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  Predicting Intelligence from FMRI Data of the Human Brain

Lohmann, G., Lacosse, E., Ethofer, T., Kumar, V., Scheffler, K., & Jost, J. (2022). Predicting Intelligence from FMRI Data of the Human Brain. Poster presented at FENS Forum 2022, Paris, France.

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Lohmann, G, Author           
Lacosse, E1, Author           
Ethofer, T, Author                 
Kumar, V1, Author           
Scheffler, K1, Author           
Jost, J, Author
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1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              

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 Abstract: In recent years, the prediction of individual behaviour from the fMRI-based functional connectome has become a major focus of research. The motivation behind this research is to find generalizable neuromarkers of cognitive functions. However, insufficient prediction accuracies and long scan time requirements are still unsolved issues. Here we propose a new machine learning algorithm for predicting intelligence scores of healthy human subjects from resting state (rsfMRI) or task-based fMRI (tfMRI). 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 when results from two tasks are combined. Surprisingly, we found that the tfMRI data were significantly more predictive of intelligence than rsfMRI even though they were acquired at much shorter scan times (approximately 10~minutes versus 1~hour). Existing methods that we investigated in a benchmark comparison underperformed on tfMRI data and produced prediction accuracies well below our results. Our proposed algorithm differs from existing methods in that 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. Reference: G. Lohmann, E. Lacosse, T. Ethofer, V.J. Kumar, K. Scheffler, J. Jost, Predicting intelligence from fMRI data of the human brain in a few minutes of scan time, biorxiv (2021), doi: https://doi.org/10.1101/2021.03.18.435935

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 Dates: 2022-07
 Publication Status: Published online
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Title: FENS Forum 2022
Place of Event: Paris, France
Start-/End Date: 2022-07-09 - 2022-07-13

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Title: FENS Forum 2022
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: S02-508 Start / End Page: 1982 Identifier: -