Reuters Health – Researchers at the University of Illinois in collaboration with researchers around the world have measured functional connectivity of a subset of brain regions that are not easily characterized using traditional functional magnetic resonance imaging.
In an article published in the journal Neuroimage the researchers report measurements of brain activity (neuroreports) by measuring magnetic resonance imaging signals in the entorhinal cortex which integrates information on the frequency of brain activity as well as indicates the location of its connections (sensory areas) in the brain.
Now that we have readily available neuroregions that can be characterized using these gold standard tools the investigation and analysis of these types of brain regions holds it promise to move forward said the studys leader Pooya N. Moghissi professor of neuroengineering mechanics and behavior at the University of Illinois. This is an important step in realizing the promise that AI and machine learning algorithms will present in personalized medicine.
To characterize entorhinal cortical regions in the study of robot vision co-author Mr. Glenn K. Hough research assistant professor of mechanical engineering at the University of Illinois constructed a novel measurement tool that can be used for any neuroscience study in which measurement of entorhinal cortex activity is required.
To measure entorhinal cortex activity using this measurement tool a scan of 256 subjects was obtained allowing for the collection of 60 samples collected in the entorhinal cortex before switching to the new more easily collected test. The data was analyzed (averaged) from each of the experimental locations for each sample yielding measurement-level averages across the entire prefrontal cortex.
One limitation of the study is that this measurement tool is based on a zoom-in MRI (Z-Mo)) scanner as opposed to an effective magnetic resonance imaging (MRI) system and it only allowed for dynamic scanning a single point in time; a step that adds a critical element to the analysis Mr. Hough said.
Another issue is that the visualization tools currently in existence for neurons in specific brain areas are only across the top portion of the frontal lobe – not down the cerebral spinal cord towards subcortical areas or anywhere in the brain. The brain areas to be measured were also very similar to those that have previously been used in animal brain research Mr. Hough said.
Still the findings have potential implications for principles of artificial intelligence and machine learning that will be applied in psychiatry and other fields the researchers said.