Treatment of depression
Inventors
Etkin, Amit • Zhang, Yu • Fonzo, Greg • Trivedi, Madhukar
Assignees
University of Texas System • US Department of Veterans Affairs
Publication Number
US-12150777-B2
Publication Date
2024-11-26
Expiration Date
2039-03-15
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Abstract
Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.
Core Innovation
The invention provides methods for identifying subjects suffering from depression who are likely to respond to treatment with an antidepressant. The method involves administering specific trials of an emotional conflict task—first incongruent trial followed by a second incongruent trial, and a congruent trial followed by a third incongruent trial—and measuring brain activity levels in multiple brain regions in response to these trials. A difference between brain activity levels in these tasks is quantified and analyzed using a machine learning model to identify likelihood of antidepressant response.
The problem addressed is that although antidepressants have been widely used for major depression, their overall advantage over placebo is small, especially when patients are unselected. Depression is biologically heterogeneous, and the small average benefit of antidepressants over placebo conceals critical biological differences among patients. Therefore, objective measures are needed to stratify depressed patients into those who will show clinically significant benefit from antidepressants compared to placebo and those who will not.
The invention presents methods using brain activity measures derived from an emotional conflict task and machine learning models to provide this stratification. This approach allows for objective identification of antidepressant-responsive patients, facilitating more personalized treatment. The emotional conflict regulation capacity, as measured in specific brain regions such as the frontopolar cortex, lateral prefrontal cortex, dorsal anterior cingulate cortex, and anterior insula, serves as a biomarker to moderate antidepressant response compared to placebo.
Claims Coverage
The patent contains two independent claims, each detailing methods involving emotional conflict task trials and brain activity measurement for predicting antidepressant response.
Method to identify antidepressant responders using brain activity differences during emotional conflict tasks
A method comprising administering specific sequences of incongruent and congruent trials forming an emotional conflict task to a depressed subject, measuring brain activity levels in multiple brain regions (including two or more among frontopolar cortex, lateral prefrontal cortex, dorsal anterior cingulate cortex, and anterior insula), quantifying differences between brain activity levels, and applying a machine learning model to these differences to identify if the subject will respond to antidepressant treatment.
Brain activity measurement method across multiple distinct brain regions during emotional conflict tasks
A method comprising administering sequences of incongruent and congruent trials as part of an emotional conflict task to a subject, measuring first and second brain activity levels in a first brain region in response to these trials, quantifying the difference between these activity levels, performing these steps across four different brain regions selected from frontopolar cortex, lateral prefrontal cortex, dorsal anterior cingulate cortex, and anterior insula, and determining antidepressant responsiveness based on the brain activity difference where a greater second brain activity level relative to the first indicates responsiveness.
The claims cover methods that use brain activity differences measured during specific emotional conflict trials in defined brain regions combined with machine learning techniques to identify whether a subject suffering from depression will respond to antidepressant treatment, thus providing an objective predictive biomarker method.
Stated Advantages
Provides an objective measure to stratify depressed patients into those likely to benefit from antidepressants over placebo and those unlikely to benefit.
Enables clinically and mechanistically meaningful personalization of depression treatment based on neurobiological markers.
Offers a method to identify patients with a high remission rate who should receive antidepressants and those who may better pursue alternative treatments.
Allows prediction of sertraline-specific treatment outcome, explaining a significant percentage of variance in response, independent of clinical severity.
Documented Applications
Stratification of depressed patients to guide antidepressant treatment decisions to improve remission rates.
Selection of participants in clinical studies to enrich for medication responders or non-responders based on brain activity biomarkers.
Use of emotional conflict regulation measured via neuroimaging to predict and personalize treatment response, potentially reducing ineffective antidepressant trials.
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