Predicting autism early using AI

Predicting autism early using AI


Autism and other neurodevelopmental disorders often aren't diagnosed until a child is a few years of age, when behavioral interventions and speech/occupational therapy become less effective. But new research in PNAS suggests that two simple, quantifiable measures -- spontaneous fluctuations in pupil dilation or heart rate-- could enable much earlier diagnosis of Rett syndrome and possibly other disorders with autism-like features.

The study unveils a machine-learning algorithm that can spot abnormalities in pupil dilation that are predictive of autism spectrum disorder (ASD) in mouse models. It further shows that the algorithm can accurately detect if a girl has Rett syndrome, a genetic disorder that impairs cognitive, sensory, motor, and autonomic function starting at 6 to 18 months of age, as well as autism-like behaviors.

The authors hope this system could provide an early warning signal not just for Rett syndrome but for ASD in general. In the future, they believe it could also be used to monitor patients' responses to treatments; currently, a clinical trial is testing the drug ketamine for Rett syndrome, and a gene therapy trial is planned.

The researchers began with the idea that people on the autism spectrum have altered behavioral states. Prior evidence indicates that the brain's cholinergic circuits, which are involved in arousal, are especially perturbed, and that altered arousal affects both spontaneous pupil dilation/constriction and heart rate.

The team set out to measure pupil fluctuations in several mouse models of ASD, including mice with the mutations causing Rett syndrome or CDKL5 disorder, as well as BTBR mice. Spontaneous pupil dilation and constriction were altered even before the animals began showing ASD-like symptoms, the team found.

Moreover, in mice lacking MeCP2, the gene mutated in Rett syndrome, restoring a normal copy of the gene, in cholinergic brain circuits only, prevented the onset of pupillary abnormalities as well as behavioral symptoms.

To systematically link the observed arousal changes to the cholinergic system, the team took advantage of an earlier discovery: mice lacking the LYNX1 protein exhibit enhanced cholinergic signaling. Based on about 60 hours of observation of these mice, the investigators "trained" a deep learning algorithm to recognize abnormal pupillary patterns. The same algorithm accurately estimated cholinergic dysfunction in the BTBR, CDKL5, and MeCP2-deficient mice.

The team then brought this algorithm to 35 young girls with Rett syndrome and 40 typically developing controls. Instead of measuring the girls' pupils (as patients may fidget), they used heart rate fluctuations as the measure of arousal. The algorithm nonetheless successfully identified the girls with Rett, with an accuracy of 80 percent in the first and second year of life.

"These two biomarkers fluctuate in a similar way because they are proxies of the activity of autonomic arousal," says the author. "It is the so-called 'fight or flight response."

Autonomic arousal, a property of the brain that is strongly preserved across different species, is a robust indicator of an altered developmental trajectory, the authors found.

https://www.pnas.org/content/early/2019/07/16/1820847116

http://sciencemission.com/site/index.php?page=news&type=view&id=publications%2Fdeep-learning-of&filter=22

Edited

Rating

Unrated
Rating: