Evidence map›Paper›PMID 39729407›Full record

ArticlePLoS computational biology2024

Therapeutic dose prediction of α5-GABA receptor modulation from simulated EEG of depression severity.

Alexandre Guet-McCreight, Frank Mazza, Thomas D Prevot, Etienne Sibille, Etay Hay

Abstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Alexandre Guet-McCreightKrembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada.ORCID 0000-0002-2377-0337
Frank MazzaKrembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada.
Thomas D PrevotDepartment of Psychiatry, University of Toronto, Toronto, Canada.
Etienne SibilleDepartment of Psychiatry, University of Toronto, Toronto, Canada.
Etay HayKrembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada.ORCID 0000-0001-8136-6124

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Treatment for major depressive disorder (depression) often has partial efficacy and a large portion of patients are treatment resistant. Recent studies implicate reduced somatostatin (SST) interneuron inhibition in depression, and new pharmacology boosting this inhibition via positive allosteric modulators of α5-GABAA receptors (α5-PAM) offers a promising effective treatment. However, testing the effect of α5-PAM on human brain activity is limited, meriting the use of detailed simulations. We utilized our previous detailed computational models of human depression microcircuits with reduced SST interneuron inhibition and α5-PAM effects, to simulate EEG of individual microcircuits across depression severity and α5-PAM doses. We developed machine learning models that predicted optimal dose from EEG with high accuracy and recovered microcircuit activity and EEG. This study provides dose prediction models for α5-PAM administration based on EEG biomarkers of depression severity. Given limitations in doing the above in the living human brain, the results and tools we developed will facilitate translation of α5-PAM treatment to clinical use.

Indexed as

ElectroencephalographyMajor Depressive DisorderReceptors, GABA-AAntidepressive AgentsBrainComputational BiologyComputer SimulationDepressionDose-Response Relationship, DrugHumansInterneuronsMachine LearningModels, NeurologicalSomatostatinAntidepressive AgentsReceptors, GABA-ASomatostatin

Identifiers

PMID39729407
PMCPMC11676559

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.