New Tech Structural Brain Imaging Has Revolutionized

New Tech Structural brain imaging has revolutionized the fields of neuroscience and medicine. The American neurosurgeon Walter Dandy first introduced ventriculography and later developed pneumoencephalography, early imaging methods in the early 1900s; however, both procedures carried significant risks and could be quite painful. The technique of cerebral angiography was introduced in the late 1920's by neurologist Egas Moniz and this technique became refined and is still an important tool that is used in neurosurgery (White, Bell, & Mellick, 1973).

Further advancements such as computerized tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) have led to researchers and physicians being able to visualize the brain and other areas of the body in order for diagnostic, treatment, and research purposes. The advancements in neuro-imaging have led to many remarkable discoveries. One of the truly new cutting edge technologies in brain imaging may allow researchers to actually watch a person's dreams or visualize the experiences of a patient in a coma.

Functional magnetic resonance imaging (fMRI) is an established technique that allows for researchers to view how the brain is activated or reacts to stimuli. Using a combination of computational models and fMRI techniques researchers at the UC Berkeley have recently started to decode brain activation patterns and restructure people's visual experiences. The early results of this research have been targeted at recreating computerized images of movie trailers (Nishimoto et al., 2011).

Earlier efforts at the reconstruction of visual patterns by means of using brain scans were unsuccessful because the blood flow signals measured from fMRI scans change more slowly than do the actual neural signals that encode visual information. Due to this problem most of the previous research to decode brain activity focused on stationary visual images. In the current study the brain activity of the participants was recorded while they viewed a first set of movie trailers.

This data was then fed into a computer program that was able to learn and associate the visual patterns from the movie with the matching brain activity from the scans that occurred in the brains of the participants. The brain activation pattern that were produced by a second set of movie trailers tested the movie reconstruction algorithm by feeding 18 million seconds of random videos from YouTube into the computer to allow it to predict the brain activity that the first set of film clips most likely produced in each person.

Finally, the top 100 clips that the computer determined were most similar to the film clip participants viewed were merged together to produce a reconstruction of the original movie.