Brain imaging combined with machine learning could reveal subtypes of depression and anxiety, according to the study led by researchers at Stanford Medicine in the US, published in the journal Nature Medicine.
Using a machine learning approach called cluster analysis to group images of the patients' brains, the team identified six distinct patterns of activity in the brain regions they studied.
"Better ways to match patients with treatment are desperately needed," said Leanne Williams, director of Stanford Medicine's Center for Precision Mental Health and Wellness.
In the study, patients with one subtype, which is characterized by hyperactivity in cognitive areas of the brain, experienced the best response to the antidepressant venlafaxine (commonly known as Effexor) compared with those with other biotypes. Those with the second subtype, who had higher levels of brain activity at rest among three regions associated with depression and problem-solving, had better symptom relief with behavioral talk therapy.Those with the third subtype, who had lower levels of resting-time activity in the brain circuits that control attention, were less likely to see improvement in their symptoms with talk therapy than people with other biotypes, the team noted.
"To our knowledge, this is the first time we have been able to demonstrate that depression can be explained by different disruptions in brain functioning," Williams said.
In another recently published study, Williams and his team showed that using fMRI brain imaging improves their ability to identify individuals likely to respond to antidepressant treatment.
Williams and his team are now expanding the imaging study to include more participants. "The goal of our work is to figure out how we can get it right the first time," Williams said.,
“Living in the depression zone and not having any better alternative to this one-size-fits-all approach is very frustrating.”
Using a machine learning approach called cluster analysis to group images of the patients' brains, the team identified six distinct patterns of activity in the brain regions they studied.
"Better ways to match patients with treatment are desperately needed," said Leanne Williams, director of Stanford Medicine's Center for Precision Mental Health and Wellness.
In the study, patients with one subtype, which is characterized by hyperactivity in cognitive areas of the brain, experienced the best response to the antidepressant venlafaxine (commonly known as Effexor) compared with those with other biotypes. Those with the second subtype, who had higher levels of brain activity at rest among three regions associated with depression and problem-solving, had better symptom relief with behavioral talk therapy.Those with the third subtype, who had lower levels of resting-time activity in the brain circuits that control attention, were less likely to see improvement in their symptoms with talk therapy than people with other biotypes, the team noted.
"To our knowledge, this is the first time we have been able to demonstrate that depression can be explained by different disruptions in brain functioning," Williams said.
In another recently published study, Williams and his team showed that using fMRI brain imaging improves their ability to identify individuals likely to respond to antidepressant treatment.
Williams and his team are now expanding the imaging study to include more participants. "The goal of our work is to figure out how we can get it right the first time," Williams said.,
“Living in the depression zone and not having any better alternative to this one-size-fits-all approach is very frustrating.”
