Summary: A new AI model that mimics the neural architecture and connections of the hippocampus is able to alter its synaptic connections as it moves a car-like virtual robot. HBP researchers at the Institute of Biophysics of the National Research Council (IBF-CNR) in Palermo, Italy, have mimicked the neuronal architecture and connections of the brain's hippocampus to develop a robotic platform capable of learning as humans do while the robot navigates around a space. The simulated hippocampus is able to alter its own synaptic connections as it moves a car-like virtual robot. Crucially, this means it needs to navigate to a specific destination only once before it is able to remember the path. This is a marked improvement over current autonomous navigation methods that rely on deep learning, and which have to calculate thousands of possible paths instead.

Summary: Artificial intelligence can detect signs of mild cognitive decline and Alzheimer's disease, even when no symptoms are apparent, by analyzing a person's speech. The technology could be used as a simple screening method to identify early signs of cognitive impairment. New technologies that can capture subtle changes in a patient's voice may help physicians diagnose cognitive impairment and Alzheimer's disease before symptoms begin to show, according to a UT Southwestern Medical Center researcher who led a study published in the Alzheimer's Association publication Diagnosis, Assessment & Disease Monitoring. "Our focus was on identifying subtle language and audio changes that are present in the very early stages of Alzheimer's disease but not easily recognizable by family members or an individual's primary care physician," said Ihab Hajjar, M.D., Professor of Neurology at UT Southwestern's Peter O'Donnell Jr. Brain Institute. Researchers used advanced machine learning and natural language processing (NLP) tools to assess speech patterns in 206 people – 114 who met the criteria for mild cognitive decline and 92 who were unimpaired.

Summary: Utilizing data from Twitter and applying natural language processing artificial intelligence algorithms, researchers created a new, accurate prediction model for depression and anxiety. Researchers at the University of São Paulo (USP) in Brazil are using artificial intelligence (AI) and Twitter, one of the world's largest social media platforms, to try to create anxiety and depression prediction models that could in future provide signs of these disorders before clinical diagnosis. The study is reported in an article published in the journal Language Resources and Evaluation. Construction of a database, called SetembroBR, was the first step in the study. The name is a reference to Yellow September, an annual suicide awareness and prevention campaign, and also to the fact that data collection for the study began one day in September. The second step is still in progress but has provided some preliminary findings, such as the possibility of detecting whether a person is likely to develop depression solely on the basis of their social media friends and followers, without taking their own posts into account.

Summary: Ocular drift, or tiny eye movements that seem random can be influenced by prior knowledge of an expected visual target, researchers report. A very subtle and seemingly random type of eye movement called ocular drift can be influenced by prior knowledge of the expected visual target, suggesting a surprising level of cognitive control over the eyes, according to a study led by Weill Cornell Medicine neuroscientists. The discovery, described Apr. 3 in Current Biology, adds to the scientific understanding of how vision--far from being a mere absorption of incoming signals from the retina--is controlled and directed by cognitive processes. "These eye movements are so tiny that we're not even conscious of them, and yet our brains somehow can use the knowledge of the visual task to control them," says study lead author Dr. Yen-Chu Lin, who carried out the work as a Fred Plum Fellow in Systems Neurology and Neuroscience in the Feil Family Brain and Mind Research Institute at Weill Cornell Medicine. Dr. Lin works in the laboratory of study senior author Dr. Jonathan Victor, the Fred Plum Professor of Neurology at Weill Cornell Medicine. The study involved a close collaboration with the laboratory of Dr. Michele Rucci, professor of brain and cognitive sciences and neuroscience at the University of Rochester.

Summary: Using machine learning technology, researchers concluded the risk of genetic risk may outweigh age as a predictor of whether a person will develop Alzheimer's disease. Once adults reach age 65, the threshold age for the onset of Alzheimer's disease, the extent of their genetic risk may outweigh age as a predictor of whether they will develop the fatal brain disorder, a new study suggests. The study, published recently in the journal Scientific Reports, is the first to construct machine learning models with genetic risk scores, non-genetic information and electronic health record data from nearly half a million individuals to rank risk factors in order of how strong their association is with eventual development of Alzheimer's disease. Researchers used the models to rank predictive risk factors for two populations from the UK Biobank: White individuals aged 40 and older, and a subset of those adults who were 65 or older. Results showed that age – which constitutes one-third of total risk by age 85, according to the Alzheimer's Association – was the biggest risk factor for Alzheimer's in the entire population, but for the older adults, genetic risk as determined by a polygenic risk score was more predictive.

Summary: New artificial intelligence technology is able to screen for genetic mutations in brain cancer tumors in less than 90 seconds. Using artificial intelligence, researchers have discovered how to screen for genetic mutations in cancerous brain tumors in under 90 seconds -- and possibly streamline the diagnosis and treatment of gliomas, a study suggests. A team of neurosurgeons and engineers at Michigan Medicine, in collaboration with investigators from New York University, University of California, San Francisco and others, developed an AI-based diagnostic screening system called DeepGlioma that uses rapid imaging to analyze tumor specimens taken during an operation and detect genetic mutations more rapidly. In a study of more than 150 patients with diffuse glioma, the most common and deadly primary brain tumor, the newly developed system identified mutations used by the World Health Organization to define molecular subgroups of the condition with an average accuracy over 90%. The results are published in Nature Medicine. "This AI-based tool has the potential to improve the access and speed of diagnosis and care of patients with deadly brain tumors," said lead author and creator of DeepGlioma Todd Hollon, M.D., a neurosurgeon at University of Michigan Health and assistant professor of neurosurgery at U-M Medical School.

Summary: Using ECG data, a new machine learning algorithm was able to predict death within 5 years of a patient being admitted to hospital with 87% accuracy. The AI was able to sort patients into 5 categories ranging from low to high risk of death. If you've ever been admitted to hospital or visited an emergency department, you've likely had an electrocardiogram, or ECG, a standard test involving tiny electrodes taped to your chest that checks your heart's rhythm and electrical activity. Hospital ECGs are usually read by a doctor or nurse at your bedside, but now researchers are using artificial intelligence to glean even more information from those results to improve your care and the health-care system all at once. In recently published findings, the research team built and trained machine learning programs based on 1.6 million ECGs done on 244,077 patients in northern Alberta between 2007 and 2020.

Summary: Text-to-image generation deep learning models like OpenAI's DALL-E 2 can be a promising new tool for image augmentation, generation, and manipulation in a healthcare setting. A new paper published in the Journal of Medical Internet Research describes how generative models such as DALL-E 2, a novel deep learning model for text-to-image generation, could represent a promising future tool for image generation, augmentation, and manipulation in health care. Do generative models have sufficient medical domain knowledge to provide accurate and useful results? Dr Lisa C Adams and colleagues explore this topic in their latest viewpoint titled "What Does DALL-E 2 Know About Radiology?" First introduced by OpenAI in April 2022, DALL-E 2 is an artificial intelligence (AI) tool that has gained popularity for generating novel photorealistic images or artwork based on textual input.

Summary: Utilizing a classic neural network, researchers have created a new artificial intelligence model based on recent biological findings that shows improved memory performance. Computer models are an important tool for studying how the brain makes and stores memories and other types of complex information. But creating such models is a tricky business. Somehow, a symphony of signals – both biochemical and electrical – and a tangle of connections between neurons and other cell types creates the hardware for memories to take hold. Yet because neuroscientists don't fully understand the underlying biology of the brain, encoding the process into a computer model in order to study it further has been a challenge.

Summary: Examining the cognitive abilities of the AI language model, GPT-3, researchers found the algorithm can keep up and compete with humans in some areas but falls behind in others due to a lack of real-world experience and interactions. Researchers at the Max Planck Institute for Biological Cybernetics in Tübingen have examined the general intelligence of the language model GPT-3, a powerful AI tool. Using psychological tests, they studied competencies such as causal reasoning and deliberation, and compared the results with the abilities of humans. Their findings paint a heterogeneous picture: while GPT-3 can keep up with humans in some areas, it falls behind in others, probably due to a lack of interaction with the real world. Neural networks can learn to respond to input given in natural language and can themselves generate a wide variety of texts.