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New Developments In fMRI

8 April, 2012 | imaging equipment, MRI

Brain MRI - MedWOW.com

On November 1, 1991, the first paper on functional MRI (fMRI) was published in the journal, Science. Since then, fMRI has helped to develop a surge of interest in cognitive neuroscience. Many world-class research centers are dedicated to using fMRI as the main tool of investigation. fMRI has not only focused on cognitive research but also social research, for instance, how the brain reacts to advertising or pictures of people of different ethnicities. In fact, fMRI has been applied to almost every aspect of brain research since its inception. However, some problems regarding fMRI research remain. In a Nature News Feature published on April 4, entitled, “Brain Imaging: fMRI 2.0” several researchers were interviewed as to what they think about the future of fMRI. Their commentary is summarized below.

One of the limitations of fMRI is it does not detect brain activity directly. There is still contention over whether the mapping of deoxygenated hemoglobin correlates directly with activity in the brain. John George, an MRI physicist at Los Alamos National Laboratory in New Mexico has developed a way to measure the magnetic field of each neuron as it conducts electrical signals. The technology is called SQUIDs (superconducting quantum interference devices). He admits the technology is in its early stages but is confident about its promise for future research.

Another problem with fMRI is that the data of brain activity it produces does not necessarily correlate to brain function. The same areas of “blobs” are activated on fMRI in many different cognitive tasks. Neuroscientists are seeking ways to build a more detailed model of the brain’s organization, its neural networks, so that they can interpret the patterns of activation with more confidence. Several teams across the world are compiling data with sophisticated statistical techniques to pick out detailed patterns from fMRI scans. One statistical technique called a multivariate analysis, charts the trends in the activation patterns from fMRI, rather than averaging them together into a blob. This is useful because when data is averaged together you miss smaller important patterns of brain activity. Peter Bandettini, who heads the US NIMH in Bethesda, Maryland was quoted, “what was previously noise was now suddenly signal.” Based on this method of analysis the teams are able to construct useful computational models to predict brain activity, not just decode it.

fMRI allows the investigator to see brain activity in human subjects noninvasively and without injecting tracer compounds. However, the scans tend to generate small signals and lots of noise. Stephen Smith at Oxford University put it, “(Y)ou need quite a lot of neurons firing in synchrony with each other to see a change in blood oxygenation.” This means subtle changes in activity often are overlooked. To overcome this lack of resolution, much research is combining fMRI with other technology. A group of Minnesota neuroscientists led by Dr. Olman say they have found a way create 3D images of the cerebral cortex as opposed to 2D. They used a powerful 7 Tesla MRI scanner and a T2-weighted 3D GRASE pulse sequence that provides extremely high spatial resolution. This technology was possible from the use of specially designed MRI parts. In an experimental trial on a group of volunteers, the technology successfully distinguished M cells and P cells in different layers of visual cortex. Other scientists are using fMRI with stronger Magnets and combining it with EEG and PET scan data in the same cohort of subjects.

Functional neuroimaging has expanded dramatically since its inception, however the technology has largely remained in research settings. Researchers are struggling to find a way to apply the technology in clinical settings to individual patients. Most fMRI data are averages of many people doing the same task. Researchers are now developing statistical methods to pull meaningful information out of a single scan. They do this relying on a reference set compiled by a large data set over many age ranges. The individual scan can then be compared to the large reference to get useful information and then be used to recommend a psychiatric or neurologic treatment. Such a technique could be used to diagnose mental illness. One such research team led by Arthur Toga at UCLA is building a reference set for Alzheimer’s disease called the Alzheimer’s Disease Neuroimaging Initiative. This study has scanned 800 people looking at the onset and progression of Alzheimer’s disease through genetic analyses, brain function, and blood biomarkers. The investigators hope that the database will be useful in the clinical practice of taking care of patient’s with Alzheimer’s disease.

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On April 8, 2012
Last modified:May 3, 2012

Summary:

Great Review of fMRI developments started in 1991.