There depression it’s not just one: at least they exist six subtypes different, involving distinct regions of the brain and responding differently to therapies. A research, with the help of artificial intelligence, examined the brain MRI images of 800 patientsdiscovering these variations. The study, which paves the way for increasingly personalized treatments, was published in the journal Nature Medicine by a team international led by Italian neuroscientist Leonardo Tozzi at Stanford University, California.
Research: A step forward towards personalized medicine
The work represents “demonstration of a personalized medicine approach to mental health based on objective measures of brain function”observes study coordinator Leanne M. Williams of Stanford University. Leanne, who lost her partner to depression almost a decade ago, has decided to focus her research on precision psychiatry. The goal is to find new methods that allow each patient to be directed toward the most effective therapeutic path, considering that today 30% of depression cases do not respond to therapies and two-thirds of treated subjects fail to achieve full recovery of quality of life.
Artificial intelligence at the service of diagnosis and to fight depression
Artificial intelligence applied to diagnostic imaging is now helping. Researchers subjected 801 patients suffering from depression or anxiety to a functional magnetic resonance imaging of the brain, to detect the activity of specific areas linked to depression, both at rest and during the execution of certain tasks. The images obtained were examined with a machine learning algorithm which allowed them to be grouped into six different typologies.
Next, 250 study participants were randomly assigned to receive antidepressant medication or cognitive-behavioral therapy. It turns out that a subtype of depression, characterized by hyperactivity in the cognitive regions of the brain, responds better to the antidepressant venlafaxine. Cognitive-behavioral therapy was more effective in another subtype of patients where the resting brain had higher levels of activity in three regions associated with depression and problem-solving. The least responsive to cognitive-behavioral therapy were the patients belonging to a third subtype who, at rest, had lower levels of activity in the brain circuit that controls attention.
By identifying the subtype of depression with MRI, the researchers were able to predict the probability of disease remission in 63% of cases versus 36% obtained without diagnostic imaging.
This discovery represents an important step after you towards the personalization of therapies for depression, offering new hope to those patients who have so far not found relief in traditional therapies. With a approach more targeted, based on the specificity of each individual’s brain functioning, it will be possible to develop more effective treatments and significantly improve the quality of life of those suffering from this disease.
What do you think about this finding? Could it influence the way other mental illnesses are treated?
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