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The results are evidence that long-term analysis is a useful tool, Poldrack says, and hopefully make it attractive to a larger population of subjects. In many ways he was an ideal subject—he was available, committed to the long haul, and could sit still in the scanner—but diversity will ultimately lead to more interesting discoveries.
“I’m generally a pretty happy and even-keeled person,” he says. “My positive mood is almost always high, and my negative mood is almost always non-existent. It would be interesting to scan people with a wider emotional variation and see how their connections look over time.”
In the meantime, Poldrack and his colleagues have made the entire dataset and the ready-built tools to analyze it available. The dataset is so large and deep, Poldrack says, that he hopes people will approach it from innovative angles and uncover connections that will help advance the research.
Poldrack, meanwhile, plans to hone software to elucidate the interplay between brain function and gene expression.
“It’s a hard dataset to know what to do with, because it’s hard to tell if something is noise or if it’s real with just one person. But there’s potentially some really interesting stuff here,” he adds. “There are a ton of relationships between brain connectivity and gene expression in the blood, that are clearly there and seem to be strong, but we just don’t have a way to understand them based on current neuroscience.”
Republished as a derivative work from Futurity.org under the Creative Commons Attribution 4.0 International license. Original article posted on Futurity by Bjorn Carey-Stanford. The research paper was published in Nature Communications.
Featured Image Credit: Stanford University