Home Life Style Are Three Brain Imaging Techniques Better Than One?

Are Three Brain Imaging Techniques Better Than One?

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bRAIN

Imaging studies show that different parts of the brain of autistic children do not connect with each other in typical ways. Initially, most researchers thought that the autistic brain has fewer connections between key regions. The most recent studies, however, point to an opposite conclusion: The brains of people with autism exhibit over connectivity. Till now, most of the

studies of autistic children have used single imaging technology to explore connectivity. None of those attempts have been successful in capturing a clear view of brain abnormalities that autism brings.

 

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Now, the study of autism takes new turns as two new grants from the National Institute of Mental Health enable Professor Ralph-Axel Muller (San Diego State University, Psychology) in combining three imaging techniques to harness the better of each one. The term “brain imaging” is used a lot for any kind of latest advances in neuroscientific research and psychology, but you need to know that there is a range of different brain imaging techniques. Each of the techniques gives scientists a unique view of the inner workings of the brain, and each has its own strengths and weaknesses.

 

For instance we can take the most frequently used technique of functional magnetic resonance imaging. It measures blood flow in all the different parts of the brain at specific time intervals. The results are based on the knowledge premise that increase in blood flow indicates increase in activity of the nerve cells in that part of the brain. This technique is powerful, but is limited when detecting dynamic changes in the brain that happen very fast, i.e. within milliseconds.

 

EEG (electroencephalography), as we all know is a much older technique for mental imaging but is actually better at detecting dynamic changes in the brain activity. Although it too has its limitations that it cannot successfully pinpoint the location in the brain, wherein the activity occurs.

A more powerful and recent technique is MEG (magnetoencephalography). It can successfully detect dynamic activities and changes in the brain that happen within a short span of time, as short as a few milliseconds.

 

Muller says it’s important to look for brain activities that have disorganized patterns and could be responsible for some characteristics of autism spectrum disorder in many children. These symptoms may include

lack of attention to social cues and recurring and obsessive behavior.

 

We take the example of last year, where Muller and his colleagues discovered that autistic children have lesser connectivity between the cerebral cortex and the thalamus. It happens to be a deep brain

Structure that plays an essential role in sensor motor functions and attention.

 

$4.2 million funding from the NIH, will help Müller, Ksenija Marinkovic at SDSU and other collaborators, i.e. Thomas Liu at the University of California, San Diego to apply EEG, MEG and MRI to effectively study autistic, non-autistic and typically-developing children and teenagers during a variety of tests. An array of language tests will also be employed that are typically designed to kindle activity in various areas of the brain.

 

This project will have 2 components. One will concern with the visual system. Previous researches in autism show people with autism to be relying on their visual cortex more than normal. Using fMRI and

MEG together, Muller and his team aim at determining the dynamic processes in the brain when its different regions work together to creates a response, and how autism alters these processes.

 

This study also aims to examine brain functioning in resting state for identifying abnormalities and disharmony in brain network. The combination of EEG and MEG with fMRI techniques, reveal brain anatomy

and will help create a complete picture of the abnormalities in the brain organization in autistic patients.

 

Ultimate goal of Müller and his colleagues is to identify biomarkers in the brain. It can be very reliable in indicating whether the participant falls on the autism spectrum or not.

 

References

 

http://science.education.nih.gov/supplements/nih2/addiction/guide/lesson1-1.htm

 

http://newscenter.sdsu.edu/sdsu_newscenter/news.aspx?s=75130