ISLAMABAD, June 12 (ONLINE): A new machine learning model can predict the eventual occurrence of dementia, according to a new study.
\nIts authors say the model is greater than 80% accurate and can predict disease up to nine years early.
\nThe study focused on all-cause dementia, as well as risk factors for Alzheimer\u2019s disease, the most common type of dementia.
\nA new test can predict dementia with 82% accuracy, according to researchers at Queen Mary University of London in the U.K.<\/p>\n
Analyzing 1,111 functional magnetic resonance imaging (fMRI) scans of people with and without dementia, the researchers produced a model using machine learning. They say it spots the telltale signs of dementia up to nine years before diagnosis.<\/p>\n
The model identifies changes in the brain\u2019s default mode network (DMN)Trusted Source. The DMN is active when a person is lying in an fMRI scanner, not doing anything. The DMN state is believed to play a role in daydreaming and introspection, among other activities. It is more active when a person is at rest or engaged in self-referential thought.<\/p>\n
The researchers collected the fMRI scans used in the study from the UK Biobank. The participants included 81 people who had not been diagnosed with dementia at the time their scans were taken, but who developed the condition up to nine years afterward. For comparison, 1,030 people served as matched controls for the study.<\/p>\n
The researchers examined disconnects between 10 key regions within the DMN, using machine learning to construct their model.<\/p>\n
When they applied their model to medical records of the participants, they found it accurately predicted dementia within a two-year window during which is when such a diagnosis would occur.<\/p>\n
The authors of the study also looked for associations between DMN disconnectivity and various dementia risk factors. They found links between the model and social isolation \u2014 which has been linked to the likelihood of developing Alzheimer\u2019s disease \u2014 as well as a genetic risk for the condition.<\/p>\n
The hope is that early detection may one day provide a chance for early interventions that may forestall or prevent dementia.<\/p>\n
Diagnosing different types of dementias
\nDr. Claire Sexton, DPhil, the Alzheimer\u2019s Association\u2019s U.S. senior director of scientific programs and outreach, who was not involved in the study, said the DMN seems to be implicated in Alzheimer\u2019s.<\/p>\n
\u201cA number of studies,\u201d she said, \u201chave found that Alzheimer\u2019s is associated with decreased functional connectivity within the DMN.\u201d<\/p>\n
\u201cThis is congruent with our current knowledge of Alzheimer\u2019s, with other characteristic brain changes known to begin years, even decades, prior to diagnosis,\u201d said Sexton.<\/p>\n
The predictive tool is aimed at detecting all-cause dementia, not just Alzheimer\u2019s, and dementias can be quite different, pointed out Clifford Segil, DO, neurologist at Providence Saint John\u2019s Health Center in Santa Monica, CA. (He was likewise not involved in the study.)<\/p>\n
Segil explained that Alzheimer\u2019s is a \u201ccortical dementia with damage to the cortex of the brain, and there is vascular dementia, which is a subcortical dementia that involves damage to the white matter of the brain.\u201d<\/p>\n
Still, pointed out the senior author of the study, Professor Charles Marshall, clinical senior lecturer in dementia in the Preventive Neurology Unit at Queen Mary, \u201cIn practice, a large majority of dementia is due to either Alzheimer\u2019s disease on its own or mixed Alzheimer\u2019s and vascular dementia.\u201d<\/p>\n
\u201cWe need to extend the work to show whether or not it is relevant to rarer dementias such as frontotemporal dementia and Lewy body dementiaTrusted Source,\u201d Marshall told Medical News Today.<\/p>\n
Seeking a pattern in the brain
\nThe model developed by Marshall\u2019s team tracked various sorts of activity.<\/p>\n
\u201cSome brain areas show reduced activity, but others show increased activity, probably as a compensatory response. We trained a machine learning tool to recognize patterns that were \u2018dementia-like,\u2019\u201d said Marshall.<\/p>\n
A practical predictive model for diagnosing dementia
\nIn general, Segil was supportive of the continued development of new models for diagnosing cognitive problems before they manifest as symptoms, particularly in the DMN.<\/p>\n
He pointed out, however, that \u201cone of the issues with using fMRI, similar to nuclear medicine studies used in neurology, is the reproducibility of reading these studies.\u201d<\/p>\n
\u201cFor example,\u201d explained Segil, \u201cneurologists and neuro-radiologists often see the same findings when they look at structural 3T MRITrusted Source scans, but fMRIs, unfortunately in clinical practice, have different readers arrive at completely different conclusions at this time.\u201d<\/p>\n
Sexton listed three limitations she found with the current study:<\/p>\n
\u201cthe definition and examination of DMN disconnectivity varies substantially across studies
\nthe current study reports all-cause dementia based on clinician coding rather than on diagnostic criteria
\nthe cohort from which this study was drawn, UK Biobank, is predominantly white, healthier than average, with a higher than average socioeconomic status.\u201d
\nThese factors, she said, limit the generalizability of the study\u2019s findings.<\/p>\n
\u201cReplication of results with standardized methods and in study populations that accurately represent the population living with, and at risk of, Alzheimer\u2019s is crucial,\u201d said Sexton.<\/p>\n
No protective dementia medications \u2014 yet
\nEarly detection of cognitive issues is one vital part of helping patients, with effective treatments being the other.<\/p>\n
\u201cIf these tests pan out to have some clinical utility in the future,\u201d said Segil, \u201cthese patients would be followed by more frequent 3T structural MRI scans to determine if there were anatomical changes that would correlate with their memory loss.\u201d<\/p>\n
\u201cUnfortunately, in the year 2024, even if we could target patients with early onset dementia, we do not have any neuroprotective medications to be used at this time,\u201d Segil said.<\/p>\n
Marshall said that as such interventions are eventually developed, he hoped that \u201cour test could be used to select the most appropriate people to go into these trials.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"
ISLAMABAD, June 12 (ONLINE): A new machine learning model can predict the eventual occurrence of dementia, according to a new study. Its authors say the model is greater than 80% accurate and can predict disease up to nine years early. The study focused on all-cause […]<\/p>\n
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