AI News, Machine Learning Could Predict Medication Responses in Patients with Complex Mood Disorders

Machine Learning Could Predict Medication Responses in Patients with Complex Mood Disorders

Summary: Researchers have developed a new machine learning algorithm which is 94% accurate in predicting which patients with complex mood disorders will respond to medications.Source: Lawson Health Research Institute.

In a collaborative study by Lawson Health Research Institute, The Mind Research Network and Brainnetome Center, researchers have developed an artificial intelligence (AI) algorithm that analyzes brain scans to better classify illness in patients with a complex mood disorder and help predict their response to medication.

The first part of the study involved 66 patients who had already completed treatment for a clear diagnosis of either MDD or bipolar type I (bipolar I), which is a form of bipolar disorder that features full manic episodes, as well as an additional 33 research participants with no history of mental illness.

The research team analyzed and compared the scans of those with MDD, bipolar I and no history of mental illness, and found the three groups differed in particular brain networks.

“It also suggests that we may one day have an objective measure of psychiatric illness through brain imaging that would make diagnosis faster, more effective and more consistent across health care providers.”

doi:10.1111/acps.12945 Abstract Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication‐class of response in complex patients Objective This study determined the clinical utility of an fMRI classification algorithm predicting medication‐class of response in patients with challenging mood diagnoses.

A predictive algorithm was trained and cross‐validated on the known‐diagnosis patients using maximally spatially independent components (ICs), constructing a similarity matrix among subjects, partitioning the matrix in kernel space and optimizing support vector machine classifiers and IC combinations.

Applied to the complicated patients, the algorithm diagnosis was consistent with optimal medication‐class of response to sustained recovery in 11 of 12 cases (i.e., almost 92% accuracy).

Mood disorders like major depressive disorder (MDD) and bipolar disorder are often complex and hard to diagnose, especially among youth when the illness is just evolving.

The first part of the study involved 66 patients who had already completed treatment for a clear diagnosis of either MDD or bipolar type I (bipolar I), which is a form of bipolar disorder that features full manic episodes, as well as an additional 33 research participants with no history of mental illness.

Each individual participated in scanning to examine different brain networks using Lawson’s functional magnetic resonance imaging (fMRI) capabilities at St. Joseph’s Health Care London.  The research team analyzed and compared the scans of those with MDD, bipolar I and no history of mental illness, and found the three groups differed in particular brain networks.

When tested against the research participants with a known diagnosis, the algorithm correctly classified their illness with 92.4 per cent accuracy.  The research team then performed imaging with 12 additional participants with complex mood disorders for whom a diagnosis was not clear.

They used the algorithm to study a participant’s brain function to predict his or her diagnosis and, more importantly, examined the participant’s response to medication.  “Antidepressants are the gold standard pharmaceutical therapy for MDD while mood stabilizers are the gold standard for bipolar I,” says Dr. Elizabeth Osuch, a clinician-scientist at Lawson, medical director at FEMAP and co-lead investigator on the study.

Researchers develop algorithm that identifies mental illnesses, predicts response to medication

Researchers say a new algorithm can help distinguish between two mental illnesses and predict patient reactions to different medications.

The first part of the study included 66 adult patients from mental health programs at London Health Sciences Centre who had been previously diagnosed with either major depressive disorder or bipolar disorder.

Using machine learning, the algorithm was able to examine the scans and determine whether a patient had MDD or bipolar disorder with 92.4 per cent accuracy.

WATCH: MRI scans suggest transgender people’s brains resemble their identified gender: study The algorithm was then applied to 12 additional participants with complex mood disorders for whom a diagnosis was not clear.

A new artificial (AI) algorithm developed by researchers from the Lawson Health Research Institute in Ontario, Canada and The Mind Research Network in Albuquerque, New Mexico, could help predict whether a patient will successfully respond to medication for a mood disorder.   The AI algorithm can to analyze brain scans to better classify mood disorders and predict medication response in patients.

6 in the journal Acta Psychiatrica Scandinavica, also may suggest that biomarkers may help distinguish certain mood disorders from others, such as major depressive disorder (MDD) and bipolar disorder.    “Antidepressants are the gold standard pharmaceutical therapy for MDD while mood stabilizers are the gold standard for bipolar I,” said co-author Elizabeth Osuch, MD, a clinician-scientist at the Lawson Health Research Institute, in a prepared statement.

Machine learning could predict medication response in patients with complex mood disorders

Mood disorders like major depressive disorder (MDD) and bipolar disorder are often complex and hard to diagnose, especially among youth when the illness is just evolving.

In a collaborative study by Lawson Health Research Institute, The Mind Research Network and Brainnetome Center, researchers have developed an artificial intelligence (AI) algorithm that analyzes brain scans to better classify illness in patients with a complex mood disorder and help predict their response to medication.

The first part of the study involved 66 patients who had already completed treatment for a clear diagnosis of either MDD or bipolar type I (bipolar I), which is a form of bipolar disorder that features full manic episodes, as well as an additional 33 research participants with no history of mental illness.

These included regions in the default mode network, a set of regions thought to be important for self-reflection, as well as in the thalamus, a ‘gateway’ that connects multiple cortical regions and helps control arousal and alertness.

“It also suggests that we may one day have an objective measure of psychiatric illness through brain imaging that would make diagnosis faster, more effective and more consistent across health care providers.” Psychiatrists currently make a diagnosis based on the history and behavior of a patient.

Machine learning could predict medication response in patients with complex mood disorders

Mood disorders like major depressive disorder (MDD) and bipolar disorder are often complex and hard to diagnose, especially among youth when the illness is ...

Relief from severe depression and suicidal ideation within hours: from synapses to symptoms

Relief from severe depression and suicidal ideation within hours: from synapses to symptoms Air date: Wednesday, November 04, 2015, 3:00:00 PM Category: ...

Robots Can Now Sense Your Emotions Via Radio Waves

Scientists have developed a machine that can read your emotions - even when your facial expression remains constant. How does it work? Why You Shouldn't ...

Using twitter to predict heart disease | Lyle Ungar | TEDxPenn

Can Twitter predict heart disease? Day in and day out, we use social media, making it the center of our social lives, work lives, and private lives. Lyle Ungar ...

Peter D. Kramer on depression, antidepressants, and psychotherapy - Full interview | VIEWPOINT

What is depression, how is it diagnosed, and how is it treated? How effective are the treatments doctors use to treat depression? Peter D. Kramer, psychiatrist ...

Connecting Emotions, Brain, and Behavior with Wearables — Dr. Rosalind Picard

NIMH Director's Innovation Speaker Series March 3, 2016 Dr. Rosalind Picard is Director of Affective Computing Research at the MIT Media Lab, Faculty Chair ...

HGP10: Translating Pharmacogenetics Research to Practice: The Case Example of Smoking Cessation

May 6, 2013 - Human Genome Project (HGP) 10th Anniversary Seminar Series Speaker: Caryn Lerman, Ph.D. More:

The Sleep-Deprived Human Brain | Nora Volkow || Radcliffe Institute

The Sleep-Deprived Human Brain A presentation by Nora D. Volkow There is increased recognition that sleep deprivation interferes with cognition and ...

Everything You Wanted to Know about Treatment for Alcohol Use Disorder: A Primer for Non-Clinicians

At the 2017 meeting of the Research Society on Alcoholism, the symposium “Everything You Ever Wanted To Know About Alcohol Treatment But Were Afraid To ...

The Promise of AI

Building on our popular primer on artificial intelligence [a16z.com/2016/06/10/ai-deep-learning-machines/] -- and a companion microsite [aiplaybook.a16z.com/] ...