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For the first time, researchers can provide early detection of plaques that have a high likelihood of clotting and/or rupture. Boston University School of Medicine (BUSM) scientists have observed the development and evolution of atherosclerotic plaques at the highest risk for thrombosis (clotting) by using non-invasive Magnetic Resonance Imaging (MRI).
Atherosclerosis is an important contributor to heart attacks and strokes, the leading causes of death in developed nations despite progress in their prevention. Atherosclerosis is a complex disease with many stages, ranging from plaques that can remain clinically silent for decades (‘stable’) to dangerous (‘vulnerable’) plaques that in their most highly advanced stage (‘highest risk’) can suddenly form a blood clot (thrombus) in the vessel, leading to myocardial infarction or stroke.
Researchers at BUSM, under the direction of James A. Hamilton, PhD, Professor of Physiology and Biophysics and Research Professor of medicine at BUSM, pioneered the use of MRI to identify high-risk plaques in a unique experimental model encompassing both atherosclerosis and thrombosis (atherothrombosis). The group established criteria for distinguishing vulnerable and stable plaques from analysis of in vivo (from a living organism) images of mature plaques. The plaques were then tested to determine their stability.
In the new study, images were obtained at monthly intervals to provide information about the pathways of progression (plaque history) of individual plaques in the vessel and to determine whether MRI can discriminate vulnerable and stable plaques at early times.
‘Vulnerable plaques and stable plaques showed different physiological progression patterns beginning after one month. Stable plaques exhibited no features of vulnerability at any time, whereas vulnerable plaques showed a progression of vulnerable features, especially in the last month,’ explained Hamilton, who is the corresponding author.
According to Hamilton, successive MRIs could provide a non-invasive means of identifying plaques that are evolving to become a high risk for rupture. ‘This work provides us a unique model for evaluating potential therapies after vulnerable plaques are clearly established.’ EurekAlert
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MIT researchers have developed a biomedical imaging system that could ultimately replace a $100,000 piece of a lab equipment with components that cost just hundreds of dollars.
The system uses a technique called fluorescence lifetime imaging, which has applications in DNA sequencing and cancer diagnosis, among other things. So the new work could have implications for both biological research and clinical practice.
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A method for data analysis used in medical diagnostics has been tested for the first time on resting state functional magnetic resonance imaging (fMRI) data. The method, which relies on ‘fuzziness’, proved to be as robust as the well-known and regularly used sample entropy (SampEn) method but with the advantage of offering greater detail than sample entropy.
Do not be misled by the word ‘fuzzy’: Fuzzy Approximate Entropy (fApEn) is a method that offers better sensitivity for understanding the complexity of noisy images produced by functional magnetic resonance imaging (fMRI). fMRI is a medical imaging technique which, when applied to the brain allows non-invasive observation of neural activity associated with specific human behaviour . However, just ‘looking’ at these images is not enough to understand what is going on, and different methods exist that analyse, filter and reconstruct the signals to enable scientists to understand the brain’s complex activity.
fApEn has been used to analyse electrocardiograms, electroencephalograms and
electromyograms, but this is the first time it is used with fMRI because 3D fMRI computation is complex. ‘Until now scientists have preferred to use a reliable method, Sample Entropy (sampEn), which, however, suffers several limitations’, explains Moses Sokunbi, research scientist at the International School for Advanced Studies (SISSA) in Trieste and first author of the study.
‘In this paper we demonstrated not only that fApEn can indeed be used but that compared with sampEn analysis on the same recordings, it gave superior results which were not detected by SampEn ‘.
‘The advantage of fApEn is that it’s a non-linear method’, Sokunbi points out. ‘All too often, in fact, data from the brain are analysed using linear methods, but the brain is a complex system that produces signals that are non-linear and dynamic in nature and analysing with these linear methods results in loss of information ‘.
The non-linear fApEn method was used to test a hypothesis regarding brain activity. ‘ We tested the fMRI data of 86 healthy individuals with age ranging between 19 and 85 years’, explains Sokunbi. ‘The complexity of brain activity is thought to decrease over the years: a young adult brain is more complex than an older adult brain. This hypothesis is supported by several observations so we decided to test it by scanning the brains of individuals of varying age with functional magnetic resonance imaging and analysing the data both with fApEn and SampEn’.
fApEn showed better signal detection in comparison to SampEn. With sampEn there was a tendency in the direction predicted by the hypothesis, but this was not significant. In contrast, fApEn analysis on the same data provided a clear and significant tendency in the expected direction’.
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