Using brain scans to predict intelligence - 07/03/18 11:33 PM
New algorithm uses brain scans to tell how smart you are
Medical News Today
July 2, 2018
The paper is
A distributed brain network predicts general intelligence from resting-state human neuroimaging data
Abstract
Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, since it is the single best predictor of long-term life success, and since individual differences in a similar broad ability are found across animal species. The most replicated neural correlate of human intelligence to date is total brain volume. However, this coarse morphometric correlate gives no insights into mechanisms; it says little about function. Here we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project dataset (N=884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age, and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state fMRI data. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.
Medical News Today
July 2, 2018
Quote
A new machine-learning algorithm, which was developed by scientists at Caltech, can predict a person's intellectual ability with unprecedented accuracy.
doctors looking at brain scans
Soon, we might be able to tell whether someone has a mental health condition just by looking at a brain scan.
Scientists led by Ralph Adolphs — a professor of psychology, neuroscience, and biology from the Caltech Brain Imaging Center in Pasadena, CA — developed a machine-learning tool that can predict how intelligent a person is based on their brain activity patterns.
Specifically, the new algorithm relies on data culled by a functional MRI (fMRI) scanner.
Also, the algorithm only needs information on the brain's resting state, so the people being scanned do not need to solve any logical puzzles or math problems in order to have their mental abilities tested.
The scientists tested their newly developed algorithm on almost 900 participants, and the findings will soon be published in the journal Philosophical Transactions of the Royal Society.
doctors looking at brain scans
Soon, we might be able to tell whether someone has a mental health condition just by looking at a brain scan.
Scientists led by Ralph Adolphs — a professor of psychology, neuroscience, and biology from the Caltech Brain Imaging Center in Pasadena, CA — developed a machine-learning tool that can predict how intelligent a person is based on their brain activity patterns.
Specifically, the new algorithm relies on data culled by a functional MRI (fMRI) scanner.
Also, the algorithm only needs information on the brain's resting state, so the people being scanned do not need to solve any logical puzzles or math problems in order to have their mental abilities tested.
The scientists tested their newly developed algorithm on almost 900 participants, and the findings will soon be published in the journal Philosophical Transactions of the Royal Society.
A distributed brain network predicts general intelligence from resting-state human neuroimaging data
Abstract
Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, since it is the single best predictor of long-term life success, and since individual differences in a similar broad ability are found across animal species. The most replicated neural correlate of human intelligence to date is total brain volume. However, this coarse morphometric correlate gives no insights into mechanisms; it says little about function. Here we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project dataset (N=884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age, and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state fMRI data. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.