New Brain Imaging Technology Provides Better Understanding Of Brain Activity

Rats And Mice In A Medical School Laboratory
(Photo : Getty Images/China Photos)

Researchers currently rely on functional magnetic resonance imaging (fMRI) scans to detect brain activity.

While fMRI identifies active neurons in the brain by magnetic changes in the blood during specific tasks, the images are relatively low resolution.

Also, fMRI images do not explain how individual cells work or how individual cells create complex networks that produce thought and behavior.

In a new study, researchers at Cold Spring Harbor Laboratory (CSHL), MIT, Boston University and the Allen Institute of Brain Science have developed an automated method of detecting neuronal activity during specific behaviors at a higher resolution for a whole mouse brain, reports Science Daily.

The method uses the presence of a protein called c-fos to indicate where individual neurons are active.

Serial two-photon (STP) tomography can scan a whole mouse brain and produce an image that glows where the c-fos gene is expressed.

For the study, the researchers tested the technology by determining brain activation patterns in male mice when recognizing other mice and determining the gender of another mouse.

Mice sniff other mice to determine if they are the same or the opposite gender, which determines their behavior.

"When we compared male and female whole-brain activation patterns using our technology and analytical pipeline, we were able to make satisfying side-by-side comparisons," said Pavel Osten, a researcher at CSHL.

The brain images were synchronized with a reference brain called the Allen Mouse Brain Atlas.

The researchers found specific parts of the brain linked to behavioral motivation were found to be strongly activated during male-female interaction.

They also found some brain regions were activated by both male and female interactions.

With the new imaging technology, the researchers were able to count the number of neurons which were active in during specific behaviors.

In the future, the researchers hope that the technology will help determine what is causing disorders such as autism and schizophrenia which involve problems with social interaction.

The research was published in the journal Cell Reports.


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