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Smartphone app shown to improve memory recall

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A new smartphone app, designed by University of Toronto researchers, has been shown to significantly improve memory recall in older adults.

In a study published in the Proceedings of the National Academy of Sciences, University of Toronto (U of T) researchers have demonstrated that their new smartphone application helps to significantly improve memory recall, which could prove beneficial for individuals in the early stages of Alzheimer’s disease or other forms of memory impairment.

Known as HippoCamera for its ability to mimic the function of the brain’s hippocampus in memory construction and retention, the app enhances the encoding of memories stored in the brain by boosting attention to daily events and consolidating them more distinctly, thus later enabling richer, more comprehensive recall.

In a two-step process, HippoCamera users record a short video of up to 24 seconds of a moment they want to remember with a brief eight-second audio description of the event. The app combines the two elements just as the brain’s hippocampus would, with the video component sped up to mimic aspects of hippocampal function and to facilitate efficient review. Users then replay cues produced by HippoCamera at later times on a curated and regular basis to reinforce the memory and enable detailed recall.

“We found that memories with an associated HippoCamera cue were long-lasting, and that it worked for everyone in the study – healthy older adults, those starting to show cognitive decline, and even one case with severe amnesia due to an acquired brain injury,” said study co-author and professor Morgan Barense, Canada Research Chair in Cognitive Neuroscience in the Department of Psychology in the Faculty of Arts & Science at U of T who is leading the development of the app. 

“Many months after the initial part of the study ended, and participants had not watched their HippoCamera cues, they were able to recall these memories in rich detail.”

The study shows that regular users of the app were able to recall over 50 per cent more details about everyday experiences that took place as many as six months earlier, than if they had only recorded events and never replayed them.

The new research suggests that systematic reactivation of memories for recent real-world experiences can help to maintain a bridge between the present and past in older adults and holds promise for people in the early stages of Alzheimer’s disease or other forms of memory impairment.

The study also found that reviewing memory cues with HippoCamera resulted in more positive sentiment during later retrieval.

“So, there’s something about being better able to remember these events that made people feel closer to them and more positive,” said Professor Barense, who is also an adjunct scientist at the Rotman Research Institute at Baycrest.

“This is a really important finding given what we know about dementia and the fact that positive reminiscence or focusing on positive life events and positive emotions can improve both memory and well-being in dementia.”

For the study, participants recorded unique HippoCamera clips for everyday events that they wanted to remember and subsequently replayed these memory cues approximately eight times over a two-week period in one experiment, and over a ten-week period in a second experiment. The researchers then initiated a cued recall task, where they showed the participants their memory cues and asked them to describe everything they could remember about each event.

This was followed by fMRI brain scanning sessions where researchers measured patterns of brain activity while participants saw their cues and completed a memory test. Three months later, after not practicing their HippoCamera memories and not having access to the cues, the participants were asked to recall these events a second time.

“On average, we saw on later recall an increase of more than 50 per cent in the amount of rich, detailed information that someone was able to remember, about events that happened as many as 200 days ago, which is significant,” said Chris Martin, an assistant professor in the Department of Psychology at Florida State University and lead author of the study. 

“Memory is truly self-sustaining – a strong memory cue can bring along another memory, which can feed into another. You just have to focus on the cue in the first place.”

Further, the brain scans showed that replaying HippoCamera memory cues changed the way in which these everyday experiences were coded in the hippocampus, which has a well-established role in storing detailed memories for recent experiences. 

Recall-related activity in the hippocampus was more distinctive, meaning that HippoCamera replay helps to ensure that memories for different events remain separate from one another in the brain.

“The more detailed recollection seen earlier in the study was associated with more differentiated memory signals in the hippocampus,” said Martin. 

“That HippoCamera is aiding the hippocampus in distinctly encoding memories, so they do not become confused with one another, explains why users are able to recall past events in such great detail. It’s evidence that rich and detailed memory reactivation promotes memory differentiation at the neural level, and that this allows us to mentally re-experience the past with vivid detail.”

One key factor in HippoCamera’s effectiveness, the researchers say, is the sense of purpose and intention inherent in its use. By its very design, the intervention prompts users to think about what it is that they want to remember and why a particular moment is important to them, and then regularly re-engage with the memories in a meaningful way.

“Someone who is committed to using HippoCamera is going to go through their lives paying attention to what is happening to them, asking themselves if this is an event they want to capture,” said Professor Barense. 

“If it is, they’re going to take the time to stop and describe that event. And that act of approaching events in our lives with more attention is going to be good for memory.

“Then later, there’s an intention with how we study those memories, taking the time to review them using optimal learning techniques.”

The researchers note that as people begin to lose their existing memories at any point in their lives, as well as their ability to create new ones, they start to lose their sense of self. As a result, they often become disengaged from the people and events in their lives.

“Memory and our sense of identity are very closely linked,” said Professor Barense, who is receiving support from U of T startup accelerator UTEST to take the app from lab to market. 

“We understand who we are as people by remembering the things that we’ve done. Our hope with HippoCamera is that by helping people feel closer to these people and events in their lives, we can help give them back their sense of self.”

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Opinion: How robots could help to ease the social care crisis

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Robear is an animatronic bear that lifts elders with mobility problems, Paro is a fuzzy robotic seal intended to provide a futuristic form of animal therapy, and Pepper is a humanoid with remote-monitoring capabilities and learning tools.

Pet robots have revolutionised and enhanced the standard of care, improving the wellbeing of care home residents. Here Stephen Hayes, managing director at automation tech firm Beckhoff UK, explores the benefits of integrating robots in social care homes.

There is a wide variety of care robots on the market. Some are aimed at physical care, including machines that can assist with mobility and exercise, feed their owner and help them with hygiene tasks. These could greatly benefit caregivers, freeing their time and preventing them from suffering from long-term health conditions or disability due to the physical effort associated with giving care.

Others play the role of a companion, engaging older people emotionally to reduce and even prevent cognitive decline, providing companionship for lonely older people, and making those with cognitive conditions easier for care staff to manage.

Research by the University of Plymouth, conducted in care homes using these pets found decreased neuropsychiatric symptoms such as delusions, depression, anxiety, apathy and occupational disruptiveness because they provided a sense of responsibility and purpose.

Social care vacancies are higher than before the COVID-19 pandemic, and data shows one in ten social care posts are unfilled in a staffing crisis that could have harmful results for residents. In England, 152,000 social care posts remain empty, according to a report released by Skills for Care.

Steve Barclay, Secretary of State for Health and Social Care commented for The Telegraph earlier this year, stating that robots and AI are key to better supporting patients and reducing demand on social care staff. He said that there was a need to adopt an innovative approach to health and attempt to cut NHS waiting times while improving care for the elderly.

However, these robots present limitations, such as superficiality and lack of personalisation. Also, the content of their conversations can be very limited, making them less entertaining with the pass of time.

This was the case of the humanoid Pepper, for which production ceased in 2021 due to a weak demand as care homes did not see the long-term benefit of his interactions. Nevertheless, robots like Paro, which move and respond to touch, have had a positive impact on the wellbeing of care home residents. However, with a cost of £5,000, care homes are looking for more affordable options.

Japan is a pioneer in developing this kind of technology. The nation is facing a ‘greying’ crisis due to the aging of its population, so the country has invested heavily in developing caretech able to serve and provide emotional support.

In the UK, there are currently almost 12 million people who are aged 65 or over, and the number of people coping with illnesses such as arthritis or dementia is expected to increase. In fact, a recent machine learning study by the Journal of Medical Systems suggests that 135 million people might be affected by dementia by 2050.

To allow for continuous innovation in this field, the UK Government announced its commitment to invest at least 2.4 per cent of GDP in R&D by 2027. The programme supports the UK Government’s Ageing Society Grand Challenge and Future of Mobility Grand Challenge, which will ensure we meet the needs of an ageing society.

With this in mind, there are certain technologies that we are likely to see more of in care homes over the coming years, including robots that can connect to each other and other devices.

This includes devices such as oximeters, thermometers, or even thermal cameras, enabling the elderly to have consultations any time of the day, from home, and send out emergency notices to staff or hospitals. However, for care robots to be a success, state-of-the-art control technology is required.

Beckhoff’s Ethernet-based fieldbus system, EtherCAT, has extension modules that are compatible with third party hardware for integration. This platform process data and transports it directly, has a flexible topology and simple line or tree structure that requires no expensive infrastructure components and includes the environments for programming, diagnostics and configuration.

This global standard for real-time Ethernet communication provides workers with real-time information about elders like location, health condition, or learning progress. This data also allows carers focus their time on other urgent tasks, optimise resources and personalise treatment.

The advancement and implementation of robot pets could improve awareness of preventative care, reduce anxiety on disease and enhance stakeholder relationship.

Further research on caretech would tackle functional problems, making these devices an essential asset for any caregiver. By investing in the right control technology now, social care homes will be better prepared to take care of their residents.

Beckhoff provides PC-based control and EtherCAT to connect caretech systems. See more here.

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New AI program could predict likelihood of Alzheimer’s disease

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By analysing speech patterns, a new machine learning model can predict with a high degree of accuracy whether someone with mild cognitive impairment will develop Alzheimer’s-associated dementia within six years.

Trying to figure out whether someone has Alzheimer’s disease usually involves a battery of assessments—interviews, brain imaging, blood and cerebrospinal fluid tests. But, by then, it’s probably already too late: memories have started slipping away, long established personality traits have begun subtly shifting.

If caught early, new pioneering treatments can slow the disease’s remorseless progression, but there’s no surefire way to predict who will develop the dementia associated with Alzheimer’s.

Now, Boston University researchers say they have designed a promising new artificial intelligence computer program, or model, that could one day help change that—just by analysing a patient’s speech.

Their model can predict, with an accuracy rate of 78.5%, whether someone with mild cognitive impairment is likely to remain stable over the next six years—or fall into the dementia associated with Alzheimer’s disease.

While allowing clinicians to peer into the future and make earlier diagnoses, the researchers say their work could also help make cognitive impairment screening more accessible by automating parts of the process—no expensive lab tests, imaging exams, or even office visits required.

The model is powered by machine learning, a subset of AI where computer scientists teach a program to independently analyse data.

“We wanted to predict what would happen in the next six years—and we found we can reasonably make that prediction with relatively good confidence and accuracy,” says Ioannis (Yannis) Paschalidis, director of the BU Rafik B. Hariri Institute for Computing and Computational Science & Engineering. “It shows the power of AI.”

The multidisciplinary team of engineers, neurobiologists, and computer and data scientists published their findings in Alzheimer’s & Dementia, the journal of the Alzheimer’s Association.

“We hope, as everyone does, that there will be more and more Alzheimer’s treatments made available,” says Paschalidis, a BU College of Engineering Distinguished Professor of Engineering and founding member of the Faculty of Computing & Data Sciences.

“If you can predict what will happen, you have more of an opportunity and time window to intervene with drugs, and at least try to maintain the stability of the condition and prevent the transition to more severe forms of dementia.”

Calculating the probability of Alzheimer’s Disease

To train and build their new model, the researchers turned to data from one of the nation’s oldest and longest-running studies—the BU-led Framingham Heart Study. Although the Framingham study is focused on cardiovascular health, participants showing signs of cognitive decline undergo regular neuropsychological tests and interviews, producing a wealth of longitudinal information on their cognitive well-being.

Paschalidis and his colleagues were given audio recordings of 166 initial interviews with people, between ages 63 and 97, diagnosed with mild cognitive impairment—76 who would remain stable for the next six years and 90 whose cognitive function would progressively decline.

They then used a combination of speech recognition tools—similar to the programs powering your smart speaker—and machine learning to train a model to spot connections between speech, demographics, diagnosis, and disease progression.

After training it on a subset of the study population, they tested its predictive prowess on the rest of the participants.

“We combine the information we extract from the audio recordings with some very basic demographics—age, gender, and so on—and we get the final score,” says Paschalidis.

“You can think of the score as the likelihood, the probability, that someone will remain stable or transition to dementia. It had significant predictive ability.”

Rather than using acoustic features of speech, like enunciation or speed, the model is just pulling from the content of the interview—the words spoken, how they’re structured. And Paschalidis says the information they put into the machine learning program is rough around the edges: the recordings, for example, are messy—low-quality and filled with background noise.

“It’s a very casual recording,” he says. “And still, with this dirty data, the model is able to make something out of it.”

That’s important, because the project was partly about testing AI’s ability to make the process of dementia diagnosis more efficient and automated, with little human involvement. In the future, the researchers say, models like theirs could be used to bring care to patients who aren’t near medical centres or to provide routine monitoring through interaction with an at-home app, drastically increasing the number of people who get screened.

According to Alzheimer’s Disease International, the majority of people with dementia worldwide never receive a formal diagnosis, leaving them shut off from treatment and care.

Rhoda Au, a co-author on the paper, says AI has the power to create “equal opportunity science and healthcare.” The study builds on the same team’s previous work, where they found AI could accurately detect cognitive impairment using voice recordings.

“Technology can overcome the bias of work that can only be done by those with resources, or care that has relied on specialized expertise that is not available to everyone,” says Au, a BU Chobanian & Avedisian School of Medicine professor of anatomy and neurobiology.

For her, one of the most exciting findings was “that a method for cognitive assessment that has the potential to be maximally inclusive—possibly independent of age, sex/gender, education, language, culture, income, geography—could serve as a potential screening tool for detecting and monitoring symptoms related to Alzheimer’s disease.”

A dementia diagnosis from home

In future research, Paschalidis would like to explore using data not just from formal clinician-patient interviews—with their scripted questions and predictable back-and-forth—but also from more natural, everyday conversations.

He’s already looking ahead to a project on if AI can help diagnose dementia via a smartphone app, as well as expanding the current study beyond speech analysis—the Framingham tests also include patient drawings and data on daily life patterns—to boost the model’s predictive accuracy.

“Digital is the new blood,” says Au. “You can collect it, analyse it for what is known today, store it, and reanalyse it for whatever new emerges tomorrow.”

This research was funded, in part, by the National Science Foundation, the National Institutes of Health, and the BU Rajen Kilachand Fund for Integrated Life Science and Engineering.

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UK body calls for more ageing research backing

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The British Society for Research on Ageing (BSRA) is calling for more public backing in the UK for research to help people stay healthier for longer, as an alternative to charities that support research on diseases.

The greatest risk factor for disease is ageing, but we have very little charitable support for research into how to slow ageing, the organisation warns.

Many diseases such as cancers and heart disease tragically shorten lives far too early, or like Alzheimer’s and arthritis, destroy quality of life for patients and carers. There is understandably huge public charitable support for more research. However, the greatest risk factor for those diseases, and even infectious diseases like COVID, is ageing.

Yet in comparison there is currently very little support for research to understand how we can slow ageing to prevent disease. This approach may be more productive in the long term to fight disease. Furthermore, keeping people healthier for longer, or avoiding chronic diseases all together, would be the most favourable outcome.

The UK population is ageing fast, putting pressure on the NHS and the economy. Despite this pressing problem all around us, there is no accessible way for people to support research into ageing in the UK. The BSRA aims to change that.

With a very small budget and almost completely run by volunteers, the BSRA has successfully funded several small research projects but progress needs to be accelerated. More funding is needed because it takes years to see the effects of ageing, so studies are long. Also ageing affects individuals in different ways, meaning that large numbers of people must be studied to make firm conclusions.

Therefore, there is an urgency to get studies funded and the BSRA has decided to launch an ambitious fundraising campaign to boost research into ageing. Initially, the Society aims to fund a series of one year research projects at the Masters degree level at universities across the UK and with plans to raise much more in the future to support longer and more ambitious projects that will impact the lives of the general public.

Chair of the BSRA, Prof David Weinkove from Durham University, says “The time is now to really get behind research into the biology of ageing. We have fantastic researchers across the country, but they are held back by a lack of funding. Evidence-based research is needed to understand how we people can stay healthier for longer, and to then we must make that knowledge available to as many people as possible”.

Dr Jed Lye says “This is a great opportunity for the public to help, for corporations to contribute, or philanthropists wanting a large impact with a relatively small donation; every £20,000 we raise can fund an entire year of research into ageing and longevity, and gets a budding scientist their research qualification.”

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