Predicting treatment response to ketamine in treatment-resistant depression using auditory mismatch negativity: Study protocol.
<h4>Background</h4>Ketamine has recently attracted considerable attention for its rapid effects on patients with major depressive disorder, including treatment-resistant depression (TRD). Despite ketamine's promising results in treating depression, a significant number of patients d...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0308413 |
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| author | Josh Martin Fatemeh Gholamali Nezhad Alice Rueda Gyu Hee Lee Colleen E Charlton Milad Soltanzadeh Karim S Ladha Sridhar Krishnan Andreea O Diaconescu Venkat Bhat |
| author_facet | Josh Martin Fatemeh Gholamali Nezhad Alice Rueda Gyu Hee Lee Colleen E Charlton Milad Soltanzadeh Karim S Ladha Sridhar Krishnan Andreea O Diaconescu Venkat Bhat |
| author_sort | Josh Martin |
| collection | DOAJ |
| description | <h4>Background</h4>Ketamine has recently attracted considerable attention for its rapid effects on patients with major depressive disorder, including treatment-resistant depression (TRD). Despite ketamine's promising results in treating depression, a significant number of patients do not respond to the treatment, and predicting who will benefit remains a challenge. Although its antidepressant effects are known to be linked to its action as an antagonist of the N-methyl-D-aspartate (NMDA) receptor, the precise mechanisms that determine why some patients respond and others do not are still unclear.<h4>Objective</h4>This study aims to understand the computational mechanisms underlying changes in the auditory mismatch negativity (MMN) response following treatment with intravenous ketamine. Moreover, we aim to link the computational mechanisms to their underlying neural causes and use the parameters of the neurocomputational model to make individual treatment predictions.<h4>Methods</h4>This is a prospective study of 30 patients with TRD who are undergoing intravenous ketamine therapy. Prior to 3 out of 4 ketamine infusions, EEG will be recorded while patients complete the auditory MMN task. Depression, suicidality, and anxiety will be assessed throughout the study and a week after the last ketamine infusion. To translate the effects of ketamine on the MMN to computational mechanisms, we will model changes in the auditory MMN using the hierarchical Gaussian filter, a hierarchical Bayesian model. Furthermore, we will employ a conductance-based neural mass model of the electrophysiological data to link these computational mechanisms to their neural causes.<h4>Conclusion</h4>The findings of this study may improve understanding of the mechanisms underlying response and resistance to ketamine treatment in patients with TRD. The parameters obtained from fitting computational models to EEG recordings may facilitate single-patient treatment predictions, which could provide clinically useful prognostic information.<h4>Trial registration</h4>Clinicaltrials.gov NCT05464264. Registered June 24, 2022. |
| format | Article |
| id | doaj-art-70673749f1a442148206e58b8392f18b |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-70673749f1a442148206e58b8392f18b2025-02-05T05:32:24ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-01198e030841310.1371/journal.pone.0308413Predicting treatment response to ketamine in treatment-resistant depression using auditory mismatch negativity: Study protocol.Josh MartinFatemeh Gholamali NezhadAlice RuedaGyu Hee LeeColleen E CharltonMilad SoltanzadehKarim S LadhaSridhar KrishnanAndreea O DiaconescuVenkat Bhat<h4>Background</h4>Ketamine has recently attracted considerable attention for its rapid effects on patients with major depressive disorder, including treatment-resistant depression (TRD). Despite ketamine's promising results in treating depression, a significant number of patients do not respond to the treatment, and predicting who will benefit remains a challenge. Although its antidepressant effects are known to be linked to its action as an antagonist of the N-methyl-D-aspartate (NMDA) receptor, the precise mechanisms that determine why some patients respond and others do not are still unclear.<h4>Objective</h4>This study aims to understand the computational mechanisms underlying changes in the auditory mismatch negativity (MMN) response following treatment with intravenous ketamine. Moreover, we aim to link the computational mechanisms to their underlying neural causes and use the parameters of the neurocomputational model to make individual treatment predictions.<h4>Methods</h4>This is a prospective study of 30 patients with TRD who are undergoing intravenous ketamine therapy. Prior to 3 out of 4 ketamine infusions, EEG will be recorded while patients complete the auditory MMN task. Depression, suicidality, and anxiety will be assessed throughout the study and a week after the last ketamine infusion. To translate the effects of ketamine on the MMN to computational mechanisms, we will model changes in the auditory MMN using the hierarchical Gaussian filter, a hierarchical Bayesian model. Furthermore, we will employ a conductance-based neural mass model of the electrophysiological data to link these computational mechanisms to their neural causes.<h4>Conclusion</h4>The findings of this study may improve understanding of the mechanisms underlying response and resistance to ketamine treatment in patients with TRD. The parameters obtained from fitting computational models to EEG recordings may facilitate single-patient treatment predictions, which could provide clinically useful prognostic information.<h4>Trial registration</h4>Clinicaltrials.gov NCT05464264. Registered June 24, 2022.https://doi.org/10.1371/journal.pone.0308413 |
| spellingShingle | Josh Martin Fatemeh Gholamali Nezhad Alice Rueda Gyu Hee Lee Colleen E Charlton Milad Soltanzadeh Karim S Ladha Sridhar Krishnan Andreea O Diaconescu Venkat Bhat Predicting treatment response to ketamine in treatment-resistant depression using auditory mismatch negativity: Study protocol. PLoS ONE |
| title | Predicting treatment response to ketamine in treatment-resistant depression using auditory mismatch negativity: Study protocol. |
| title_full | Predicting treatment response to ketamine in treatment-resistant depression using auditory mismatch negativity: Study protocol. |
| title_fullStr | Predicting treatment response to ketamine in treatment-resistant depression using auditory mismatch negativity: Study protocol. |
| title_full_unstemmed | Predicting treatment response to ketamine in treatment-resistant depression using auditory mismatch negativity: Study protocol. |
| title_short | Predicting treatment response to ketamine in treatment-resistant depression using auditory mismatch negativity: Study protocol. |
| title_sort | predicting treatment response to ketamine in treatment resistant depression using auditory mismatch negativity study protocol |
| url | https://doi.org/10.1371/journal.pone.0308413 |
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