Approximately 30% of people with depression have treatment-resistant depression and up to two-thirds do not fully recover from symptoms despite treatment.
Brain activity patterns from functional magnetic resonance imaging (fMRI) were used to identify these subtypes using machine learning algorithms.
Stanford Medicine researchers identify six distinct subtypes of depression based on brain imaging and machine learning.
Subtypes had unique characteristics, such as overactive cognitive regions responding best to antidepressants and lower activity at rest in the attention region being less likely to see improvement with talk therapy.
Using fMRI brain imaging during a screening assessment for depression could lead to more effective treatments by personalizing treatments based on brain activity.
In a groundbreaking study, researchers from Stanford Medicine have identified six distinct subtypes of depression based on brain imaging and machine learning. These findings could revolutionize the way mental health conditions are diagnosed and treated by enabling personalized treatment plans for each individual.
The research team used functional magnetic resonance imaging (fMRI) to analyze brain activity patterns in individuals with major depressive disorder. They then applied machine learning algorithms to identify distinct subtypes of depression based on these patterns. The identified subtypes had unique characteristics, such as overactive cognitive regions responding best to antidepressants, higher levels of activity in certain regions indicating better response to behavioral talk therapy, and lower activity at rest in the attention region being less likely to see improvement with talk therapy.
The study also showed that using fMRI brain imaging improves the ability to identify individuals likely to respond to antidepressant treatment. This could lead to more effective treatments for those who don't respond well to standard antidepressants, as approximately 30% of people with depression have treatment-resistant depression and up to two-thirds do not fully recover from symptoms despite treatment.
The goal is to use brain scans during a screening assessment for depression to identify the best treatment for each individual. This approach could significantly improve efficacy by personalizing treatments based on brain activity, potentially reducing the number of people with depression who don't respond well to current treatments.
This study builds upon previous research that has used task-free fMRI and found aberrant connectivity in frontostriatal and limbic networks, hyper- and hypoconnectivity of the default mode network, and differences in anxiety within depression. These findings highlight the importance of understanding neurobiological dysfunctions to develop effective treatments for mental health disorders.
It is crucial to note that this study does not draw conclusions or make calls to action. The role of a journalist is to provide a complete and factual story without bias, allowing readers to form their own opinions based on the information presented.
Stanford Medicine researchers have identified six subtypes of depression using brain imaging and machine learning.
Three of these subtypes have been linked to specific treatments: overactive cognitive regions respond best to antidepressants, higher levels of activity in certain regions indicate better response to behavioral talk therapy, and lower activity at rest in the attention region is less likely to see improvement with talk therapy.
The goal is to use brain scans during a screening assessment for depression to identify the best treatment for each individual.
Approximately 30% of people with depression have treatment-resistant depression, and up to two-thirds do not fully recover from symptoms despite treatment. This study aims to improve efficacy by personalizing treatments based on brain activity.
One subtype, characterized by overactivity in cognitive regions, had the best response to venlafaxine (Effexor) antidepressants compared to other biotypes. Another subtype with higher levels of activity at rest in three depression-related regions responded better to behavioral talk therapy. A third subtype with lower levels of activity at rest in the attention region was less likely to see improvement from talk therapy.
The study also showed that using fMRI brain imaging improves the ability to identify individuals likely to respond to antidepressant treatment, specifically for a subtype called the cognitive biotype. This improved accuracy could lead to more effective treatments for those who don’t respond well to standard antidepressants.
Depression and anxiety disorders are a significant global health issue with over a third of major depressive disorder patients and approximately half of generalized anxiety disorder patients not responding to first-line treatment.
Personalized measures for quantifying neurobiological dysfunctions in patients is required for precision medicine approach to mental health care.
Current psychiatric diagnostic system assigns one label to syndromes involving multiple neurobiological processes, hindering effective treatment.
Efforts to identify biotypes of depressed and anxious patients have used task-free fMRI and found aberrant connectivity in frontostriatal and limbic networks, hyper- and hypoconnectivity of the default mode network, and differences in anxiety within depression.
Accuracy
Approximately a third of major depressive disorder patients and approximately half of generalized anxiety disorder patients do not respond to first-line treatment.
, A precision medicine approach to mental health care requires personalized measures for quantifying neurobiological dysfunctions in patients.
, The current psychiatric diagnostic system assigns one label to syndromes involving multiple neurobiological processes, hindering effective treatment.
, Efforts to identify biotypes of depressed and anxious patients have used task-free fMRI and found aberrant connectivity in frontostriatal and limbic networks, hyper- and hypoconnectivity of the default mode network, and differences in anxiety within depression.
Stanford Medicine-led study identifies six distinct types of depression based on brain imaging and machine learning
Three subtypes of depression respond better to specific treatments: venlafaxine for overactive cognitive regions, behavioral talk therapy for higher levels of activity in problem-solving regions, and less likely improvement with talk therapy for lower activity in attention control regions
