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Predicting Fluid Intelligence from Structural MRI Using Random Forest regression

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Adolescent Brain Cognitive Development Neurocognitive Prediction (ABCD-NP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11791))

Abstract

Fluid intelligence (FI) indicates a set of general abilities like pattern recognition, abstract thinking, and problem-solving. FI is related to inherent, biological factors. We present a method to predict the fluid intelligence score in children (9–10 y/o) from their structural brain scans. For the purposes of this work, we used features derived from the T1-weighted Magnetic Resonance scans from the ABCD study. We used data from 3739 subjects for training and 415 for validation of the model. As features we used the volumes of gray matter regions of interest provided by the challenge organizers, as well as three additional groups of features. These include signal intensity features based on the ROIs, as well as shape-based features derived from the anterior and posterior cross sectional area of the corpus callosum. We used the random forest regressor model for prediction. We compare its performance to other regression-based models (XGBoost Regression and Support Vector Regression). Additionally, we ran a mean decrease accuracy (MDA) algorithm to select features that had high influence on the prediction results. The results we have obtained for the validation set were as follows: MSE = 67.39, R-squared = 0.0762. The proposed method showed promising results and has the potential to provide a good prediction of fluid intelligence based on structural brain scans.

Majority of the work was done at the BrainHack Warsaw hackathon.

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Acknowledgement

The authors would like to thank Professor Mark Jenkinson for his helpful comments regarding the manuscript.

Author information

Authors and Affiliations

  1. Tooploox, Wroclaw, Poland

    Agata Wlaszczyk

  2. Adam Mickiewicz University in Poznan, Poznan, Poland

    Agnieszka Kaminska

  3. Lodz, Poland

    Agnieszka Pietraszek

  4. Poznan University of Technology, Poznan, Poland

    Jakub Dabrowski

  5. Poznan University of Medical Sciences, Poznan, Poland

    Mikolaj A. Pawlak

  6. Inteneural Networks Inc., Chicago, USA

    Mikolaj A. Pawlak

  7. FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK

    Hanna Nowicka

Authors
  1. Agata Wlaszczyk
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  2. Agnieszka Kaminska
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  3. Agnieszka Pietraszek
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  4. Jakub Dabrowski
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  5. Mikolaj A. Pawlak
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  6. Hanna Nowicka
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Corresponding author

Correspondence to Hanna Nowicka .

Editor information

Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Kilian M. Pohl

  2. University of California, San Diego, La Jolla, CA, USA

    Wesley K. Thompson

  3. Stanford University, Stanford, CA, USA

    Ehsan Adeli

  4. Children’s National Health System, Washington, DC, USA

    Marius George Linguraru

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Wlaszczyk, A., Kaminska, A., Pietraszek, A., Dabrowski, J., Pawlak, M.A., Nowicka, H. (2019). Predicting Fluid Intelligence from Structural MRI Using Random Forest regression. In: Pohl, K., Thompson, W., Adeli, E., Linguraru, M. (eds) Adolescent Brain Cognitive Development Neurocognitive Prediction. ABCD-NP 2019. Lecture Notes in Computer Science(), vol 11791. Springer, Cham. https://doi.org/10.1007/978-3-030-31901-4_10

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  • DOI: https://doi.org/10.1007/978-3-030-31901-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31900-7

  • Online ISBN: 978-3-030-31901-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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