Brain Imaging Accurately Predict Autism Diagnosis

Carnegie Mellon University researchers have created brain-reading techniques to use neural representations of social thoughts to predict autism diagnoses with 97 percent accuracy. This establishes the first biologically based diagnostic tool that measures a person's thoughts to detect the disorder that affects many children and adults worldwide.
Researchers have created brain-reading techniques to use neural
 representations of social thoughts to predict autism diagnoses
 with 97 percent accuracy. This establishes the first biologically
based diagnostic tool that measures a person's thoughts to
detect the disorder that affects many children and adults
worldwide. (Credit: Carnegie Mellon University)
Psychiatric disorders - including autism - are characterized and diagnosed based on a clinical assessment of verbal and physical behavior. However, brain imaging and cognitive neuroscience are poised to provide a powerful advanced new tool.

Researchers have created brain-reading techniques to use neural representations of social thoughts to predict autism diagnoses with 97 percent accuracy. This establishes the first biologically based diagnostic tool that measures a person's thoughts to detect the disorder that affects many children and adults worldwide.

Published in PLOS ONE, the study combined functional magnetic resonance imaging (fMRI) and machine-learning techniques that use brain activation patterns to scan and decode the contents of a person's thoughts of objects or emotions. The previous work also demonstrated that specific thoughts and emotions have a very similar neural signature across normal individuals, suggesting that brain disorders may display detectable alterations in thought activation patterns. Now, the research team has successfully used this approach to identify autism by detecting changes in the way certain concepts are represented in the brains of autistic individuals. They call these alterations "thought-markers" because they indicate abnormalities in the brain representations of certain thoughts that are diagnostic of the disorder.

For the study, the researchers scanned the brains of 17 adults with high-functioning autism and 17 neurotypical control participants. The participants were asked to think about 16 different social interactions, such as "persuade," "adore" and "hug". The resulting brain images showed that the control participants' thoughts of social interaction clearly included activation indicating a representation of the "self", manifested in the brain's posterior midline regions. However, the self-related activation was near absent in the autism group. Machine-learning algorithms classified individuals as autistic or non-autistic with 97 percent accuracy based on the fMRI thought-markers.

Implications of this research could extend to other psychiatric disorders, such as being suicidal or having obsessive-compulsive disorder, in which certain types of thoughts are altered. By providing a brain-based measure of the altered thoughts to use in conjunction with clinical assessments, this new research could enable clinicians to make quicker and more certain diagnoses and more quickly implement targeted therapies that focus on the alteration.

This neuroscience research is on the vanguard of two fronts: it advances the scientific mission of classifying and diagnosing mental disorders based on behavioral and neurobiological measures (rather than conventional symptoms), and it integrates the conception of brain and mind by assessing thoughts in terms of brain function.

For more information about how brain representations of social thoughts accurately predict autism diagnosis, watch one of the scientists discuss the research in the video below: