Machine learning helps to find dimensions of mental illnesses

A new study at Penn Medicine using machine learning has identified brain-based dimensions of mental health disorders, an advance towards much-needed biomarkers to more accurately diagnose and treat patients. A team led by Theodore D. Satterthwaite, MD, an assistant professor in the department of Psychiatry, mapped abnormalities in brain networks to four dimensions of psychopathology: mood, psychosis, fear, and disruptive externalizing behavior.

Photo
Connectivity-informed dimensions of psychopathology cross clinical diagnostic categories.
Source: Penn Medicine

Psychiatry is behind the rest of medicine when it comes to diagnosing illness,” said Satterthwaite. “For example, when a patient comes in to see a doctor with most problems, in addition to talking to the patient, the physician will recommend lab tests and imaging studies to help diagnose their condition. Right now, that is not how things work in psychiatry. In most cases, all psychiatric diagnoses rely on just talking to the patient. One of the reasons for this is that we don’t understand how abnormalities in the brain lead to psychiatric symptoms. This research effort aims to link mental health issues and their associated brain network abnormalities to psychiatric symptoms using a data-driven approach.”

To uncover the brain networks associated with psychiatric disorders, the team studied a large sample of adolescents and young adults (999 participants, ages 8 to 22). All participants completed both functional MRI scans and a comprehensive evaluation of psychiatric symptoms as part of the Philadelphia Neurodevelopmental Cohort (PNC), an effort lead by Raquel E. Gur, MD, Ph.D., professor of Psychiatry, Neurology, and Radiology, that was funded by the National Institute of Mental Health. The brain and symptom data were then jointly analyzed using a machine learning method called sparse canonical correlation analysis.

Patterns of changes in brain networks

The researchers found that each brain-guided dimension contained symptoms from several different clinical diagnostic categories. For example, the mood dimension was comprised of symptoms from three categories, e.g. depression (feeling sad), mania (irritability), and obsessive-compulsive disorder (recurrent thoughts of self-harm). Similarly, the disruptive externalizing behavior dimension was driven primarily by symptoms of both Attention Deficit Hyperactivity Disorder (ADHD) and Oppositional Defiant Disorder (ODD), but also included the irritability item from the depression domain. These findings suggest that when both brain and symptomatic data are taken into consideration, psychiatric symptoms do not neatly fall into established categories. Instead, groups of symptoms emerge from diverse clinical domains to form dimensions that are linked to specific patterns of abnormal connectivity in the brain.

“In addition to these specific brain patterns in each dimension, we also found common brain connectivity abnormalities that are shared across dimensions,” said Cedric Xia, a MD-Ph.D. candidate and the paper’s lead author. “Specifically, a pair of brain networks called default mode network and frontal-parietal network, whose connections usually grow apart during brain development, become abnormally integrated in all dimensions.”

These two brain networks have long intrigued psychiatrists and neuroscientists because of their crucial role in complex mental processes such as self-control, memory, and social interactions. The findings in this study support the theory that many types of psychiatric illness are related to abnormalities of brain development. “This study shows that we can start to use the brain to guide our understanding of psychiatric disorders in a way that’s fundamentally different than grouping symptoms into clinical diagnostic categories. By moving away from clinical labels developed decades ago, perhaps we can let the biology speak for itself,” said Satterthwaite. “Our ultimate hope is that understanding the biology of mental illnesses will allow us to develop better treatments for our patients.”

Subscribe to our newsletter

Related articles

Video game to treat children with ADHD

Video game to treat children with ADHD

Researchers have developed advanced brain-computer interface technology that harnesses machine learning to personalise brain-training for children with ADHD.

Mental health game changer

Mental health game changer

Using a simple computer game and AI techniques, researchers were able to identify behavioural patterns in subjects with depression and bipolar disorder.

Digital phenotyping helps to treat mental illness

Digital phenotyping helps to treat mental illness

Research shows that digital phenotyping can provide valuable information to mental health professionals about mental illness symptom severity and relapse.

AI may help spot autism early

AI may help spot autism early

A machine learning algorithm can spot abnormalities in pupil dilation that are predictive of autism spectrum disorder in mouse models.

AI could change managing of Alzheimer’s disease

AI could change managing of Alzheimer’s disease

A study from Florida Atlantic University introduces machine learning as new potantial tactic in assessing cognitive brain health and patient care.

Machine learning finds ‘sound’ words predict psychosis

Machine learning finds ‘sound’ words predict psychosis

A machine learning method discovered a clue in people’s language predictive of the emergence of psychosis — the frequent use of words associated with sound.

AI can detect depression in a child’s speech

AI can detect depression in a child’s speech

Researchers have used artificial intelligence to detect hidden depression in young children, a condition that can lead to increased risk of substance abuse and suicide later in life if left untreated.

The progress and risks of artificial intelligence

The progress and risks of artificial intelligence

Artificial intelligence has reached a critical turning point in its evolution, according to an international panel of experts.

New 3D facial scans to give genetic clues to autism

New 3D facial scans to give genetic clues to autism

High-tech 3D facial scans to give us a better understanding of the genetic causes of autism.

Popular articles

Subscribe to Newsletter