Neurology


Artificial intelligence boosts MRI detection of ADHD

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Deep learning, a type of artificial intelligence, can boost the power of MRI in predicting attention deficit hyperactivity disorder (ADHD), according to a study published in Radiology: Artificial Intelligence. Researchers said the approach could also have applications for other neurological conditions. The human brain is a complex set of networks. Advances in functional MRI, a type of imaging that measures brain activity by detecting changes in blood flow, have helped with the mapping of connections within and between brain networks. This comprehensive brain map is referred to as the connectome.


Transforming Clinical Trials with the Power of AI

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Developing prescription drugs is a high-cost, high-risk endeavor. Average research and development for an approved prescription drug requires an investment of $2.9 billion and takes more than 11 years. Clinical trials alone can cost an average of $1.1 billion over 6.6 years. In fact, clinical trials account for a staggering 40 percent of the pharmaceutical industry's research budget. To make matters worse, only 14 percent of drugs that enter clinical trials are eventually approved.


This AI can detect ADHD better than humans

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A team of researchers used a type of artificial intelligence to predict attention deficit hyperactivity disorder (ADHD) in patients by having it analyze magnetic resonance imaging (MRI) scans. According to a new paper published in the journal Radiology: Artificial Intelligence, their technique could also be used to spot other neurological conditions. Health care professionals have increasingly been relying on MRI scans to understand ADHD, a brain disorder that often causes patients to be restless, and makes it more difficult for them to pay attention. More than eight percent of children in the U.S. have been diagnosed with the condition according to The American Psychiatric Association (APA). Research suggests that a breakdown in the connections between the different regions of the brain, the so-called connectome, causes ADHD.


r/artificial - For Neuralink run tests to see if you can put thoughts into someone or something's head

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Easy enough to abstract information from someone's mind, but you'll know you're getting somewhere when you put information "in." Like maybe if you can get a monkey to "get the red ball" and they routinely do after having the thought put in their mind. Or for human trials have then be given a question they could know the answer to if the thought insertion worked. You shouldn't be trying to get a brain and a computer to work directly in tandem. Not at all compatible, but you can translate thoughts into computer code, have the computer do the processing and then insert the thought back.


Artificial Intelligence Boosts MRI Detection of ADHD Artificial Intelligence Research

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Deep learning, a type of artificial intelligence, can boost the power of MRI in predicting attention deficit hyperactivity disorder (ADHD), according to a study published in Radiology: Artificial Intelligence. Researchers said the approach could also have applications for other neurological conditions. The human brain is a complex set of networks. Advances in functional MRI, a type of imaging that measures brain activity by detecting changes in blood flow, have helped with the mapping of connections within and between brain networks. This comprehensive brain map is referred to as the connectome.


FastMRI initiative releases neuroimaging data set

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FastMRI, a joint research collaboration between Facebook AI and NYU Langone Health to use AI to speed up magnetic resonance imaging (MRI) scans, is announcing a new open source data set from NYU Langone Health, along with baseline models and a newly expanded research paper to help the AI research community accelerate and broaden research in this area. Our research collaborators at NYU Langone Health are making available 6,970 fully de-identified cases of neuro MRIs in raw (k-space) format and 10,000 more cases containing 370,000 image slices in DICOM format as part of the fastMRI project. Researchers can request access to the new brain MRI data set on NYU Langone Health's fastMRI site, and the accompanying research paper and baseline models can be found here: This is the largest public data set of raw k-space format brain MRIs available to researchers, and it follows our release last year of the largest knee MRI data set and the recently completed fastMRI image reconstruction challenge. K-space data is collected during scanning but typically discarded after it's used to generate images. The information can be used to train models, validate their performance, and generally simulate how image reconstruction techniques would be used in real-world conditions.


Scientists finally develop artificial neurons that mimic our brain cells

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Neurons in a human brain have been somewhat of a mystery for scientists. Unlike the traditional electrical circuits, the inner workings of the biological circuitry in the brain have always been less than predictable, apart from the complex biology they exhibit. Scientists at the University of Bath now seem to have decoded the bizarre behavior of our brain cells and replicated it on tiny silicon chips. Researchers from the Universities of Bristol, Zurich & Auckland collaborated on this effort. Designing artificial neurons has been a challenge for medical researchers for decades.



Harnessing the power of machine learning for earlier autism diagnosis

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When Grayson Kollins was two and a half years old--just shortly after the birth of his younger sister--his parents noticed that he had all but stopped uttering the sentences and phrases that up until then he had been using to communicate. In addition, his daycare provider mentioned that Grayson had begun repeating phrases over and over, and lacked interest in playing with other children. Grayson's father Scott Kollins, Ph.D., a clinical psychologist and professor of psychiatry and behavioral sciences in the School of Medicine at Duke, was well aware of the symptoms of autism spectrum disorder, or ASD, a neurodevelopmental disorder that affects the ability to socially interact and communicate with others. Although it usually manifests early in life, it is a lifelong condition and can have profound effects on learning, employment, and personal relationships. Prompted by these early symptoms, Grayson's parents subsequently had him assessed, and he received a clinical diagnosis of ASD.


Harnessing the power of machine learning for earlier autism diagnosis

#artificialintelligence

When Grayson Kollins was two and a half years old--just shortly after the birth of his younger sister--his parents noticed that he had all but stopped uttering the sentences and phrases that up until then he had been using to communicate. In addition, his daycare provider mentioned that Grayson had begun repeating phrases over and over, and lacked interest in playing with other children. Grayson's father Scott Kollins, Ph.D., a clinical psychologist and professor of psychiatry and behavioral sciences in the School of Medicine at Duke, was well aware of the symptoms of autism spectrum disorder, or ASD, a neurodevelopmental disorder that affects the ability to socially interact and communicate with others. Although it usually manifests early in life, it is a lifelong condition and can have profound effects on learning, employment, and personal relationships. Prompted by these early symptoms, Grayson's parents subsequently had him assessed, and he received a clinical diagnosis of ASD.