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AI can spot patterns in the brain linked to Alzheimer's, schizophrenia and autism

Daily Mail - Science & tech

A new artificial intelligence (AI) is capable of spotting mental health conditions by sifting through brain imaging data to find patterns linked to autism, schizophrenia and Alzheimer's - and it can do so before the symptoms set in. The model was first trained with brain images from healthy adults and then shown those with mental health issues, allowing it to identify tiny changes that go unnoticed by the human eye. The sophisticated computer program was developed by a team of researchers led by Georgia State who note the it could one day detect Alzheimer's in someone as young as 40 years old, which is about 25 years before symptoms start to appear. By catching such diseases early would help patients receive treatment that lessens or even eliminates the mental illness. The AI was trained on a massive dataset of more than 10,000 people to understand functional magnetic resonance imaging (fMRI), which measures brain activity by detecting changes with blood flow.


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Summary: Children on the autism spectrum may not always process bodily movements correctly, especially if they are distracted by something else. Noticing and understanding what it means when a person leans into a conversation or takes a step back and crosses their arms is a vital part of human communication. Researchers at the Del Monte Institute for Neuroscience at the University of Rochester have found that children with autism spectrum disorder may not always process body movements effectively, especially if they are distracted by something else. "Being able to read and respond to someone's body language is important in our daily interactions with others," said Emily Knight, M.D., Ph.D., clinical and postdoctoral fellow in Pediatrics and Neuroscience, is the first author of the study recently published in Molecular Autism. "Our findings suggest that when children with autism are distracted by something else, their brains process the movements of another person differently than their peers."


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According to observations, children with autism frequently speak more slowly than similarly developing kids. They differ in their speech in other ways, most notably in tone, intonation, and rhythm. It is very challenging to consistently and objectively describe these "prosodic" distinctions, and it has been decades since their roots have been identified. Researchers from Northwestern University and Hong Kong collaborated on a study to shed light on the causes and diagnoses of this illness. This method uses machine learning to find speech patterns in autistic children that are similar in Cantonese and English.


Northwestern University Researchers Used Machine Learning To Identify Speech Patterns In Children With Autism That Were Consistent Between English And Cantonese

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According to observations, children with autism frequently speak more slowly than similarly developing kids. They differ in their speech in other ways, most notably in tone, intonation, and rhythm. It is very challenging to consistently and objectively describe these "prosodic" distinctions, and it has been decades since their roots have been identified. Researchers from Northwestern University and Hong Kong collaborated on a study to shed light on the causes and diagnoses of this illness. This method uses machine learning to find speech patterns in autistic children that are similar in Cantonese and English.


AI Detects Autism Speech Patterns Across Different Languages - AI Summary

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Summary: Machine learning algorithms help researchers identify speech patterns in children on the autism spectrum that are consistent between different languages. The data used to train the algorithm were recordings of English- and Cantonese-speaking young people with and without autism telling their own version of the story depicted in a wordless children's picture book called "Frog, Where Are You?" "Using this method, we were able to identify features of speech that can predict the diagnosis of autism," said Lau, a postdoctoral researcher working with Losh in the Roxelyn and Richard Pepper Department of Communication Sciences and Disorders at Northwestern. Finally, the results of the study could inform efforts to identify and understand the role of specific genes and brain processing mechanisms implicated in genetic susceptibility to autism, the authors said. Using a supervised machine-learning analytic approach, we examined acoustic features relevant to rhythmic and intonational aspects of prosody derived from narrative samples elicited in English and Cantonese, two typologically and prosodically distinct languages. Summary: Machine learning algorithms help researchers identify speech patterns in children on the autism spectrum that are consistent between different languages.


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A new study led by Northwestern University researchers used machine learning--a branch of artificial intelligence--to identify speech patterns in children with autism that were consistent between English and Cantonese, suggesting that features of speech might be a useful tool for diagnosing the condition. Undertaken with collaborators in Hong Kong, the study yielded insights that could help scientists distinguish between genetic and environmental factors shaping the communication abilities of people with autism, potentially helping them learn more about the origin of the condition and develop new therapies.


AI Detects Autism Speech Patterns Across Different Languages - Neuroscience News

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Summary: Machine learning algorithms help researchers identify speech patterns in children on the autism spectrum that are consistent between different languages. A new study led by Northwestern University researchers used machine learning--a branch of artificial intelligence--to identify speech patterns in children with autism that were consistent between English and Cantonese, suggesting that features of speech might be a useful tool for diagnosing the condition. Undertaken with collaborators in Hong Kong, the study yielded insights that could help scientists distinguish between genetic and environmental factors shaping the communication abilities of people with autism, potentially helping them learn more about the origin of the condition and develop new therapies. Children with autism often talk more slowly than typically developing children, and exhibit other differences in pitch, intonation and rhythm. But those differences (called "prosodic differences'" by researchers) have been surprisingly difficult to characterize in a consistent, objective way, and their origins have remained unclear for decades. However, a team of researchers led by Northwestern scientists Molly Losh and Joseph C.Y. Lau, along with Hong Kong-based collaborator Patrick Wong and his team, successfully used supervised machine learning to identify speech differences associated with autism.


How a tech startup is using AI to find better treatments for autism

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Some parents may flounder to find an effective ABA care provider because the data simply doesn't exist in convenient form. SpectrumAi aims to bring more clarity around what works and produce better treatment outcomes by measuring it in an empirical way via a digital platform. Rather than having to scribble largely unquantifiable and subjective paper notes that interrupts the flow of ABA therapy, which requires deep engagement by the therapist, SpectrumAi's software helps automate the data collection process and provides a widely accessible information hub that can also help therapists zero in on the most effective ABA techniques for different patient groups. Shao did not divulge SpectrumAi's specific customers, but said they include major players across the U.S. health insurance and ABA provider industries.


Bot Discovers Why Some Autistic Adults Can't Detect Emotion

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One common symptom that people with autism struggle with is the inability to interpret facial expressions. This can lead to difficulty in reading social cues in their personal lives, school, workplace, and even media like movies and TV shows. However, researchers at MIT have created an AI that helped shed light on why exactly this is. A paper published on Wednesday in The Journal of Neuroscience unveiled research that found that neurotypical adults (those not displaying autistic characteristics) and adults with autism might have key differences in a region of their brain called the IT cortex. These differences could determine whether or not they can detect emotions via facial expressions.


AI detects autism speech patterns across different languages

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A new study led by Northwestern University researchers used machine learning--a branch of artificial intelligence--to identify speech patterns in children with autism that were consistent between English and Cantonese, suggesting that features of speech might be a useful tool for diagnosing the condition. Undertaken with collaborators in Hong Kong, the study yielded insights that could help scientists distinguish between genetic and environmental factors shaping the communication abilities of people with autism, potentially helping them learn more about the origin of the condition and develop new therapies. Children with autism often talk more slowly than typically developing children, and exhibit other differences in pitch, intonation and rhythm. But those differences (called "prosodic differences'" by researchers) have been surprisingly difficult to characterize in a consistent, objective way, and their origins have remained unclear for decades. However, a team of researchers led by Northwestern scientists Molly Losh and Joseph C.Y. Lau, along with Hong Kong-based collaborator Patrick Wong and his team, successfully used supervised machine learning to identify speech differences associated with autism. The data used to train the algorithm were recordings of English- and Cantonese-speaking young people with and without autism telling their own version of the story depicted in a wordless children's picture book called "Frog, Where Are You?" The results were published in the journal PLOS One on June 8, 2022.