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  Predicting intelligence from fMRI data of the human brain in a few minutes of scan time

Lohmann, G., Lacosse, E., Ethofer, T., Kumar, V., Scheffler, K., & Jost, J. (submitted). Predicting intelligence from fMRI data of the human brain in a few minutes of scan time.

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 Creators:
Lohmann, G1, 2, Author              
Lacosse, E1, 2, Author              
Ethofer, T, Author
Kumar, VJ1, 2, Author              
Scheffler, K1, 2, Author              
Jost, J, Author
Affiliations:
1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: A number of recent studies have investigated machine learning techniques for predicting individual behaviour from fMRI. Even though encouraging results have been obtained, excessive scan times – especially in resting state fMRI – are a limiting factor. Here we present a new machine learning algorithm for predicting individual behaviour of healthy human subjects using both resting state (rsfMRI) as well as task-based fMRI (tfMRI). It achieves dimensionality reduction via ensemble learning and partial least squares regression rather than via brain parcellations or ICA decompositions. In addition, it introduces Ricci-Forman curvature as a novel type of edge weight. As a proof of concept, we focus on predicting fluid, crystallized and general intelligence scores. In a cohort of 390 unrelated test subjects of the Human Connectome Project, we found correlations between the observed and the predicted general intelligence of more than 50 percent in tfMRI, and of around 59 percent (R2 ≈ 0.29) when results from two tasks are combined. We compare these results against a benchmark of existing methods that produced correlations below 50 percent in both rsfMRI and tfMRI. We conclude that with novel machine learning techniques applied to tfMRI it is possible to obtain significantly better prediction accuracies at a fraction of the scan time.

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 Dates: 2021-03
 Publication Status: Submitted
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1101/2021.03.18.435935
 Degree: -

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