How old is your brain, really? Just like people who look older than they really are, scientists say a person's brain can age faster than the rest of their body. With that in mind, researchers at USC have created an artificial intelligence program which can accurately tell how old someone's brain is -- while also pointing out warning signs for Alzheimer's disease. The AI program analyzes MRI brain scans, looking for signs of cognitive decline which have a link to neurodegenerative diseases, like Alzheimer's. Brain aging is one of the most reliable markers for neurodegenerative disease risk.

Summary: OpenAI's ChatGPT program can identify clues from spontaneous speech that are 80% accurate in predicting the early stages of dementia. The artificial intelligence algorithms behind the chatbot program ChatGPT--which has drawn attention for its ability to generate humanlike written responses to some of the most creative queries--might one day be able to help doctors detect Alzheimer's disease in its early stages. Research from Drexel University's School of Biomedical Engineering, Science and Health Systems recently demonstrated that OpenAI's GPT-3 program can identify clues from spontaneous speech that are 80% accurate in predicting the early stages of dementia. Reported in the journal PLOS Digital Health, the Drexel study is the latest in a series of efforts to show the effectiveness of natural language processing programs for early prediction of Alzheimer's--leveraging current research suggesting that language impairment can be an early indicator of neurodegenerative disorders. The current practice for diagnosing Alzheimer's Disease typically involves a medical history review and lengthy set of physical and neurological evaluations and tests.

After its earlier schedule was postponed, Neuralink, Elon Musk's neurotechnology company that creates implantable brain-machine interfaces, is finally set to hold its "Show and Tell" event on November 30. The official Twitter account of Neuralink tweeted on Friday a brief video invitation asking people to join them in its "Show and Tell" event. The tweet also came with the caption, "Nov 30, 6 pm PT," which is presumably the date of the event. In April, Musk shared that Neuralink's first human trials were still set for the end of 2022. In August Musk announced Neuralink's "Show and Tell" event planned for October, but given the tech billionaire's acquisition of Twitter at around that time, the event was pushed to the end of November.

Work with the platform development team to assist with the data collection, processing and validation. This may include software development tasks to enable the linkage of mHealth data with other health and non-health datasets. Work with the platform development team to assist with the data collection, processing and validation. This may include software development tasks to enable the linkage of mHealth data with other health and non-health datasets.

Citizen Science Salon is a partnership between Discover and SciStarter.org. In 2016, a team of Alzheimer's disease researchers at Cornell University hit a dead end. The scientists were studying mice, looking for links between Alzheimer's and blood flow changes in the brain. For years, scientists have known that reduced blood flow in the brain is a symptom of Alzheimer's disease. More recent research has also shown that this reduced blood flow can be caused by clogged blood vessels -- or "stalls." And by reversing these stalls in mice, scientists were able to restore their memory.

Traditionally, nonlinear inversion, direct inversion, or wave estimation methods have been used for reconstructing images from MRE displacement data. In this work, we propose a convolutional neural network architecture that can map MRE displacement data directly into elastograms, circumventing the costly and computationally intensive classical approaches. In addition to the mean squared error reconstruction objective, we also introduce a secondary loss inspired by the MRE mechanical models for training the neural network. Our network is demonstrated to be effective for generating MRE images that compare well with equivalents from the nonlinear inversion method.

We present hidden fluid mechanics (HFM), a physics informed deep learning framework capable of encoding an important class of physical laws governing fluid motions, namely the Navier-Stokes equations. In particular, we seek to leverage the underlying conservation laws (i.e., for mass, momentum, and energy) to infer hidden quantities of interest such as velocity and pressure fields merely from spatio-temporal visualizations of a passive scaler (e.g., dye or smoke), transported in arbitrarily complex domains (e.g., in human arteries or brain aneurysms). Our approach towards solving the aforementioned data assimilation problem is unique as we design an algorithm that is agnostic to the geometry or the initial and boundary conditions. This makes HFM highly flexible in choosing the spatio-temporal domain of interest for data acquisition as well as subsequent training and predictions. Consequently, the predictions made by HFM are among those cases where a pure machine learning strategy or a mere scientific computing approach simply cannot reproduce. The proposed algorithm achieves accurate predictions of the pressure and velocity fields in both two and three dimensional flows for several benchmark problems motivated by real-world applications. Our results demonstrate that this relatively simple methodology can be used in physical and biomedical problems to extract valuable quantitative information (e.g., lift and drag forces or wall shear stresses in arteries) for which direct measurements may not be possible.

We have constructed an information extraction system called the Mars Target Encyclopedia that takes in planetary science publications and extracts scientific knowledge about target compositions. The extracted knowledge is stored in a searchable database that can greatly accelerate the ability of scientists to compare new discoveries with what is already known. To date, we have applied this system to ~6000 documents and achieved 41-56% precision in the extracted information.

Transcranial direct current stimulation has been claimed to enhance learning.Credit: Liz Hafalia/Polaris/eyevine Is there a common element that binds diverse mental abilities, from language to mental arithmetic? Or do these skills compete for our brains' limited resources? In The Genius Within, Dav...

Building off of work published in 2015, the team used AI to analyze the speech patterns of 59 individuals who had participated in a separate study. Transcripts of an interview the participants took part in were broken down into parts of speech and were scored on how coherent the sentences were. Then, the machine learning model determined, based on those speech patterns, who was at risk of developing psychosis and who wasn't. Of those participants, 19 developed a psychotic disorder within two years while 40 did not and the model was able to predict that with 83 percent accuracy. It was also able to differentiate speech patterns of patients who had recently developed psychosis from those of healthy patients with 72 percent accuracy.