Which Brain Imaging Technique Allows Scientists To Draw Causal Inferences Between Variables?

ATLANTA—Sergey Plis, associate professor of computer science at Georgia State University, and his collaborators have received $1.iii million from the Collaborative Research in Computational Neuroscience Plan, jointly run by the National Science Foundation and the National Institutes of Health (NIH), to written report causal connections in the brain.

The iv-year award, funded through the NIH, will support interdisciplinary research to build causal learning models that can produce a blueprint of how encephalon regions interact.

Overview

A maxim in scientific discipline is that "correlation is non causation." In other words, merely because you lot observe an association between 2 variables does not mean there is a cause-and-effect human relationship between them. Ethically, nonetheless, brain researchers are ofttimes limited to collecting observational information and using patterns of correlation to infer patterns of causation.

"We can't just poke around in the brain to see how it works," said Plis, who is besides director of machine learning at the university'due south Center for Translational Inquiry in Neuroimaging & Data Science (TReNDS).

There is a longstanding problem associated with observational information nearly the encephalon: the speed of human being brain connections and the speed of measurement modality are non equal. For example, Plis noted, human neurons fire much faster than functional magnetic resonance imaging (fMRI) can measure out brain activity.

"The inferences that scientists draw using this information are statistically sound, but they rely on a false assumption—that the timescales are the same," said Plis. "As a result, these methods tin produce incorrect or unreliable information near how brain regions influence each other."

Other types of brain imaging, such equally magnetoencephalography (MEG) or electroencephalography (EEG), can measure processes at a faster rate than fMRI—but they produce less detailed data. Scientists lack methods to integrate causal information across multiple imaging modalities with significantly varied timescales.

Projection goals

The project aims to develop novel theories and methods that enable learning well-nigh the brain's causal construction and connectivity, fifty-fifty when at that place is a meaning mismatch betwixt the speed of the brain and the measurement. The resulting set of algorithms volition provide the neuroimaging community with a more robust, reliable understanding of directed connectivity in the brain.

"We will have data collected at unlike speeds by unlike modalities and combine it to reveal more than virtually how encephalon regions influence each other," said Plis. "For instance, nosotros can have dull modality like fMRI and larn causal information at faster neural scale, and then fuse information technology with what we larn from Meg or EEG. By combining them, ane could partially right the other."

In addition to providing scientists with a new set of methodological tools, the project will advance scientific noesis nearly the neural bases of diseases. The team plans to apply their models to schizophrenia, which is considered a disorder of "disconnectivity."

"Nosotros know that something has gone wrong with the connectivity inside these patients' brains, but there are competing theories near what exactly has happened," said Plis. "Using our dynamic causal fusion models, we'll be able to test predictions and validate those theories."

The computational models could also be applied toward other issues that deal with varied, complex data sets, Plis added, such every bit questions around causal relationships related to weather condition or climate.

Researchers

Plis'due south collaborators include Vince Calhoun, Distinguished University Professor of Psychology and director of TReNDS; Godfrey Pearlson, professor of psychiatry and neuroscience at Yale Schoolhouse of Medicine; and David Danks, professor of philosophy and psychology at the University of California-San Diego. They are seeking boosted collaborators to join the project.

An abstract of the grant, 1R01MH129047, is bachelor at the NIH Reporter website.

Featured Researcher

Sergey Plis
Associate professor
Computer Science

With a background in engineering, artificial intelligence and information science, Plis is focused on developing computational instruments that enable noesis extraction from observational multimodal information nerveless at different temporal and spatial scales.

Source: https://learningsolutionsmag.com/articles/research-causal-relationships-in-the-brain

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