MVPA is non-linear. Assume for instance that there's a set of neurons dedicated to recognizing the meaning of a stop sign. These neurons are not active when we see something red or something octagonal because there's not a one-to-one linear mapping between being red and being a stop sign (an apple isn't a stop sign), nor between being octagonal and being a stop sign (a board room table isn't a stop sign).
"A non-linear response ensures that they do light up when we see an object that is both red and octagonal," explains McNorgan. "For this reason, non-linear methods like MVPA have been at the core of so-called 'Deep Learning' approaches behind technologies, such as the computer vision software required for self-driving cars."
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But MVPA uses brute force machine-learning techniques. The process is opportunistic, sometimes confusing coincidence with correlation. Even ideal models require researchers to provide evidence that activity in the theoretical model would also be present under the same conditions in the brain.
On their own, both traditional functional connectivity and MVPA approaches have limitations, and integrating results generated by each of these approaches requires considerable effort and expertise for brain researchers to puzzle out the evidence.
When combined, however, the limitations are mutually constrained -- and McNorgan is the first researcher to successfully integrate functional connectivity and MVPA to develop a machine-learning model that's explicitly grounded in real-world functional connections among brain regions. In other words, the mutually constrained results are a self-assembling puzzle.
"It was my chocolate and peanut butter moment," says McNorgan, an expert in neuroimaging and computational modeling.
"I've had a particular career trajectory that has allowed me to work extensively with different theoretical models. That background provided a particular set of experiences that made the combination seem obvious in hindsight."
To build his models, McNorgan begins by gathering the brain data that will teach them the patterns of brain activity that are associated with each of three categories - in this case, tools, musical instruments and fruits. These data came from 11 participants who imagined the appearance and sound of familiar category examples, like hammers, guitars and apples, while undergoing an MRI scan. These scans indicate which areas are more or less active based on blood oxygen levels.
