Self-regulation discovery through data-driven ontology!

Self-regulation discovery through data-driven ontology!

In recent years, efforts to understand the workings of the mind have taken on new-found urgency. Not only are psychological and neurological disorders -- from Alzheimer's disease and strokes to autism and anxiety -- becoming more widespread, new tools and methods have emerged that allow scientists to explore the structure of, and activity within, the brain with greater granularity.

"Science is meant to be cumulative, but both methodological and conceptual problems have impeded cumulative progress in psychological science," authors wrote in a Nature Communications paper.


"I'd thought for a long time that data sharing was important for a number of reasons," explained Poldrack, "for transparency and reproducibility and also to help us aggregate across lots of small studies to improve our power to answer questions."

OpenFMRI grew to nearly a hundred datasets, and in 2016 was subsumed into OpenNeuro, a more general platform for hosting brain imaging studies. That platform today has more than 220 datasets, including some like "The Stockholm Sleepy Brain Study" and "Neural Processing of Emotional Musical and Nonmusical Stimuli in Depression," that have been downloaded hundreds of times.



Part of the success of the project is due to the development of a common standard, BIDS -- Brain Imaging Data Structure (BIDS) -- that allows researchers to compare and combine studies in an apples-to-apples way. Introduced by Poldrack and others in 2016, it earned near-immediate acceptance and has grown into the lingua franca for neuroimaging data.

As part of the standard creation, Poldrack and his collaborators built a web-based validator to make it easy to determine whether one's data meets the standard.

"Researchers convert their data into BIDS format, upload their data and it gets validated on upload," Poldrack said. "Once it passes the validator and gets uploaded, with a click of a button it can be shared."


He is also working with another project called based at Indiana University, which uses XSEDE resources, including those at TACC, to process data, including data from OpenNeuro.


"In addition to sharing data, one of the things that having this common data standard affords us is the ability to automatically analyze data and do the kind of pre-processing and quality control that we often do on imaging data," he explained. "You just point the container at the data set, and it just runs it."



Poldrack and his collaborators addressed both the methodological and assumption problems in their recent Nature Communications paper by applying more rigorous statistical methods to try to uncover the underlying structures of the mind, a process they call 'data-driven ontology discovery.'

Applying the approach to studies of self-regulation, the researchers tested the ability of survey questionnaires and task-based studies to predict an individual's likelihood of being at risk for alcoholism, obesity, drug abuse, or other self-regulation-related issues.

In their study, 522 participants took 23 self-report studies and performed 37 behavioral tasks. From each of these 60 measures, the team derived multiple dependent variables thought to capture psychological constructs. Using the dependent variables, the team first tried to create "a psychological space" -- a way of quantifying the distance between dependent variables to determine how various types of behavior that are often seen as separate cluster or correlate to each other. They used these "ontological fingerprints" to determine the contribution of various psychological constructs to the final predictive model.


"We're bringing to bear serious machine learning methods to determine what's correlated with what, and what has generalizable predictive accuracy, using methods that are still fairly new to this area of research," Poldrack said.

They found that some predicted targets, like mental health and obesity, had simple ontological fingerprints, such as "emotional control" and "problematic eating," but that other fingerprints were more complicated. They also found that task-based studies -- common in psychological research -- had almost no predictive ability.



"Breaking these disorders into diagnostic categories like schizophrenia, bipolar disorder, or depression, is just not biologically realistic," he said. "Both genetics and neuroscience show that those disorders have way more overlap in terms of their genetics and their neurobiology, than differences. So, I think that there are new paradigms that might emerge that would be helped by a better understanding of the brain."


"We can use sampling techniques to randomize the data 5,000 times and re-run big models many times," Poldrack said. "That's not realistically possible without supercomputers."


"And," said Poldrack, "there's a lot of questions that, without high performance computing, you can't feasibly get an answer to."


"We're trying to understand really complex things," he said. "It has to be realized that everything we say is probably wrong, but the hope is that it can get us a little bit closer to what's right."