#cognitive impairment
A new study from Uppsala University shows that compared to non-shift workers, shift workers needed more time to complete a test that is frequently used by physicians to screen for cognitive impairment. However, those who had quit shift work more than five years ago completed the test just as quick as the non-shift workers. The findings are published in the journal Neurobiology of Aging.
By utilizing data from around 7000 individuals participating in the Swedish cohort study EpiHealth, researchers from Uppsala University and Malmö University sought to examine whether shift work history would be linked to performance. The test that was used is called the “Trail Making Test”, which consists of two parts. Part A requires participants to connect circles labeled with numbers 1-25 in an ascending order. In part B, participants must alternate between numbers and letters in an ascending order. Time to complete these tests has been shown to increase with age.
‘Our results indicate that shift work is linked to poorer performance on a test that is frequently used to screen for cognitive impairment in humans’, says Christian Benedict, associate professor at the Department of Neuroscience at Uppsala University and corresponding author of the study.
‘The poorer performance was only observed in current shift workers and those who worked shifts during the past 5 years. In contrast, no difference was observed between non-shift workers and those who had quit shift work more than 5 years ago. The latter could suggest that it may take at least 5 years for previous shift workers to recover brain functions that are relevant to the performance on this test’, says Christian Benedict.
Hit the sleep ‘sweet spot’ to keep brain sharp
Like so many other good things in life, sleep is best in moderation. A multiyear study of older adults found that both short and long sleepers experienced greater cognitive decline than people who slept a moderate amount, even when the effects of early Alzheimer’s disease were taken into account. The study was led by researchers at Washington University School of Medicine in St. Louis.
Poor sleep and Alzheimer’s disease are both associated with cognitive decline, and separating out the effects of each has proven challenging. By tracking cognitive function in a large group of older adults over several years and analyzing it against levels of Alzheimer’s-related proteins and measures of brain activity during sleep, the researchers generated crucial data that help untangle the complicated relationship among sleep, Alzheimer’s and cognitive function. The findings could aid efforts to help keep people’s minds sharp as they age.
The findings were published in the journal Brain.
“It’s been challenging to determine how sleep and different stages of Alzheimer’s disease are related, but that’s what you need to know to start designing interventions,” said first author Brendan Lucey, MD, an associate professor of neurology and director of the Washington University Sleep Medicine Center. “Our study suggests that there is a middle range, or ‘sweet spot,’ for total sleep time where cognitive performance was stable over time. Short and long sleep times were associated with worse cognitive performance, perhaps due to insufficient sleep or poor sleep quality. An unanswered question is if we can intervene to improve sleep, such as increasing sleep time for short sleepers by an hour or so, would that have a positive effect on their cognitive performance so they no longer decline? We need more longitudinal data to answer this question.”
Alzheimer’s is the main cause of cognitive decline in older adults, contributing to about 70% of dementia cases. Poor sleep is a common symptom of the disease and a driving force that can accelerate the disease’s progression. Studies have shown that self-reported short and long sleepers are both more likely to perform poorly on cognitive tests, but such sleep studies typically do not include assessments of Alzheimer’s disease.
To tease apart the separate effects of sleep and Alzheimer’s disease on cognition, Lucey and colleagues turned to volunteers who participate in Alzheimer’s studies through the university’s Charles F. and Joanne Knight Alzheimer Disease Research Center. Such volunteers undergo annual clinical and cognitive assessments, and provide a blood sample to be tested for the high-risk Alzheimer’s genetic variant APOE4. For this study, the participants also provided samples of cerebrospinal fluid to measure levels of Alzheimer’s proteins, and each slept with a tiny electroencephalogram (EEG) monitor strapped to their foreheads for four to six nights to measure brain activity during sleep.
In total, the researchers obtained sleep and Alzheimer’s data on 100 participants whose cognitive function had been monitored for an average of 4 ½ years. Most (88) had no cognitive impairments, 11 were very mildly impaired, and one had mild cognitive impairment. The average age was 75 at the time of the sleep study.
The researchers found a U-shaped relationship between sleep and cognitive decline. Overall, cognitive scores declined for the groups that slept less than 4.5 or more than 6.5 hours per night — as measured by EEG — while scores stayed stable for those in the middle of the range. EEG tends to yield estimates of sleep time that are about an hour shorter than self-reported sleep time, so the findings correspond to 5.5 to 7.5 hours of self-reported sleep, Lucey said.
The U-shaped relationship held true for measures of specific sleep phases, including rapid-eye movement (REM), or dreaming, sleep; and non-REM sleep. Moreover, the relationship held even after adjusting for factors that can affect both sleep and cognition, such as age, sex, levels of Alzheimer’s proteins, and the presence of APOE4.
“It was particularly interesting to see that not only those with short amounts of sleep but also those with long amounts of sleep had more cognitive decline,” said co-senior author David Holtzman, MD, a professor of neurology. “It suggests that sleep quality may be key, as opposed to simply total sleep.”
Each person’s sleep needs are unique, and people who wake up feeling rested on short or long sleep schedules should not feel compelled to change their habits, Lucey said. But those who are not sleeping well should be aware that sleep problems often can be treated.
“I ask many of my patients, ‘How’s your sleep?’” said co-senior author Beau M. Ances, MD, PhD, the Daniel J. Brennan, MD, Professor of Neurology. Ances treats patients with dementia and other neurodegenerative conditions at Barnes-Jewish Hospital. “Often patients report that they’re not sleeping well. Often once their sleep issues are treated, they may have improvements in cognition. Physicians who are seeing patients with cognitive complaints should ask them about their quality of sleep. This is potentially a modifiable factor.”
Algorithm can predict possible Alzheimer’s with nearly 100 per cent accuracy
Researchers from Kaunas universities in Lithuania developed a deep learning-based method that can predict the possible onset of Alzheimer’s disease from brain images with an accuracy of over 99 per cent. The method was developed while analysing functional MRI images obtained from 138 subjects and performed better in terms of accuracy, sensitivity and specificity than previously developed methods.
According to World Health Organisation, Alzheimer’s disease is the most frequent cause of dementia, contributing to up to 70 per cent of dementia cases. Worldwide, approximately 24 million people are affected, and this number is expected to double every 20 years. Owing to societal ageing, the disease will become a costly public health burden in the years to come.
“Medical professionals all over the world attempt to raise awareness of an early Alzheimer’s diagnosis, which provides the affected with a better chance of benefiting from treatment. This was one of the most important issues for choosing a topic for Modupe Odusami, a PhD student from Nigeria”, says Rytis Maskeliūnas, a researcher at the Department of Multimedia Engineering, Faculty of Informatics, Kaunas University of Technology (KTU), Odusami’s PhD supervisor.
Image processing delegated to the machine
One of the possible Alzheimer’s first signs is mild cognitive impairment (MCI), which is the stage between the expected cognitive decline of normal ageing and dementia. Based on the previous research, functional magnetic resonance imaging (fMRI) can be used to identify the regions in the brain which can be associated with the onset of Alzheimer’s disease, according to Maskeliūnas. The earliest stages of MCI often have almost no clear symptoms, but in quite a few cases can be detected by neuroimaging.
However, although theoretically possible, manual analysing of fMRI images attempting to identify the changes associated with Alzheimer’s not only requires specific knowledge but is also time-consuming – application of Deep learning and other AI methods can speed this up by a significant time margin. Finding MCI features does not necessarily mean the presence of illness, as it can also be a symptom of other related diseases, but it is more of an indicator and possible helper to steer toward an evaluation by a medical professional.
“Modern signal processing allows delegating the image processing to the machine, which can complete it faster and accurately enough. Of course, we don’t dare to suggest that a medical professional should ever rely on any algorithm one-hundred-per cent. Think of a machine as a robot capable of doing the most tedious task of sorting the data and searching for features. In this scenario, after the computer algorithm selects potentially affected cases, the specialist can look into them more closely, and at the end, everybody benefits as the diagnosis and the treatment reaches the patient much faster”, says Maskeliūnas, who supervised the team working on the model.
We need to make the most of data
The deep learning-based model was developed as a fruitful collaboration of leading Lithuanian researchers in the Artificial Intelligence sector, using a modification of well-known fine-tuned ResNet 18 (residual neural network) to classify functional MRI images obtained from 138 subjects. The images fell into six different categories: from healthy through the spectre of mild cognitive impairment (MCI) to Alzheimer’s disease. In total, 51,443 and 27,310 images from The Alzheimer’s Disease Neuroimaging Initiative fMRI dataset were selected for training and validation.
The model was able to effectively find the MCI features in the given dataset, achieving the best classification accuracy of 99.99%, 99.95%, and 99.95% for early MCI vs. AD, late MCI vs. AD, and MCI vs. early MCI, respectively.
“Although this was not the first attempt to diagnose the early onset of Alzheimer’s from similar data, our main breakthrough is the accuracy of the algorithm. Obviously, such high numbers are not indicators of true real-life performance, but we’re working with medical institutions to get more data”, says Maskeliūnas.
According to him, the algorithm could be developed into software, which would analyse the collected data from vulnerable groups (those over 65, having a history of brain injury, high blood pressure, etc.) and notify the medical personnel about the anomalies related to the early onset of Alzheimer’s.
“We need to make the most of data”, says Maskeliūnas, “that’s why our research group focuses on the European open science principle, so anyone can use our knowledge and develop it further. I believe that this principle contributes greatly to societal advancement”.
Maskeliūnas, the chief researcher, whose main area focuses on the application of modern methods of artificial intelligence on signal processing and multimodal interfaces, says that the above-described model can be integrated into a more complex system, analysing several different parameters, for example, also monitoring eye movements’ tracking, face reading, voice analysing, etc. Such technology could then be used for self-check and alert to seek professional advice if anything is causing concern.
“Technologies can make medicine more accessible and cheaper. Although they will never (or at least not soon) truly replace the medical professional, technologies can encourage seeking timely diagnosis and help”, says Maskeliūnas.
American Women Have Seen a 23% Decline in Serious Cognitive Issues Over the Past Decade
Go to college. Eat your veggies. Don’t smoke.
Poor eyesight unfairly mistaken for brain decline
Poor eyesight unfairly mistaken for brain decline
Millions of older people with poor vision are at risk of being misdiagnosed with mild cognitive impairments, according to a new study by the University of South Australia.
Cognitive tests that rely on vision-dependent tasks could be skewing results in up to a quarter of people aged over 50 who have undiagnosed visual problems such as cataracts or age-related macular degeneration (AMD).
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