A team of international researchers led by the University of Cambridge and the U.S. National Institutes of Health has used a new technique to map the connectivity of human brains by using brain scans from a magnetic resonance imaging (MRI) scanner and found a correlation between the level of brain connectivity and IQ.<\/p>\n
The study is published in the journal <\/span>Neuron<\/span><\/a>. <\/span><\/p>\n Recently, many researchers are joining in the effort to map the connections in the brain and to understand how this relates to important human behaviors, such as intelligence and mental health disorders. <\/span><\/p>\n \u201c<\/span>Studying the \u2018connectome,\u2019 or the map of connectivity of the brain, is crucial for our understanding of the basic neural architecture,\u201d said <\/span>Jakob Seidlitz<\/span><\/a>, the lead researcher and a PhD candidate <\/span>at the <\/span>NIH Oxford-Cambridge Scholars Programme<\/span><\/a>. <\/span><\/p>\n The team used a conventional 3T MRI scanner (3T indicating the strength of the magnetic field), to take brain scans of 296 typically-developing adolescent volunteers. After comparing all of them, the researchers validated the results by comparing brain scans of another 124 volunteers. <\/span><\/p>\n Through a typical MRI scan, researchers can see a single image of the brain, from which they can calculate the brain\u2019s multiple structural features. There can be as many as 10 different characteristics describing every region of the brain. <\/span><\/p>\n Whenever researchers saw two brain regions sharing similar characteristics, they assumed the regions to be a connected network and called it a \u201cmorphometric similarity.\u201d And they called the major connection points between different regions of the brain network \u201chubs.\u201d <\/span><\/p>\n Using this morphometric similarity network (MSN), <\/span>they were able to build up a map of connectome, showing how well connected the hubs were. <\/span><\/p>\n \u201cWe use non-invasive tools such as MRI to obtain these maps, but there are always limitations,\u201d Seidlitz said. \u201cOur new method addresses some of these limitations, and by doing so we believe it can help provide new insight into how individual brains are organized.\u201d <\/span><\/p>\n The researchers found that the level of connectivity in the MSNs in brain regions is related to higher order functions, such as problem solving and language and intelligence. The stronger the connections, the higher the person\u2019s IQ level. While IQ varied across the participants, the MSNs accounted for around 40 percent of this variation. <\/span><\/p>\n \u201c<\/span>We saw a clear link between the \u2018hubbiness\u2019 of higher-order brain regions \u2013 in other words, how densely connected they were to the rest of the network \u2013 and an individual\u2019s IQ,\u201d Seidlitz said in a <\/span>statement<\/span><\/a>. \u201cThis makes sense if you think of the hubs as enabling the flow of information around the brain \u2013 the stronger the connections, the better the brain is at processing information.\u201d<\/span><\/p>\n Through this simple technique of mapping the brain, researchers can understand and explore many things. For example, rather than relying on IQ tests, we can get a better look at a person\u2019s intelligence by looking at the connectome created by the person\u2019s MRI brain scans. <\/span><\/p>\n Researchers also hope to use brain connectivity to understand how the symptoms of mental health disorders, such as anxiety and depression, arise from differences in connectivity within the brain. <\/span><\/p>\n \u201cThe results \u2026 suggest that individual intelligence may arise, in part, from the connectivity profile of that individual’s brain,\u201d said Seidlitz. \u201cWe hope that these results provide a proof-of-concept that the Morphometric Similarity Networks (MSNs) not only relate to the anatomy but also to behavioral constructs.\u201d <\/span><\/p>\nHow connected is your brain?<\/b><\/h2>\n
What a connectome can tell us <\/b><\/h2>\n