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Predicting treatment response to ketamine in treatment-resistant depression using auditory mismatch negativity: Study
Josh Martin1, Fatemeh Gholamali Nezhad1, Alice Rueda1
1Interventional Psychiatry Program, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada.
Plos One
|August 8, 2024
Summary
This study explores how ketamine affects brain responses in major depressive disorder (MDD) patients. By modeling auditory mismatch negativity (MMN) changes, researchers aim to predict individual treatment outcomes for better depression management.
Area of Science:
- Neuroscience
- Computational Psychiatry
- Pharmacology
Background:
- Ketamine shows rapid antidepressant effects for major depressive disorder (MDD), including treatment-resistant depression (TRD).
- Predicting patient response to ketamine remains challenging, as not all individuals benefit.
- The exact mechanisms behind ketamine's antidepressant action and variable patient response are not fully understood, despite its known N-methyl-D-aspartate (NMDA) receptor antagonism.
Purpose of the Study:
- To investigate the computational mechanisms of auditory mismatch negativity (MMN) changes after intravenous ketamine treatment.
- To correlate these computational mechanisms with their neural underpinnings.
- To utilize neurocomputational model parameters for predicting individual patient responses to ketamine therapy.
Main Methods:
- A prospective study involving 30 TRD patients receiving intravenous ketamine therapy.
- Electroencephalography (EEG) recorded during an auditory MMN task before ketamine infusions.
- Application of hierarchical Gaussian filter and a neural mass model to analyze MMN changes and their neural correlates.
Main Results:
- Analysis of computational mechanisms underlying MMN alterations post-ketamine.
- Linking computational parameters to specific neural activity patterns.
- Development of a model for predicting individual treatment response based on EEG data.
Conclusions:
- Findings may elucidate mechanisms of ketamine response and resistance in TRD.
- Computational model parameters derived from EEG could enable personalized treatment predictions.
- This approach may offer valuable prognostic information for clinicians and patients.
