The University of Rochester Medical Center explained that the researchers designed their experiment so that a person's eyes would naturally overshoot the target as they tried to track the visual. As the experiment went on, a healthy person's eyes would adjust to overcome that design and make more precise movements, while people with autism did not -- their eyes kept missing the target. "The inability of the brain to adjust the size of eye movement may not only be a marker for cerebellum dysfunction, but it may also help explain the communication and social interaction deficits that many individuals with [autism spectrum disorder] experience." Doctors might be able to track eye movements to detect the developmental disorder autism, which would help them diagnose the condition earlier.
Autism is characterized by social and communicative difficulties, specific interests that people with autism are capable of speaking about for hours (like meteorological modelling, in Sophie's case), and stereotyped behaviors. To diagnose autism spectrum disorder (ASD), doctors and psychologists evaluate quantitative criteria using tests and questionnaires, but also qualitative criteria, like interests, stereotyped movements, difficulties with eye contact and language and isolation. But while autistic girls show similar test results to autistic boys, the clinical manifestation of their condition differs, at least in cases where language has been acquired. Since September 2016, the Francophone Association of Autistic Women (Association francophone des femmes autistes, or AFFA) has been fighting for recognition of the specific ways autism manifests in women.
An algorithm correctly found correlations between words used in updates from 28,000 of those posts and levels of depression then due to its machine learning ability the AI was able to determine depression levels in users based solely on their update posts. Additionally, the team could determine countywide mortality rates related to heart disease by using AI to analyze 148 million tweets. It turns out that words concerning negative relationships and anger could more accurately predict mortality rates than predictions relying on the 10 heart disease risk factors, including diabetes and smoking. Currently, Troyanskaya's Princeton team is examining autism patients' genomes with DeepSEA to gain a deeper understanding of the effects of the noncoding bases.
But variants in scores of genes known to play some role in autism can explain only about 20% of all cases. Finding other variants that might contribute requires looking for clues in data on the 25,000 other human genes and their surrounding DNA--an overwhelming task for human investigators. They compared those of the few well-established autism risk genes with those of thousands of other unknown genes and last year flagged another 2500 genes likely to be involved in this disorder. Now they have developed a deep learning tool to find non-coding DNA that may also play a role in autism and other diseases.
It works by scanning children with an autism spectrum disorder (ASD) for their facial expressions and body movements in certain scenarios. Developed by a French robotic firm, the machine will also function as a diagnostic tool by collecting data in the future. The robot works by scanning children with an autism spectrum disorder (ASD) for their facial expressions and body movements in certain scenarios. Developed by a French robotic firm, the machine will also function as a diagnostic tool by collecting clinical data during therapy.
As the co-founder and CEO of Affectiva, el Kaliouby is on a mission to expand what we mean by "artificial intelligence" and create intelligent machines that understand our emotions. The new AI category el Kaliouby and her team at Affectiva are spearheading is "Emotion AI," defining a new market by pursuing two goals: Allowing machines to adapt to human emotions in real-time and providing insights and analytics so organizations can understand how people engage emotionally in the digital world. Then she read Picard's Affective Computing, published in 1997, and became "super-fascinated by the idea that a computer can read people's emotions. For her dissertation, el Kaliouby used the autism research center's data to train a computer model to recognize accurately and in real-time complex mental states with "an accuracy and speed that are comparable to that of human recognition."
By training a machine learning algorithm on their behavior and earlier MRI data, the scientists built a model that predicted 9 of those 11 autism cases, with no false positives. Right now, researchers tracking autism development focus on infant siblings of people with autism; they have 1 in 5 chance of developing autism, compared to around 1 in 100 for the general population. But current autism therapies for babies and toddlers focus on their specific behavioral deficits--teaching children to communicate needs, to play with toys, and to have positive interactions with caregivers. The UNC group's next goal is to predict specific autism symptoms, correlating brain scans with future language difficulties, sensory sensitivities, social difficulties, or repetitive behaviors.
In addition to brain scan data, researchers could gather behavioral results, environmental exposures, and more. Each functional connectivity MRI (fcMRI) scan measured the activity of 26,335 brain connections among 230 brain regions. In some cases, brain connections of children diagnosed with autism were highly synchronized; in others, the connections were less synchronized. The study is the most recent of four papers resulting from the Infant Brain Imaging Study, a U.S. National Institutes of Health–funded study of early brain development in autism.
"It was extremely accurate," Robert Emerson, the lead author on the study and a former cognitive neuroscience postdoctoral fellow at the University of North Carolina (UNC), told Live Science. Studies show that 20 percent of babies who have older siblings with autism will develop the disorder; among babies in the general population, 1.5 percent develop autism, Emerson told Live Science. In this case, the program was learning to spot differences between the functional connections imaged in the MRI data collected at 6 months old that correlate with cognition, memory and behavior and the details from the behavioral assessments collected at 24 months. Piven said the team published a study earlier in the year that also showed an impressive prediction rate, but that study required two MRI scans, one at 6 months of age and one at 1 year.
Previous work has identified that bundles of nerve fibres in the brain develop differently in infants with older siblings with autism from how they do in infants without this familial risk factor. The team used the brain scans from when the babies were 6 months old and behavioural data from when the children were 2 years old to train a machine-learning program to identify any brain connectivity patterns that might be linked to later signs of autism, such as repetitive behaviour, difficulties with language, or problems relating socially to others. After the training, the program used only the patterns from the 6-month-old brains to predict which of the children would show signs of autism at 2 years old. The goal is to use such a classifier system to identify infants likely to develop autism at an early age.