher spread0.266 · 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 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
Published2021
Admission routes1
Has abstractyes

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