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Eyesight tests could predict which people with Parkinson's will develop dementia 18 months on

Daily Mail - Science & tech

Eyesight tests could be used to identify which people with Parkinson's disease are likely to suffer from cognitive impairment and possible dementia 18 months later. UK researchers have found that people with Parkinson's who perform less well in eye tests show worse cognitive performance a year and a half later. The study is one of two by University College London (UCL) published this month looking at people with Parkinson's – the progressive nervous system disorder that causes shakiness and stiffness. The second study found structural and functional connections of brain regions become'decoupled' throughout the entire brain in people with Parkinson's disease, particularly among people with vision problems. The findings support previous evidence that vision changes precede the cognitive decline that occurs in many, but not all, people with Parkinson's.

Interview with Guillem Alenyà – discussing assistive robotics, human-robot interaction, and more


His research activities include assistive robotics, robot adaptation, human-robot interactions and grasping of deformables. We spoke about some of the projects he is involved in and his plans for future work. The SOCRATES project is about quality of interaction between robots and users, and our role is focussed on adapting the robot behaviour to user needs. We have concentrated on a very nice use case: cognitive training of mild dementia patients. We are working with a day-care facility in Barcelona and asked if we could provide some technological help for the caregiver.

EEG Plus Machine-learning Identifies Mild Cognitive Impairment...


Using non-invasive, dry-electroencephalography (EEG) and machine-learning computer algorithms, researchers were able to distinguish between Parkinson's disease patients with and without mild cognitive impairments with 80% accuracy, early results of a study found. Follow-up assessments will be conducted after 12 months to validate the method's ability to predict cognitive impairment in this patient population. The results were presented in a poster, "The Identification of Mild Cognitive Impairment in Parkinson's disease using EEG and Machine Learning, "at the Alzheimer's Association International Conference 2020. The poster abstract was published in the journal Alzheimer's & Dementia. Cognitive impairment is a common symptom of Parkinson's disease characterized by difficulties in executive function, attention, vision, word-finding, and problems with learning and remembering information.

Experimental drug can reverse age-related cognitive decline within THREE DAYS, new mouse study finds

Daily Mail - Science & tech

Scientists believe they may have created the first drug that reverses age-related cognitive decline in just days. It works by rebooting the protein production machinery of the cells in our brains after it gets smothered by a stress response from our body. Research conducted on aged mice showed the compound restored memory and mental flexibility as well rejuvenated brain and immune cells. The team, from the University of California, San Francisco (UCSF) says the findings shed fresh light on age-related brain diseases and could lead to a treatment that stops the progression of symptoms. An integrated stress response (ISR) can stop the protein production machine of cells if it notices something is wrong, but can also damage cells in our brains if left'on' (file image) In the past, the team had been studying integrated stress response (ISR), which can be triggered within a cell due to certain conditions.

This AI task-based app hopes to improve dementia care


Mindset takes people through three "fun" cognitive tasks with the aim of creating a significant dementia database and one day screen for the syndrome. A group of UK medical students have released medical app Mindset which hopes to become the "world's largest dementia AI initiative". The brain syndrome – which can cause memory loss and changes in behaviour – is a significant and growing problem, particularly for people over the age of 65. It is thought that around 62% of individuals suffering from dementia are undiagnosed. By 2050, the number of people who suffer from the condition is expected to triple, mostly because of an aging population.

SK Telecom offers AI voice analysis for dementia diagnosis


SK Telecom and Seoul National University College of Medicine have developed an artificial intelligence (AI) tool to help identify dementia earlier. The AI tool analyses the voices of patients by having a 10-minute conversation with them to determine if they may have dementia, the two organisations said on Monday. They said the AI is able to do this as voice emitted from the vocal cord changes as it passes through the vocal tract and the way this happens is different for healthy individuals and dementia patients. The AI looks for this difference when making its diagnosis. This accessibility will assist in early diagnosis, SK Telecom said, which it explained is crucial for slowing down the progression of dementia.

IBM scientists hope to detect early signs of dementia using AI


Researchers from IBM and Pfizer have published details on a new AI model that interprets written speech, which they claim can predict whether a person will develop Alzheimer's seven years before they show symptoms. The idea is attractive for its simplicity: The model's only input is a written sample from the "cookie-theft picture description task," a common cognitive test that asks participants to describe what's happening in a drawing (three guesses what the drawing is of). Researchers trained the AI to pore over participants' responses, picking up on hints of cognitive decline like repetition, misspellings, two-word sentences, and limited vocabulary. Now, hold your applause: The model is in early days, and it isn't any better than current cognitive assessments. The initial study--based on data gathered from just 270 Americans over the course of four decades--showed the AI could predict a future Alzheimer's diagnosis 70% of the time.

Multitask Bandit Learning through Heterogeneous Feedback Aggregation Machine Learning

Online multi-armed bandit learning has many important real-world applications (see Villar et al., 2015; Shen et al., 2015; Li et al., 2010, for a few examples). In practice, a group of online bandit learning agents are often deployed for similar tasks, and they learn to perform these tasks in similar yet nonidentical environments. For example, a group of assistive healthcare robots may be deployed to provide personalized cognitive training to people with dementia (PwD), e.g., by playing cognitive training games with people (Kubota et al., 2020). Each robot seeks to learn the preferences of its paired PwD so as to recommend tailored health intervention based on how the PwD reacts to and is engaged with the activities (as captured by sensors on the robots) (Kubota et al., 2020). As PwD may have similar preferences and may therefore exhibit similar reactions, one natural question arises--can the robots as a multi-agent system learn to perform their respective tasks faster through collaboration? In this paper, we develop multi-agent bandit learning algorithms where each agent can robustly aggregate data from other agents to better perform its respective task. We generalize the the multi-armed bandit problem (Auer et al., 2002) and formulate the ɛ-Multi-Player Multi-Armed Bandit (ɛ-MPMAB) problem, which models heterogeneous multitask learning in a multi-agent bandit learning setting. In an ɛ-MPMAB problem instance, a set of M players are deployed to perform similar tasks--simultaneously they interact with a set of actions/arms, and for each arm, different players receive feedback from similar but not necessarily identical reward distributions. In the above assistive robotics example, each player corresponds to a robot; each arm corresponds to one of the cognitive activities to choose from; for each player and each arm, there is a separate reward distribution which reflects a PwD's

USC leads massive new artificial intelligence study of Alzheimer's


A massive problem like Alzheimer's disease (AD) — which affects nearly 50 million people worldwide — requires bold solutions. New funding expected to total $17.8 million, awarded to the Keck School of Medicine of USC's Mark and Mary Stevens Neuroimaging and Informatics Institute (INI) and its collaborators, is one key piece of that puzzle. The five-year National Institutes of Health (NIH)-funded effort, "Ultrascale Machine Learning to Empower Discovery in Alzheimer's Disease Biobanks," known as AI4AD, will develop state-of-the-art artificial intelligence (AI) methods and apply them to giant databases of genetic, imaging and cognitive data collected from AD patients. Forty co-investigators at 11 research centers will team up to leverage AI and machine learning to bolster precision diagnostics, prognosis and the development of new treatments for AD. "Our team of experts in computer science, genetics, neuroscience and imaging sciences will create algorithms that analyze data at a previously impossible scale," said Paul Thompson, PhD, associate director of the INI and project leader for the new grant. "Collectively, this will enable the discovery of new features in the genome that influence the biological processes involved in Alzheimer's disease." Predicting a diagnosis The project's first objective is to identify genetic and biological markers that predict an AD diagnosis — and to distinguish between several subtypes of the disease. To accomplish this, the research team will apply sophisticated AI and machine learning methods to a variety of data types, including tens of thousands of brain images and whole genome sequences. The investigators then will relate these findings to the clinical progression of AD, including in patients who have not yet developed dementia symptoms. The researchers will train AI methods on large databases of brain scans to identify patterns that can help detect the disease as it emerges in individual patients. "As we get older, each of us has a unique mix of brain changes that occur for decades before we develop any signs of Alzheimer's disease — changes in our blood vessels, the buildup of abnormal protein deposits and brain cell loss," said Thompson, who also directs INI's Imaging Genetics Center. "Our new AI methods will help us determine what changes are happening in each patient, as well as drivers of these processes in their DNA, that we can target with new drugs." The team is even creating a dedicated "Drug Repurposing Core" to identify ways to repurpose existing drugs to target newly identified segments of the genome, molecules or neurobiological processes involved in the disease. "We predict that combining AI with whole genome data and advanced brain scans will outperform methods used today to predict Alzheimer's disease progression," Thompson said. Advancing AI The AI4AD effort is part of the "Cognitive Systems Analysis of Alzheimer's Disease Genetic and Phenotypic Data" and "Harmonization of Alzheimer's Disease and Related Dementias (AD/ADRD) Genetic, Epidemiologic, and Clinical Data to Enhance Therapeutic Target Discovery" initiatives from the NIH's National Institute on Aging. These initiatives aim to create and develop advanced AI methods and apply them to extensive and harmonized rich genomic, imaging and cognitive data. Collectively, the goals of AI4AD leverage the promise of machine learning to contribute to precision diagnostics, prognostication, and targeted and novel treatments. Thompson and his USC team will collaborate with four co-principal investigators at the University of Pennsylvania, the University of Pittsburgh and the Indiana University School of Medicine. The researchers will also host regular training events at major AD neuroimaging and genetics conferences to help disseminate newly developed AI tools to investigators across the field. Research reported in this publication will be supported by the National Institute on Aging of the National Institutes of Health under Award Number U01AG068057. Also involved in the project are INI faculty members Neda Jahanshad and Lauren Salminen, as well as consortium manager Sophia Thomopoulos. — Zara Greenbaum

IoT + AI = Divine Healthcare Pair for helping Elderly


As we age our bodies becomes a host to several diseases and inabilities. It's like the reverse cycle of a young-one growing up that executes in the opposite direction. Here's how AI and IoT in healthcare can help dementia patients. With age, senior people tend to lose their ability to walk correctly, hear well, speak sharply, and they get blurred vision. Dementia is the dysfunction of several mental conditions like memory loss, decision making, or thinking potential.